diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml new file mode 100644 index 000000000000..514c9327e231 --- /dev/null +++ b/.github/FUNDING.yml @@ -0,0 +1,12 @@ +# These are supported funding model platforms + +github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2] +patreon: # Replace with a single Patreon username +open_collective: # Replace with a single Open Collective username +ko_fi: # Replace with a single Ko-fi username +tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel +community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry +liberapay: TheAlgorithms +issuehunt: # Replace with a single IssueHunt username +otechie: # Replace with a single Otechie username +custom: ['http://paypal.me/TheAlgorithms/1000', 'https://donorbox.org/thealgorithms'] diff --git a/.github/stale.yml b/.github/stale.yml new file mode 100644 index 000000000000..70032115fc2c --- /dev/null +++ b/.github/stale.yml @@ -0,0 +1,18 @@ +# Number of days of inactivity before an issue becomes stale +daysUntilStale: 30 +# Number of days of inactivity before a stale issue is closed +daysUntilClose: 7 +# Issues with these labels will never be considered stale +exemptLabels: + - bug + - help wanted + - OK to merge +# Label to use when marking an issue as stale +staleLabel: wontfix +# Comment to post when marking an issue as stale. Set to `false` to disable +markComment: > + This issue has been automatically marked as stale because it has not had + recent activity. It will be closed if no further activity occurs. Thank you + for your contributions. +# Comment to post when closing a stale issue. Set to `false` to disable +closeComment: true diff --git a/.github/workflows/autoblack.yml b/.github/workflows/autoblack.yml new file mode 100644 index 000000000000..98310ac80b11 --- /dev/null +++ b/.github/workflows/autoblack.yml @@ -0,0 +1,34 @@ +# GitHub Action that uses Black to reformat the Python code in an incoming pull request. +# If all Python code in the pull request is complient with Black then this Action does nothing. +# Othewrwise, Black is run and its changes are committed back to the incoming pull request. +# https://github.com/cclauss/autoblack + +name: autoblack +on: [pull_request] +jobs: + build: + runs-on: ubuntu-latest + strategy: + max-parallel: 1 + matrix: + python-version: [3.7] + steps: + - uses: actions/checkout@v1 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v1 + with: + python-version: ${{ matrix.python-version }} + - name: Install psf/black + run: pip install black + - name: Run black --check . + run: black --check . + - name: If needed, commit black changes to the pull request + if: failure() + run: | + black . + git config --global user.name 'autoblack' + git config --global user.email 'cclauss@users.noreply.github.com' + git remote set-url origin https://x-access-token:${{ secrets.GITHUB_TOKEN }}@github.com/$GITHUB_REPOSITORY + git checkout $GITHUB_HEAD_REF + git commit -am "fixup: Format Python code with psf/black" + git push diff --git a/.gitignore b/.gitignore index 5f9132236c26..b840d4ed0490 100644 --- a/.gitignore +++ b/.gitignore @@ -7,9 +7,7 @@ __pycache__/ *.so # Distribution / packaging -.vscode/ .Python -env/ build/ develop-eggs/ dist/ @@ -21,9 +19,11 @@ lib64/ parts/ sdist/ var/ +wheels/ *.egg-info/ .installed.cfg *.egg +MANIFEST # PyInstaller # Usually these files are written by a python script from a template @@ -43,8 +43,9 @@ htmlcov/ .cache nosetests.xml coverage.xml -*,cover +*.cover .hypothesis/ +.pytest_cache/ # Translations *.mo @@ -53,6 +54,7 @@ coverage.xml # Django stuff: *.log local_settings.py +db.sqlite3 # Flask stuff: instance/ @@ -67,7 +69,7 @@ docs/_build/ # PyBuilder target/ -# IPython Notebook +# Jupyter Notebook .ipynb_checkpoints # pyenv @@ -76,17 +78,32 @@ target/ # celery beat schedule file celerybeat-schedule -# dotenv -.env +# SageMath parsed files +*.sage.py -# virtualenv +# Environments +.env +.venv +env/ venv/ ENV/ +env.bak/ +venv.bak/ # Spyder project settings .spyderproject +.spyproject # Rope project settings .ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + +.DS_Store .idea -.DS_Store \ No newline at end of file +.try +.vscode/ diff --git a/.lgtm.yml b/.lgtm.yml deleted file mode 100644 index ec550ab72705..000000000000 --- a/.lgtm.yml +++ /dev/null @@ -1,12 +0,0 @@ -extraction: - python: - python_setup: - version: 3 - after_prepare: - - python3 -m pip install --upgrade --user flake8 - before_index: - - python3 -m flake8 --version # flake8 3.6.0 on CPython 3.6.5 on Linux - # stop the build if there are Python syntax errors or undefined names - - python3 -m flake8 . --count --select=E901,E999,F821,F822,F823 --show-source --statistics - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - - python3 -m flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics diff --git a/.travis.yml b/.travis.yml index 5fba6987bb66..0c7c9fd0e1c7 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,26 +1,14 @@ language: python +python: 3.7 cache: pip -python: - - 2.7 - - 3.6 - #- nightly - #- pypy - #- pypy3 -matrix: - allow_failures: - - python: nightly - - python: pypy - - python: pypy3 -install: - #- pip install -r requirements.txt - - pip install flake8 # pytest # add another testing frameworks later +before_install: pip install --upgrade pip setuptools +install: pip install -r requirements.txt before_script: - # stop the build if there are Python syntax errors or undefined names - - flake8 . --count --select=E901,E999,F821,F822,F823 --show-source --statistics - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - black --check . || true + - flake8 . --count --select=E9,F4,F63,F7,F82 --show-source --statistics script: - - true # pytest --capture=sys # add other tests here -notifications: - on_success: change - on_failure: change # `always` will be the setting once code changes slow down + - scripts/validate_filenames.py # no uppercase, no spaces, in a directory + - mypy --ignore-missing-imports . + - pytest . --doctest-modules +after_success: + - scripts/build_directory_md.py 2>&1 | tee DIRECTORY.md diff --git a/.vs/Python/v15/.suo b/.vs/Python/v15/.suo deleted file mode 100644 index 0e3f4807567d..000000000000 Binary files a/.vs/Python/v15/.suo and /dev/null differ diff --git a/.vs/slnx.sqlite b/.vs/slnx.sqlite deleted file mode 100644 index 2fe4a449f121..000000000000 Binary files a/.vs/slnx.sqlite and /dev/null differ diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 000000000000..ce2f03886e01 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,160 @@ +# Contributing guidelines + +## Before contributing + +Welcome to [TheAlgorithms/Python](https://github.com/TheAlgorithms/Python)! Before sending your pull requests, make sure that you **read the whole guidelines**. If you have any doubt on the contributing guide, please feel free to [state it clearly in an issue](https://github.com/TheAlgorithms/Python/issues/new) or ask the community in [Gitter](https://gitter.im/TheAlgorithms). + +## Contributing + +### Contributor + +We are very happy that you consider implementing algorithms and data structure for others! This repository is referenced and used by learners from all over the globe. Being one of our contributors, you agree and confirm that: + +- You did your work - no plagiarism allowed + - Any plagiarized work will not be merged. +- Your work will be distributed under [MIT License](License) once your pull request is merged +- You submitted work fulfils or mostly fulfils our styles and standards + +**New implementation** is welcome! For example, new solutions for a problem, different representations for a graph data structure or algorithm designs with different complexity. + +**Improving comments** and **writing proper tests** are also highly welcome. + +### Contribution + +We appreciate any contribution, from fixing a grammar mistake in a comment to implementing complex algorithms. Please read this section if you are contributing your work. + +Your contribution will be tested by our [automated testing on Travis CI](https://travis-ci.org/TheAlgorithms/Python/pull_requests) to save time and mental energy. After you have submitted your pull request, you should see the Travis tests start to run at the bottom of your submission page. If those tests fail, then click on the ___details___ button try to read through the Travis output to understand the failure. If you do not understand, please leave a comment on your submission page and a community member will try to help. + +#### Coding Style + +We want your work to be readable by others; therefore, we encourage you to note the following: + +- Please write in Python 3.7+. __print()__ is a function in Python 3 so __print "Hello"__ will _not_ work but __print("Hello")__ will. +- Please focus hard on naming of functions, classes, and variables. Help your reader by using __descriptive names__ that can help you to remove redundant comments. + - Single letter variable names are _old school_ so please avoid them unless their life only spans a few lines. + - Expand acronyms because __gcd()__ is hard to understand but __greatest_common_divisor()__ is not. + - Please follow the [Python Naming Conventions](https://pep8.org/#prescriptive-naming-conventions) so variable_names and function_names should be lower_case, CONSTANTS in UPPERCASE, ClassNames should be CamelCase, etc. + + + +- We encourage the use of Python [f-strings](https://realpython.com/python-f-strings/#f-strings-a-new-and-improved-way-to-format-strings-in-python) where the make the code easier to read. + + + +- Please consider running [__psf/black__](https://github.com/python/black) on your Python file(s) before submitting your pull request. This is not yet a requirement but it does make your code more readable and automatically aligns it with much of [PEP 8](https://www.python.org/dev/peps/pep-0008/). There are other code formatters (autopep8, yapf) but the __black__ style is now the recommendation of the Python Core Team. To use it, + + ```bash + pip3 install black # only required the first time + black . + ``` + +- All submissions will need to pass the test __flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics__ before they will be accepted so if possible, try this test locally on your Python file(s) before submitting your pull request. + + ```bash + pip3 install flake8 # only required the first time + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + ``` + + + +- Original code submission require docstrings or comments to describe your work. + +- More on docstrings and comments: + + If you are using a Wikipedia article or some other source material to create your algorithm, please add the URL in a docstring or comment to help your reader. + + The following are considered to be bad and may be requested to be improved: + + ```python + x = x + 2 # increased by 2 + ``` + + This is too trivial. Comments are expected to be explanatory. For comments, you can write them above, on or below a line of code, as long as you are consistent within the same piece of code. + + We encourage you to put docstrings inside your functions but please pay attention to indentation of docstrings. The following is acceptable in this case: + + ```python + def sumab(a, b): + """ + This function returns the sum of two integers a and b + Return: a + b + """ + return a + b + ``` + +- Write tests (especially [__doctests__](https://docs.python.org/3/library/doctest.html)) to illustrate and verify your work. We highly encourage the use of _doctests on all functions_. + + ```python + def sumab(a, b): + """ + This function returns the sum of two integers a and b + Return: a + b + >>> sumab(2, 2) + 4 + >>> sumab(-2, 3) + 1 + >>> sumab(4.9, 5.1) + 10.0 + """ + return a + b + ``` + + These doctests will be run by pytest as part of our automated testing so please try to run your doctests locally and make sure that they are found and pass: + + ```bash + python3 -m doctest -v my_submission.py + ``` + + The use of the Python builtin __input()__ function is **not** encouraged: + + ```python + input('Enter your input:') + # Or even worse... + input = eval(input("Enter your input: ")) + ``` + + However, if your code uses __input()__ then we encourage you to gracefully deal with leading and trailing whitespace in user input by adding __.strip()__ as in: + + ```python + starting_value = int(input("Please enter a starting value: ").strip()) + ``` + + The use of [Python type hints](https://docs.python.org/3/library/typing.html) is encouraged for function parameters and return values. Our automated testing will run [mypy](http://mypy-lang.org) so run that locally before making your submission. + + ```python + def sumab(a: int, b: int) --> int: + pass + ``` + + + +- [__List comprehensions and generators__](https://docs.python.org/3/tutorial/datastructures.html#list-comprehensions) are preferred over the use of `lambda`, `map`, `filter`, `reduce` but the important thing is to demonstrate the power of Python in code that is easy to read and maintain. + + + +- Avoid importing external libraries for basic algorithms. Only use those libraries for complicated algorithms. +- If you need a third party module that is not in the file __requirements.txt__, please add it to that file as part of your submission. + +#### Other Standard While Submitting Your Work + +- File extension for code should be `.py`. Jupiter notebook files are acceptable in machine learning algorithms. +- Strictly use snake_case (underscore_separated) in your file_name, as it will be easy to parse in future using scripts. +- Please avoid creating new directories if at all possible. Try to fit your work into the existing directory structure. +- If possible, follow the standard *within* the folder you are submitting to. + + + +- If you have modified/added code work, make sure the code compiles before submitting. +- If you have modified/added documentation work, ensure your language is concise and contains no grammar errors. +- Do not update the README.md or DIRECTORY.md file which will be periodically autogenerated by our Travis CI processes. +- Add a corresponding explanation to [Algorithms-Explanation](https://github.com/TheAlgorithms/Algorithms-Explanation) (Optional but recommended). +- All submissions will be tested with [__mypy__](http://www.mypy-lang.org) so we encourage to add [__Python type hints__](https://docs.python.org/3/library/typing.html) where it makes sense to do so. + + + +- Most importantly, + - **Be consistent in the use of these guidelines when submitting.** + - **Join** [Gitter](https://gitter.im/TheAlgorithms) **now!** + - Happy coding! + +Writer [@poyea](https://github.com/poyea), Jun 2019. diff --git a/DIRECTORY.md b/DIRECTORY.md new file mode 100644 index 000000000000..e2d74d39828f --- /dev/null +++ b/DIRECTORY.md @@ -0,0 +1,496 @@ + +## Arithmetic Analysis + * [Bisection](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/bisection.py) + * [Gaussian Elimination](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/gaussian_elimination.py) + * [In Static Equilibrium](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/in_static_equilibrium.py) + * [Intersection](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/intersection.py) + * [Lu Decomposition](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/lu_decomposition.py) + * [Newton Forward Interpolation](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_forward_interpolation.py) + * [Newton Method](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_method.py) + * [Newton Raphson Method](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_raphson_method.py) + +## Backtracking + * [All Combinations](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_combinations.py) + * [All Permutations](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_permutations.py) + * [All Subsequences](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_subsequences.py) + * [Minimax](https://github.com/TheAlgorithms/Python/blob/master/backtracking/minimax.py) + * [N Queens](https://github.com/TheAlgorithms/Python/blob/master/backtracking/n_queens.py) + * [Sudoku](https://github.com/TheAlgorithms/Python/blob/master/backtracking/sudoku.py) + * [Sum Of Subsets](https://github.com/TheAlgorithms/Python/blob/master/backtracking/sum_of_subsets.py) + +## Blockchain + * [Chinese Remainder Theorem](https://github.com/TheAlgorithms/Python/blob/master/blockchain/chinese_remainder_theorem.py) + * [Diophantine Equation](https://github.com/TheAlgorithms/Python/blob/master/blockchain/diophantine_equation.py) + * [Modular Division](https://github.com/TheAlgorithms/Python/blob/master/blockchain/modular_division.py) + +## Boolean Algebra + * [Quine Mc Cluskey](https://github.com/TheAlgorithms/Python/blob/master/boolean_algebra/quine_mc_cluskey.py) + +## Ciphers + * [Affine Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/affine_cipher.py) + * [Atbash](https://github.com/TheAlgorithms/Python/blob/master/ciphers/atbash.py) + * [Base16](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base16.py) + * [Base32](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base32.py) + * [Base64 Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base64_cipher.py) + * [Base85](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base85.py) + * [Brute Force Caesar Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/brute_force_caesar_cipher.py) + * [Caesar Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/caesar_cipher.py) + * [Cryptomath Module](https://github.com/TheAlgorithms/Python/blob/master/ciphers/cryptomath_module.py) + * [Elgamal Key Generator](https://github.com/TheAlgorithms/Python/blob/master/ciphers/elgamal_key_generator.py) + * [Hill Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/hill_cipher.py) + * [Morse Code Implementation](https://github.com/TheAlgorithms/Python/blob/master/ciphers/morse_code_implementation.py) + * [Onepad Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/onepad_cipher.py) + * [Playfair Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/playfair_cipher.py) + * [Rabin Miller](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rabin_miller.py) + * [Rot13](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rot13.py) + * [Rsa Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rsa_cipher.py) + * [Rsa Key Generator](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rsa_key_generator.py) + * [Shuffled Shift Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/shuffled_shift_cipher.py) + * [Simple Substitution Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/simple_substitution_cipher.py) + * [Trafid Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/trafid_cipher.py) + * [Transposition Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/transposition_cipher.py) + * [Transposition Cipher Encrypt Decrypt File](https://github.com/TheAlgorithms/Python/blob/master/ciphers/transposition_cipher_encrypt_decrypt_file.py) + * [Vigenere Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/vigenere_cipher.py) + * [Xor Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/xor_cipher.py) + +## Compression + * [Burrows Wheeler](https://github.com/TheAlgorithms/Python/blob/master/compression/burrows_wheeler.py) + * [Huffman](https://github.com/TheAlgorithms/Python/blob/master/compression/huffman.py) + * [Peak Signal To Noise Ratio](https://github.com/TheAlgorithms/Python/blob/master/compression/peak_signal_to_noise_ratio.py) + +## Conversions + * [Decimal To Binary](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_binary.py) + * [Decimal To Hexadecimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_hexadecimal.py) + * [Decimal To Octal](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_octal.py) + +## Data Structures + * Binary Tree + * [Avl Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/avl_tree.py) + * [Basic Binary Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/basic_binary_tree.py) + * [Binary Search Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/binary_search_tree.py) + * [Fenwick Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/fenwick_tree.py) + * [Lazy Segment Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/lazy_segment_tree.py) + * [Lca](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/lca.py) + * [Red Black Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/red_black_tree.py) + * [Segment Tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/segment_tree.py) + * [Treap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/treap.py) + * Disjoint Set + * [Disjoint Set](https://github.com/TheAlgorithms/Python/blob/master/data_structures/disjoint_set/disjoint_set.py) + * Hashing + * [Double Hash](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/double_hash.py) + * [Hash Table](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/hash_table.py) + * [Hash Table With Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/hash_table_with_linked_list.py) + * Number Theory + * [Prime Numbers](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/number_theory/prime_numbers.py) + * [Quadratic Probing](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/quadratic_probing.py) + * Heap + * [Binomial Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/binomial_heap.py) + * [Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/heap.py) + * [Min Heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/min_heap.py) + * Linked List + * [Doubly Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/doubly_linked_list.py) + * [Is Palindrome](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/is_palindrome.py) + * [Singly Linked List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/singly_linked_list.py) + * [Swap Nodes](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/swap_nodes.py) + * Queue + * [Double Ended Queue](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/double_ended_queue.py) + * [Linked Queue](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/linked_queue.py) + * [Queue On List](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/queue_on_list.py) + * [Queue On Pseudo Stack](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/queue_on_pseudo_stack.py) + * Stacks + * [Balanced Parentheses](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/balanced_parentheses.py) + * [Infix To Postfix Conversion](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/infix_to_postfix_conversion.py) + * [Infix To Prefix Conversion](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/infix_to_prefix_conversion.py) + * [Linked Stack](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/linked_stack.py) + * [Next Greater Element](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/next_greater_element.py) + * [Postfix Evaluation](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/postfix_evaluation.py) + * [Stack](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/stack.py) + * [Stock Span Problem](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/stock_span_problem.py) + * Trie + * [Trie](https://github.com/TheAlgorithms/Python/blob/master/data_structures/trie/trie.py) + +## Digital Image Processing + * [Change Contrast](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/change_contrast.py) + * Edge Detection + * [Canny](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/edge_detection/canny.py) + * Filters + * [Convolve](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/convolve.py) + * [Gaussian Filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/gaussian_filter.py) + * [Median Filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/median_filter.py) + * [Sobel Filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/sobel_filter.py) + * Rotation + * [Rotation](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/rotation/rotation.py) + * [Test Digital Image Processing](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/test_digital_image_processing.py) + +## Divide And Conquer + * [Closest Pair Of Points](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/closest_pair_of_points.py) + * [Convex Hull](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/convex_hull.py) + * [Inversions](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/inversions.py) + * [Max Subarray Sum](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/max_subarray_sum.py) + * [Mergesort](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/mergesort.py) + +## Dynamic Programming + * [Abbreviation](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/abbreviation.py) + * [Bitmask](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/bitmask.py) + * [Climbing Stairs](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/climbing_stairs.py) + * [Coin Change](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/coin_change.py) + * [Edit Distance](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/edit_distance.py) + * [Factorial](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/factorial.py) + * [Fast Fibonacci](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fast_fibonacci.py) + * [Fibonacci](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fibonacci.py) + * [Floyd Warshall](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/floyd_warshall.py) + * [Fractional Knapsack](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fractional_knapsack.py) + * [Integer Partition](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/integer_partition.py) + * [K Means Clustering Tensorflow](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/k_means_clustering_tensorflow.py) + * [Knapsack](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/knapsack.py) + * [Longest Common Subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_common_subsequence.py) + * [Longest Increasing Subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_increasing_subsequence.py) + * [Longest Increasing Subsequence O(Nlogn)](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_increasing_subsequence_o(nlogn).py) + * [Longest Sub Array](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_sub_array.py) + * [Matrix Chain Order](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/matrix_chain_order.py) + * [Max Sub Array](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_sub_array.py) + * [Minimum Partition](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/minimum_partition.py) + * [Rod Cutting](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/rod_cutting.py) + * [Subset Generation](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/subset_generation.py) + * [Sum Of Subset](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/sum_of_subset.py) + +## File Transfer + * [Recieve File](https://github.com/TheAlgorithms/Python/blob/master/file_transfer/recieve_file.py) + * [Send File](https://github.com/TheAlgorithms/Python/blob/master/file_transfer/send_file.py) + +## Fuzzy Logic + * [Fuzzy Operations](https://github.com/TheAlgorithms/Python/blob/master/fuzzy_logic/fuzzy_operations.py) + +## Graphs + * [A Star](https://github.com/TheAlgorithms/Python/blob/master/graphs/a_star.py) + * [Articulation Points](https://github.com/TheAlgorithms/Python/blob/master/graphs/articulation_points.py) + * [Basic Graphs](https://github.com/TheAlgorithms/Python/blob/master/graphs/basic_graphs.py) + * [Bellman Ford](https://github.com/TheAlgorithms/Python/blob/master/graphs/bellman_ford.py) + * [Bfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/bfs.py) + * [Bfs Shortest Path](https://github.com/TheAlgorithms/Python/blob/master/graphs/bfs_shortest_path.py) + * [Breadth First Search](https://github.com/TheAlgorithms/Python/blob/master/graphs/breadth_first_search.py) + * [Check Bipartite Graph Bfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/check_bipartite_graph_bfs.py) + * [Check Bipartite Graph Dfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/check_bipartite_graph_dfs.py) + * [Depth First Search](https://github.com/TheAlgorithms/Python/blob/master/graphs/depth_first_search.py) + * [Dfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/dfs.py) + * [Dijkstra](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra.py) + * [Dijkstra 2](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra_2.py) + * [Dijkstra Algorithm](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra_algorithm.py) + * [Dinic](https://github.com/TheAlgorithms/Python/blob/master/graphs/dinic.py) + * [Directed And Undirected (Weighted) Graph](https://github.com/TheAlgorithms/Python/blob/master/graphs/directed_and_undirected_(weighted)_graph.py) + * [Edmonds Karp Multiple Source And Sink](https://github.com/TheAlgorithms/Python/blob/master/graphs/edmonds_karp_multiple_source_and_sink.py) + * [Eulerian Path And Circuit For Undirected Graph](https://github.com/TheAlgorithms/Python/blob/master/graphs/eulerian_path_and_circuit_for_undirected_graph.py) + * [Even Tree](https://github.com/TheAlgorithms/Python/blob/master/graphs/even_tree.py) + * [Finding Bridges](https://github.com/TheAlgorithms/Python/blob/master/graphs/finding_bridges.py) + * [G Topological Sort](https://github.com/TheAlgorithms/Python/blob/master/graphs/g_topological_sort.py) + * [Graph List](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_list.py) + * [Graph Matrix](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_matrix.py) + * [Graphs Floyd Warshall](https://github.com/TheAlgorithms/Python/blob/master/graphs/graphs_floyd_warshall.py) + * [Kahns Algorithm Long](https://github.com/TheAlgorithms/Python/blob/master/graphs/kahns_algorithm_long.py) + * [Kahns Algorithm Topo](https://github.com/TheAlgorithms/Python/blob/master/graphs/kahns_algorithm_topo.py) + * [Minimum Spanning Tree Kruskal](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_kruskal.py) + * [Minimum Spanning Tree Prims](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_prims.py) + * [Multi Hueristic Astar](https://github.com/TheAlgorithms/Python/blob/master/graphs/multi_hueristic_astar.py) + * [Page Rank](https://github.com/TheAlgorithms/Python/blob/master/graphs/page_rank.py) + * [Prim](https://github.com/TheAlgorithms/Python/blob/master/graphs/prim.py) + * [Scc Kosaraju](https://github.com/TheAlgorithms/Python/blob/master/graphs/scc_kosaraju.py) + * [Tarjans Scc](https://github.com/TheAlgorithms/Python/blob/master/graphs/tarjans_scc.py) + +## Hashes + * [Chaos Machine](https://github.com/TheAlgorithms/Python/blob/master/hashes/chaos_machine.py) + * [Enigma Machine](https://github.com/TheAlgorithms/Python/blob/master/hashes/enigma_machine.py) + * [Md5](https://github.com/TheAlgorithms/Python/blob/master/hashes/md5.py) + * [Sha1](https://github.com/TheAlgorithms/Python/blob/master/hashes/sha1.py) + +## Linear Algebra + * Src + * [Lib](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/lib.py) + * [Polynom-For-Points](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/polynom-for-points.py) + * [Test Linear Algebra](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/test_linear_algebra.py) + +## Machine Learning + * [Decision Tree](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/decision_tree.py) + * [Gradient Descent](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/gradient_descent.py) + * [K Means Clust](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/k_means_clust.py) + * [K Nearest Neighbours](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/k_nearest_neighbours.py) + * [Knn Sklearn](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/knn_sklearn.py) + * [Linear Regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/linear_regression.py) + * [Logistic Regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/logistic_regression.py) + * [Polymonial Regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/polymonial_regression.py) + * [Scoring Functions](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/scoring_functions.py) + * [Sequential Minimum Optimization](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/sequential_minimum_optimization.py) + * [Support Vector Machines](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/support_vector_machines.py) + +## Maths + * [3N+1](https://github.com/TheAlgorithms/Python/blob/master/maths/3n+1.py) + * [Abs](https://github.com/TheAlgorithms/Python/blob/master/maths/abs.py) + * [Abs Max](https://github.com/TheAlgorithms/Python/blob/master/maths/abs_max.py) + * [Abs Min](https://github.com/TheAlgorithms/Python/blob/master/maths/abs_min.py) + * [Average Mean](https://github.com/TheAlgorithms/Python/blob/master/maths/average_mean.py) + * [Average Median](https://github.com/TheAlgorithms/Python/blob/master/maths/average_median.py) + * [Basic Maths](https://github.com/TheAlgorithms/Python/blob/master/maths/basic_maths.py) + * [Binary Exponentiation](https://github.com/TheAlgorithms/Python/blob/master/maths/binary_exponentiation.py) + * [Collatz Sequence](https://github.com/TheAlgorithms/Python/blob/master/maths/collatz_sequence.py) + * [Explicit Euler](https://github.com/TheAlgorithms/Python/blob/master/maths/explicit_euler.py) + * [Extended Euclidean Algorithm](https://github.com/TheAlgorithms/Python/blob/master/maths/extended_euclidean_algorithm.py) + * [Factorial Python](https://github.com/TheAlgorithms/Python/blob/master/maths/factorial_python.py) + * [Factorial Recursive](https://github.com/TheAlgorithms/Python/blob/master/maths/factorial_recursive.py) + * [Fermat Little Theorem](https://github.com/TheAlgorithms/Python/blob/master/maths/fermat_little_theorem.py) + * [Fibonacci](https://github.com/TheAlgorithms/Python/blob/master/maths/fibonacci.py) + * [Fibonacci Sequence Recursion](https://github.com/TheAlgorithms/Python/blob/master/maths/fibonacci_sequence_recursion.py) + * [Find Max](https://github.com/TheAlgorithms/Python/blob/master/maths/find_max.py) + * [Find Max Recursion](https://github.com/TheAlgorithms/Python/blob/master/maths/find_max_recursion.py) + * [Find Min](https://github.com/TheAlgorithms/Python/blob/master/maths/find_min.py) + * [Find Min Recursion](https://github.com/TheAlgorithms/Python/blob/master/maths/find_min_recursion.py) + * [Gaussian](https://github.com/TheAlgorithms/Python/blob/master/maths/gaussian.py) + * [Greatest Common Divisor](https://github.com/TheAlgorithms/Python/blob/master/maths/greatest_common_divisor.py) + * [Hardy Ramanujanalgo](https://github.com/TheAlgorithms/Python/blob/master/maths/hardy_ramanujanalgo.py) + * [Is Square Free](https://github.com/TheAlgorithms/Python/blob/master/maths/is_square_free.py) + * [Jaccard Similarity](https://github.com/TheAlgorithms/Python/blob/master/maths/jaccard_similarity.py) + * [Karatsuba](https://github.com/TheAlgorithms/Python/blob/master/maths/karatsuba.py) + * [Kth Lexicographic Permutation](https://github.com/TheAlgorithms/Python/blob/master/maths/kth_lexicographic_permutation.py) + * [Largest Of Very Large Numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/largest_of_very_large_numbers.py) + * [Least Common Multiple](https://github.com/TheAlgorithms/Python/blob/master/maths/least_common_multiple.py) + * [Lucas Lehmer Primality Test](https://github.com/TheAlgorithms/Python/blob/master/maths/lucas_lehmer_primality_test.py) + * [Lucas Series](https://github.com/TheAlgorithms/Python/blob/master/maths/lucas_series.py) + * [Matrix Exponentiation](https://github.com/TheAlgorithms/Python/blob/master/maths/matrix_exponentiation.py) + * [Mobius Function](https://github.com/TheAlgorithms/Python/blob/master/maths/mobius_function.py) + * [Modular Exponential](https://github.com/TheAlgorithms/Python/blob/master/maths/modular_exponential.py) + * [Newton Raphson](https://github.com/TheAlgorithms/Python/blob/master/maths/newton_raphson.py) + * [Polynomial Evaluation](https://github.com/TheAlgorithms/Python/blob/master/maths/polynomial_evaluation.py) + * [Prime Check](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_check.py) + * [Prime Factors](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_factors.py) + * [Prime Sieve Eratosthenes](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_sieve_eratosthenes.py) + * [Qr Decomposition](https://github.com/TheAlgorithms/Python/blob/master/maths/qr_decomposition.py) + * [Quadratic Equations Complex Numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/quadratic_equations_complex_numbers.py) + * [Radix2 Fft](https://github.com/TheAlgorithms/Python/blob/master/maths/radix2_fft.py) + * [Runge Kutta](https://github.com/TheAlgorithms/Python/blob/master/maths/runge_kutta.py) + * [Segmented Sieve](https://github.com/TheAlgorithms/Python/blob/master/maths/segmented_sieve.py) + * [Sieve Of Eratosthenes](https://github.com/TheAlgorithms/Python/blob/master/maths/sieve_of_eratosthenes.py) + * [Simpson Rule](https://github.com/TheAlgorithms/Python/blob/master/maths/simpson_rule.py) + * [Softmax](https://github.com/TheAlgorithms/Python/blob/master/maths/softmax.py) + * [Sum Of Arithmetic Series](https://github.com/TheAlgorithms/Python/blob/master/maths/sum_of_arithmetic_series.py) + * [Test Prime Check](https://github.com/TheAlgorithms/Python/blob/master/maths/test_prime_check.py) + * [Trapezoidal Rule](https://github.com/TheAlgorithms/Python/blob/master/maths/trapezoidal_rule.py) + * [Volume](https://github.com/TheAlgorithms/Python/blob/master/maths/volume.py) + * [Zellers Congruence](https://github.com/TheAlgorithms/Python/blob/master/maths/zellers_congruence.py) + +## Matrix + * [Matrix Class](https://github.com/TheAlgorithms/Python/blob/master/matrix/matrix_class.py) + * [Matrix Operation](https://github.com/TheAlgorithms/Python/blob/master/matrix/matrix_operation.py) + * [Nth Fibonacci Using Matrix Exponentiation](https://github.com/TheAlgorithms/Python/blob/master/matrix/nth_fibonacci_using_matrix_exponentiation.py) + * [Rotate Matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/rotate_matrix.py) + * [Searching In Sorted Matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/searching_in_sorted_matrix.py) + * [Sherman Morrison](https://github.com/TheAlgorithms/Python/blob/master/matrix/sherman_morrison.py) + * [Spiral Print](https://github.com/TheAlgorithms/Python/blob/master/matrix/spiral_print.py) + * Tests + * [Test Matrix Operation](https://github.com/TheAlgorithms/Python/blob/master/matrix/tests/test_matrix_operation.py) + +## Networking Flow + * [Ford Fulkerson](https://github.com/TheAlgorithms/Python/blob/master/networking_flow/ford_fulkerson.py) + * [Minimum Cut](https://github.com/TheAlgorithms/Python/blob/master/networking_flow/minimum_cut.py) + +## Neural Network + * [Back Propagation Neural Network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/back_propagation_neural_network.py) + * [Convolution Neural Network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/convolution_neural_network.py) + * [Perceptron](https://github.com/TheAlgorithms/Python/blob/master/neural_network/perceptron.py) + +## Other + * [Activity Selection](https://github.com/TheAlgorithms/Python/blob/master/other/activity_selection.py) + * [Anagrams](https://github.com/TheAlgorithms/Python/blob/master/other/anagrams.py) + * [Autocomplete Using Trie](https://github.com/TheAlgorithms/Python/blob/master/other/autocomplete_using_trie.py) + * [Binary Exponentiation](https://github.com/TheAlgorithms/Python/blob/master/other/binary_exponentiation.py) + * [Binary Exponentiation 2](https://github.com/TheAlgorithms/Python/blob/master/other/binary_exponentiation_2.py) + * [Detecting English Programmatically](https://github.com/TheAlgorithms/Python/blob/master/other/detecting_english_programmatically.py) + * [Euclidean Gcd](https://github.com/TheAlgorithms/Python/blob/master/other/euclidean_gcd.py) + * [Fischer Yates Shuffle](https://github.com/TheAlgorithms/Python/blob/master/other/fischer_yates_shuffle.py) + * [Food Wastage Analysis From 1961-2013 Fao](https://github.com/TheAlgorithms/Python/blob/master/other/food_wastage_analysis_from_1961-2013_fao.ipynb) + * [Frequency Finder](https://github.com/TheAlgorithms/Python/blob/master/other/frequency_finder.py) + * [Game Of Life](https://github.com/TheAlgorithms/Python/blob/master/other/game_of_life.py) + * [Greedy](https://github.com/TheAlgorithms/Python/blob/master/other/greedy.py) + * [Largest Subarray Sum](https://github.com/TheAlgorithms/Python/blob/master/other/largest_subarray_sum.py) + * [Linear Congruential Generator](https://github.com/TheAlgorithms/Python/blob/master/other/linear_congruential_generator.py) + * [Magicdiamondpattern](https://github.com/TheAlgorithms/Python/blob/master/other/magicdiamondpattern.py) + * [Nested Brackets](https://github.com/TheAlgorithms/Python/blob/master/other/nested_brackets.py) + * [Palindrome](https://github.com/TheAlgorithms/Python/blob/master/other/palindrome.py) + * [Password Generator](https://github.com/TheAlgorithms/Python/blob/master/other/password_generator.py) + * [Primelib](https://github.com/TheAlgorithms/Python/blob/master/other/primelib.py) + * [Sierpinski Triangle](https://github.com/TheAlgorithms/Python/blob/master/other/sierpinski_triangle.py) + * [Tower Of Hanoi](https://github.com/TheAlgorithms/Python/blob/master/other/tower_of_hanoi.py) + * [Two Sum](https://github.com/TheAlgorithms/Python/blob/master/other/two_sum.py) + * [Word Patterns](https://github.com/TheAlgorithms/Python/blob/master/other/word_patterns.py) + +## Project Euler + * Problem 01 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol1.py) + * [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol2.py) + * [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol3.py) + * [Sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol4.py) + * [Sol5](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol5.py) + * [Sol6](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol6.py) + * [Sol7](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol7.py) + * Problem 02 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_02/sol1.py) + * [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_02/sol2.py) + * [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_02/sol3.py) + * [Sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_02/sol4.py) + * [Sol5](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_02/sol5.py) + * Problem 03 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_03/sol1.py) + * [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_03/sol2.py) + * Problem 04 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_04/sol1.py) + * [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_04/sol2.py) + * Problem 05 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_05/sol1.py) + * [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_05/sol2.py) + * Problem 06 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_06/sol1.py) + * [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_06/sol2.py) + * [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_06/sol3.py) + * [Sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_06/sol4.py) + * Problem 07 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_07/sol1.py) + * [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_07/sol2.py) + * [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_07/sol3.py) + * Problem 08 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_08/sol1.py) + * [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_08/sol2.py) + * Problem 09 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_09/sol1.py) + * [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_09/sol2.py) + * [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_09/sol3.py) + * Problem 10 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_10/sol1.py) + * [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_10/sol2.py) + * [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_10/sol3.py) + * Problem 11 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_11/sol1.py) + * [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_11/sol2.py) + * Problem 12 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_12/sol1.py) + * [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_12/sol2.py) + * Problem 13 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_13/sol1.py) + * Problem 14 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_14/sol1.py) + * [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_14/sol2.py) + * Problem 15 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_15/sol1.py) + * Problem 16 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_16/sol1.py) + * [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_16/sol2.py) + * Problem 17 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_17/sol1.py) + * Problem 18 + * [Solution](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_18/solution.py) + * Problem 19 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_19/sol1.py) + * Problem 20 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_20/sol1.py) + * [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_20/sol2.py) + * [Sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_20/sol3.py) + * Problem 21 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_21/sol1.py) + * Problem 22 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_22/sol1.py) + * [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_22/sol2.py) + * Problem 23 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_23/sol1.py) + * Problem 234 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_234/sol1.py) + * Problem 24 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_24/sol1.py) + * Problem 25 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_25/sol1.py) + * [Sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_25/sol2.py) + * Problem 28 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_28/sol1.py) + * Problem 29 + * [Solution](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_29/solution.py) + * Problem 31 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_31/sol1.py) + * Problem 32 + * [Sol32](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_32/sol32.py) + * Problem 36 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_36/sol1.py) + * Problem 40 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_40/sol1.py) + * Problem 42 + * [Solution42](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_42/solution42.py) + * Problem 48 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_48/sol1.py) + * Problem 52 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_52/sol1.py) + * Problem 53 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_53/sol1.py) + * Problem 551 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_551/sol1.py) + * Problem 56 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_56/sol1.py) + * Problem 67 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_67/sol1.py) + * Problem 76 + * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_76/sol1.py) + +## Searches + * [Binary Search](https://github.com/TheAlgorithms/Python/blob/master/searches/binary_search.py) + * [Fibonacci Search](https://github.com/TheAlgorithms/Python/blob/master/searches/fibonacci_search.py) + * [Interpolation Search](https://github.com/TheAlgorithms/Python/blob/master/searches/interpolation_search.py) + * [Jump Search](https://github.com/TheAlgorithms/Python/blob/master/searches/jump_search.py) + * [Linear Search](https://github.com/TheAlgorithms/Python/blob/master/searches/linear_search.py) + * [Quick Select](https://github.com/TheAlgorithms/Python/blob/master/searches/quick_select.py) + * [Sentinel Linear Search](https://github.com/TheAlgorithms/Python/blob/master/searches/sentinel_linear_search.py) + * [Simple-Binary-Search](https://github.com/TheAlgorithms/Python/blob/master/searches/simple-binary-search.py) + * [Tabu Search](https://github.com/TheAlgorithms/Python/blob/master/searches/tabu_search.py) + * [Ternary Search](https://github.com/TheAlgorithms/Python/blob/master/searches/ternary_search.py) + +## Sorts + * [Bitonic Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bitonic_sort.py) + * [Bogo Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bogo_sort.py) + * [Bubble Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bubble_sort.py) + * [Bucket Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bucket_sort.py) + * [Cocktail Shaker Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/cocktail_shaker_sort.py) + * [Comb Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/comb_sort.py) + * [Counting Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/counting_sort.py) + * [Cycle Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/cycle_sort.py) + * [Double Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/double_sort.py) + * [External Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/external_sort.py) + * [Gnome Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/gnome_sort.py) + * [Heap Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/heap_sort.py) + * [I Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/i_sort.py) + * [Insertion Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/insertion_sort.py) + * [Merge Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/merge_sort.py) + * [Merge Sort Fastest](https://github.com/TheAlgorithms/Python/blob/master/sorts/merge_sort_fastest.py) + * [Odd Even Transposition Parallel](https://github.com/TheAlgorithms/Python/blob/master/sorts/odd_even_transposition_parallel.py) + * [Odd Even Transposition Single Threaded](https://github.com/TheAlgorithms/Python/blob/master/sorts/odd_even_transposition_single_threaded.py) + * [Pancake Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/pancake_sort.py) + * [Pigeon Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/pigeon_sort.py) + * [Quick Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/quick_sort.py) + * [Quick Sort 3 Partition](https://github.com/TheAlgorithms/Python/blob/master/sorts/quick_sort_3_partition.py) + * [Radix Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/radix_sort.py) + * [Random Normal Distribution Quicksort](https://github.com/TheAlgorithms/Python/blob/master/sorts/random_normal_distribution_quicksort.py) + * [Random Pivot Quick Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/random_pivot_quick_sort.py) + * [Selection Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/selection_sort.py) + * [Shell Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/shell_sort.py) + * [Stooge Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/stooge_sort.py) + * [Tim Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/tim_sort.py) + * [Topological Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/topological_sort.py) + * [Tree Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/tree_sort.py) + * [Wiggle Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/wiggle_sort.py) + +## Strings + * [Aho-Corasick](https://github.com/TheAlgorithms/Python/blob/master/strings/aho-corasick.py) + * [Boyer Moore Search](https://github.com/TheAlgorithms/Python/blob/master/strings/boyer_moore_search.py) + * [Knuth Morris Pratt](https://github.com/TheAlgorithms/Python/blob/master/strings/knuth_morris_pratt.py) + * [Levenshtein Distance](https://github.com/TheAlgorithms/Python/blob/master/strings/levenshtein_distance.py) + * [Manacher](https://github.com/TheAlgorithms/Python/blob/master/strings/manacher.py) + * [Min Cost String Conversion](https://github.com/TheAlgorithms/Python/blob/master/strings/min_cost_string_conversion.py) + * [Naive String Search](https://github.com/TheAlgorithms/Python/blob/master/strings/naive_string_search.py) + * [Rabin Karp](https://github.com/TheAlgorithms/Python/blob/master/strings/rabin_karp.py) + +## Traversals + * [Binary Tree Traversals](https://github.com/TheAlgorithms/Python/blob/master/traversals/binary_tree_traversals.py) + +## Web Programming + * [Crawl Google Results](https://github.com/TheAlgorithms/Python/blob/master/web_programming/crawl_google_results.py) diff --git a/License b/LICENSE.md similarity index 96% rename from License rename to LICENSE.md index c84ae570c084..a20869d96300 100644 --- a/License +++ b/LICENSE.md @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2016 The Algorithms +Copyright (c) 2019 The Algorithms Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/README.md b/README.md index 1e43deb6bdef..51b2cf8c854c 100644 --- a/README.md +++ b/README.md @@ -1,355 +1,31 @@ -# The Algorithms - Python -[![Donate](https://img.shields.io/badge/Donate-PayPal-green.svg)](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=JP3BLXA6KMDGW) +# The Algorithms - Python +[![Donate](https://img.shields.io/badge/Donate-PayPal-green.svg?logo=paypal&style=flat-square)](https://www.paypal.me/TheAlgorithms/100)  +[![Build Status](https://img.shields.io/travis/TheAlgorithms/Python.svg?label=Travis%20CI&logo=travis&style=flat-square)](https://travis-ci.com/TheAlgorithms/Python)  +[![LGTM](https://img.shields.io/lgtm/alerts/github/TheAlgorithms/Python.svg?label=LGTM&logo=LGTM&style=flat-square)](https://lgtm.com/projects/g/TheAlgorithms/Python/alerts)  +[![Gitter chat](https://img.shields.io/badge/Chat-Gitter-ff69b4.svg?label=Chat&logo=gitter&style=flat-square)](https://gitter.im/TheAlgorithms)  +[![contributions welcome](https://img.shields.io/static/v1.svg?label=Contributions&message=Welcome&color=0059b3&style=flat-square)](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md)  +![](https://img.shields.io/github/repo-size/TheAlgorithms/Python.svg?label=Repo%20size&style=flat-square)  + ### All algorithms implemented in Python (for education) -These implementations are for demonstration purposes. They are less efficient than the implementations in the Python standard library. +These implementations are for learning purposes. They may be less efficient than the implementations in the Python standard library. -## Sorting Algorithms +## Contribution Guidelines +Read our [Contribution Guidelines](CONTRIBUTING.md) before you contribute. -### Bubble Sort -![alt text][bubble-image] +## Community Channel -**Bubble sort**, sometimes referred to as *sinking sort*, is a simple sorting algorithm that repeatedly steps through the list to be sorted, compares each pair of adjacent items and swaps them if they are in the wrong order. The pass through the list is repeated until no swaps are needed, which indicates that the list is sorted. +We're on [Gitter](https://gitter.im/TheAlgorithms)! Please join us. -__Properties__ -* Worst case performance O(n2) -* Best case performance O(n) -* Average case performance O(n2) +## List of Algorithms -###### Source: [Wikipedia][bubble-wiki] -###### View the algorithm in [action][bubble-toptal] +See our [directory](DIRECTORY.md). -### Bucket -![alt text][bucket-image-1] -![alt text][bucket-image-2] -**Bucket sort**, or _bin sort_, is a sorting algorithm that works by distributing the elements of an array into a number of buckets. Each bucket is then sorted individually, either using a different sorting algorithm, or by recursively applying the bucket sorting algorithm. -__Properties__ -* Worst case performance O(n2) -* Best case performance O(n+k) -* Average case performance O(n+k) -###### Source: [Wikipedia][bucket-wiki] - -### Cocktail shaker -![alt text][cocktail-shaker-image] - -**Cocktail shaker sort**, also known as _bidirectional bubble sort_, _cocktail sort_, _shaker sort_ (which can also refer to a variant of _selection sort_), _ripple sort_, _shuffle sort_, or _shuttle sort_, is a variation of bubble sort that is both a stable sorting algorithm and a comparison sort. The algorithm differs from a bubble sort in that it sorts in both directions on each pass through the list. - -__Properties__ -* Worst case performance O(n2) -* Best case performance O(n) -* Average case performance O(n2) - -###### Source: [Wikipedia][cocktail-shaker-wiki] - - -### Insertion Sort -![alt text][insertion-image] - -**Insertion sort** is a simple sorting algorithm that builds the final sorted array (or list) one item at a time. It is much less efficient on *large* lists than more advanced algorithms such as quicksort, heapsort, or merge sort. - -__Properties__ -* Worst case performance O(n2) -* Best case performance O(n) -* Average case performance O(n2) - -###### Source: [Wikipedia][insertion-wiki] -###### View the algorithm in [action][insertion-toptal] - - -### Merge Sort -![alt text][merge-image] - -**Merge sort** (also commonly spelled *mergesort*) is an efficient, general-purpose, comparison-based sorting algorithm. Most implementations produce a stable sort, which means that the implementation preserves the input order of equal elements in the sorted output. Mergesort is a divide and conquer algorithm that was invented by John von Neumann in 1945. - -__Properties__ -* Worst case performance O(n log n) -* Best case performance O(n log n) -* Average case performance O(n log n) - -###### Source: [Wikipedia][merge-wiki] -###### View the algorithm in [action][merge-toptal] - -### Quick -![alt text][quick-image] - -**Quicksort** (sometimes called *partition-exchange sort*) is an efficient sorting algorithm, serving as a systematic method for placing the elements of an array in order. - -__Properties__ -* Worst case performance O(n2) -* Best case performance O(*n* log *n*) or O(n) with three-way partition -* Average case performance O(*n* log *n*) - -###### Source: [Wikipedia][quick-wiki] -###### View the algorithm in [action][quick-toptal] - - -### Heap -![alt text][heapsort-image] - -**Heapsort** is a _comparison-based_ sorting algorithm. It can be thought of as an improved selection sort. It divides its input into a sorted and an unsorted region, and it iteratively shrinks the unsorted region by extracting the largest element and moving that to the sorted region. - -__Properties__ -* Worst case performance O(*n* log *n*) -* Best case performance O(*n* log *n*) -* Average case performance O(*n* log *n*) - -###### Source: [Wikipedia][heap-wiki] -###### View the algorithm in [action](https://www.toptal.com/developers/sorting-algorithms/heap-sort) - - -### Radix - -From [Wikipedia][radix-wiki]: Radix sort is a non-comparative integer sorting algorithm that sorts data with integer keys by grouping keys by the individual digits which share the same significant position and value. - -__Properties__ -* Worst case performance O(wn) -* Best case performance O(wn) -* Average case performance O(wn) - -###### Source: [Wikipedia][radix-wiki] - - -### Selection -![alt text][selection-image] - -**Selection sort** is an algorithm that divides the input list into two parts: the sublist of items already sorted, which is built up from left to right at the front (left) of the list, and the sublist of items remaining to be sorted that occupy the rest of the list. Initially, the sorted sublist is empty and the unsorted sublist is the entire input list. The algorithm proceeds by finding the smallest (or largest, depending on sorting order) element in the unsorted sublist, exchanging (swapping) it with the leftmost unsorted element (putting it in sorted order), and moving the sublist boundaries one element to the right. - -__Properties__ -* Worst case performance O(n2) -* Best case performance O(n2) -* Average case performance O(n2) - -###### Source: [Wikipedia][selection-wiki] -###### View the algorithm in [action][selection-toptal] - - -### Shell -![alt text][shell-image] - -**Shellsort** is a generalization of *insertion sort* that allows the exchange of items that are far apart. The idea is to arrange the list of elements so that, starting anywhere, considering every nth element gives a sorted list. Such a list is said to be h-sorted. Equivalently, it can be thought of as h interleaved lists, each individually sorted. - -__Properties__ -* Worst case performance O(*n*log2*n*) -* Best case performance O(*n* log *n*) -* Average case performance depends on gap sequence - -###### Source: [Wikipedia][shell-wiki] -###### View the algorithm in [action][shell-toptal] - - -### Topological - -From [Wikipedia][topological-wiki]: **Topological sort**, or _topological ordering of a directed graph_ is a linear ordering of its vertices such that for every directed edge _uv_ from vertex _u_ to vertex _v_, _u_ comes before _v_ in the ordering. For instance, the vertices of the graph may represent tasks to be performed, and the edges may represent constraints that one task must be performed before another; in this application, a topological ordering is just a valid sequence for the tasks. A topological ordering is possible if and only if the graph has no directed cycles, that is, if it is a _directed acyclic graph_ (DAG). Any DAG has at least one topological ordering, and algorithms are known for constructing a topological ordering of any DAG in linear time. - -### Time-Complexity Graphs - -Comparing the complexity of sorting algorithms (*Bubble Sort*, *Insertion Sort*, *Selection Sort*) - -![Complexity Graphs](https://github.com/prateekiiest/Python/blob/master/sorts/sortinggraphs.png) - -Comparing the sorting algorithms: -
-Quicksort is a very fast algorithm but can be pretty tricky to implement -
-Bubble sort is a slow algorithm but is very easy to implement. To sort small sets of data, bubble sort may be a better option since it can be implemented quickly, but for larger datasets, the speedup from quicksort might be worth the trouble implementing the algorithm. - ----------------------------------------------------------------------------------- - -## Search Algorithms - -### Linear -![alt text][linear-image] - -**Linear search** or *sequential search* is a method for finding a target value within a list. It sequentially checks each element of the list for the target value until a match is found or until all the elements have been searched. Linear search runs in at worst linear time and makes at most n comparisons, where n is the length of the list. - -__Properties__ -* Worst case performance O(n) -* Best case performance O(1) -* Average case performance O(n) -* Worst case space complexity O(1) iterative - -###### Source: [Wikipedia][linear-wiki] - - -### Binary -![alt text][binary-image] - -**Binary search**, also known as *half-interval search* or *logarithmic search*, is a search algorithm that finds the position of a target value within a sorted array. It compares the target value to the middle element of the array; if they are unequal, the half in which the target cannot lie is eliminated and the search continues on the remaining half until it is successful. - -__Properties__ -* Worst case performance O(log n) -* Best case performance O(1) -* Average case performance O(log n) -* Worst case space complexity O(1) - -###### Source: [Wikipedia][binary-wiki] - - -## Interpolation -**Interpolation search** is an algorithm for searching for a key in an array that has been ordered by numerical values assigned to the keys (key values). It was first described by W. W. Peterson in 1957. Interpolation search resembles the method by which people search a telephone directory for a name (the key value by which the book's entries are ordered): in each step the algorithm calculates where in the remaining search space the sought item might be, based on the key values at the bounds of the search space and the value of the sought key, usually via a linear interpolation. The key value actually found at this estimated position is then compared to the key value being sought. If it is not equal, then depending on the comparison, the remaining search space is reduced to the part before or after the estimated position. This method will only work if calculations on the size of differences between key values are sensible. - -By comparison, binary search always chooses the middle of the remaining search space, discarding one half or the other, depending on the comparison between the key found at the estimated position and the key sought — it does not require numerical values for the keys, just a total order on them. The remaining search space is reduced to the part before or after the estimated position. The linear search uses equality only as it compares elements one-by-one from the start, ignoring any sorting. - -On average the interpolation search makes about log(log(n)) comparisons (if the elements are uniformly distributed), where n is the number of elements to be searched. In the worst case (for instance where the numerical values of the keys increase exponentially) it can make up to O(n) comparisons. - -In interpolation-sequential search, interpolation is used to find an item near the one being searched for, then linear search is used to find the exact item. - -###### Source: [Wikipedia][interpolation-wiki] - - -## Jump Search -**Jump search** or _block search_ refers to a search algorithm for ordered lists. It works by first checking all items Lkm, where {\displaystyle k\in \mathbb {N} } k\in \mathbb {N} and m is the block size, until an item is found that is larger than the search key. To find the exact position of the search key in the list a linear search is performed on the sublist L[(k-1)m, km]. - -The optimal value of m is √n, where n is the length of the list L. Because both steps of the algorithm look at, at most, √n items the algorithm runs in O(√n) time. This is better than a linear search, but worse than a binary search. The advantage over the latter is that a jump search only needs to jump backwards once, while a binary can jump backwards up to log n times. This can be important if a jumping backwards takes significantly more time than jumping forward. - -The algorithm can be modified by performing multiple levels of jump search on the sublists, before finally performing the linear search. For an k-level jump search the optimum block size ml for the lth level (counting from 1) is n(k-l)/k. The modified algorithm will perform k backward jumps and runs in O(kn1/(k+1)) time. - -###### Source: [Wikipedia][jump-wiki] - - -## Quick Select -![alt text][QuickSelect-image] - -**Quick Select** is a selection algorithm to find the kth smallest element in an unordered list. It is related to the quicksort sorting algorithm. Like quicksort, it was developed by Tony Hoare, and thus is also known as Hoare's selection algorithm.[1] Like quicksort, it is efficient in practice and has good average-case performance, but has poor worst-case performance. Quickselect and its variants are the selection algorithms most often used in efficient real-world implementations. - -Quickselect uses the same overall approach as quicksort, choosing one element as a pivot and partitioning the data in two based on the pivot, accordingly as less than or greater than the pivot. However, instead of recursing into both sides, as in quicksort, quickselect only recurses into one side – the side with the element it is searching for. This reduces the average complexity from O(n log n) to O(n), with a worst case of O(n2). - -As with quicksort, quickselect is generally implemented as an in-place algorithm, and beyond selecting the k'th element, it also partially sorts the data. See selection algorithm for further discussion of the connection with sorting. - -###### Source: [Wikipedia][quick-wiki] - - -## Tabu -**Tabu search** uses a local or neighborhood search procedure to iteratively move from one potential solution {\displaystyle x} x to an improved solution {\displaystyle x'} x' in the neighborhood of {\displaystyle x} x, until some stopping criterion has been satisfied (generally, an attempt limit or a score threshold). Local search procedures often become stuck in poor-scoring areas or areas where scores plateau. In order to avoid these pitfalls and explore regions of the search space that would be left unexplored by other local search procedures, tabu search carefully explores the neighborhood of each solution as the search progresses. The solutions admitted to the new neighborhood, {\displaystyle N^{*}(x)} N^*(x), are determined through the use of memory structures. Using these memory structures, the search progresses by iteratively moving from the current solution {\displaystyle x} x to an improved solution {\displaystyle x'} x' in {\displaystyle N^{*}(x)} N^*(x). - -These memory structures form what is known as the tabu list, a set of rules and banned solutions used to filter which solutions will be admitted to the neighborhood {\displaystyle N^{*}(x)} N^*(x) to be explored by the search. In its simplest form, a tabu list is a short-term set of the solutions that have been visited in the recent past (less than {\displaystyle n} n iterations ago, where {\displaystyle n} n is the number of previous solutions to be stored — is also called the tabu tenure). More commonly, a tabu list consists of solutions that have changed by the process of moving from one solution to another. It is convenient, for ease of description, to understand a “solution” to be coded and represented by such attributes. - -###### Source: [Wikipedia][tabu-wiki] - ----------------------------------------------------------------------------------------------------------------------- - -## Ciphers - -### Caesar -![alt text][caesar] - -**Caesar cipher**, also known as _Caesar's cipher_, the _shift cipher_, _Caesar's code_ or _Caesar shift_, is one of the simplest and most widely known encryption techniques.
-It is **a type of substitution cipher** in which each letter in the plaintext is replaced by a letter some fixed number of positions down the alphabet. For example, with a left shift of 3, D would be replaced by A, E would become B, and so on.
-The method is named after **Julius Caesar**, who used it in his private correspondence.
-The encryption step performed by a Caesar cipher is often incorporated as part of more complex schemes, such as the Vigenère cipher, and still has modern application in the ROT13 system. As with all single-alphabet substitution ciphers, the Caesar cipher is easily broken and in modern practice offers essentially no communication security. - -###### Source: [Wikipedia](https://en.wikipedia.org/wiki/Caesar_cipher) - - -### Vigenère - -**Vigenère cipher** is a method of encrypting alphabetic text by using a series of **interwoven Caesar ciphers** based on the letters of a keyword. It is **a form of polyalphabetic substitution**.
-The Vigenère cipher has been reinvented many times. The method was originally described by Giovan Battista Bellaso in his 1553 book La cifra del. Sig. Giovan Battista Bellaso; however, the scheme was later misattributed to Blaise de Vigenère in the 19th century, and is now widely known as the "Vigenère cipher".
-Though the cipher is easy to understand and implement, for three centuries it resisted all attempts to break it; this earned it the description **le chiffre indéchiffrable**(French for 'the indecipherable cipher'). -Many people have tried to implement encryption schemes that are essentially Vigenère ciphers. Friedrich Kasiski was the first to publish a general method of deciphering a Vigenère cipher in 1863. - -###### Source: [Wikipedia](https://en.wikipedia.org/wiki/Vigen%C3%A8re_cipher) - - -### Transposition - -**Transposition cipher** is a method of encryption by which the positions held by units of *plaintext* (which are commonly characters or groups of characters) are shifted according to a regular system, so that the *ciphertext* constitutes a permutation of the plaintext. That is, the order of the units is changed (the plaintext is reordered).
- -Mathematically a bijective function is used on the characters' positions to encrypt and an inverse function to decrypt. - -###### Source: [Wikipedia](https://en.wikipedia.org/wiki/Transposition_cipher) - - -### RSA (Rivest–Shamir–Adleman) -**RSA** _(Rivest–Shamir–Adleman)_ is one of the first public-key cryptosystems and is widely used for secure data transmission. In such a cryptosystem, the encryption key is public and it is different from the decryption key which is kept secret (private). In RSA, this asymmetry is based on the practical difficulty of the factorization of the product of two large prime numbers, the "factoring problem". The acronym RSA is made of the initial letters of the surnames of Ron Rivest, Adi Shamir, and Leonard Adleman, who first publicly described the algorithm in 1978. Clifford Cocks, an English mathematician working for the British intelligence agency Government Communications Headquarters (GCHQ), had developed an equivalent system in 1973, but this was not declassified until 1997.[1] - -A user of RSA creates and then publishes a public key based on two large prime numbers, along with an auxiliary value. The prime numbers must be kept secret. Anyone can use the public key to encrypt a message, but with currently published methods, and if the public key is large enough, only someone with knowledge of the prime numbers can decode the message feasibly.[2] Breaking RSA encryption is known as the RSA problem. Whether it is as difficult as the factoring problem remains an open question. - -###### Source: [Wikipedia](https://en.wikipedia.org/wiki/RSA_(cryptosystem)) - - -## ROT13 -![alt text][ROT13-image] - -**ROT13** ("rotate by 13 places", sometimes hyphenated _ROT-13_) is a simple letter substitution cipher that replaces a letter with the 13th letter after it, in the alphabet. ROT13 is a special case of the Caesar cipher which was developed in ancient Rome. - -Because there are 26 letters (2×13) in the basic Latin alphabet, ROT13 is its own inverse; that is, to undo ROT13, the same algorithm is applied, so the same action can be used for encoding and decoding. The algorithm provides virtually no cryptographic security, and is often cited as a canonical example of weak encryption.[1] - -###### Source: [Wikipedia](https://en.wikipedia.org/wiki/ROT13) - - -## XOR -**XOR cipher** is a simple type of additive cipher,[1] an encryption algorithm that operates according to the principles: - -A {\displaystyle \oplus } \oplus 0 = A, -A {\displaystyle \oplus } \oplus A = 0, -(A {\displaystyle \oplus } \oplus B) {\displaystyle \oplus } \oplus C = A {\displaystyle \oplus } \oplus (B {\displaystyle \oplus } \oplus C), -(B {\displaystyle \oplus } \oplus A) {\displaystyle \oplus } \oplus A = B {\displaystyle \oplus } \oplus 0 = B, -where {\displaystyle \oplus } \oplus denotes the exclusive disjunction (XOR) operation. This operation is sometimes called modulus 2 addition (or subtraction, which is identical).[2] With this logic, a string of text can be encrypted by applying the bitwise XOR operator to every character using a given key. To decrypt the output, merely reapplying the XOR function with the key will remove the cipher. - -###### Source: [Wikipedia](https://en.wikipedia.org/wiki/XOR_cipher) - - -[bubble-toptal]: https://www.toptal.com/developers/sorting-algorithms/bubble-sort -[bubble-wiki]: https://en.wikipedia.org/wiki/Bubble_sort -[bubble-image]: https://upload.wikimedia.org/wikipedia/commons/thumb/8/83/Bubblesort-edited-color.svg/220px-Bubblesort-edited-color.svg.png "Bubble Sort" - -[bucket-wiki]: https://en.wikipedia.org/wiki/Bucket_sort -[bucket-image-1]: https://upload.wikimedia.org/wikipedia/commons/thumb/6/61/Bucket_sort_1.svg/311px-Bucket_sort_1.svg.png "Bucket Sort" -[bucket-image-2]: https://upload.wikimedia.org/wikipedia/commons/thumb/e/e3/Bucket_sort_2.svg/311px-Bucket_sort_2.svg.png "Bucket Sort" - -[cocktail-shaker-wiki]: https://en.wikipedia.org/wiki/Cocktail_shaker_sort -[cocktail-shaker-image]: https://upload.wikimedia.org/wikipedia/commons/e/ef/Sorting_shaker_sort_anim.gif "Cocktail Shaker Sort" - -[insertion-toptal]: https://www.toptal.com/developers/sorting-algorithms/insertion-sort -[insertion-wiki]: https://en.wikipedia.org/wiki/Insertion_sort -[insertion-image]: https://upload.wikimedia.org/wikipedia/commons/7/7e/Insertionsort-edited.png "Insertion Sort" - -[quick-toptal]: https://www.toptal.com/developers/sorting-algorithms/quick-sort -[quick-wiki]: https://en.wikipedia.org/wiki/Quicksort -[quick-image]: https://upload.wikimedia.org/wikipedia/commons/6/6a/Sorting_quicksort_anim.gif "Quick Sort" - -[heapsort-image]: https://upload.wikimedia.org/wikipedia/commons/4/4d/Heapsort-example.gif "Heap Sort" -[heap-wiki]: https://en.wikipedia.org/wiki/Heapsort - -[radix-wiki]: https://en.wikipedia.org/wiki/Radix_sort - -[merge-toptal]: https://www.toptal.com/developers/sorting-algorithms/merge-sort -[merge-wiki]: https://en.wikipedia.org/wiki/Merge_sort -[merge-image]: https://upload.wikimedia.org/wikipedia/commons/c/cc/Merge-sort-example-300px.gif "Merge Sort" - -[selection-toptal]: https://www.toptal.com/developers/sorting-algorithms/selection-sort -[selection-wiki]: https://en.wikipedia.org/wiki/Selection_sort -[selection-image]: https://upload.wikimedia.org/wikipedia/commons/thumb/b/b0/Selection_sort_animation.gif/250px-Selection_sort_animation.gif "Selection Sort Sort" - -[shell-toptal]: https://www.toptal.com/developers/sorting-algorithms/shell-sort -[shell-wiki]: https://en.wikipedia.org/wiki/Shellsort -[shell-image]: https://upload.wikimedia.org/wikipedia/commons/d/d8/Sorting_shellsort_anim.gif "Shell Sort" - -[topological-wiki]: https://en.wikipedia.org/wiki/Topological_sorting - -[linear-wiki]: https://en.wikipedia.org/wiki/Linear_search -[linear-image]: http://www.tutorialspoint.com/data_structures_algorithms/images/linear_search.gif "Linear Search" - -[binary-wiki]: https://en.wikipedia.org/wiki/Binary_search_algorithm -[binary-image]: https://upload.wikimedia.org/wikipedia/commons/f/f7/Binary_search_into_array.png "Binary Search" - - -[interpolation-wiki]: https://en.wikipedia.org/wiki/Interpolation_search - -[jump-wiki]: https://en.wikipedia.org/wiki/Jump_search - -[quick-wiki]: https://en.wikipedia.org/wiki/Quickselect - -[tabu-wiki]: https://en.wikipedia.org/wiki/Tabu_search - -[ROT13-image]: https://upload.wikimedia.org/wikipedia/commons/3/33/ROT13_table_with_example.svg "ROT13" - -[JumpSearch-image]: https://i1.wp.com/theoryofprogramming.com/wp-content/uploads/2016/11/jump-search-1.jpg "Jump Search" - -[QuickSelect-image]: https://upload.wikimedia.org/wikipedia/commons/0/04/Selecting_quickselect_frames.gif "Quick Select" +[![Open in Gitpod](https://gitpod.io/button/open-in-gitpod.svg?style=flat-square)](https://gitpod.io/#https://github.com/TheAlgorithms/Python) diff --git a/Travis_CI_tests_are_failing.md b/Travis_CI_tests_are_failing.md new file mode 100644 index 000000000000..10bf5a6655d2 --- /dev/null +++ b/Travis_CI_tests_are_failing.md @@ -0,0 +1,9 @@ +# Travis CI test are failing +### How do I find out what is wrong with my pull request? +1. In your PR look for the failing test and click the `Details` link: ![Travis_CI_fail_1.png](images/Travis_CI_fail_1.png) +2. On the next page, click `The build failed` link: ![Travis_CI_fail_2.png](images/Travis_CI_fail_2.png) +3. Now scroll down and look for `red` text describing the error(s) in the test log. + +Pull requests will __not__ be merged if the Travis CI tests are failing. + +If anything is unclear, please read through [CONTRIBUTING.md](CONTRIBUTING.md) and attempt to run the failing tests on your computer before asking for assistance. diff --git a/arithmetic_analysis/bisection.py b/arithmetic_analysis/bisection.py index c81fa84f81e1..78582b025880 100644 --- a/arithmetic_analysis/bisection.py +++ b/arithmetic_analysis/bisection.py @@ -1,7 +1,9 @@ import math -def bisection(function, a, b): # finds where the function becomes 0 in [a,b] using bolzano +def bisection( + function, a, b +): # finds where the function becomes 0 in [a,b] using bolzano start = a end = b @@ -9,25 +11,28 @@ def bisection(function, a, b): # finds where the function becomes 0 in [a,b] us return a elif function(b) == 0: return b - elif function(a) * function(b) > 0: # if none of these are root and they are both positive or negative, + elif ( + function(a) * function(b) > 0 + ): # if none of these are root and they are both positive or negative, # then his algorithm can't find the root print("couldn't find root in [a,b]") return else: - mid = (start + end) / 2 - while abs(start - mid) > 10**-7: # until we achieve precise equals to 10^-7 + mid = start + (end - start) / 2.0 + while abs(start - mid) > 10 ** -7: # until we achieve precise equals to 10^-7 if function(mid) == 0: return mid elif function(mid) * function(start) < 0: end = mid else: start = mid - mid = (start + end) / 2 + mid = start + (end - start) / 2.0 return mid def f(x): - return math.pow(x, 3) - 2*x - 5 + return math.pow(x, 3) - 2 * x - 5 + if __name__ == "__main__": print(bisection(f, 1, 1000)) diff --git a/arithmetic_analysis/gaussian_elimination.py b/arithmetic_analysis/gaussian_elimination.py new file mode 100644 index 000000000000..51207686c12a --- /dev/null +++ b/arithmetic_analysis/gaussian_elimination.py @@ -0,0 +1,83 @@ +""" +Gaussian elimination method for solving a system of linear equations. +Gaussian elimination - https://en.wikipedia.org/wiki/Gaussian_elimination +""" + + +import numpy as np + + +def retroactive_resolution(coefficients: np.matrix, vector: np.array) -> np.array: + """ + This function performs a retroactive linear system resolution + for triangular matrix + + Examples: + 2x1 + 2x2 - 1x3 = 5 2x1 + 2x2 = -1 + 0x1 - 2x2 - 1x3 = -7 0x1 - 2x2 = -1 + 0x1 + 0x2 + 5x3 = 15 + >>> gaussian_elimination([[2, 2, -1], [0, -2, -1], [0, 0, 5]], [[5], [-7], [15]]) + array([[2.], + [2.], + [3.]]) + >>> gaussian_elimination([[2, 2], [0, -2]], [[-1], [-1]]) + array([[-1. ], + [ 0.5]]) + """ + + rows, columns = np.shape(coefficients) + + x = np.zeros((rows, 1), dtype=float) + for row in reversed(range(rows)): + sum = 0 + for col in range(row + 1, columns): + sum += coefficients[row, col] * x[col] + + x[row, 0] = (vector[row] - sum) / coefficients[row, row] + + return x + + +def gaussian_elimination(coefficients: np.matrix, vector: np.array) -> np.array: + """ + This function performs Gaussian elimination method + + Examples: + 1x1 - 4x2 - 2x3 = -2 1x1 + 2x2 = 5 + 5x1 + 2x2 - 2x3 = -3 5x1 + 2x2 = 5 + 1x1 - 1x2 + 0x3 = 4 + >>> gaussian_elimination([[1, -4, -2], [5, 2, -2], [1, -1, 0]], [[-2], [-3], [4]]) + array([[ 2.3 ], + [-1.7 ], + [ 5.55]]) + >>> gaussian_elimination([[1, 2], [5, 2]], [[5], [5]]) + array([[0. ], + [2.5]]) + """ + # coefficients must to be a square matrix so we need to check first + rows, columns = np.shape(coefficients) + if rows != columns: + return [] + + # augmented matrix + augmented_mat = np.concatenate((coefficients, vector), axis=1) + augmented_mat = augmented_mat.astype("float64") + + # scale the matrix leaving it triangular + for row in range(rows - 1): + pivot = augmented_mat[row, row] + for col in range(row + 1, columns): + factor = augmented_mat[col, row] / pivot + augmented_mat[col, :] -= factor * augmented_mat[row, :] + + x = retroactive_resolution( + augmented_mat[:, 0:columns], augmented_mat[:, columns : columns + 1] + ) + + return x + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/arithmetic_analysis/image_data/2D_problems.JPG b/arithmetic_analysis/image_data/2D_problems.JPG new file mode 100644 index 000000000000..8887cf641685 Binary files /dev/null and b/arithmetic_analysis/image_data/2D_problems.JPG differ diff --git a/arithmetic_analysis/image_data/2D_problems_1.JPG b/arithmetic_analysis/image_data/2D_problems_1.JPG new file mode 100644 index 000000000000..aa9f45362014 Binary files /dev/null and b/arithmetic_analysis/image_data/2D_problems_1.JPG differ diff --git a/arithmetic_analysis/in_static_equilibrium.py b/arithmetic_analysis/in_static_equilibrium.py new file mode 100644 index 000000000000..addaff888f7f --- /dev/null +++ b/arithmetic_analysis/in_static_equilibrium.py @@ -0,0 +1,86 @@ +""" +Checks if a system of forces is in static equilibrium. + +python/black : true +flake8 : passed +mypy : passed +""" + +from numpy import array, cos, sin, radians, cross # type: ignore +from typing import List + + +def polar_force( + magnitude: float, angle: float, radian_mode: bool = False +) -> List[float]: + """ + Resolves force along rectangular components. + (force, angle) => (force_x, force_y) + >>> polar_force(10, 45) + [7.0710678118654755, 7.071067811865475] + >>> polar_force(10, 3.14, radian_mode=True) + [-9.999987317275394, 0.01592652916486828] + """ + if radian_mode: + return [magnitude * cos(angle), magnitude * sin(angle)] + return [magnitude * cos(radians(angle)), magnitude * sin(radians(angle))] + + +def in_static_equilibrium( + forces: array, location: array, eps: float = 10 ** -1 +) -> bool: + """ + Check if a system is in equilibrium. + It takes two numpy.array objects. + forces ==> [ + [force1_x, force1_y], + [force2_x, force2_y], + ....] + location ==> [ + [x1, y1], + [x2, y2], + ....] + >>> force = array([[1, 1], [-1, 2]]) + >>> location = array([[1, 0], [10, 0]]) + >>> in_static_equilibrium(force, location) + False + """ + # summation of moments is zero + moments: array = cross(location, forces) + sum_moments: float = sum(moments) + return abs(sum_moments) < eps + + +if __name__ == "__main__": + # Test to check if it works + forces = array( + [polar_force(718.4, 180 - 30), polar_force(879.54, 45), polar_force(100, -90)] + ) + + location = array([[0, 0], [0, 0], [0, 0]]) + + assert in_static_equilibrium(forces, location) + + # Problem 1 in image_data/2D_problems.jpg + forces = array( + [ + polar_force(30 * 9.81, 15), + polar_force(215, 180 - 45), + polar_force(264, 90 - 30), + ] + ) + + location = array([[0, 0], [0, 0], [0, 0]]) + + assert in_static_equilibrium(forces, location) + + # Problem in image_data/2D_problems_1.jpg + forces = array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]]) + + location = array([[0, 0], [6, 0], [10, 0], [12, 0]]) + + assert in_static_equilibrium(forces, location) + + import doctest + + doctest.testmod() diff --git a/arithmetic_analysis/intersection.py b/arithmetic_analysis/intersection.py index 2f25f76ebd96..0fdcfbf1943e 100644 --- a/arithmetic_analysis/intersection.py +++ b/arithmetic_analysis/intersection.py @@ -1,17 +1,24 @@ import math -def intersection(function,x0,x1): #function is the f we want to find its root and x0 and x1 are two random starting points + +def intersection( + function, x0, x1 +): # function is the f we want to find its root and x0 and x1 are two random starting points x_n = x0 x_n1 = x1 while True: - x_n2 = x_n1-(function(x_n1)/((function(x_n1)-function(x_n))/(x_n1-x_n))) - if abs(x_n2 - x_n1) < 10**-5: + x_n2 = x_n1 - ( + function(x_n1) / ((function(x_n1) - function(x_n)) / (x_n1 - x_n)) + ) + if abs(x_n2 - x_n1) < 10 ** -5: return x_n2 - x_n=x_n1 - x_n1=x_n2 + x_n = x_n1 + x_n1 = x_n2 + def f(x): - return math.pow(x , 3) - (2 * x) -5 + return math.pow(x, 3) - (2 * x) - 5 + if __name__ == "__main__": - print(intersection(f,3,3.5)) + print(intersection(f, 3, 3.5)) diff --git a/arithmetic_analysis/lu_decomposition.py b/arithmetic_analysis/lu_decomposition.py index f291d2dfe003..4372621d74cb 100644 --- a/arithmetic_analysis/lu_decomposition.py +++ b/arithmetic_analysis/lu_decomposition.py @@ -1,32 +1,34 @@ +"""Lower-Upper (LU) Decomposition.""" + # lower–upper (LU) decomposition - https://en.wikipedia.org/wiki/LU_decomposition import numpy -def LUDecompose (table): + +def LUDecompose(table): # Table that contains our data # Table has to be a square array so we need to check first - rows,columns=numpy.shape(table) - L=numpy.zeros((rows,columns)) - U=numpy.zeros((rows,columns)) - if rows!=columns: + rows, columns = numpy.shape(table) + L = numpy.zeros((rows, columns)) + U = numpy.zeros((rows, columns)) + if rows != columns: return [] - for i in range (columns): - for j in range(i-1): - sum=0 - for k in range (j-1): - sum+=L[i][k]*U[k][j] - L[i][j]=(table[i][j]-sum)/U[j][j] - L[i][i]=1 - for j in range(i-1,columns): - sum1=0 - for k in range(i-1): - sum1+=L[i][k]*U[k][j] - U[i][j]=table[i][j]-sum1 - return L,U + for i in range(columns): + for j in range(i - 1): + sum = 0 + for k in range(j - 1): + sum += L[i][k] * U[k][j] + L[i][j] = (table[i][j] - sum) / U[j][j] + L[i][i] = 1 + for j in range(i - 1, columns): + sum1 = 0 + for k in range(i - 1): + sum1 += L[i][k] * U[k][j] + U[i][j] = table[i][j] - sum1 + return L, U + if __name__ == "__main__": - matrix =numpy.array([[2,-2,1], - [0,1,2], - [5,3,1]]) - L,U = LUDecompose(matrix) + matrix = numpy.array([[2, -2, 1], [0, 1, 2], [5, 3, 1]]) + L, U = LUDecompose(matrix) print(L) print(U) diff --git a/arithmetic_analysis/newton_forward_interpolation.py b/arithmetic_analysis/newton_forward_interpolation.py new file mode 100644 index 000000000000..09adb5113f82 --- /dev/null +++ b/arithmetic_analysis/newton_forward_interpolation.py @@ -0,0 +1,55 @@ +# https://www.geeksforgeeks.org/newton-forward-backward-interpolation/ + +import math + +# for calculating u value +def ucal(u, p): + """ + >>> ucal(1, 2) + 0 + >>> ucal(1.1, 2) + 0.11000000000000011 + >>> ucal(1.2, 2) + 0.23999999999999994 + """ + temp = u + for i in range(1, p): + temp = temp * (u - i) + return temp + + +def main(): + n = int(input("enter the numbers of values")) + y = [] + for i in range(n): + y.append([]) + for i in range(n): + for j in range(n): + y[i].append(j) + y[i][j] = 0 + + print("enter the values of parameters in a list") + x = list(map(int, input().split())) + + print("enter the values of corresponding parameters") + for i in range(n): + y[i][0] = float(input()) + + value = int(input("enter the value to interpolate")) + u = (value - x[0]) / (x[1] - x[0]) + + # for calculating forward difference table + + for i in range(1, n): + for j in range(n - i): + y[j][i] = y[j + 1][i - 1] - y[j][i - 1] + + summ = y[0][0] + for i in range(1, n): + summ += (ucal(u, i) * y[0][i]) / math.factorial(i) + + print("the value at {} is {}".format(value, summ)) + + +if __name__ == "__main__": + main() diff --git a/arithmetic_analysis/newton_method.py b/arithmetic_analysis/newton_method.py index 2ed29502522e..1408a983041d 100644 --- a/arithmetic_analysis/newton_method.py +++ b/arithmetic_analysis/newton_method.py @@ -1,18 +1,25 @@ +"""Newton's Method.""" + # Newton's Method - https://en.wikipedia.org/wiki/Newton%27s_method -def newton(function,function1,startingInt): #function is the f(x) and function1 is the f'(x) - x_n=startingInt - while True: - x_n1=x_n-function(x_n)/function1(x_n) - if abs(x_n-x_n1) < 10**-5: - return x_n1 - x_n=x_n1 - + +# function is the f(x) and function1 is the f'(x) +def newton(function, function1, startingInt): + x_n = startingInt + while True: + x_n1 = x_n - function(x_n) / function1(x_n) + if abs(x_n - x_n1) < 10 ** -5: + return x_n1 + x_n = x_n1 + + def f(x): - return (x**3) - (2 * x) -5 + return (x ** 3) - (2 * x) - 5 + def f1(x): - return 3 * (x**2) -2 + return 3 * (x ** 2) - 2 + if __name__ == "__main__": - print(newton(f,f1,3)) + print(newton(f, f1, 3)) diff --git a/arithmetic_analysis/newton_raphson_method.py b/arithmetic_analysis/newton_raphson_method.py index 5e7e2f930abc..646b352a923c 100644 --- a/arithmetic_analysis/newton_raphson_method.py +++ b/arithmetic_analysis/newton_raphson_method.py @@ -1,36 +1,34 @@ # Implementing Newton Raphson method in Python -# Author: Haseeb - +# Author: Syed Haseeb Shah (github.com/QuantumNovice) +# The Newton-Raphson method (also known as Newton's method) is a way to +# quickly find a good approximation for the root of a real-valued function from sympy import diff from decimal import Decimal + def NewtonRaphson(func, a): - ''' Finds root from the point 'a' onwards by Newton-Raphson method ''' + """ Finds root from the point 'a' onwards by Newton-Raphson method """ while True: - c = Decimal(a) - ( Decimal(eval(func)) / Decimal(eval(str(diff(func)))) ) - + c = Decimal(a) - (Decimal(eval(func)) / Decimal(eval(str(diff(func))))) + a = c # This number dictates the accuracy of the answer - if abs(eval(func)) < 10**-15: - return c - + if abs(eval(func)) < 10 ** -15: + return c + # Let's Execute -if __name__ == '__main__': +if __name__ == "__main__": # Find root of trigonometric function # Find value of pi - print ('sin(x) = 0', NewtonRaphson('sin(x)', 2)) - + print("sin(x) = 0", NewtonRaphson("sin(x)", 2)) + # Find root of polynomial - print ('x**2 - 5*x +2 = 0', NewtonRaphson('x**2 - 5*x +2', 0.4)) - + print("x**2 - 5*x +2 = 0", NewtonRaphson("x**2 - 5*x +2", 0.4)) + # Find Square Root of 5 - print ('x**2 - 5 = 0', NewtonRaphson('x**2 - 5', 0.1)) + print("x**2 - 5 = 0", NewtonRaphson("x**2 - 5", 0.1)) # Exponential Roots - print ('exp(x) - 1 = 0', NewtonRaphson('exp(x) - 1', 0)) - - - - + print("exp(x) - 1 = 0", NewtonRaphson("exp(x) - 1", 0)) diff --git a/arithmetic_analysis/secant_method.py b/arithmetic_analysis/secant_method.py new file mode 100644 index 000000000000..b05d44c627d8 --- /dev/null +++ b/arithmetic_analysis/secant_method.py @@ -0,0 +1,28 @@ +# Implementing Secant method in Python +# Author: dimgrichr + + +from math import exp + + +def f(x): + """ + >>> f(5) + 39.98652410600183 + """ + return 8 * x - 2 * exp(-x) + + +def SecantMethod(lower_bound, upper_bound, repeats): + """ + >>> SecantMethod(1, 3, 2) + 0.2139409276214589 + """ + x0 = lower_bound + x1 = upper_bound + for i in range(0, repeats): + x0, x1 = x1, x1 - (f(x1) * (x1 - x0)) / (f(x1) - f(x0)) + return x1 + + +print(f"The solution is: {SecantMethod(1, 3, 2)}") diff --git a/backtracking/all_combinations.py b/backtracking/all_combinations.py new file mode 100644 index 000000000000..23fe378f5462 --- /dev/null +++ b/backtracking/all_combinations.py @@ -0,0 +1,41 @@ +# -*- coding: utf-8 -*- + +""" + In this problem, we want to determine all possible combinations of k + numbers out of 1 ... n. We use backtracking to solve this problem. + Time complexity: O(C(n,k)) which is O(n choose k) = O((n!/(k! * (n - k)!))) +""" + + +def generate_all_combinations(n: int, k: int) -> [[int]]: + """ + >>> generate_all_combinations(n=4, k=2) + [[1, 2], [1, 3], [1, 4], [2, 3], [2, 4], [3, 4]] + """ + + result = [] + create_all_state(1, n, k, [], result) + return result + + +def create_all_state(increment, total_number, level, current_list, total_list): + if level == 0: + total_list.append(current_list[:]) + return + + for i in range(increment, total_number - level + 2): + current_list.append(i) + create_all_state(i + 1, total_number, level - 1, current_list, total_list) + current_list.pop() + + +def print_all_state(total_list): + for i in total_list: + print(*i) + + +if __name__ == "__main__": + n = 4 + k = 2 + total_list = generate_all_combinations(n, k) + print_all_state(total_list) diff --git a/backtracking/all_permutations.py b/backtracking/all_permutations.py new file mode 100644 index 000000000000..b0955bf53a31 --- /dev/null +++ b/backtracking/all_permutations.py @@ -0,0 +1,45 @@ +""" + In this problem, we want to determine all possible permutations + of the given sequence. We use backtracking to solve this problem. + + Time complexity: O(n! * n), + where n denotes the length of the given sequence. +""" + + +def generate_all_permutations(sequence): + create_state_space_tree(sequence, [], 0, [0 for i in range(len(sequence))]) + + +def create_state_space_tree(sequence, current_sequence, index, index_used): + """ + Creates a state space tree to iterate through each branch using DFS. + We know that each state has exactly len(sequence) - index children. + It terminates when it reaches the end of the given sequence. + """ + + if index == len(sequence): + print(current_sequence) + return + + for i in range(len(sequence)): + if not index_used[i]: + current_sequence.append(sequence[i]) + index_used[i] = True + create_state_space_tree(sequence, current_sequence, index + 1, index_used) + current_sequence.pop() + index_used[i] = False + + +""" +remove the comment to take an input from the user + +print("Enter the elements") +sequence = list(map(int, input().split())) +""" + +sequence = [3, 1, 2, 4] +generate_all_permutations(sequence) + +sequence = ["A", "B", "C"] +generate_all_permutations(sequence) diff --git a/backtracking/all_subsequences.py b/backtracking/all_subsequences.py new file mode 100644 index 000000000000..4a22c05d29a8 --- /dev/null +++ b/backtracking/all_subsequences.py @@ -0,0 +1,42 @@ +""" + In this problem, we want to determine all possible subsequences + of the given sequence. We use backtracking to solve this problem. + + Time complexity: O(2^n), + where n denotes the length of the given sequence. +""" + + +def generate_all_subsequences(sequence): + create_state_space_tree(sequence, [], 0) + + +def create_state_space_tree(sequence, current_subsequence, index): + """ + Creates a state space tree to iterate through each branch using DFS. + We know that each state has exactly two children. + It terminates when it reaches the end of the given sequence. + """ + + if index == len(sequence): + print(current_subsequence) + return + + create_state_space_tree(sequence, current_subsequence, index + 1) + current_subsequence.append(sequence[index]) + create_state_space_tree(sequence, current_subsequence, index + 1) + current_subsequence.pop() + + +""" +remove the comment to take an input from the user + +print("Enter the elements") +sequence = list(map(int, input().split())) +""" + +sequence = [3, 1, 2, 4] +generate_all_subsequences(sequence) + +sequence = ["A", "B", "C"] +generate_all_subsequences(sequence) diff --git a/backtracking/minimax.py b/backtracking/minimax.py new file mode 100644 index 000000000000..af07b8d8171a --- /dev/null +++ b/backtracking/minimax.py @@ -0,0 +1,34 @@ +import math + +""" Minimax helps to achieve maximum score in a game by checking all possible moves + depth is current depth in game tree. + nodeIndex is index of current node in scores[]. + if move is of maximizer return true else false + leaves of game tree is stored in scores[] + height is maximum height of Game tree +""" + + +def minimax(Depth, nodeIndex, isMax, scores, height): + + if Depth == height: + return scores[nodeIndex] + + if isMax: + return max( + minimax(Depth + 1, nodeIndex * 2, False, scores, height), + minimax(Depth + 1, nodeIndex * 2 + 1, False, scores, height), + ) + return min( + minimax(Depth + 1, nodeIndex * 2, True, scores, height), + minimax(Depth + 1, nodeIndex * 2 + 1, True, scores, height), + ) + + +if __name__ == "__main__": + + scores = [90, 23, 6, 33, 21, 65, 123, 34423] + height = math.log(len(scores), 2) + + print("Optimal value : ", end="") + print(minimax(0, 0, True, scores, height)) diff --git a/backtracking/n_queens.py b/backtracking/n_queens.py new file mode 100644 index 000000000000..f95357c82e21 --- /dev/null +++ b/backtracking/n_queens.py @@ -0,0 +1,88 @@ +""" + + The nqueens problem is of placing N queens on a N * N + chess board such that no queen can attack any other queens placed + on that chess board. + This means that one queen cannot have any other queen on its horizontal, vertical and + diagonal lines. + +""" +solution = [] + + +def isSafe(board, row, column): + """ + This function returns a boolean value True if it is safe to place a queen there considering + the current state of the board. + + Parameters : + board(2D matrix) : board + row ,column : coordinates of the cell on a board + + Returns : + Boolean Value + + """ + for i in range(len(board)): + if board[row][i] == 1: + return False + for i in range(len(board)): + if board[i][column] == 1: + return False + for i, j in zip(range(row, -1, -1), range(column, -1, -1)): + if board[i][j] == 1: + return False + for i, j in zip(range(row, -1, -1), range(column, len(board))): + if board[i][j] == 1: + return False + return True + + +def solve(board, row): + """ + It creates a state space tree and calls the safe function untill it receives a + False Boolean and terminates that brach and backtracks to the next + poosible solution branch. + """ + if row >= len(board): + """ + If the row number exceeds N we have board with a successful combination + and that combination is appended to the solution list and the board is printed. + + """ + solution.append(board) + printboard(board) + print() + return + for i in range(len(board)): + """ + For every row it iterates through each column to check if it is feesible to place a + queen there. + If all the combinations for that particaular branch are successfull the board is + reinitialized for the next possible combination. + """ + if isSafe(board, row, i): + board[row][i] = 1 + solve(board, row + 1) + board[row][i] = 0 + return False + + +def printboard(board): + """ + Prints the boards that have a successfull combination. + """ + for i in range(len(board)): + for j in range(len(board)): + if board[i][j] == 1: + print("Q", end=" ") + else: + print(".", end=" ") + print() + + +# n=int(input("The no. of queens")) +n = 8 +board = [[0 for i in range(n)] for j in range(n)] +solve(board, 0) +print("The total no. of solutions are :", len(solution)) diff --git a/backtracking/sudoku.py b/backtracking/sudoku.py new file mode 100644 index 000000000000..b33351fd4911 --- /dev/null +++ b/backtracking/sudoku.py @@ -0,0 +1,151 @@ +""" + + Given a partially filled 9×9 2D array, the objective is to fill a 9×9 + square grid with digits numbered 1 to 9, so that every row, column, and + and each of the nine 3×3 sub-grids contains all of the digits. + + This can be solved using Backtracking and is similar to n-queens. + We check to see if a cell is safe or not and recursively call the + function on the next column to see if it returns True. if yes, we + have solved the puzzle. else, we backtrack and place another number + in that cell and repeat this process. + +""" + +# assigning initial values to the grid +initial_grid = [ + [3, 0, 6, 5, 0, 8, 4, 0, 0], + [5, 2, 0, 0, 0, 0, 0, 0, 0], + [0, 8, 7, 0, 0, 0, 0, 3, 1], + [0, 0, 3, 0, 1, 0, 0, 8, 0], + [9, 0, 0, 8, 6, 3, 0, 0, 5], + [0, 5, 0, 0, 9, 0, 6, 0, 0], + [1, 3, 0, 0, 0, 0, 2, 5, 0], + [0, 0, 0, 0, 0, 0, 0, 7, 4], + [0, 0, 5, 2, 0, 6, 3, 0, 0], +] +# a grid with no solution +no_solution = [ + [5, 0, 6, 5, 0, 8, 4, 0, 3], + [5, 2, 0, 0, 0, 0, 0, 0, 2], + [1, 8, 7, 0, 0, 0, 0, 3, 1], + [0, 0, 3, 0, 1, 0, 0, 8, 0], + [9, 0, 0, 8, 6, 3, 0, 0, 5], + [0, 5, 0, 0, 9, 0, 6, 0, 0], + [1, 3, 0, 0, 0, 0, 2, 5, 0], + [0, 0, 0, 0, 0, 0, 0, 7, 4], + [0, 0, 5, 2, 0, 6, 3, 0, 0], +] + + +def is_safe(grid, row, column, n): + """ + This function checks the grid to see if each row, + column, and the 3x3 subgrids contain the digit 'n'. + It returns False if it is not 'safe' (a duplicate digit + is found) else returns True if it is 'safe' + + """ + + for i in range(9): + if grid[row][i] == n or grid[i][column] == n: + return False + + for i in range(3): + for j in range(3): + if grid[(row - row % 3) + i][(column - column % 3) + j] == n: + return False + + return True + + +def is_completed(grid): + """ + This function checks if the puzzle is completed or not. + it is completed when all the cells are assigned with a number(not zero) + and There is no repeating number in any column, row or 3x3 subgrid. + + """ + + for row in grid: + for cell in row: + if cell == 0: + return False + + return True + + +def find_empty_location(grid): + """ + This function finds an empty location so that we can assign a number + for that particular row and column. + + """ + + for i in range(9): + for j in range(9): + if grid[i][j] == 0: + return i, j + + +def sudoku(grid): + """ + Takes a partially filled-in grid and attempts to assign values to + all unassigned locations in such a way to meet the requirements + for Sudoku solution (non-duplication across rows, columns, and boxes) + + >>> sudoku(initial_grid) # doctest: +NORMALIZE_WHITESPACE + [[3, 1, 6, 5, 7, 8, 4, 9, 2], + [5, 2, 9, 1, 3, 4, 7, 6, 8], + [4, 8, 7, 6, 2, 9, 5, 3, 1], + [2, 6, 3, 4, 1, 5, 9, 8, 7], + [9, 7, 4, 8, 6, 3, 1, 2, 5], + [8, 5, 1, 7, 9, 2, 6, 4, 3], + [1, 3, 8, 9, 4, 7, 2, 5, 6], + [6, 9, 2, 3, 5, 1, 8, 7, 4], + [7, 4, 5, 2, 8, 6, 3, 1, 9]] + >>> sudoku(no_solution) + False + """ + + if is_completed(grid): + return grid + + row, column = find_empty_location(grid) + + for digit in range(1, 10): + if is_safe(grid, row, column, digit): + grid[row][column] = digit + + if sudoku(grid): + return grid + + grid[row][column] = 0 + + return False + + +def print_solution(grid): + """ + A function to print the solution in the form + of a 9x9 grid + + """ + + for row in grid: + for cell in row: + print(cell, end=" ") + print() + + +if __name__ == "__main__": + + # make a copy of grid so that you can compare with the unmodified grid + for grid in (initial_grid, no_solution): + grid = list(map(list, grid)) + solution = sudoku(grid) + if solution: + print("grid after solving:") + print_solution(solution) + else: + print("Cannot find a solution.") diff --git a/backtracking/sum_of_subsets.py b/backtracking/sum_of_subsets.py new file mode 100644 index 000000000000..d96552d39997 --- /dev/null +++ b/backtracking/sum_of_subsets.py @@ -0,0 +1,55 @@ +""" + The sum-of-subsetsproblem states that a set of non-negative integers, and a value M, + determine all possible subsets of the given set whose summation sum equal to given M. + + Summation of the chosen numbers must be equal to given number M and one number can + be used only once. +""" + + +def generate_sum_of_subsets_soln(nums, max_sum): + result = [] + path = [] + num_index = 0 + remaining_nums_sum = sum(nums) + create_state_space_tree(nums, max_sum, num_index, path, result, remaining_nums_sum) + return result + + +def create_state_space_tree(nums, max_sum, num_index, path, result, remaining_nums_sum): + """ + Creates a state space tree to iterate through each branch using DFS. + It terminates the branching of a node when any of the two conditions + given below satisfy. + This algorithm follows depth-fist-search and backtracks when the node is not branchable. + + """ + if sum(path) > max_sum or (remaining_nums_sum + sum(path)) < max_sum: + return + if sum(path) == max_sum: + result.append(path) + return + for num_index in range(num_index, len(nums)): + create_state_space_tree( + nums, + max_sum, + num_index + 1, + path + [nums[num_index]], + result, + remaining_nums_sum - nums[num_index], + ) + + +""" +remove the comment to take an input from the user + +print("Enter the elements") +nums = list(map(int, input().split())) +print("Enter max_sum sum") +max_sum = int(input()) + +""" +nums = [3, 34, 4, 12, 5, 2] +max_sum = 9 +result = generate_sum_of_subsets_soln(nums, max_sum) +print(*result) diff --git a/blockchain/chinese_remainder_theorem.py b/blockchain/chinese_remainder_theorem.py new file mode 100644 index 000000000000..8c3eb9b4b01e --- /dev/null +++ b/blockchain/chinese_remainder_theorem.py @@ -0,0 +1,91 @@ +# Chinese Remainder Theorem: +# GCD ( Greatest Common Divisor ) or HCF ( Highest Common Factor ) + +# If GCD(a,b) = 1, then for any remainder ra modulo a and any remainder rb modulo b there exists integer n, +# such that n = ra (mod a) and n = ra(mod b). If n1 and n2 are two such integers, then n1=n2(mod ab) + +# Algorithm : + +# 1. Use extended euclid algorithm to find x,y such that a*x + b*y = 1 +# 2. Take n = ra*by + rb*ax + + +# Extended Euclid +def extended_euclid(a, b): + """ + >>> extended_euclid(10, 6) + (-1, 2) + + >>> extended_euclid(7, 5) + (-2, 3) + + """ + if b == 0: + return (1, 0) + (x, y) = extended_euclid(b, a % b) + k = a // b + return (y, x - k * y) + + +# Uses ExtendedEuclid to find inverses +def chinese_remainder_theorem(n1, r1, n2, r2): + """ + >>> chinese_remainder_theorem(5,1,7,3) + 31 + + Explanation : 31 is the smallest number such that + (i) When we divide it by 5, we get remainder 1 + (ii) When we divide it by 7, we get remainder 3 + + >>> chinese_remainder_theorem(6,1,4,3) + 14 + + """ + (x, y) = extended_euclid(n1, n2) + m = n1 * n2 + n = r2 * x * n1 + r1 * y * n2 + return (n % m + m) % m + + +# ----------SAME SOLUTION USING InvertModulo instead ExtendedEuclid---------------- + +# This function find the inverses of a i.e., a^(-1) +def invert_modulo(a, n): + """ + >>> invert_modulo(2, 5) + 3 + + >>> invert_modulo(8,7) + 1 + + """ + (b, x) = extended_euclid(a, n) + if b < 0: + b = (b % n + n) % n + return b + + +# Same a above using InvertingModulo +def chinese_remainder_theorem2(n1, r1, n2, r2): + """ + >>> chinese_remainder_theorem2(5,1,7,3) + 31 + + >>> chinese_remainder_theorem2(6,1,4,3) + 14 + + """ + x, y = invert_modulo(n1, n2), invert_modulo(n2, n1) + m = n1 * n2 + n = r2 * x * n1 + r1 * y * n2 + return (n % m + m) % m + + +# import testmod for testing our function +from doctest import testmod + +if __name__ == "__main__": + testmod(name="chinese_remainder_theorem", verbose=True) + testmod(name="chinese_remainder_theorem2", verbose=True) + testmod(name="invert_modulo", verbose=True) + testmod(name="extended_euclid", verbose=True) diff --git a/blockchain/diophantine_equation.py b/blockchain/diophantine_equation.py new file mode 100644 index 000000000000..ec2ed26e40ec --- /dev/null +++ b/blockchain/diophantine_equation.py @@ -0,0 +1,128 @@ +# Diophantine Equation : Given integers a,b,c ( at least one of a and b != 0), the diophantine equation +# a*x + b*y = c has a solution (where x and y are integers) iff gcd(a,b) divides c. + +# GCD ( Greatest Common Divisor ) or HCF ( Highest Common Factor ) + + +def diophantine(a, b, c): + """ + >>> diophantine(10,6,14) + (-7.0, 14.0) + + >>> diophantine(391,299,-69) + (9.0, -12.0) + + But above equation has one more solution i.e., x = -4, y = 5. + That's why we need diophantine all solution function. + + """ + + assert ( + c % greatest_common_divisor(a, b) == 0 + ) # greatest_common_divisor(a,b) function implemented below + (d, x, y) = extended_gcd(a, b) # extended_gcd(a,b) function implemented below + r = c / d + return (r * x, r * y) + + +# Lemma : if n|ab and gcd(a,n) = 1, then n|b. + +# Finding All solutions of Diophantine Equations: + +# Theorem : Let gcd(a,b) = d, a = d*p, b = d*q. If (x0,y0) is a solution of Diophantine Equation a*x + b*y = c. +# a*x0 + b*y0 = c, then all the solutions have the form a(x0 + t*q) + b(y0 - t*p) = c, where t is an arbitrary integer. + +# n is the number of solution you want, n = 2 by default + + +def diophantine_all_soln(a, b, c, n=2): + """ + >>> diophantine_all_soln(10, 6, 14) + -7.0 14.0 + -4.0 9.0 + + >>> diophantine_all_soln(10, 6, 14, 4) + -7.0 14.0 + -4.0 9.0 + -1.0 4.0 + 2.0 -1.0 + + >>> diophantine_all_soln(391, 299, -69, n = 4) + 9.0 -12.0 + 22.0 -29.0 + 35.0 -46.0 + 48.0 -63.0 + + """ + (x0, y0) = diophantine(a, b, c) # Initial value + d = greatest_common_divisor(a, b) + p = a // d + q = b // d + + for i in range(n): + x = x0 + i * q + y = y0 - i * p + print(x, y) + + +# Euclid's Lemma : d divides a and b, if and only if d divides a-b and b + +# Euclid's Algorithm + + +def greatest_common_divisor(a, b): + """ + >>> greatest_common_divisor(7,5) + 1 + + Note : In number theory, two integers a and b are said to be relatively prime, mutually prime, or co-prime + if the only positive integer (factor) that divides both of them is 1 i.e., gcd(a,b) = 1. + + >>> greatest_common_divisor(121, 11) + 11 + + """ + if a < b: + a, b = b, a + + while a % b != 0: + a, b = b, a % b + + return b + + +# Extended Euclid's Algorithm : If d divides a and b and d = a*x + b*y for integers x and y, then d = gcd(a,b) + + +def extended_gcd(a, b): + """ + >>> extended_gcd(10, 6) + (2, -1, 2) + + >>> extended_gcd(7, 5) + (1, -2, 3) + + """ + assert a >= 0 and b >= 0 + + if b == 0: + d, x, y = a, 1, 0 + else: + (d, p, q) = extended_gcd(b, a % b) + x = q + y = p - q * (a // b) + + assert a % d == 0 and b % d == 0 + assert d == a * x + b * y + + return (d, x, y) + + +# import testmod for testing our function +from doctest import testmod + +if __name__ == "__main__": + testmod(name="diophantine", verbose=True) + testmod(name="diophantine_all_soln", verbose=True) + testmod(name="extended_gcd", verbose=True) + testmod(name="greatest_common_divisor", verbose=True) diff --git a/blockchain/modular_division.py b/blockchain/modular_division.py new file mode 100644 index 000000000000..1255f04328d5 --- /dev/null +++ b/blockchain/modular_division.py @@ -0,0 +1,151 @@ +# Modular Division : +# An efficient algorithm for dividing b by a modulo n. + +# GCD ( Greatest Common Divisor ) or HCF ( Highest Common Factor ) + +# Given three integers a, b, and n, such that gcd(a,n)=1 and n>1, the algorithm should return an integer x such that +# 0≤x≤n−1, and b/a=x(modn) (that is, b=ax(modn)). + +# Theorem: +# a has a multiplicative inverse modulo n iff gcd(a,n) = 1 + + +# This find x = b*a^(-1) mod n +# Uses ExtendedEuclid to find the inverse of a + + +def modular_division(a, b, n): + """ + >>> modular_division(4,8,5) + 2 + + >>> modular_division(3,8,5) + 1 + + >>> modular_division(4, 11, 5) + 4 + + """ + assert n > 1 and a > 0 and greatest_common_divisor(a, n) == 1 + (d, t, s) = extended_gcd(n, a) # Implemented below + x = (b * s) % n + return x + + +# This function find the inverses of a i.e., a^(-1) +def invert_modulo(a, n): + """ + >>> invert_modulo(2, 5) + 3 + + >>> invert_modulo(8,7) + 1 + + """ + (b, x) = extended_euclid(a, n) # Implemented below + if b < 0: + b = (b % n + n) % n + return b + + +# ------------------ Finding Modular division using invert_modulo ------------------- + +# This function used the above inversion of a to find x = (b*a^(-1))mod n +def modular_division2(a, b, n): + """ + >>> modular_division2(4,8,5) + 2 + + >>> modular_division2(3,8,5) + 1 + + >>> modular_division2(4, 11, 5) + 4 + + """ + s = invert_modulo(a, n) + x = (b * s) % n + return x + + +# Extended Euclid's Algorithm : If d divides a and b and d = a*x + b*y for integers x and y, then d = gcd(a,b) + + +def extended_gcd(a, b): + """ + >>> extended_gcd(10, 6) + (2, -1, 2) + + >>> extended_gcd(7, 5) + (1, -2, 3) + + ** extended_gcd function is used when d = gcd(a,b) is required in output + + """ + assert a >= 0 and b >= 0 + + if b == 0: + d, x, y = a, 1, 0 + else: + (d, p, q) = extended_gcd(b, a % b) + x = q + y = p - q * (a // b) + + assert a % d == 0 and b % d == 0 + assert d == a * x + b * y + + return (d, x, y) + + +# Extended Euclid +def extended_euclid(a, b): + """ + >>> extended_euclid(10, 6) + (-1, 2) + + >>> extended_euclid(7, 5) + (-2, 3) + + """ + if b == 0: + return (1, 0) + (x, y) = extended_euclid(b, a % b) + k = a // b + return (y, x - k * y) + + +# Euclid's Lemma : d divides a and b, if and only if d divides a-b and b +# Euclid's Algorithm + + +def greatest_common_divisor(a, b): + """ + >>> greatest_common_divisor(7,5) + 1 + + Note : In number theory, two integers a and b are said to be relatively prime, mutually prime, or co-prime + if the only positive integer (factor) that divides both of them is 1 i.e., gcd(a,b) = 1. + + >>> greatest_common_divisor(121, 11) + 11 + + """ + if a < b: + a, b = b, a + + while a % b != 0: + a, b = b, a % b + + return b + + +# Import testmod for testing our function +from doctest import testmod + +if __name__ == "__main__": + testmod(name="modular_division", verbose=True) + testmod(name="modular_division2", verbose=True) + testmod(name="invert_modulo", verbose=True) + testmod(name="extended_gcd", verbose=True) + testmod(name="extended_euclid", verbose=True) + testmod(name="greatest_common_divisor", verbose=True) diff --git a/boolean_algebra/quine_mc_cluskey.py b/boolean_algebra/quine_mc_cluskey.py index db4d153cbfd7..7762d712a01a 100644 --- a/boolean_algebra/quine_mc_cluskey.py +++ b/boolean_algebra/quine_mc_cluskey.py @@ -1,116 +1,168 @@ def compare_string(string1, string2): - l1 = list(string1); l2 = list(string2) - count = 0 - for i in range(len(l1)): - if l1[i] != l2[i]: - count += 1 - l1[i] = '_' - if count > 1: - return -1 - else: - return("".join(l1)) + """ + >>> compare_string('0010','0110') + '0_10' + + >>> compare_string('0110','1101') + -1 + """ + l1 = list(string1) + l2 = list(string2) + count = 0 + for i in range(len(l1)): + if l1[i] != l2[i]: + count += 1 + l1[i] = "_" + if count > 1: + return -1 + else: + return "".join(l1) + def check(binary): - pi = [] - while 1: - check1 = ['$']*len(binary) - temp = [] - for i in range(len(binary)): - for j in range(i+1, len(binary)): - k=compare_string(binary[i], binary[j]) - if k != -1: - check1[i] = '*' - check1[j] = '*' - temp.append(k) - for i in range(len(binary)): - if check1[i] == '$': - pi.append(binary[i]) - if len(temp) == 0: - return pi - binary = list(set(temp)) + """ + >>> check(['0.00.01.5']) + ['0.00.01.5'] + """ + pi = [] + while 1: + check1 = ["$"] * len(binary) + temp = [] + for i in range(len(binary)): + for j in range(i + 1, len(binary)): + k = compare_string(binary[i], binary[j]) + if k != -1: + check1[i] = "*" + check1[j] = "*" + temp.append(k) + for i in range(len(binary)): + if check1[i] == "$": + pi.append(binary[i]) + if len(temp) == 0: + return pi + binary = list(set(temp)) + def decimal_to_binary(no_of_variable, minterms): - temp = [] - s = '' - for m in minterms: - for i in range(no_of_variable): - s = str(m%2) + s - m //= 2 - temp.append(s) - s = '' - return temp + """ + >>> decimal_to_binary(3,[1.5]) + ['0.00.01.5'] + """ + temp = [] + s = "" + for m in minterms: + for i in range(no_of_variable): + s = str(m % 2) + s + m //= 2 + temp.append(s) + s = "" + return temp + def is_for_table(string1, string2, count): - l1 = list(string1);l2=list(string2) - count_n = 0 - for i in range(len(l1)): - if l1[i] != l2[i]: - count_n += 1 - if count_n == count: - return True - else: - return False + """ + >>> is_for_table('__1','011',2) + True + + >>> is_for_table('01_','001',1) + False + """ + l1 = list(string1) + l2 = list(string2) + count_n = 0 + for i in range(len(l1)): + if l1[i] != l2[i]: + count_n += 1 + if count_n == count: + return True + else: + return False + def selection(chart, prime_implicants): - temp = [] - select = [0]*len(chart) - for i in range(len(chart[0])): - count = 0 - rem = -1 - for j in range(len(chart)): - if chart[j][i] == 1: - count += 1 - rem = j - if count == 1: - select[rem] = 1 - for i in range(len(select)): - if select[i] == 1: - for j in range(len(chart[0])): - if chart[i][j] == 1: - for k in range(len(chart)): - chart[k][j] = 0 - temp.append(prime_implicants[i]) - while 1: - max_n = 0; rem = -1; count_n = 0 - for i in range(len(chart)): - count_n = chart[i].count(1) - if count_n > max_n: - max_n = count_n - rem = i - - if max_n == 0: - return temp - - temp.append(prime_implicants[rem]) - - for i in range(len(chart[0])): - if chart[rem][i] == 1: - for j in range(len(chart)): - chart[j][i] = 0 - -def prime_implicant_chart(prime_implicants, binary): - chart = [[0 for x in range(len(binary))] for x in range(len(prime_implicants))] - for i in range(len(prime_implicants)): - count = prime_implicants[i].count('_') - for j in range(len(binary)): - if(is_for_table(prime_implicants[i], binary[j], count)): - chart[i][j] = 1 + """ + >>> selection([[1]],['0.00.01.5']) + ['0.00.01.5'] - return chart + >>> selection([[1]],['0.00.01.5']) + ['0.00.01.5'] + """ + temp = [] + select = [0] * len(chart) + for i in range(len(chart[0])): + count = 0 + rem = -1 + for j in range(len(chart)): + if chart[j][i] == 1: + count += 1 + rem = j + if count == 1: + select[rem] = 1 + for i in range(len(select)): + if select[i] == 1: + for j in range(len(chart[0])): + if chart[i][j] == 1: + for k in range(len(chart)): + chart[k][j] = 0 + temp.append(prime_implicants[i]) + while 1: + max_n = 0 + rem = -1 + count_n = 0 + for i in range(len(chart)): + count_n = chart[i].count(1) + if count_n > max_n: + max_n = count_n + rem = i + + if max_n == 0: + return temp + + temp.append(prime_implicants[rem]) + + for i in range(len(chart[0])): + if chart[rem][i] == 1: + for j in range(len(chart)): + chart[j][i] = 0 + + +def prime_implicant_chart(prime_implicants, binary): + """ + >>> prime_implicant_chart(['0.00.01.5'],['0.00.01.5']) + [[1]] + """ + chart = [[0 for x in range(len(binary))] for x in range(len(prime_implicants))] + for i in range(len(prime_implicants)): + count = prime_implicants[i].count("_") + for j in range(len(binary)): + if is_for_table(prime_implicants[i], binary[j], count): + chart[i][j] = 1 + + return chart + def main(): - no_of_variable = int(input("Enter the no. of variables\n")) - minterms = [int(x) for x in input("Enter the decimal representation of Minterms 'Spaces Seprated'\n").split()] - binary = decimal_to_binary(no_of_variable, minterms) - - prime_implicants = check(binary) - print("Prime Implicants are:") - print(prime_implicants) - chart = prime_implicant_chart(prime_implicants, binary) - - essential_prime_implicants = selection(chart,prime_implicants) - print("Essential Prime Implicants are:") - print(essential_prime_implicants) + no_of_variable = int(input("Enter the no. of variables\n")) + minterms = [ + int(x) + for x in input( + "Enter the decimal representation of Minterms 'Spaces Seprated'\n" + ).split() + ] + binary = decimal_to_binary(no_of_variable, minterms) + + prime_implicants = check(binary) + print("Prime Implicants are:") + print(prime_implicants) + chart = prime_implicant_chart(prime_implicants, binary) + + essential_prime_implicants = selection(chart, prime_implicants) + print("Essential Prime Implicants are:") + print(essential_prime_implicants) + + +if __name__ == "__main__": + import doctest -if __name__ == '__main__': - main() + doctest.testmod() + main() diff --git a/ciphers/affine_cipher.py b/ciphers/affine_cipher.py index af5f4e0ff4c6..eb50acf8fc20 100644 --- a/ciphers/affine_cipher.py +++ b/ciphers/affine_cipher.py @@ -1,44 +1,57 @@ -from __future__ import print_function import sys, random, cryptomath_module as cryptoMath SYMBOLS = r""" !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~""" + def main(): - message = input('Enter message: ') - key = int(input('Enter key [2000 - 9000]: ')) - mode = input('Encrypt/Decrypt [E/D]: ') - - if mode.lower().startswith('e'): - mode = 'encrypt' - translated = encryptMessage(key, message) - elif mode.lower().startswith('d'): - mode = 'decrypt' - translated = decryptMessage(key, message) - print('\n%sed text: \n%s' % (mode.title(), translated)) + message = input("Enter message: ") + key = int(input("Enter key [2000 - 9000]: ")) + mode = input("Encrypt/Decrypt [E/D]: ") + + if mode.lower().startswith("e"): + mode = "encrypt" + translated = encryptMessage(key, message) + elif mode.lower().startswith("d"): + mode = "decrypt" + translated = decryptMessage(key, message) + print("\n%sed text: \n%s" % (mode.title(), translated)) + def getKeyParts(key): keyA = key // len(SYMBOLS) keyB = key % len(SYMBOLS) return (keyA, keyB) + def checkKeys(keyA, keyB, mode): - if keyA == 1 and mode == 'encrypt': - sys.exit('The affine cipher becomes weak when key A is set to 1. Choose different key') - if keyB == 0 and mode == 'encrypt': - sys.exit('The affine cipher becomes weak when key A is set to 1. Choose different key') + if keyA == 1 and mode == "encrypt": + sys.exit( + "The affine cipher becomes weak when key A is set to 1. Choose different key" + ) + if keyB == 0 and mode == "encrypt": + sys.exit( + "The affine cipher becomes weak when key A is set to 1. Choose different key" + ) if keyA < 0 or keyB < 0 or keyB > len(SYMBOLS) - 1: - sys.exit('Key A must be greater than 0 and key B must be between 0 and %s.' % (len(SYMBOLS) - 1)) + sys.exit( + "Key A must be greater than 0 and key B must be between 0 and %s." + % (len(SYMBOLS) - 1) + ) if cryptoMath.gcd(keyA, len(SYMBOLS)) != 1: - sys.exit('Key A %s and the symbol set size %s are not relatively prime. Choose a different key.' % (keyA, len(SYMBOLS))) + sys.exit( + "Key A %s and the symbol set size %s are not relatively prime. Choose a different key." + % (keyA, len(SYMBOLS)) + ) + def encryptMessage(key, message): - ''' + """ >>> encryptMessage(4545, 'The affine cipher is a type of monoalphabetic substitution cipher.') 'VL}p MM{I}p~{HL}Gp{vp pFsH}pxMpyxIx JHL O}F{~pvuOvF{FuF{xIp~{HL}Gi' - ''' + """ keyA, keyB = getKeyParts(key) - checkKeys(keyA, keyB, 'encrypt') - cipherText = '' + checkKeys(keyA, keyB, "encrypt") + cipherText = "" for symbol in message: if symbol in SYMBOLS: symIndex = SYMBOLS.find(symbol) @@ -47,14 +60,15 @@ def encryptMessage(key, message): cipherText += symbol return cipherText + def decryptMessage(key, message): - ''' + """ >>> decryptMessage(4545, 'VL}p MM{I}p~{HL}Gp{vp pFsH}pxMpyxIx JHL O}F{~pvuOvF{FuF{xIp~{HL}Gi') 'The affine cipher is a type of monoalphabetic substitution cipher.' - ''' + """ keyA, keyB = getKeyParts(key) - checkKeys(keyA, keyB, 'decrypt') - plainText = '' + checkKeys(keyA, keyB, "decrypt") + plainText = "" modInverseOfkeyA = cryptoMath.findModInverse(keyA, len(SYMBOLS)) for symbol in message: if symbol in SYMBOLS: @@ -64,6 +78,7 @@ def decryptMessage(key, message): plainText += symbol return plainText + def getRandomKey(): while True: keyA = random.randint(2, len(SYMBOLS)) @@ -71,7 +86,9 @@ def getRandomKey(): if cryptoMath.gcd(keyA, len(SYMBOLS)) == 1: return keyA * len(SYMBOLS) + keyB -if __name__ == '__main__': + +if __name__ == "__main__": import doctest + doctest.testmod() main() diff --git a/ciphers/atbash.py b/ciphers/atbash.py new file mode 100644 index 000000000000..4cf003859856 --- /dev/null +++ b/ciphers/atbash.py @@ -0,0 +1,15 @@ +def atbash(): + output = "" + for i in input("Enter the sentence to be encrypted ").strip(): + extract = ord(i) + if 65 <= extract <= 90: + output += chr(155 - extract) + elif 97 <= extract <= 122: + output += chr(219 - extract) + else: + output += i + print(output) + + +if __name__ == "__main__": + atbash() diff --git a/ciphers/base16.py b/ciphers/base16.py index 9bc0e5d8337a..0210315d54e6 100644 --- a/ciphers/base16.py +++ b/ciphers/base16.py @@ -1,11 +1,13 @@ import base64 + def main(): - inp = input('->') - encoded = inp.encode('utf-8') #encoded the input (we need a bytes like object) - b16encoded = base64.b16encode(encoded) #b16encoded the encoded string + inp = input("->") + encoded = inp.encode("utf-8") # encoded the input (we need a bytes like object) + b16encoded = base64.b16encode(encoded) # b16encoded the encoded string print(b16encoded) - print(base64.b16decode(b16encoded).decode('utf-8'))#decoded it + print(base64.b16decode(b16encoded).decode("utf-8")) # decoded it + -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/ciphers/base32.py b/ciphers/base32.py index 2ac29f441e94..5bba8c4dd685 100644 --- a/ciphers/base32.py +++ b/ciphers/base32.py @@ -1,11 +1,13 @@ import base64 + def main(): - inp = input('->') - encoded = inp.encode('utf-8') #encoded the input (we need a bytes like object) - b32encoded = base64.b32encode(encoded) #b32encoded the encoded string + inp = input("->") + encoded = inp.encode("utf-8") # encoded the input (we need a bytes like object) + b32encoded = base64.b32encode(encoded) # b32encoded the encoded string print(b32encoded) - print(base64.b32decode(b32encoded).decode('utf-8'))#decoded it + print(base64.b32decode(b32encoded).decode("utf-8")) # decoded it + -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/ciphers/base64_cipher.py b/ciphers/base64_cipher.py index fa3451c0cbae..f95403c7b426 100644 --- a/ciphers/base64_cipher.py +++ b/ciphers/base64_cipher.py @@ -1,64 +1,88 @@ -def encodeBase64(text): - base64chars = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/" - - r = "" #the result - c = 3 - len(text) % 3 #the length of padding - p = "=" * c #the padding - s = text + "\0" * c #the text to encode - - i = 0 +def encode_base64(text): + r""" + >>> encode_base64('WELCOME to base64 encoding 😁') + 'V0VMQ09NRSB0byBiYXNlNjQgZW5jb2Rpbmcg8J+YgQ==' + >>> encode_base64('AÅᐃ𐀏🤓') + 'QcOF4ZCD8JCAj/CfpJM=' + >>> encode_base64('A'*60) + 'QUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFB\r\nQUFB' + """ + base64_chars = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/" + + byte_text = bytes(text, "utf-8") # put text in bytes for unicode support + r = "" # the result + c = -len(byte_text) % 3 # the length of padding + p = "=" * c # the padding + s = byte_text + b"\x00" * c # the text to encode + + i = 0 while i < len(s): if i > 0 and ((i / 3 * 4) % 76) == 0: - r = r + "\r\n" - - n = (ord(s[i]) << 16) + (ord(s[i+1]) << 8 ) + ord(s[i+2]) - + r = r + "\r\n" # for unix newline, put "\n" + + n = (s[i] << 16) + (s[i + 1] << 8) + s[i + 2] + n1 = (n >> 18) & 63 n2 = (n >> 12) & 63 - n3 = (n >> 6) & 63 + n3 = (n >> 6) & 63 n4 = n & 63 - - r += base64chars[n1] + base64chars[n2] + base64chars[n3] + base64chars[n4] + + r += base64_chars[n1] + base64_chars[n2] + base64_chars[n3] + base64_chars[n4] i += 3 - return r[0: len(r)-len(p)] + p - -def decodeBase64(text): - base64chars = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/" + return r[0 : len(r) - len(p)] + p + + +def decode_base64(text): + r""" + >>> decode_base64('V0VMQ09NRSB0byBiYXNlNjQgZW5jb2Rpbmcg8J+YgQ==') + 'WELCOME to base64 encoding 😁' + >>> decode_base64('QcOF4ZCD8JCAj/CfpJM=') + 'AÅᐃ𐀏🤓' + >>> decode_base64("QUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFB\r\nQUFB") + 'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA' + """ + base64_chars = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/" s = "" - + for i in text: - if i in base64chars: + if i in base64_chars: s += i c = "" else: - if i == '=': - c += '=' - + if i == "=": + c += "=" + p = "" if c == "=": - p = 'A' + p = "A" else: if c == "==": p = "AA" - - r = "" + + r = b"" s = s + p - + i = 0 while i < len(s): - n = (base64chars.index(s[i]) << 18) + (base64chars.index(s[i+1]) << 12) + (base64chars.index(s[i+2]) << 6) +base64chars.index(s[i+3]) - - r += chr((n >> 16) & 255) + chr((n >> 8) & 255) + chr(n & 255) - + n = ( + (base64_chars.index(s[i]) << 18) + + (base64_chars.index(s[i + 1]) << 12) + + (base64_chars.index(s[i + 2]) << 6) + + base64_chars.index(s[i + 3]) + ) + + r += bytes([(n >> 16) & 255]) + bytes([(n >> 8) & 255]) + bytes([n & 255]) + i += 4 - - return r[0: len(r) - len(p)] + + return str(r[0 : len(r) - len(p)], "utf-8") + def main(): - print(encodeBase64("WELCOME to base64 encoding")) - print(decodeBase64(encodeBase64("WELCOME to base64 encoding"))) - + print(encode_base64("WELCOME to base64 encoding 😁")) + print(decode_base64(encode_base64("WELCOME to base64 encoding 😁"))) + -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/ciphers/base85.py b/ciphers/base85.py index 5fd13837f662..ebfd0480f794 100644 --- a/ciphers/base85.py +++ b/ciphers/base85.py @@ -1,11 +1,13 @@ import base64 + def main(): - inp = input('->') - encoded = inp.encode('utf-8') #encoded the input (we need a bytes like object) - a85encoded = base64.a85encode(encoded) #a85encoded the encoded string + inp = input("->") + encoded = inp.encode("utf-8") # encoded the input (we need a bytes like object) + a85encoded = base64.a85encode(encoded) # a85encoded the encoded string print(a85encoded) - print(base64.a85decode(a85encoded).decode('utf-8'))#decoded it + print(base64.a85decode(a85encoded).decode("utf-8")) # decoded it + -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/ciphers/brute_force_caesar_cipher.py b/ciphers/brute_force_caesar_cipher.py index 3b0716442fc5..2586803ba5ff 100644 --- a/ciphers/brute_force_caesar_cipher.py +++ b/ciphers/brute_force_caesar_cipher.py @@ -1,4 +1,3 @@ -from __future__ import print_function def decrypt(message): """ >>> decrypt('TMDETUX PMDVU') @@ -43,12 +42,15 @@ def decrypt(message): translated = translated + symbol print("Decryption using Key #%s: %s" % (key, translated)) + def main(): message = input("Encrypted message: ") message = message.upper() decrypt(message) -if __name__ == '__main__': + +if __name__ == "__main__": import doctest + doctest.testmod() main() diff --git a/ciphers/caesar_cipher.py b/ciphers/caesar_cipher.py index 39c069c95a7c..200f868051d4 100644 --- a/ciphers/caesar_cipher.py +++ b/ciphers/caesar_cipher.py @@ -1,63 +1,67 @@ -import sys -def encrypt(strng, key): - encrypted = '' - for x in strng: - indx = (ord(x) + key) % 256 - if indx > 126: - indx = indx - 95 - encrypted = encrypted + chr(indx) - return encrypted - - -def decrypt(strng, key): - decrypted = '' - for x in strng: - indx = (ord(x) - key) % 256 - if indx < 32: - indx = indx + 95 - decrypted = decrypted + chr(indx) - return decrypted - -def brute_force(strng): +def encrypt(input_string: str, key: int) -> str: + result = "" + for x in input_string: + if not x.isalpha(): + result += x + elif x.isupper(): + result += chr((ord(x) + key - 65) % 26 + 65) + elif x.islower(): + result += chr((ord(x) + key - 97) % 26 + 97) + return result + + +def decrypt(input_string: str, key: int) -> str: + result = "" + for x in input_string: + if not x.isalpha(): + result += x + elif x.isupper(): + result += chr((ord(x) - key - 65) % 26 + 65) + elif x.islower(): + result += chr((ord(x) - key - 97) % 26 + 97) + return result + + +def brute_force(input_string: str) -> None: key = 1 - decrypted = '' + result = "" while key <= 94: - for x in strng: + for x in input_string: indx = (ord(x) - key) % 256 if indx < 32: indx = indx + 95 - decrypted = decrypted + chr(indx) - print("Key: {}\t| Message: {}".format(key, decrypted)) - decrypted = '' + result = result + chr(indx) + print(f"Key: {key}\t| Message: {result}") + result = "" key += 1 return None def main(): while True: - print('-' * 10 + "\n**Menu**\n" + '-' * 10) - print("1.Encrpyt") - print("2.Decrypt") - print("3.BruteForce") - print("4.Quit") + print(f'{"-" * 10}\n Menu\n{"-", * 10}') + print(*["1.Encrpyt", "2.Decrypt", "3.BruteForce", "4.Quit"], sep="\n") choice = input("What would you like to do?: ") - if choice not in ['1', '2', '3', '4']: - print ("Invalid choice, please enter a valid choice") - elif choice == '1': - strng = input("Please enter the string to be encrypted: ") - key = int(input("Please enter off-set between 1-94: ")) + if choice not in ["1", "2", "3", "4"]: + print("Invalid choice, please enter a valid choice") + elif choice == "1": + input_string = input("Please enter the string to be encrypted: ") + key = int(input("Please enter off-set between 0-25: ")) if key in range(1, 95): - print (encrypt(strng.lower(), key)) - elif choice == '2': - strng = input("Please enter the string to be decrypted: ") + print(encrypt(input_string.lower(), key)) + elif choice == "2": + input_string = input("Please enter the string to be decrypted: ") key = int(input("Please enter off-set between 1-94: ")) - if key in range(1,95): - print(decrypt(strng, key)) - elif choice == '3': - strng = input("Please enter the string to be decrypted: ") - brute_force(strng) + if key in range(1, 95): + print(decrypt(input_string, key)) + elif choice == "3": + input_string = input("Please enter the string to be decrypted: ") + brute_force(input_string) main() - elif choice == '4': - print ("Goodbye.") + elif choice == "4": + print("Goodbye.") break -main() + + +if __name__ == "__main__": + main() diff --git a/ciphers/cryptomath_module.py b/ciphers/cryptomath_module.py index 3e8e71b117ed..fc38e4bd2a22 100644 --- a/ciphers/cryptomath_module.py +++ b/ciphers/cryptomath_module.py @@ -3,6 +3,7 @@ def gcd(a, b): a, b = b % a, a return b + def findModInverse(a, m): if gcd(a, m) != 1: return None @@ -10,5 +11,5 @@ def findModInverse(a, m): v1, v2, v3 = 0, 1, m while v3 != 0: q = u3 // v3 - v1, v2, v3, u1, u2, u3 = (u1 - q * v1), (u2 - q * v2), (u3 - q *v3), v1, v2, v3 - return u1 % m + v1, v2, v3, u1, u2, u3 = (u1 - q * v1), (u2 - q * v2), (u3 - q * v3), v1, v2, v3 + return u1 % m diff --git a/ciphers/deterministic_miller_rabin.py b/ciphers/deterministic_miller_rabin.py new file mode 100644 index 000000000000..37845d6c9b41 --- /dev/null +++ b/ciphers/deterministic_miller_rabin.py @@ -0,0 +1,135 @@ +"""Created by Nathan Damon, @bizzfitch on github +>>> test_miller_rabin() +""" + + +def miller_rabin(n, allow_probable=False): + """Deterministic Miller-Rabin algorithm for primes ~< 3.32e24. + + Uses numerical analysis results to return whether or not the passed number + is prime. If the passed number is above the upper limit, and + allow_probable is True, then a return value of True indicates that n is + probably prime. This test does not allow False negatives- a return value + of False is ALWAYS composite. + + Parameters + ---------- + n : int + The integer to be tested. Since we usually care if a number is prime, + n < 2 returns False instead of raising a ValueError. + allow_probable: bool, default False + Whether or not to test n above the upper bound of the deterministic test. + + Raises + ------ + ValueError + + Reference + --------- + https://en.wikipedia.org/wiki/Miller%E2%80%93Rabin_primality_test + """ + if n == 2: + return True + if not n % 2 or n < 2: + return False + if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit + return False + if n > 3_317_044_064_679_887_385_961_981 and not allow_probable: + raise ValueError( + "Warning: upper bound of deterministic test is exceeded. " + "Pass allow_probable=True to allow probabilistic test. " + "A return value of True indicates a probable prime." + ) + # array bounds provided by analysis + bounds = [2_047, + 1_373_653, + 25_326_001, + 3_215_031_751, + 2_152_302_898_747, + 3_474_749_660_383, + 341_550_071_728_321, + 1, + 3_825_123_056_546_413_051, + 1, + 1, + 318_665_857_834_031_151_167_461, + 3_317_044_064_679_887_385_961_981] + + primes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41] + for idx, _p in enumerate(bounds, 1): + if n < _p: + # then we have our last prime to check + plist = primes[:idx] + break + d, s = n - 1, 0 + # break up n -1 into a power of 2 (s) and + # remaining odd component + # essentially, solve for d * 2 ** s == n - 1 + while d % 2 == 0: + d //= 2 + s += 1 + for prime in plist: + pr = False + for r in range(s): + m = pow(prime, d * 2 ** r, n) + # see article for analysis explanation for m + if (r == 0 and m == 1) or ((m + 1) % n == 0): + pr = True + # this loop will not determine compositeness + break + if pr: + continue + # if pr is False, then the above loop never evaluated to true, + # and the n MUST be composite + return False + return True + + +def test_miller_rabin(): + """Testing a nontrivial (ends in 1, 3, 7, 9) composite + and a prime in each range. + """ + assert not miller_rabin(561) + assert miller_rabin(563) + # 2047 + + assert not miller_rabin(838_201) + assert miller_rabin(838_207) + # 1_373_653 + + assert not miller_rabin(17_316_001) + assert miller_rabin(17_316_017) + # 25_326_001 + + assert not miller_rabin(3_078_386_641) + assert miller_rabin(3_078_386_653) + # 3_215_031_751 + + assert not miller_rabin(1_713_045_574_801) + assert miller_rabin(1_713_045_574_819) + # 2_152_302_898_747 + + assert not miller_rabin(2_779_799_728_307) + assert miller_rabin(2_779_799_728_327) + # 3_474_749_660_383 + + assert not miller_rabin(113_850_023_909_441) + assert miller_rabin(113_850_023_909_527) + # 341_550_071_728_321 + + assert not miller_rabin(1_275_041_018_848_804_351) + assert miller_rabin(1_275_041_018_848_804_391) + # 3_825_123_056_546_413_051 + + assert not miller_rabin(79_666_464_458_507_787_791_867) + assert miller_rabin(79_666_464_458_507_787_791_951) + # 318_665_857_834_031_151_167_461 + + assert not miller_rabin(552_840_677_446_647_897_660_333) + assert miller_rabin(552_840_677_446_647_897_660_359) + # 3_317_044_064_679_887_385_961_981 + # upper limit for probabilistic test + + +if __name__ == '__main__': + test_miller_rabin() diff --git a/ciphers/diffie.py b/ciphers/diffie.py new file mode 100644 index 000000000000..6b0cca1f45e6 --- /dev/null +++ b/ciphers/diffie.py @@ -0,0 +1,26 @@ +def find_primitive(n): + for r in range(1, n): + li = [] + for x in range(n-1): + val = pow(r,x,n) + if val in li: + break + li.append(val) + else: + return r + + +if __name__ == "__main__": + q = int(input('Enter a prime number q: ')) + a = find_primitive(q) + a_private = int(input('Enter private key of A: ')) + a_public = pow(a, a_private, q) + b_private = int(input('Enter private key of B: ')) + b_public = pow(a, b_private, q) + + a_secret = pow(b_public, a_private, q) + b_secret = pow(a_public, b_private, q) + + print('The key value generated by A is: ', a_secret) + print('The key value generated by B is: ', b_secret) + diff --git a/ciphers/elgamal_key_generator.py b/ciphers/elgamal_key_generator.py index 6a8751f69524..cc6b297f2daf 100644 --- a/ciphers/elgamal_key_generator.py +++ b/ciphers/elgamal_key_generator.py @@ -7,9 +7,9 @@ def main(): - print('Making key files...') - makeKeyFiles('elgamal', 2048) - print('Key files generation successful') + print("Making key files...") + makeKeyFiles("elgamal", 2048) + print("Key files generation successful") # I have written my code naively same as definition of primitive root @@ -19,7 +19,7 @@ def main(): def primitiveRoot(p_val): print("Generating primitive root of p") while True: - g = random.randrange(3,p_val) + g = random.randrange(3, p_val) if pow(g, 2, p_val) == 1: continue if pow(g, p_val, p_val) == 1: @@ -28,7 +28,7 @@ def primitiveRoot(p_val): def generateKey(keySize): - print('Generating prime p...') + print("Generating prime p...") p = rabinMiller.generateLargePrime(keySize) # select large prime number. e_1 = primitiveRoot(p) # one primitive root on modulo p. d = random.randrange(3, p) # private_key -> have to be greater than 2 for safety. @@ -41,23 +41,28 @@ def generateKey(keySize): def makeKeyFiles(name, keySize): - if os.path.exists('%s_pubkey.txt' % name) or os.path.exists('%s_privkey.txt' % name): - print('\nWARNING:') - print('"%s_pubkey.txt" or "%s_privkey.txt" already exists. \n' - 'Use a different name or delete these files and re-run this program.' % - (name, name)) + if os.path.exists("%s_pubkey.txt" % name) or os.path.exists( + "%s_privkey.txt" % name + ): + print("\nWARNING:") + print( + '"%s_pubkey.txt" or "%s_privkey.txt" already exists. \n' + "Use a different name or delete these files and re-run this program." + % (name, name) + ) sys.exit() publicKey, privateKey = generateKey(keySize) - print('\nWriting public key to file %s_pubkey.txt...' % name) - with open('%s_pubkey.txt' % name, 'w') as fo: - fo.write('%d,%d,%d,%d' % (publicKey[0], publicKey[1], publicKey[2], publicKey[3])) + print("\nWriting public key to file %s_pubkey.txt..." % name) + with open("%s_pubkey.txt" % name, "w") as fo: + fo.write( + "%d,%d,%d,%d" % (publicKey[0], publicKey[1], publicKey[2], publicKey[3]) + ) - print('Writing private key to file %s_privkey.txt...' % name) - with open('%s_privkey.txt' % name, 'w') as fo: - fo.write('%d,%d' % (privateKey[0], privateKey[1])) + print("Writing private key to file %s_privkey.txt..." % name) + with open("%s_privkey.txt" % name, "w") as fo: + fo.write("%d,%d" % (privateKey[0], privateKey[1])) -if __name__ == '__main__': +if __name__ == "__main__": main() - \ No newline at end of file diff --git a/ciphers/hill_cipher.py b/ciphers/hill_cipher.py index 89b88beed17e..e01b6a3f48a8 100644 --- a/ciphers/hill_cipher.py +++ b/ciphers/hill_cipher.py @@ -44,7 +44,7 @@ def gcd(a, b): if a == 0: return b - return gcd(b%a, a) + return gcd(b % a, a) class HillCipher: @@ -59,25 +59,29 @@ class HillCipher: modulus = numpy.vectorize(lambda x: x % 36) toInt = numpy.vectorize(lambda x: round(x)) - + def __init__(self, encrypt_key): """ encrypt_key is an NxN numpy matrix """ - self.encrypt_key = self.modulus(encrypt_key) # mod36 calc's on the encrypt key - self.checkDeterminant() # validate the determinant of the encryption key + self.encrypt_key = self.modulus(encrypt_key) # mod36 calc's on the encrypt key + self.checkDeterminant() # validate the determinant of the encryption key self.decrypt_key = None self.break_key = encrypt_key.shape[0] def checkDeterminant(self): det = round(numpy.linalg.det(self.encrypt_key)) - + if det < 0: det = det % len(self.key_string) req_l = len(self.key_string) if gcd(det, len(self.key_string)) != 1: - raise ValueError("discriminant modular {0} of encryption key({1}) is not co prime w.r.t {2}.\nTry another key.".format(req_l, det, req_l)) + raise ValueError( + "discriminant modular {0} of encryption key({1}) is not co prime w.r.t {2}.\nTry another key.".format( + req_l, det, req_l + ) + ) def processText(self, text): text = list(text.upper()) @@ -87,25 +91,27 @@ def processText(self, text): while len(text) % self.break_key != 0: text.append(last) - return ''.join(text) - + return "".join(text) + def encrypt(self, text): text = self.processText(text.upper()) - encrypted = '' + encrypted = "" for i in range(0, len(text) - self.break_key + 1, self.break_key): - batch = text[i:i+self.break_key] + batch = text[i : i + self.break_key] batch_vec = list(map(self.replaceLetters, batch)) batch_vec = numpy.matrix([batch_vec]).T - batch_encrypted = self.modulus(self.encrypt_key.dot(batch_vec)).T.tolist()[0] - encrypted_batch = ''.join(list(map(self.replaceNumbers, batch_encrypted))) + batch_encrypted = self.modulus(self.encrypt_key.dot(batch_vec)).T.tolist()[ + 0 + ] + encrypted_batch = "".join(list(map(self.replaceNumbers, batch_encrypted))) encrypted += encrypted_batch return encrypted def makeDecryptKey(self): det = round(numpy.linalg.det(self.encrypt_key)) - + if det < 0: det = det % len(self.key_string) det_inv = None @@ -114,22 +120,27 @@ def makeDecryptKey(self): det_inv = i break - inv_key = det_inv * numpy.linalg.det(self.encrypt_key) *\ - numpy.linalg.inv(self.encrypt_key) + inv_key = ( + det_inv + * numpy.linalg.det(self.encrypt_key) + * numpy.linalg.inv(self.encrypt_key) + ) return self.toInt(self.modulus(inv_key)) - + def decrypt(self, text): self.decrypt_key = self.makeDecryptKey() text = self.processText(text.upper()) - decrypted = '' + decrypted = "" for i in range(0, len(text) - self.break_key + 1, self.break_key): - batch = text[i:i+self.break_key] + batch = text[i : i + self.break_key] batch_vec = list(map(self.replaceLetters, batch)) batch_vec = numpy.matrix([batch_vec]).T - batch_decrypted = self.modulus(self.decrypt_key.dot(batch_vec)).T.tolist()[0] - decrypted_batch = ''.join(list(map(self.replaceNumbers, batch_decrypted))) + batch_decrypted = self.modulus(self.decrypt_key.dot(batch_vec)).T.tolist()[ + 0 + ] + decrypted_batch = "".join(list(map(self.replaceNumbers, batch_decrypted))) decrypted += decrypted_batch return decrypted @@ -147,21 +158,22 @@ def main(): hc = HillCipher(numpy.matrix(hill_matrix)) print("Would you like to encrypt or decrypt some text? (1 or 2)") - option = input(""" + option = input( + """ 1. Encrypt 2. Decrypt """ - ) + ) - if option == '1': + if option == "1": text_e = input("What text would you like to encrypt?: ") print("Your encrypted text is:") print(hc.encrypt(text_e)) - elif option == '2': + elif option == "2": text_d = input("What text would you like to decrypt?: ") print("Your decrypted text is:") print(hc.decrypt(text_d)) - + if __name__ == "__main__": main() diff --git a/ciphers/mixed_keyword_cypher.py b/ciphers/mixed_keyword_cypher.py new file mode 100644 index 000000000000..c8d3ad6a535f --- /dev/null +++ b/ciphers/mixed_keyword_cypher.py @@ -0,0 +1,71 @@ +def mixed_keyword(key="college", pt="UNIVERSITY"): + """ + + For key:hello + + H E L O + A B C D + F G I J + K M N P + Q R S T + U V W X + Y Z + and map vertically + + >>> mixed_keyword("college", "UNIVERSITY") # doctest: +NORMALIZE_WHITESPACE + {'A': 'C', 'B': 'A', 'C': 'I', 'D': 'P', 'E': 'U', 'F': 'Z', 'G': 'O', 'H': 'B', + 'I': 'J', 'J': 'Q', 'K': 'V', 'L': 'L', 'M': 'D', 'N': 'K', 'O': 'R', 'P': 'W', + 'Q': 'E', 'R': 'F', 'S': 'M', 'T': 'S', 'U': 'X', 'V': 'G', 'W': 'H', 'X': 'N', + 'Y': 'T', 'Z': 'Y'} + 'XKJGUFMJST' + """ + key = key.upper() + pt = pt.upper() + temp = [] + for i in key: + if i not in temp: + temp.append(i) + l = len(temp) + # print(temp) + alpha = [] + modalpha = [] + # modalpha.append(temp) + dic = dict() + c = 0 + for i in range(65, 91): + t = chr(i) + alpha.append(t) + if t not in temp: + temp.append(t) + # print(temp) + r = int(26 / 4) + # print(r) + k = 0 + for i in range(r): + t = [] + for j in range(l): + t.append(temp[k]) + if not (k < 25): + break + k += 1 + modalpha.append(t) + # print(modalpha) + d = dict() + j = 0 + k = 0 + for j in range(l): + for i in modalpha: + if not (len(i) - 1 >= j): + break + d[alpha[k]] = i[j] + if not k < 25: + break + k += 1 + print(d) + cypher = "" + for i in pt: + cypher += d[i] + return cypher + + +print(mixed_keyword("college", "UNIVERSITY")) diff --git a/ciphers/morse_code_implementation.py b/ciphers/morse_code_implementation.py new file mode 100644 index 000000000000..6df4632af4cb --- /dev/null +++ b/ciphers/morse_code_implementation.py @@ -0,0 +1,107 @@ +# Python program to implement Morse Code Translator + + +# Dictionary representing the morse code chart +MORSE_CODE_DICT = { + "A": ".-", + "B": "-...", + "C": "-.-.", + "D": "-..", + "E": ".", + "F": "..-.", + "G": "--.", + "H": "....", + "I": "..", + "J": ".---", + "K": "-.-", + "L": ".-..", + "M": "--", + "N": "-.", + "O": "---", + "P": ".--.", + "Q": "--.-", + "R": ".-.", + "S": "...", + "T": "-", + "U": "..-", + "V": "...-", + "W": ".--", + "X": "-..-", + "Y": "-.--", + "Z": "--..", + "1": ".----", + "2": "..---", + "3": "...--", + "4": "....-", + "5": ".....", + "6": "-....", + "7": "--...", + "8": "---..", + "9": "----.", + "0": "-----", + ", ": "--..--", + ".": ".-.-.-", + "?": "..--..", + "/": "-..-.", + "-": "-....-", + "(": "-.--.", + ")": "-.--.-", +} + + +def encrypt(message): + cipher = "" + for letter in message: + if letter != " ": + + cipher += MORSE_CODE_DICT[letter] + " " + else: + + cipher += " " + + return cipher + + +def decrypt(message): + + message += " " + + decipher = "" + citext = "" + for letter in message: + + if letter != " ": + + i = 0 + + citext += letter + + else: + + i += 1 + + if i == 2: + + decipher += " " + else: + + decipher += list(MORSE_CODE_DICT.keys())[ + list(MORSE_CODE_DICT.values()).index(citext) + ] + citext = "" + + return decipher + + +def main(): + message = "Morse code here" + result = encrypt(message.upper()) + print(result) + + message = result + result = decrypt(message) + print(result) + + +if __name__ == "__main__": + main() diff --git a/ciphers/onepad_cipher.py b/ciphers/onepad_cipher.py index 6afbd45249ec..5a410bfa638a 100644 --- a/ciphers/onepad_cipher.py +++ b/ciphers/onepad_cipher.py @@ -1,32 +1,30 @@ -from __future__ import print_function - import random class Onepad: def encrypt(self, text): - '''Function to encrypt text using psedo-random numbers''' + """Function to encrypt text using psedo-random numbers""" plain = [ord(i) for i in text] key = [] cipher = [] for i in plain: k = random.randint(1, 300) - c = (i+k)*k + c = (i + k) * k cipher.append(c) key.append(k) return cipher, key - + def decrypt(self, cipher, key): - '''Function to decrypt text using psedo-random numbers.''' + """Function to decrypt text using psedo-random numbers.""" plain = [] for i in range(len(key)): - p = int((cipher[i]-(key[i])**2)/key[i]) + p = int((cipher[i] - (key[i]) ** 2) / key[i]) plain.append(chr(p)) - plain = ''.join([i for i in plain]) + plain = "".join([i for i in plain]) return plain -if __name__ == '__main__': - c, k = Onepad().encrypt('Hello') +if __name__ == "__main__": + c, k = Onepad().encrypt("Hello") print(c, k) print(Onepad().decrypt(c, k)) diff --git a/ciphers/playfair_cipher.py b/ciphers/playfair_cipher.py index 20449b161963..030fe8155a69 100644 --- a/ciphers/playfair_cipher.py +++ b/ciphers/playfair_cipher.py @@ -1,14 +1,14 @@ import string import itertools + def chunker(seq, size): it = iter(seq) while True: - chunk = tuple(itertools.islice(it, size)) - if not chunk: - return - yield chunk - + chunk = tuple(itertools.islice(it, size)) + if not chunk: + return + yield chunk def prepare_input(dirty): @@ -16,32 +16,33 @@ def prepare_input(dirty): Prepare the plaintext by up-casing it and separating repeated letters with X's """ - - dirty = ''.join([c.upper() for c in dirty if c in string.ascii_letters]) + + dirty = "".join([c.upper() for c in dirty if c in string.ascii_letters]) clean = "" - + if len(dirty) < 2: return dirty - for i in range(len(dirty)-1): + for i in range(len(dirty) - 1): clean += dirty[i] - - if dirty[i] == dirty[i+1]: - clean += 'X' - + + if dirty[i] == dirty[i + 1]: + clean += "X" + clean += dirty[-1] if len(clean) & 1: - clean += 'X' + clean += "X" return clean + def generate_table(key): # I and J are used interchangeably to allow # us to use a 5x5 table (25 letters) alphabet = "ABCDEFGHIKLMNOPQRSTUVWXYZ" - # we're using a list instead of a '2d' array because it makes the math + # we're using a list instead of a '2d' array because it makes the math # for setting up the table and doing the actual encoding/decoding simpler table = [] @@ -57,6 +58,7 @@ def generate_table(key): return table + def encode(plaintext, key): table = generate_table(key) plaintext = prepare_input(plaintext) @@ -68,14 +70,14 @@ def encode(plaintext, key): row2, col2 = divmod(table.index(char2), 5) if row1 == row2: - ciphertext += table[row1*5+(col1+1)%5] - ciphertext += table[row2*5+(col2+1)%5] + ciphertext += table[row1 * 5 + (col1 + 1) % 5] + ciphertext += table[row2 * 5 + (col2 + 1) % 5] elif col1 == col2: - ciphertext += table[((row1+1)%5)*5+col1] - ciphertext += table[((row2+1)%5)*5+col2] - else: # rectangle - ciphertext += table[row1*5+col2] - ciphertext += table[row2*5+col1] + ciphertext += table[((row1 + 1) % 5) * 5 + col1] + ciphertext += table[((row2 + 1) % 5) * 5 + col2] + else: # rectangle + ciphertext += table[row1 * 5 + col2] + ciphertext += table[row2 * 5 + col1] return ciphertext @@ -90,13 +92,13 @@ def decode(ciphertext, key): row2, col2 = divmod(table.index(char2), 5) if row1 == row2: - plaintext += table[row1*5+(col1-1)%5] - plaintext += table[row2*5+(col2-1)%5] + plaintext += table[row1 * 5 + (col1 - 1) % 5] + plaintext += table[row2 * 5 + (col2 - 1) % 5] elif col1 == col2: - plaintext += table[((row1-1)%5)*5+col1] - plaintext += table[((row2-1)%5)*5+col2] - else: # rectangle - plaintext += table[row1*5+col2] - plaintext += table[row2*5+col1] + plaintext += table[((row1 - 1) % 5) * 5 + col1] + plaintext += table[((row2 - 1) % 5) * 5 + col2] + else: # rectangle + plaintext += table[row1 * 5 + col2] + plaintext += table[row2 * 5 + col1] return plaintext diff --git a/ciphers/porta_cipher.py b/ciphers/porta_cipher.py new file mode 100644 index 000000000000..a8e79415958d --- /dev/null +++ b/ciphers/porta_cipher.py @@ -0,0 +1,110 @@ +alphabet = { + "A": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"), + "B": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"), + "C": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"), + "D": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"), + "E": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"), + "F": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"), + "G": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"), + "H": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"), + "I": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"), + "J": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"), + "K": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"), + "L": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"), + "M": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"), + "N": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"), + "O": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"), + "P": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"), + "Q": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"), + "R": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"), + "S": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"), + "T": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"), + "U": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"), + "V": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"), + "W": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"), + "X": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"), + "Y": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"), + "Z": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"), +} + + +def generate_table(key): + """ + >>> generate_table('marvin') # doctest: +NORMALIZE_WHITESPACE + [('ABCDEFGHIJKLM', 'UVWXYZNOPQRST'), ('ABCDEFGHIJKLM', 'NOPQRSTUVWXYZ'), + ('ABCDEFGHIJKLM', 'STUVWXYZNOPQR'), ('ABCDEFGHIJKLM', 'QRSTUVWXYZNOP'), + ('ABCDEFGHIJKLM', 'WXYZNOPQRSTUV'), ('ABCDEFGHIJKLM', 'UVWXYZNOPQRST')] + """ + return [alphabet[char] for char in key.upper()] + + +def encrypt(key, words): + """ + >>> encrypt('marvin', 'jessica') + 'QRACRWU' + """ + cipher = "" + count = 0 + table = generate_table(key) + for char in words.upper(): + cipher += get_opponent(table[count], char) + count = (count + 1) % len(table) + return cipher + + +def decrypt(key, words): + """ + >>> decrypt('marvin', 'QRACRWU') + 'JESSICA' + """ + return encrypt(key, words) + + +def get_position(table, char): + """ + >>> table = [ + ... ('ABCDEFGHIJKLM', 'UVWXYZNOPQRST'), ('ABCDEFGHIJKLM', 'NOPQRSTUVWXYZ'), + ... ('ABCDEFGHIJKLM', 'STUVWXYZNOPQR'), ('ABCDEFGHIJKLM', 'QRSTUVWXYZNOP'), + ... ('ABCDEFGHIJKLM', 'WXYZNOPQRSTUV'), ('ABCDEFGHIJKLM', 'UVWXYZNOPQRST')] + >>> get_position(table, 'A') + (None, None) + """ + if char in table[0]: + row = 0 + else: + row = 1 if char in table[1] else -1 + return (None, None) if row == -1 else (row, table[row].index(char)) + + +def get_opponent(table, char): + """ + >>> table = [ + ... ('ABCDEFGHIJKLM', 'UVWXYZNOPQRST'), ('ABCDEFGHIJKLM', 'NOPQRSTUVWXYZ'), + ... ('ABCDEFGHIJKLM', 'STUVWXYZNOPQR'), ('ABCDEFGHIJKLM', 'QRSTUVWXYZNOP'), + ... ('ABCDEFGHIJKLM', 'WXYZNOPQRSTUV'), ('ABCDEFGHIJKLM', 'UVWXYZNOPQRST')] + >>> get_opponent(table, 'A') + 'A' + """ + row, col = get_position(table, char.upper()) + if row == 1: + return table[0][col] + else: + return table[1][col] if row == 0 else char + + +if __name__ == "__main__": + import doctest + + doctest.testmod() # Fist ensure that all our tests are passing... + """ + ENTER KEY: marvin + ENTER TEXT TO ENCRYPT: jessica + ENCRYPTED: QRACRWU + DECRYPTED WITH KEY: JESSICA + """ + key = input("ENTER KEY: ").strip() + text = input("ENTER TEXT TO ENCRYPT: ").strip() + cipher_text = encrypt(key, text) + + print(f"ENCRYPTED: {cipher_text}") + print(f"DECRYPTED WITH KEY: {decrypt(key, cipher_text)}") diff --git a/ciphers/rabin_miller.py b/ciphers/rabin_miller.py index f71fb03c0051..c544abdf9acc 100644 --- a/ciphers/rabin_miller.py +++ b/ciphers/rabin_miller.py @@ -1,8 +1,8 @@ -from __future__ import print_function # Primality Testing with the Rabin-Miller Algorithm import random + def rabinMiller(num): s = num - 1 t = 0 @@ -24,24 +24,181 @@ def rabinMiller(num): v = (v ** 2) % num return True + def isPrime(num): - if (num < 2): + if num < 2: return False - lowPrimes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, - 61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, - 131, 137, 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, - 193, 197, 199, 211, 223, 227, 229, 233, 239, 241, 251, 257, - 263, 269, 271, 277, 281, 283, 293, 307, 311, 313, 317, 331, - 337, 347, 349, 353, 359, 367, 373, 379, 383, 389, 397, 401, - 409, 419, 421, 431, 433, 439, 443, 449, 457, 461, 463, 467, - 479, 487, 491, 499, 503, 509, 521, 523, 541, 547, 557, 563, - 569, 571, 577, 587, 593, 599, 601, 607, 613, 617, 619, 631, - 641, 643, 647, 653, 659, 661, 673, 677, 683, 691, 701, 709, - 719, 727, 733, 739, 743, 751, 757, 761, 769, 773, 787, 797, - 809, 811, 821, 823, 827, 829, 839, 853, 857, 859, 863, 877, - 881, 883, 887, 907, 911, 919, 929, 937, 941, 947, 953, 967, - 971, 977, 983, 991, 997] + lowPrimes = [ + 2, + 3, + 5, + 7, + 11, + 13, + 17, + 19, + 23, + 29, + 31, + 37, + 41, + 43, + 47, + 53, + 59, + 61, + 67, + 71, + 73, + 79, + 83, + 89, + 97, + 101, + 103, + 107, + 109, + 113, + 127, + 131, + 137, + 139, + 149, + 151, + 157, + 163, + 167, + 173, + 179, + 181, + 191, + 193, + 197, + 199, + 211, + 223, + 227, + 229, + 233, + 239, + 241, + 251, + 257, + 263, + 269, + 271, + 277, + 281, + 283, + 293, + 307, + 311, + 313, + 317, + 331, + 337, + 347, + 349, + 353, + 359, + 367, + 373, + 379, + 383, + 389, + 397, + 401, + 409, + 419, + 421, + 431, + 433, + 439, + 443, + 449, + 457, + 461, + 463, + 467, + 479, + 487, + 491, + 499, + 503, + 509, + 521, + 523, + 541, + 547, + 557, + 563, + 569, + 571, + 577, + 587, + 593, + 599, + 601, + 607, + 613, + 617, + 619, + 631, + 641, + 643, + 647, + 653, + 659, + 661, + 673, + 677, + 683, + 691, + 701, + 709, + 719, + 727, + 733, + 739, + 743, + 751, + 757, + 761, + 769, + 773, + 787, + 797, + 809, + 811, + 821, + 823, + 827, + 829, + 839, + 853, + 857, + 859, + 863, + 877, + 881, + 883, + 887, + 907, + 911, + 919, + 929, + 937, + 941, + 947, + 953, + 967, + 971, + 977, + 983, + 991, + 997, + ] if num in lowPrimes: return True @@ -52,13 +209,15 @@ def isPrime(num): return rabinMiller(num) -def generateLargePrime(keysize = 1024): + +def generateLargePrime(keysize=1024): while True: num = random.randrange(2 ** (keysize - 1), 2 ** (keysize)) if isPrime(num): return num -if __name__ == '__main__': + +if __name__ == "__main__": num = generateLargePrime() - print(('Prime number:', num)) - print(('isPrime:', isPrime(num))) + print(("Prime number:", num)) + print(("isPrime:", isPrime(num))) diff --git a/ciphers/rot13.py b/ciphers/rot13.py index 2abf981e9d7d..a7b546511967 100644 --- a/ciphers/rot13.py +++ b/ciphers/rot13.py @@ -1,18 +1,17 @@ -from __future__ import print_function def dencrypt(s, n): - out = '' + out = "" for c in s: - if c >= 'A' and c <= 'Z': - out += chr(ord('A') + (ord(c) - ord('A') + n) % 26) - elif c >= 'a' and c <= 'z': - out += chr(ord('a') + (ord(c) - ord('a') + n) % 26) + if c >= "A" and c <= "Z": + out += chr(ord("A") + (ord(c) - ord("A") + n) % 26) + elif c >= "a" and c <= "z": + out += chr(ord("a") + (ord(c) - ord("a") + n) % 26) else: out += c return out def main(): - s0 = 'HELLO' + s0 = "HELLO" s1 = dencrypt(s0, 13) print(s1) # URYYB @@ -21,5 +20,5 @@ def main(): print(s2) # HELLO -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/ciphers/rsa_cipher.py b/ciphers/rsa_cipher.py index d81f1ffc1a1e..a9b2dcc55daa 100644 --- a/ciphers/rsa_cipher.py +++ b/ciphers/rsa_cipher.py @@ -1,44 +1,44 @@ -from __future__ import print_function import sys, rsa_key_generator as rkg, os DEFAULT_BLOCK_SIZE = 128 BYTE_SIZE = 256 + def main(): - filename = 'encrypted_file.txt' - response = input(r'Encrypte\Decrypt [e\d]: ') - - if response.lower().startswith('e'): - mode = 'encrypt' - elif response.lower().startswith('d'): - mode = 'decrypt' - - if mode == 'encrypt': - if not os.path.exists('rsa_pubkey.txt'): - rkg.makeKeyFiles('rsa', 1024) - - message = input('\nEnter message: ') - pubKeyFilename = 'rsa_pubkey.txt' - print('Encrypting and writing to %s...' % (filename)) + filename = "encrypted_file.txt" + response = input(r"Encrypte\Decrypt [e\d]: ") + + if response.lower().startswith("e"): + mode = "encrypt" + elif response.lower().startswith("d"): + mode = "decrypt" + + if mode == "encrypt": + if not os.path.exists("rsa_pubkey.txt"): + rkg.makeKeyFiles("rsa", 1024) + + message = input("\nEnter message: ") + pubKeyFilename = "rsa_pubkey.txt" + print("Encrypting and writing to %s..." % (filename)) encryptedText = encryptAndWriteToFile(filename, pubKeyFilename, message) - print('\nEncrypted text:') + print("\nEncrypted text:") print(encryptedText) - elif mode == 'decrypt': - privKeyFilename = 'rsa_privkey.txt' - print('Reading from %s and decrypting...' % (filename)) + elif mode == "decrypt": + privKeyFilename = "rsa_privkey.txt" + print("Reading from %s and decrypting..." % (filename)) decryptedText = readFromFileAndDecrypt(filename, privKeyFilename) - print('writing decryption to rsa_decryption.txt...') - with open('rsa_decryption.txt', 'w') as dec: + print("writing decryption to rsa_decryption.txt...") + with open("rsa_decryption.txt", "w") as dec: dec.write(decryptedText) - print('\nDecryption:') + print("\nDecryption:") print(decryptedText) def getBlocksFromText(message, blockSize=DEFAULT_BLOCK_SIZE): - messageBytes = message.encode('ascii') + messageBytes = message.encode("ascii") blockInts = [] for blockStart in range(0, len(messageBytes), blockSize): @@ -59,7 +59,7 @@ def getTextFromBlocks(blockInts, messageLength, blockSize=DEFAULT_BLOCK_SIZE): blockInt = blockInt % (BYTE_SIZE ** i) blockMessage.insert(0, chr(asciiNumber)) message.extend(blockMessage) - return ''.join(message) + return "".join(message) def encryptMessage(message, key, blockSize=DEFAULT_BLOCK_SIZE): @@ -82,22 +82,27 @@ def decryptMessage(encryptedBlocks, messageLength, key, blockSize=DEFAULT_BLOCK_ def readKeyFile(keyFilename): with open(keyFilename) as fo: content = fo.read() - keySize, n, EorD = content.split(',') + keySize, n, EorD = content.split(",") return (int(keySize), int(n), int(EorD)) -def encryptAndWriteToFile(messageFilename, keyFilename, message, blockSize=DEFAULT_BLOCK_SIZE): +def encryptAndWriteToFile( + messageFilename, keyFilename, message, blockSize=DEFAULT_BLOCK_SIZE +): keySize, n, e = readKeyFile(keyFilename) if keySize < blockSize * 8: - sys.exit('ERROR: Block size is %s bits and key size is %s bits. The RSA cipher requires the block size to be equal to or greater than the key size. Either decrease the block size or use different keys.' % (blockSize * 8, keySize)) + sys.exit( + "ERROR: Block size is %s bits and key size is %s bits. The RSA cipher requires the block size to be equal to or greater than the key size. Either decrease the block size or use different keys." + % (blockSize * 8, keySize) + ) encryptedBlocks = encryptMessage(message, (n, e), blockSize) for i in range(len(encryptedBlocks)): encryptedBlocks[i] = str(encryptedBlocks[i]) - encryptedContent = ','.join(encryptedBlocks) - encryptedContent = '%s_%s_%s' % (len(message), blockSize, encryptedContent) - with open(messageFilename, 'w') as fo: + encryptedContent = ",".join(encryptedBlocks) + encryptedContent = "%s_%s_%s" % (len(message), blockSize, encryptedContent) + with open(messageFilename, "w") as fo: fo.write(encryptedContent) return encryptedContent @@ -106,18 +111,22 @@ def readFromFileAndDecrypt(messageFilename, keyFilename): keySize, n, d = readKeyFile(keyFilename) with open(messageFilename) as fo: content = fo.read() - messageLength, blockSize, encryptedMessage = content.split('_') + messageLength, blockSize, encryptedMessage = content.split("_") messageLength = int(messageLength) blockSize = int(blockSize) if keySize < blockSize * 8: - sys.exit('ERROR: Block size is %s bits and key size is %s bits. The RSA cipher requires the block size to be equal to or greater than the key size. Did you specify the correct key file and encrypted file?' % (blockSize * 8, keySize)) + sys.exit( + "ERROR: Block size is %s bits and key size is %s bits. The RSA cipher requires the block size to be equal to or greater than the key size. Did you specify the correct key file and encrypted file?" + % (blockSize * 8, keySize) + ) encryptedBlocks = [] - for block in encryptedMessage.split(','): + for block in encryptedMessage.split(","): encryptedBlocks.append(int(block)) return decryptMessage(encryptedBlocks, messageLength, (n, d), blockSize) -if __name__ == '__main__': + +if __name__ == "__main__": main() diff --git a/ciphers/rsa_key_generator.py b/ciphers/rsa_key_generator.py index 541e90d6e884..ce7c1f3dd12b 100644 --- a/ciphers/rsa_key_generator.py +++ b/ciphers/rsa_key_generator.py @@ -1,46 +1,54 @@ -from __future__ import print_function import random, sys, os import rabin_miller as rabinMiller, cryptomath_module as cryptoMath + def main(): - print('Making key files...') - makeKeyFiles('rsa', 1024) - print('Key files generation successful.') + print("Making key files...") + makeKeyFiles("rsa", 1024) + print("Key files generation successful.") + def generateKey(keySize): - print('Generating prime p...') + print("Generating prime p...") p = rabinMiller.generateLargePrime(keySize) - print('Generating prime q...') + print("Generating prime q...") q = rabinMiller.generateLargePrime(keySize) n = p * q - print('Generating e that is relatively prime to (p - 1) * (q - 1)...') + print("Generating e that is relatively prime to (p - 1) * (q - 1)...") while True: e = random.randrange(2 ** (keySize - 1), 2 ** (keySize)) if cryptoMath.gcd(e, (p - 1) * (q - 1)) == 1: break - print('Calculating d that is mod inverse of e...') + print("Calculating d that is mod inverse of e...") d = cryptoMath.findModInverse(e, (p - 1) * (q - 1)) publicKey = (n, e) privateKey = (n, d) return (publicKey, privateKey) + def makeKeyFiles(name, keySize): - if os.path.exists('%s_pubkey.txt' % (name)) or os.path.exists('%s_privkey.txt' % (name)): - print('\nWARNING:') - print('"%s_pubkey.txt" or "%s_privkey.txt" already exists. \nUse a different name or delete these files and re-run this program.' % (name, name)) + if os.path.exists("%s_pubkey.txt" % (name)) or os.path.exists( + "%s_privkey.txt" % (name) + ): + print("\nWARNING:") + print( + '"%s_pubkey.txt" or "%s_privkey.txt" already exists. \nUse a different name or delete these files and re-run this program.' + % (name, name) + ) sys.exit() publicKey, privateKey = generateKey(keySize) - print('\nWriting public key to file %s_pubkey.txt...' % name) - with open('%s_pubkey.txt' % name, 'w') as fo: - fo.write('%s,%s,%s' % (keySize, publicKey[0], publicKey[1])) + print("\nWriting public key to file %s_pubkey.txt..." % name) + with open("%s_pubkey.txt" % name, "w") as fo: + fo.write("%s,%s,%s" % (keySize, publicKey[0], publicKey[1])) + + print("Writing private key to file %s_privkey.txt..." % name) + with open("%s_privkey.txt" % name, "w") as fo: + fo.write("%s,%s,%s" % (keySize, privateKey[0], privateKey[1])) - print('Writing private key to file %s_privkey.txt...' % name) - with open('%s_privkey.txt' % name, 'w') as fo: - fo.write('%s,%s,%s' % (keySize, privateKey[0], privateKey[1])) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/ciphers/shuffled_shift_cipher.py b/ciphers/shuffled_shift_cipher.py new file mode 100644 index 000000000000..bbefe3305fa7 --- /dev/null +++ b/ciphers/shuffled_shift_cipher.py @@ -0,0 +1,177 @@ +import random +import string + + +class ShuffledShiftCipher(object): + """ + This algorithm uses the Caesar Cipher algorithm but removes the option to + use brute force to decrypt the message. + + The passcode is a a random password from the selection buffer of + 1. uppercase letters of the English alphabet + 2. lowercase letters of the English alphabet + 3. digits from 0 to 9 + + Using unique characters from the passcode, the normal list of characters, + that can be allowed in the plaintext, is pivoted and shuffled. Refer to docstring + of __make_key_list() to learn more about the shuffling. + + Then, using the passcode, a number is calculated which is used to encrypt the + plaintext message with the normal shift cipher method, only in this case, the + reference, to look back at while decrypting, is shuffled. + + Each cipher object can possess an optional argument as passcode, without which a + new passcode is generated for that object automatically. + cip1 = ShuffledShiftCipher('d4usr9TWxw9wMD') + cip2 = ShuffledShiftCipher() + """ + + def __init__(self, passcode: str = None): + """ + Initializes a cipher object with a passcode as it's entity + Note: No new passcode is generated if user provides a passcode + while creating the object + """ + self.__passcode = passcode or self.__passcode_creator() + self.__key_list = self.__make_key_list() + self.__shift_key = self.__make_shift_key() + + def __str__(self): + """ + :return: passcode of the cipher object + """ + return "Passcode is: " + "".join(self.__passcode) + + def __neg_pos(self, iterlist: list) -> list: + """ + Mutates the list by changing the sign of each alternate element + + :param iterlist: takes a list iterable + :return: the mutated list + + """ + for i in range(1, len(iterlist), 2): + iterlist[i] *= -1 + return iterlist + + def __passcode_creator(self) -> list: + """ + Creates a random password from the selection buffer of + 1. uppercase letters of the English alphabet + 2. lowercase letters of the English alphabet + 3. digits from 0 to 9 + + :rtype: list + :return: a password of a random length between 10 to 20 + """ + choices = string.ascii_letters + string.digits + password = [random.choice(choices) for i in range(random.randint(10, 20))] + return password + + def __make_key_list(self) -> list: + """ + Shuffles the ordered character choices by pivoting at breakpoints + Breakpoints are the set of characters in the passcode + + eg: + if, ABCDEFGHIJKLMNOPQRSTUVWXYZ are the possible characters + and CAMERA is the passcode + then, breakpoints = [A,C,E,M,R] # sorted set of characters from passcode + shuffled parts: [A,CB,ED,MLKJIHGF,RQPON,ZYXWVUTS] + shuffled __key_list : ACBEDMLKJIHGFRQPONZYXWVUTS + + Shuffling only 26 letters of the english alphabet can generate 26! + combinations for the shuffled list. In the program we consider, a set of + 97 characters (including letters, digits, punctuation and whitespaces), + thereby creating a possibility of 97! combinations (which is a 152 digit number in itself), + thus diminishing the possibility of a brute force approach. Moreover, + shift keys even introduce a multiple of 26 for a brute force approach + for each of the already 97! combinations. + """ + # key_list_options contain nearly all printable except few elements from string.whitespace + key_list_options = ( + string.ascii_letters + string.digits + string.punctuation + " \t\n" + ) + + keys_l = [] + + # creates points known as breakpoints to break the key_list_options at those points and pivot each substring + breakpoints = sorted(set(self.__passcode)) + temp_list = [] + + # algorithm for creating a new shuffled list, keys_l, out of key_list_options + for i in key_list_options: + temp_list.extend(i) + + # checking breakpoints at which to pivot temporary sublist and add it into keys_l + if i in breakpoints or i == key_list_options[-1]: + keys_l.extend(temp_list[::-1]) + temp_list = [] + + # returning a shuffled keys_l to prevent brute force guessing of shift key + return keys_l + + def __make_shift_key(self) -> int: + """ + sum() of the mutated list of ascii values of all characters where the + mutated list is the one returned by __neg_pos() + """ + num = sum(self.__neg_pos([ord(x) for x in self.__passcode])) + return num if num > 0 else len(self.__passcode) + + def decrypt(self, encoded_message: str) -> str: + """ + Performs shifting of the encoded_message w.r.t. the shuffled __key_list + to create the decoded_message + + >>> ssc = ShuffledShiftCipher('4PYIXyqeQZr44') + >>> ssc.decrypt("d>**-1z6&'5z'5z:z+-='$'>=zp:>5:#z<'.&>#") + 'Hello, this is a modified Caesar cipher' + + """ + decoded_message = "" + + # decoding shift like Caesar cipher algorithm implementing negative shift or reverse shift or left shift + for i in encoded_message: + position = self.__key_list.index(i) + decoded_message += self.__key_list[ + (position - self.__shift_key) % -len(self.__key_list) + ] + + return decoded_message + + def encrypt(self, plaintext: str) -> str: + """ + Performs shifting of the plaintext w.r.t. the shuffled __key_list + to create the encoded_message + + >>> ssc = ShuffledShiftCipher('4PYIXyqeQZr44') + >>> ssc.encrypt('Hello, this is a modified Caesar cipher') + "d>**-1z6&'5z'5z:z+-='$'>=zp:>5:#z<'.&>#" + + """ + encoded_message = "" + + # encoding shift like Caesar cipher algorithm implementing positive shift or forward shift or right shift + for i in plaintext: + position = self.__key_list.index(i) + encoded_message += self.__key_list[ + (position + self.__shift_key) % len(self.__key_list) + ] + + return encoded_message + + +def test_end_to_end(msg: str = "Hello, this is a modified Caesar cipher"): + """ + >>> test_end_to_end() + 'Hello, this is a modified Caesar cipher' + """ + cip1 = ShuffledShiftCipher() + return cip1.decrypt(cip1.encrypt(msg)) + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/ciphers/simple_substitution_cipher.py b/ciphers/simple_substitution_cipher.py index 1bdd7dc04a57..12511cc39bbc 100644 --- a/ciphers/simple_substitution_cipher.py +++ b/ciphers/simple_substitution_cipher.py @@ -1,24 +1,25 @@ -from __future__ import print_function import sys, random -LETTERS = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' +LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" + def main(): - message = input('Enter message: ') - key = 'LFWOAYUISVKMNXPBDCRJTQEGHZ' - resp = input('Encrypt/Decrypt [e/d]: ') + message = input("Enter message: ") + key = "LFWOAYUISVKMNXPBDCRJTQEGHZ" + resp = input("Encrypt/Decrypt [e/d]: ") checkValidKey(key) - if resp.lower().startswith('e'): - mode = 'encrypt' + if resp.lower().startswith("e"): + mode = "encrypt" translated = encryptMessage(key, message) - elif resp.lower().startswith('d'): - mode = 'decrypt' + elif resp.lower().startswith("d"): + mode = "decrypt" translated = decryptMessage(key, message) - print('\n%sion: \n%s' % (mode.title(), translated)) - + print("\n%sion: \n%s" % (mode.title(), translated)) + + def checkValidKey(key): keyList = list(key) lettersList = list(LETTERS) @@ -26,30 +27,33 @@ def checkValidKey(key): lettersList.sort() if keyList != lettersList: - sys.exit('Error in the key or symbol set.') + sys.exit("Error in the key or symbol set.") + def encryptMessage(key, message): """ >>> encryptMessage('LFWOAYUISVKMNXPBDCRJTQEGHZ', 'Harshil Darji') 'Ilcrism Olcvs' """ - return translateMessage(key, message, 'encrypt') + return translateMessage(key, message, "encrypt") + def decryptMessage(key, message): """ >>> decryptMessage('LFWOAYUISVKMNXPBDCRJTQEGHZ', 'Ilcrism Olcvs') 'Harshil Darji' """ - return translateMessage(key, message, 'decrypt') + return translateMessage(key, message, "decrypt") + def translateMessage(key, message, mode): - translated = '' + translated = "" charsA = LETTERS charsB = key - if mode == 'decrypt': + if mode == "decrypt": charsA, charsB = charsB, charsA - + for symbol in message: if symbol.upper() in charsA: symIndex = charsA.find(symbol.upper()) @@ -62,10 +66,12 @@ def translateMessage(key, message, mode): return translated + def getRandomKey(): key = list(LETTERS) random.shuffle(key) - return ''.join(key) + return "".join(key) + -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/ciphers/trafid_cipher.py b/ciphers/trafid_cipher.py index 0453272f26a0..0add9ee74beb 100644 --- a/ciphers/trafid_cipher.py +++ b/ciphers/trafid_cipher.py @@ -1,9 +1,10 @@ -#https://en.wikipedia.org/wiki/Trifid_cipher +# https://en.wikipedia.org/wiki/Trifid_cipher + def __encryptPart(messagePart, character2Number): one, two, three = "", "", "" tmp = [] - + for character in messagePart: tmp.append(character2Number[character]) @@ -11,8 +12,9 @@ def __encryptPart(messagePart, character2Number): one += each[0] two += each[1] three += each[2] - - return one+two+three + + return one + two + three + def __decryptPart(messagePart, character2Number): tmp, thisPart = "", "" @@ -25,62 +27,94 @@ def __decryptPart(messagePart, character2Number): tmp += digit if len(tmp) == len(messagePart): result.append(tmp) - tmp = "" + tmp = "" return result[0], result[1], result[2] + def __prepare(message, alphabet): - #Validate message and alphabet, set to upper and remove spaces + # Validate message and alphabet, set to upper and remove spaces alphabet = alphabet.replace(" ", "").upper() message = message.replace(" ", "").upper() - #Check length and characters + # Check length and characters if len(alphabet) != 27: raise KeyError("Length of alphabet has to be 27.") for each in message: if each not in alphabet: raise ValueError("Each message character has to be included in alphabet!") - #Generate dictionares - numbers = ("111","112","113","121","122","123","131","132","133","211","212","213","221","222","223","231","232","233","311","312","313","321","322","323","331","332","333") + # Generate dictionares + numbers = ( + "111", + "112", + "113", + "121", + "122", + "123", + "131", + "132", + "133", + "211", + "212", + "213", + "221", + "222", + "223", + "231", + "232", + "233", + "311", + "312", + "313", + "321", + "322", + "323", + "331", + "332", + "333", + ) character2Number = {} number2Character = {} for letter, number in zip(alphabet, numbers): character2Number[letter] = number number2Character[number] = letter - + return message, alphabet, character2Number, number2Character -def encryptMessage(message, alphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZ.", period=5): + +def encryptMessage(message, alphabet="ABCDEFGHIJKLMNOPQRSTUVWXYZ.", period=5): message, alphabet, character2Number, number2Character = __prepare(message, alphabet) encrypted, encrypted_numeric = "", "" - for i in range(0, len(message)+1, period): - encrypted_numeric += __encryptPart(message[i:i+period], character2Number) - + for i in range(0, len(message) + 1, period): + encrypted_numeric += __encryptPart(message[i : i + period], character2Number) + for i in range(0, len(encrypted_numeric), 3): - encrypted += number2Character[encrypted_numeric[i:i+3]] + encrypted += number2Character[encrypted_numeric[i : i + 3]] return encrypted -def decryptMessage(message, alphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZ.", period=5): + +def decryptMessage(message, alphabet="ABCDEFGHIJKLMNOPQRSTUVWXYZ.", period=5): message, alphabet, character2Number, number2Character = __prepare(message, alphabet) decrypted_numeric = [] decrypted = "" - for i in range(0, len(message)+1, period): - a,b,c = __decryptPart(message[i:i+period], character2Number) - + for i in range(0, len(message) + 1, period): + a, b, c = __decryptPart(message[i : i + period], character2Number) + for j in range(0, len(a)): - decrypted_numeric.append(a[j]+b[j]+c[j]) + decrypted_numeric.append(a[j] + b[j] + c[j]) for each in decrypted_numeric: decrypted += number2Character[each] return decrypted -if __name__ == '__main__': + +if __name__ == "__main__": msg = "DEFEND THE EAST WALL OF THE CASTLE." - encrypted = encryptMessage(msg,"EPSDUCVWYM.ZLKXNBTFGORIJHAQ") + encrypted = encryptMessage(msg, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ") decrypted = decryptMessage(encrypted, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ") - print ("Encrypted: {}\nDecrypted: {}".format(encrypted, decrypted)) \ No newline at end of file + print("Encrypted: {}\nDecrypted: {}".format(encrypted, decrypted)) diff --git a/ciphers/transposition_cipher.py b/ciphers/transposition_cipher.py index dbb358315d22..b6c9195b5dee 100644 --- a/ciphers/transposition_cipher.py +++ b/ciphers/transposition_cipher.py @@ -1,31 +1,33 @@ -from __future__ import print_function import math + def main(): - message = input('Enter message: ') - key = int(input('Enter key [2-%s]: ' % (len(message) - 1))) - mode = input('Encryption/Decryption [e/d]: ') + message = input("Enter message: ") + key = int(input("Enter key [2-%s]: " % (len(message) - 1))) + mode = input("Encryption/Decryption [e/d]: ") - if mode.lower().startswith('e'): + if mode.lower().startswith("e"): text = encryptMessage(key, message) - elif mode.lower().startswith('d'): + elif mode.lower().startswith("d"): text = decryptMessage(key, message) # Append pipe symbol (vertical bar) to identify spaces at the end. - print('Output:\n%s' %(text + '|')) + print("Output:\n%s" % (text + "|")) + def encryptMessage(key, message): """ >>> encryptMessage(6, 'Harshil Darji') 'Hlia rDsahrij' """ - cipherText = [''] * key + cipherText = [""] * key for col in range(key): pointer = col while pointer < len(message): cipherText[col] += message[pointer] pointer += key - return ''.join(cipherText) + return "".join(cipherText) + def decryptMessage(key, message): """ @@ -36,19 +38,26 @@ def decryptMessage(key, message): numRows = key numShadedBoxes = (numCols * numRows) - len(message) plainText = [""] * numCols - col = 0; row = 0; + col = 0 + row = 0 for symbol in message: plainText[col] += symbol col += 1 - if (col == numCols) or (col == numCols - 1) and (row >= numRows - numShadedBoxes): + if ( + (col == numCols) + or (col == numCols - 1) + and (row >= numRows - numShadedBoxes) + ): col = 0 row += 1 return "".join(plainText) -if __name__ == '__main__': + +if __name__ == "__main__": import doctest + doctest.testmod() main() diff --git a/ciphers/transposition_cipher_encrypt_decrypt_file.py b/ciphers/transposition_cipher_encrypt_decrypt_file.py index a186cf81cde7..775df354e117 100644 --- a/ciphers/transposition_cipher_encrypt_decrypt_file.py +++ b/ciphers/transposition_cipher_encrypt_decrypt_file.py @@ -1,37 +1,38 @@ -from __future__ import print_function import time, os, sys import transposition_cipher as transCipher + def main(): - inputFile = 'Prehistoric Men.txt' - outputFile = 'Output.txt' - key = int(input('Enter key: ')) - mode = input('Encrypt/Decrypt [e/d]: ') + inputFile = "Prehistoric Men.txt" + outputFile = "Output.txt" + key = int(input("Enter key: ")) + mode = input("Encrypt/Decrypt [e/d]: ") if not os.path.exists(inputFile): - print('File %s does not exist. Quitting...' % inputFile) + print("File %s does not exist. Quitting..." % inputFile) sys.exit() if os.path.exists(outputFile): - print('Overwrite %s? [y/n]' % outputFile) - response = input('> ') - if not response.lower().startswith('y'): + print("Overwrite %s? [y/n]" % outputFile) + response = input("> ") + if not response.lower().startswith("y"): sys.exit() - + startTime = time.time() - if mode.lower().startswith('e'): + if mode.lower().startswith("e"): with open(inputFile) as f: content = f.read() translated = transCipher.encryptMessage(key, content) - elif mode.lower().startswith('d'): + elif mode.lower().startswith("d"): with open(outputFile) as f: content = f.read() - translated =transCipher .decryptMessage(key, content) + translated = transCipher.decryptMessage(key, content) - with open(outputFile, 'w') as outputObj: + with open(outputFile, "w") as outputObj: outputObj.write(translated) - + totalTime = round(time.time() - startTime, 2) - print(('Done (', totalTime, 'seconds )')) - -if __name__ == '__main__': + print(("Done (", totalTime, "seconds )")) + + +if __name__ == "__main__": main() diff --git a/ciphers/vigenere_cipher.py b/ciphers/vigenere_cipher.py index 5d5be0792835..6c10e7d773f2 100644 --- a/ciphers/vigenere_cipher.py +++ b/ciphers/vigenere_cipher.py @@ -1,34 +1,37 @@ -from __future__ import print_function -LETTERS = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' +LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" + def main(): - message = input('Enter message: ') - key = input('Enter key [alphanumeric]: ') - mode = input('Encrypt/Decrypt [e/d]: ') + message = input("Enter message: ") + key = input("Enter key [alphanumeric]: ") + mode = input("Encrypt/Decrypt [e/d]: ") - if mode.lower().startswith('e'): - mode = 'encrypt' + if mode.lower().startswith("e"): + mode = "encrypt" translated = encryptMessage(key, message) - elif mode.lower().startswith('d'): - mode = 'decrypt' + elif mode.lower().startswith("d"): + mode = "decrypt" translated = decryptMessage(key, message) - print('\n%sed message:' % mode.title()) + print("\n%sed message:" % mode.title()) print(translated) + def encryptMessage(key, message): - ''' + """ >>> encryptMessage('HDarji', 'This is Harshil Darji from Dharmaj.') 'Akij ra Odrjqqs Gaisq muod Mphumrs.' - ''' - return translateMessage(key, message, 'encrypt') + """ + return translateMessage(key, message, "encrypt") + def decryptMessage(key, message): - ''' + """ >>> decryptMessage('HDarji', 'Akij ra Odrjqqs Gaisq muod Mphumrs.') 'This is Harshil Darji from Dharmaj.' - ''' - return translateMessage(key, message, 'decrypt') + """ + return translateMessage(key, message, "decrypt") + def translateMessage(key, message, mode): translated = [] @@ -38,9 +41,9 @@ def translateMessage(key, message, mode): for symbol in message: num = LETTERS.find(symbol.upper()) if num != -1: - if mode == 'encrypt': + if mode == "encrypt": num += LETTERS.find(key[keyIndex]) - elif mode == 'decrypt': + elif mode == "decrypt": num -= LETTERS.find(key[keyIndex]) num %= len(LETTERS) @@ -55,7 +58,8 @@ def translateMessage(key, message, mode): keyIndex = 0 else: translated.append(symbol) - return ''.join(translated) + return "".join(translated) + -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/ciphers/xor_cipher.py b/ciphers/xor_cipher.py index 727fac3b0703..7d8dbe41fdea 100644 --- a/ciphers/xor_cipher.py +++ b/ciphers/xor_cipher.py @@ -16,121 +16,120 @@ - encrypt_file : boolean - decrypt_file : boolean """ -class XORCipher(object): - def __init__(self, key = 0): - """ + +class XORCipher(object): + def __init__(self, key=0): + """ simple constructor that receives a key or uses default key = 0 """ - #private field - self.__key = key + # private field + self.__key = key - def encrypt(self, content, key): - """ + def encrypt(self, content, key): + """ input: 'content' of type string and 'key' of type int output: encrypted string 'content' as a list of chars if key not passed the method uses the key by the constructor. otherwise key = 1 """ - # precondition - assert (isinstance(key,int) and isinstance(content,str)) + # precondition + assert isinstance(key, int) and isinstance(content, str) - key = key or self.__key or 1 + key = key or self.__key or 1 - # make sure key can be any size - while (key > 255): - key -= 255 + # make sure key can be any size + while key > 255: + key -= 255 - # This will be returned - ans = [] + # This will be returned + ans = [] - for ch in content: - ans.append(chr(ord(ch) ^ key)) + for ch in content: + ans.append(chr(ord(ch) ^ key)) - return ans + return ans - def decrypt(self,content,key): - """ + def decrypt(self, content, key): + """ input: 'content' of type list and 'key' of type int output: decrypted string 'content' as a list of chars if key not passed the method uses the key by the constructor. otherwise key = 1 """ - # precondition - assert (isinstance(key,int) and isinstance(content,list)) - - key = key or self.__key or 1 + # precondition + assert isinstance(key, int) and isinstance(content, list) - # make sure key can be any size - while (key > 255): - key -= 255 + key = key or self.__key or 1 - # This will be returned - ans = [] + # make sure key can be any size + while key > 255: + key -= 255 - for ch in content: - ans.append(chr(ord(ch) ^ key)) + # This will be returned + ans = [] - return ans + for ch in content: + ans.append(chr(ord(ch) ^ key)) + return ans - def encrypt_string(self,content, key = 0): - """ + def encrypt_string(self, content, key=0): + """ input: 'content' of type string and 'key' of type int output: encrypted string 'content' if key not passed the method uses the key by the constructor. otherwise key = 1 """ - # precondition - assert (isinstance(key,int) and isinstance(content,str)) + # precondition + assert isinstance(key, int) and isinstance(content, str) - key = key or self.__key or 1 + key = key or self.__key or 1 - # make sure key can be any size - while (key > 255): - key -= 255 + # make sure key can be any size + while key > 255: + key -= 255 - # This will be returned - ans = "" + # This will be returned + ans = "" - for ch in content: - ans += chr(ord(ch) ^ key) + for ch in content: + ans += chr(ord(ch) ^ key) - return ans + return ans - def decrypt_string(self,content,key = 0): - """ + def decrypt_string(self, content, key=0): + """ input: 'content' of type string and 'key' of type int output: decrypted string 'content' if key not passed the method uses the key by the constructor. otherwise key = 1 """ - # precondition - assert (isinstance(key,int) and isinstance(content,str)) + # precondition + assert isinstance(key, int) and isinstance(content, str) - key = key or self.__key or 1 + key = key or self.__key or 1 - # make sure key can be any size - while (key > 255): - key -= 255 + # make sure key can be any size + while key > 255: + key -= 255 - # This will be returned - ans = "" - - for ch in content: - ans += chr(ord(ch) ^ key) + # This will be returned + ans = "" - return ans + for ch in content: + ans += chr(ord(ch) ^ key) + return ans - def encrypt_file(self, file, key = 0): - """ + def encrypt_file(self, file, key=0): + """ input: filename (str) and a key (int) output: returns true if encrypt process was successful otherwise false @@ -138,25 +137,24 @@ def encrypt_file(self, file, key = 0): otherwise key = 1 """ - #precondition - assert (isinstance(file,str) and isinstance(key,int)) - - try: - with open(file,"r") as fin: - with open("encrypt.out","w+") as fout: + # precondition + assert isinstance(file, str) and isinstance(key, int) - # actual encrypt-process - for line in fin: - fout.write(self.encrypt_string(line,key)) + try: + with open(file, "r") as fin: + with open("encrypt.out", "w+") as fout: - except: - return False + # actual encrypt-process + for line in fin: + fout.write(self.encrypt_string(line, key)) - return True + except: + return False + return True - def decrypt_file(self,file, key): - """ + def decrypt_file(self, file, key): + """ input: filename (str) and a key (int) output: returns true if decrypt process was successful otherwise false @@ -164,23 +162,21 @@ def decrypt_file(self,file, key): otherwise key = 1 """ - #precondition - assert (isinstance(file,str) and isinstance(key,int)) - - try: - with open(file,"r") as fin: - with open("decrypt.out","w+") as fout: - - # actual encrypt-process - for line in fin: - fout.write(self.decrypt_string(line,key)) + # precondition + assert isinstance(file, str) and isinstance(key, int) - except: - return False + try: + with open(file, "r") as fin: + with open("decrypt.out", "w+") as fout: - return True + # actual encrypt-process + for line in fin: + fout.write(self.decrypt_string(line, key)) + except: + return False + return True # Tests @@ -188,22 +184,22 @@ def decrypt_file(self,file, key): # key = 67 # # test enrcypt -# print crypt.encrypt("hallo welt",key) +# print(crypt.encrypt("hallo welt",key)) # # test decrypt -# print crypt.decrypt(crypt.encrypt("hallo welt",key), key) +# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key)) # # test encrypt_string -# print crypt.encrypt_string("hallo welt",key) +# print(crypt.encrypt_string("hallo welt",key)) # # test decrypt_string -# print crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key) +# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key)) # if (crypt.encrypt_file("test.txt",key)): -# print "encrypt successful" +# print("encrypt successful") # else: -# print "encrypt unsuccessful" +# print("encrypt unsuccessful") # if (crypt.decrypt_file("encrypt.out",key)): -# print "decrypt successful" +# print("decrypt successful") # else: -# print "decrypt unsuccessful" \ No newline at end of file +# print("decrypt unsuccessful") diff --git a/compression/burrows_wheeler.py b/compression/burrows_wheeler.py new file mode 100644 index 000000000000..50ee62aa0cb3 --- /dev/null +++ b/compression/burrows_wheeler.py @@ -0,0 +1,169 @@ +""" +https://en.wikipedia.org/wiki/Burrows%E2%80%93Wheeler_transform + +The Burrows–Wheeler transform (BWT, also called block-sorting compression) +rearranges a character string into runs of similar characters. This is useful +for compression, since it tends to be easy to compress a string that has runs +of repeated characters by techniques such as move-to-front transform and +run-length encoding. More importantly, the transformation is reversible, +without needing to store any additional data except the position of the first +original character. The BWT is thus a "free" method of improving the efficiency +of text compression algorithms, costing only some extra computation. +""" +from typing import List, Dict + + +def all_rotations(s: str) -> List[str]: + """ + :param s: The string that will be rotated len(s) times. + :return: A list with the rotations. + :raises TypeError: If s is not an instance of str. + Examples: + + >>> all_rotations("^BANANA|") # doctest: +NORMALIZE_WHITESPACE + ['^BANANA|', 'BANANA|^', 'ANANA|^B', 'NANA|^BA', 'ANA|^BAN', 'NA|^BANA', + 'A|^BANAN', '|^BANANA'] + >>> all_rotations("a_asa_da_casa") # doctest: +NORMALIZE_WHITESPACE + ['a_asa_da_casa', '_asa_da_casaa', 'asa_da_casaa_', 'sa_da_casaa_a', + 'a_da_casaa_as', '_da_casaa_asa', 'da_casaa_asa_', 'a_casaa_asa_d', + '_casaa_asa_da', 'casaa_asa_da_', 'asaa_asa_da_c', 'saa_asa_da_ca', + 'aa_asa_da_cas'] + >>> all_rotations("panamabanana") # doctest: +NORMALIZE_WHITESPACE + ['panamabanana', 'anamabananap', 'namabananapa', 'amabananapan', + 'mabananapana', 'abananapanam', 'bananapanama', 'ananapanamab', + 'nanapanamaba', 'anapanamaban', 'napanamabana', 'apanamabanan'] + >>> all_rotations(5) + Traceback (most recent call last): + ... + TypeError: The parameter s type must be str. + """ + if not isinstance(s, str): + raise TypeError("The parameter s type must be str.") + + return [s[i:] + s[:i] for i in range(len(s))] + + +def bwt_transform(s: str) -> Dict: + """ + :param s: The string that will be used at bwt algorithm + :return: the string composed of the last char of each row of the ordered + rotations and the index of the original string at ordered rotations list + :raises TypeError: If the s parameter type is not str + :raises ValueError: If the s parameter is empty + Examples: + + >>> bwt_transform("^BANANA") + {'bwt_string': 'BNN^AAA', 'idx_original_string': 6} + >>> bwt_transform("a_asa_da_casa") + {'bwt_string': 'aaaadss_c__aa', 'idx_original_string': 3} + >>> bwt_transform("panamabanana") + {'bwt_string': 'mnpbnnaaaaaa', 'idx_original_string': 11} + >>> bwt_transform(4) + Traceback (most recent call last): + ... + TypeError: The parameter s type must be str. + >>> bwt_transform('') + Traceback (most recent call last): + ... + ValueError: The parameter s must not be empty. + """ + if not isinstance(s, str): + raise TypeError("The parameter s type must be str.") + if not s: + raise ValueError("The parameter s must not be empty.") + + rotations = all_rotations(s) + rotations.sort() # sort the list of rotations in alphabetically order + # make a string composed of the last char of each rotation + return { + "bwt_string": "".join([word[-1] for word in rotations]), + "idx_original_string": rotations.index(s), + } + + +def reverse_bwt(bwt_string: str, idx_original_string: int) -> str: + """ + :param bwt_string: The string returned from bwt algorithm execution + :param idx_original_string: A 0-based index of the string that was used to + generate bwt_string at ordered rotations list + :return: The string used to generate bwt_string when bwt was executed + :raises TypeError: If the bwt_string parameter type is not str + :raises ValueError: If the bwt_string parameter is empty + :raises TypeError: If the idx_original_string type is not int or if not + possible to cast it to int + :raises ValueError: If the idx_original_string value is lower than 0 or + greater than len(bwt_string) - 1 + + >>> reverse_bwt("BNN^AAA", 6) + '^BANANA' + >>> reverse_bwt("aaaadss_c__aa", 3) + 'a_asa_da_casa' + >>> reverse_bwt("mnpbnnaaaaaa", 11) + 'panamabanana' + >>> reverse_bwt(4, 11) + Traceback (most recent call last): + ... + TypeError: The parameter bwt_string type must be str. + >>> reverse_bwt("", 11) + Traceback (most recent call last): + ... + ValueError: The parameter bwt_string must not be empty. + >>> reverse_bwt("mnpbnnaaaaaa", "asd") # doctest: +NORMALIZE_WHITESPACE + Traceback (most recent call last): + ... + TypeError: The parameter idx_original_string type must be int or passive + of cast to int. + >>> reverse_bwt("mnpbnnaaaaaa", -1) + Traceback (most recent call last): + ... + ValueError: The parameter idx_original_string must not be lower than 0. + >>> reverse_bwt("mnpbnnaaaaaa", 12) # doctest: +NORMALIZE_WHITESPACE + Traceback (most recent call last): + ... + ValueError: The parameter idx_original_string must be lower than + len(bwt_string). + >>> reverse_bwt("mnpbnnaaaaaa", 11.0) + 'panamabanana' + >>> reverse_bwt("mnpbnnaaaaaa", 11.4) + 'panamabanana' + """ + if not isinstance(bwt_string, str): + raise TypeError("The parameter bwt_string type must be str.") + if not bwt_string: + raise ValueError("The parameter bwt_string must not be empty.") + try: + idx_original_string = int(idx_original_string) + except ValueError: + raise TypeError( + ( + "The parameter idx_original_string type must be int or passive" + " of cast to int." + ) + ) + if idx_original_string < 0: + raise ValueError("The parameter idx_original_string must not be lower than 0.") + if idx_original_string >= len(bwt_string): + raise ValueError( + ("The parameter idx_original_string must be lower than" " len(bwt_string).") + ) + + ordered_rotations = [""] * len(bwt_string) + for x in range(len(bwt_string)): + for i in range(len(bwt_string)): + ordered_rotations[i] = bwt_string[i] + ordered_rotations[i] + ordered_rotations.sort() + return ordered_rotations[idx_original_string] + + +if __name__ == "__main__": + entry_msg = "Provide a string that I will generate its BWT transform: " + s = input(entry_msg).strip() + result = bwt_transform(s) + bwt_output_msg = "Burrows Wheeler tranform for string '{}' results in '{}'" + print(bwt_output_msg.format(s, result["bwt_string"])) + original_string = reverse_bwt(result["bwt_string"], result["idx_original_string"]) + fmt = ( + "Reversing Burrows Wheeler tranform for entry '{}' we get original" + " string '{}'" + ) + print(fmt.format(result["bwt_string"], original_string)) diff --git a/compression/huffman.py b/compression/huffman.py new file mode 100644 index 000000000000..73c084351c85 --- /dev/null +++ b/compression/huffman.py @@ -0,0 +1,87 @@ +import sys + + +class Letter: + def __init__(self, letter, freq): + self.letter = letter + self.freq = freq + self.bitstring = "" + + def __repr__(self): + return f"{self.letter}:{self.freq}" + + +class TreeNode: + def __init__(self, freq, left, right): + self.freq = freq + self.left = left + self.right = right + + +def parse_file(file_path): + """ + Read the file and build a dict of all letters and their + frequences, then convert the dict into a list of Letters. + """ + chars = {} + with open(file_path) as f: + while True: + c = f.read(1) + if not c: + break + chars[c] = chars[c] + 1 if c in chars.keys() else 1 + return sorted([Letter(c, f) for c, f in chars.items()], key=lambda l: l.freq) + + +def build_tree(letters): + """ + Run through the list of Letters and build the min heap + for the Huffman Tree. + """ + while len(letters) > 1: + left = letters.pop(0) + right = letters.pop(0) + total_freq = left.freq + right.freq + node = TreeNode(total_freq, left, right) + letters.append(node) + letters.sort(key=lambda l: l.freq) + return letters[0] + + +def traverse_tree(root, bitstring): + """ + Recursively traverse the Huffman Tree to set each + Letter's bitstring, and return the list of Letters + """ + if type(root) is Letter: + root.bitstring = bitstring + return [root] + letters = [] + letters += traverse_tree(root.left, bitstring + "0") + letters += traverse_tree(root.right, bitstring + "1") + return letters + + +def huffman(file_path): + """ + Parse the file, build the tree, then run through the file + again, using the list of Letters to find and print out the + bitstring for each letter. + """ + letters_list = parse_file(file_path) + root = build_tree(letters_list) + letters = traverse_tree(root, "") + print(f"Huffman Coding of {file_path}: ") + with open(file_path) as f: + while True: + c = f.read(1) + if not c: + break + le = list(filter(lambda l: l.letter == c, letters))[0] + print(le.bitstring, end=" ") + print() + + +if __name__ == "__main__": + # pass the file path to the huffman function + huffman(sys.argv[1]) diff --git a/analysis/compression_analysis/PSNR-example-base.png b/compression/image_data/PSNR-example-base.png similarity index 100% rename from analysis/compression_analysis/PSNR-example-base.png rename to compression/image_data/PSNR-example-base.png diff --git a/analysis/compression_analysis/PSNR-example-comp-10.jpg b/compression/image_data/PSNR-example-comp-10.jpg similarity index 100% rename from analysis/compression_analysis/PSNR-example-comp-10.jpg rename to compression/image_data/PSNR-example-comp-10.jpg diff --git a/analysis/compression_analysis/compressed_image.png b/compression/image_data/compressed_image.png similarity index 100% rename from analysis/compression_analysis/compressed_image.png rename to compression/image_data/compressed_image.png diff --git a/analysis/compression_analysis/example_image.jpg b/compression/image_data/example_image.jpg similarity index 100% rename from analysis/compression_analysis/example_image.jpg rename to compression/image_data/example_image.jpg diff --git a/analysis/compression_analysis/example_wikipedia_image.jpg b/compression/image_data/example_wikipedia_image.jpg similarity index 100% rename from analysis/compression_analysis/example_wikipedia_image.jpg rename to compression/image_data/example_wikipedia_image.jpg diff --git a/analysis/compression_analysis/original_image.png b/compression/image_data/original_image.png similarity index 100% rename from analysis/compression_analysis/original_image.png rename to compression/image_data/original_image.png diff --git a/analysis/compression_analysis/psnr.py b/compression/peak_signal_to_noise_ratio.py similarity index 68% rename from analysis/compression_analysis/psnr.py rename to compression/peak_signal_to_noise_ratio.py index 0f21aac07d34..418832a8127c 100644 --- a/analysis/compression_analysis/psnr.py +++ b/compression/peak_signal_to_noise_ratio.py @@ -9,6 +9,7 @@ import cv2 import numpy as np + def psnr(original, contrast): mse = np.mean((original - contrast) ** 2) if mse == 0: @@ -21,11 +22,13 @@ def psnr(original, contrast): def main(): dir_path = os.path.dirname(os.path.realpath(__file__)) # Loading images (original image and compressed image) - original = cv2.imread(os.path.join(dir_path, 'original_image.png')) - contrast = cv2.imread(os.path.join(dir_path, 'compressed_image.png'), 1) + original = cv2.imread(os.path.join(dir_path, "image_data/original_image.png")) + contrast = cv2.imread(os.path.join(dir_path, "image_data/compressed_image.png"), 1) - original2 = cv2.imread(os.path.join(dir_path, 'PSNR-example-base.png')) - contrast2 = cv2.imread(os.path.join(dir_path, 'PSNR-example-comp-10.jpg'), 1) + original2 = cv2.imread(os.path.join(dir_path, "image_data/PSNR-example-base.png")) + contrast2 = cv2.imread( + os.path.join(dir_path, "image_data/PSNR-example-comp-10.jpg"), 1 + ) # Value expected: 29.73dB print("-- First Test --") @@ -36,5 +39,5 @@ def main(): print(f"PSNR value is {psnr(original2, contrast2)} dB") -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/conversions/decimal_to_binary.py b/conversions/decimal_to_binary.py new file mode 100644 index 000000000000..ad4ba166745d --- /dev/null +++ b/conversions/decimal_to_binary.py @@ -0,0 +1,59 @@ +"""Convert a Decimal Number to a Binary Number.""" + + +def decimal_to_binary(num): + + """ + Convert a Integer Decimal Number to a Binary Number as str. + >>> decimal_to_binary(0) + '0b0' + >>> decimal_to_binary(2) + '0b10' + >>> decimal_to_binary(7) + '0b111' + >>> decimal_to_binary(35) + '0b100011' + >>> # negatives work too + >>> decimal_to_binary(-2) + '-0b10' + >>> # other floats will error + >>> decimal_to_binary(16.16) # doctest: +ELLIPSIS + Traceback (most recent call last): + ... + TypeError: 'float' object cannot be interpreted as an integer + >>> # strings will error as well + >>> decimal_to_binary('0xfffff') # doctest: +ELLIPSIS + Traceback (most recent call last): + ... + TypeError: 'str' object cannot be interpreted as an integer + """ + + if type(num) == float: + raise TypeError("'float' object cannot be interpreted as an integer") + if type(num) == str: + raise TypeError("'str' object cannot be interpreted as an integer") + + if num == 0: + return "0b0" + + negative = False + + if num < 0: + negative = True + num = -num + + binary = [] + while num > 0: + binary.insert(0, num % 2) + num >>= 1 + + if negative: + return "-0b" + "".join(str(e) for e in binary) + + return "0b" + "".join(str(e) for e in binary) + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/conversions/decimal_to_hexadecimal.py b/conversions/decimal_to_hexadecimal.py new file mode 100644 index 000000000000..a70e3c7b97bf --- /dev/null +++ b/conversions/decimal_to_hexadecimal.py @@ -0,0 +1,77 @@ +""" Convert Base 10 (Decimal) Values to Hexadecimal Representations """ + +# set decimal value for each hexadecimal digit +values = { + 0: "0", + 1: "1", + 2: "2", + 3: "3", + 4: "4", + 5: "5", + 6: "6", + 7: "7", + 8: "8", + 9: "9", + 10: "a", + 11: "b", + 12: "c", + 13: "d", + 14: "e", + 15: "f", +} + + +def decimal_to_hexadecimal(decimal): + """ + take integer decimal value, return hexadecimal representation as str beginning with 0x + >>> decimal_to_hexadecimal(5) + '0x5' + >>> decimal_to_hexadecimal(15) + '0xf' + >>> decimal_to_hexadecimal(37) + '0x25' + >>> decimal_to_hexadecimal(255) + '0xff' + >>> decimal_to_hexadecimal(4096) + '0x1000' + >>> decimal_to_hexadecimal(999098) + '0xf3eba' + >>> # negatives work too + >>> decimal_to_hexadecimal(-256) + '-0x100' + >>> # floats are acceptable if equivalent to an int + >>> decimal_to_hexadecimal(17.0) + '0x11' + >>> # other floats will error + >>> decimal_to_hexadecimal(16.16) # doctest: +ELLIPSIS + Traceback (most recent call last): + ... + AssertionError + >>> # strings will error as well + >>> decimal_to_hexadecimal('0xfffff') # doctest: +ELLIPSIS + Traceback (most recent call last): + ... + AssertionError + >>> # results are the same when compared to Python's default hex function + >>> decimal_to_hexadecimal(-256) == hex(-256) + True + """ + assert type(decimal) in (int, float) and decimal == int(decimal) + hexadecimal = "" + negative = False + if decimal < 0: + negative = True + decimal *= -1 + while decimal > 0: + decimal, remainder = divmod(decimal, 16) + hexadecimal = values[remainder] + hexadecimal + hexadecimal = "0x" + hexadecimal + if negative: + hexadecimal = "-" + hexadecimal + return hexadecimal + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/conversions/decimal_to_octal.py b/conversions/decimal_to_octal.py new file mode 100644 index 000000000000..0b005429d9d7 --- /dev/null +++ b/conversions/decimal_to_octal.py @@ -0,0 +1,38 @@ +"""Convert a Decimal Number to an Octal Number.""" + +import math + +# Modified from: +# https://github.com/TheAlgorithms/Javascript/blob/master/Conversions/DecimalToOctal.js + + +def decimal_to_octal(num): + """Convert a Decimal Number to an Octal Number.""" + octal = 0 + counter = 0 + while num > 0: + remainder = num % 8 + octal = octal + (remainder * math.pow(10, counter)) + counter += 1 + num = math.floor(num / 8) # basically /= 8 without remainder if any + # This formatting removes trailing '.0' from `octal`. + return "{0:g}".format(float(octal)) + + +def main(): + """Print octal equivelents of decimal numbers.""" + print("\n2 in octal is:") + print(decimal_to_octal(2)) # = 2 + print("\n8 in octal is:") + print(decimal_to_octal(8)) # = 10 + print("\n65 in octal is:") + print(decimal_to_octal(65)) # = 101 + print("\n216 in octal is:") + print(decimal_to_octal(216)) # = 330 + print("\n512 in octal is:") + print(decimal_to_octal(512)) # = 1000 + print("\n") + + +if __name__ == "__main__": + main() diff --git a/data_structures/arrays.py b/data_structures/arrays.py deleted file mode 100644 index feb061013556..000000000000 --- a/data_structures/arrays.py +++ /dev/null @@ -1,3 +0,0 @@ -arr = [10, 20, 30, 40] -arr[1] = 30 # set element 1 (20) of array to 30 -print(arr) diff --git a/data_structures/avl.py b/data_structures/avl.py deleted file mode 100644 index d01e8f825368..000000000000 --- a/data_structures/avl.py +++ /dev/null @@ -1,181 +0,0 @@ -""" -An AVL tree -""" -from __future__ import print_function - - -class Node: - - def __init__(self, label): - self.label = label - self._parent = None - self._left = None - self._right = None - self.height = 0 - - @property - def right(self): - return self._right - - @right.setter - def right(self, node): - if node is not None: - node._parent = self - self._right = node - - @property - def left(self): - return self._left - - @left.setter - def left(self, node): - if node is not None: - node._parent = self - self._left = node - - @property - def parent(self): - return self._parent - - @parent.setter - def parent(self, node): - if node is not None: - self._parent = node - self.height = self.parent.height + 1 - else: - self.height = 0 - - -class AVL: - - def __init__(self): - self.root = None - self.size = 0 - - def insert(self, value): - node = Node(value) - - if self.root is None: - self.root = node - self.root.height = 0 - self.size = 1 - else: - # Same as Binary Tree - dad_node = None - curr_node = self.root - - while True: - if curr_node is not None: - - dad_node = curr_node - - if node.label < curr_node.label: - curr_node = curr_node.left - else: - curr_node = curr_node.right - else: - node.height = dad_node.height - dad_node.height += 1 - if node.label < dad_node.label: - dad_node.left = node - else: - dad_node.right = node - self.rebalance(node) - self.size += 1 - break - - def rebalance(self, node): - n = node - - while n is not None: - height_right = n.height - height_left = n.height - - if n.right is not None: - height_right = n.right.height - - if n.left is not None: - height_left = n.left.height - - if abs(height_left - height_right) > 1: - if height_left > height_right: - left_child = n.left - if left_child is not None: - h_right = (left_child.right.height - if (left_child.right is not None) else 0) - h_left = (left_child.left.height - if (left_child.left is not None) else 0) - if (h_left > h_right): - self.rotate_left(n) - break - else: - self.double_rotate_right(n) - break - else: - right_child = n.right - if right_child is not None: - h_right = (right_child.right.height - if (right_child.right is not None) else 0) - h_left = (right_child.left.height - if (right_child.left is not None) else 0) - if (h_left > h_right): - self.double_rotate_left(n) - break - else: - self.rotate_right(n) - break - n = n.parent - - def rotate_left(self, node): - aux = node.parent.label - node.parent.label = node.label - node.parent.right = Node(aux) - node.parent.right.height = node.parent.height + 1 - node.parent.left = node.right - - - def rotate_right(self, node): - aux = node.parent.label - node.parent.label = node.label - node.parent.left = Node(aux) - node.parent.left.height = node.parent.height + 1 - node.parent.right = node.right - - def double_rotate_left(self, node): - self.rotate_right(node.getRight().getRight()) - self.rotate_left(node) - - def double_rotate_right(self, node): - self.rotate_left(node.getLeft().getLeft()) - self.rotate_right(node) - - def empty(self): - if self.root is None: - return True - return False - - def preShow(self, curr_node): - if curr_node is not None: - self.preShow(curr_node.left) - print(curr_node.label, end=" ") - self.preShow(curr_node.right) - - def preorder(self, curr_node): - if curr_node is not None: - self.preShow(curr_node.left) - self.preShow(curr_node.right) - print(curr_node.label, end=" ") - - def getRoot(self): - return self.root - -t = AVL() -t.insert(1) -t.insert(2) -t.insert(3) -# t.preShow(t.root) -# print("\n") -# t.insert(4) -# t.insert(5) -# t.preShow(t.root) -# t.preorden(t.root) diff --git a/data_structures/binary tree/fenwick_tree.py b/data_structures/binary tree/fenwick_tree.py deleted file mode 100644 index f429161c8c36..000000000000 --- a/data_structures/binary tree/fenwick_tree.py +++ /dev/null @@ -1,29 +0,0 @@ -from __future__ import print_function -class FenwickTree: - - def __init__(self, SIZE): # create fenwick tree with size SIZE - self.Size = SIZE - self.ft = [0 for i in range (0,SIZE)] - - def update(self, i, val): # update data (adding) in index i in O(lg N) - while (i < self.Size): - self.ft[i] += val - i += i & (-i) - - def query(self, i): # query cumulative data from index 0 to i in O(lg N) - ret = 0 - while (i > 0): - ret += self.ft[i] - i -= i & (-i) - return ret - -if __name__ == '__main__': - f = FenwickTree(100) - f.update(1,20) - f.update(4,4) - print (f.query(1)) - print (f.query(3)) - print (f.query(4)) - f.update(2,-5) - print (f.query(1)) - print (f.query(3)) diff --git a/data_structures/binary tree/lazy_segment_tree.py b/data_structures/binary tree/lazy_segment_tree.py deleted file mode 100644 index 9b14b24e81fa..000000000000 --- a/data_structures/binary tree/lazy_segment_tree.py +++ /dev/null @@ -1,91 +0,0 @@ -from __future__ import print_function -import math - -class SegmentTree: - - def __init__(self, N): - self.N = N - self.st = [0 for i in range(0,4*N)] # approximate the overall size of segment tree with array N - self.lazy = [0 for i in range(0,4*N)] # create array to store lazy update - self.flag = [0 for i in range(0,4*N)] # flag for lazy update - - def left(self, idx): - return idx*2 - - def right(self, idx): - return idx*2 + 1 - - def build(self, idx, l, r, A): - if l==r: - self.st[idx] = A[l-1] - else : - mid = (l+r)//2 - self.build(self.left(idx),l,mid, A) - self.build(self.right(idx),mid+1,r, A) - self.st[idx] = max(self.st[self.left(idx)] , self.st[self.right(idx)]) - - # update with O(lg N) (Normal segment tree without lazy update will take O(Nlg N) for each update) - def update(self, idx, l, r, a, b, val): # update(1, 1, N, a, b, v) for update val v to [a,b] - if self.flag[idx] == True: - self.st[idx] = self.lazy[idx] - self.flag[idx] = False - if l!=r: - self.lazy[self.left(idx)] = self.lazy[idx] - self.lazy[self.right(idx)] = self.lazy[idx] - self.flag[self.left(idx)] = True - self.flag[self.right(idx)] = True - - if r < a or l > b: - return True - if l >= a and r <= b : - self.st[idx] = val - if l!=r: - self.lazy[self.left(idx)] = val - self.lazy[self.right(idx)] = val - self.flag[self.left(idx)] = True - self.flag[self.right(idx)] = True - return True - mid = (l+r)//2 - self.update(self.left(idx),l,mid,a,b,val) - self.update(self.right(idx),mid+1,r,a,b,val) - self.st[idx] = max(self.st[self.left(idx)] , self.st[self.right(idx)]) - return True - - # query with O(lg N) - def query(self, idx, l, r, a, b): #query(1, 1, N, a, b) for query max of [a,b] - if self.flag[idx] == True: - self.st[idx] = self.lazy[idx] - self.flag[idx] = False - if l != r: - self.lazy[self.left(idx)] = self.lazy[idx] - self.lazy[self.right(idx)] = self.lazy[idx] - self.flag[self.left(idx)] = True - self.flag[self.right(idx)] = True - if r < a or l > b: - return -math.inf - if l >= a and r <= b: - return self.st[idx] - mid = (l+r)//2 - q1 = self.query(self.left(idx),l,mid,a,b) - q2 = self.query(self.right(idx),mid+1,r,a,b) - return max(q1,q2) - - def showData(self): - showList = [] - for i in range(1,N+1): - showList += [self.query(1, 1, self.N, i, i)] - print (showList) - - -if __name__ == '__main__': - A = [1,2,-4,7,3,-5,6,11,-20,9,14,15,5,2,-8] - N = 15 - segt = SegmentTree(N) - segt.build(1,1,N,A) - print (segt.query(1,1,N,4,6)) - print (segt.query(1,1,N,7,11)) - print (segt.query(1,1,N,7,12)) - segt.update(1,1,N,1,3,111) - print (segt.query(1,1,N,1,15)) - segt.update(1,1,N,7,8,235) - segt.showData() diff --git a/data_structures/binary tree/AVLtree.py b/data_structures/binary_tree/avl_tree.py similarity index 71% rename from data_structures/binary tree/AVLtree.py rename to data_structures/binary_tree/avl_tree.py index ff44963d1690..31d12c811105 100644 --- a/data_structures/binary tree/AVLtree.py +++ b/data_structures/binary_tree/avl_tree.py @@ -1,71 +1,88 @@ # -*- coding: utf-8 -*- -''' +""" An auto-balanced binary tree! -''' +""" import math import random + + class my_queue: def __init__(self): self.data = [] self.head = 0 self.tail = 0 + def isEmpty(self): return self.head == self.tail - def push(self,data): + + def push(self, data): self.data.append(data) self.tail = self.tail + 1 + def pop(self): ret = self.data[self.head] self.head = self.head + 1 return ret + def count(self): return self.tail - self.head + def print(self): print(self.data) print("**************") - print(self.data[self.head:self.tail]) - + print(self.data[self.head : self.tail]) + + class my_node: - def __init__(self,data): + def __init__(self, data): self.data = data self.left = None self.right = None self.height = 1 + def getdata(self): return self.data + def getleft(self): return self.left + def getright(self): return self.right + def getheight(self): return self.height - def setdata(self,data): + + def setdata(self, data): self.data = data return - def setleft(self,node): + + def setleft(self, node): self.left = node return - def setright(self,node): + + def setright(self, node): self.right = node return - def setheight(self,height): + + def setheight(self, height): self.height = height return + def getheight(node): if node is None: return 0 return node.getheight() -def my_max(a,b): + +def my_max(a, b): if a > b: return a return b - def leftrotation(node): - r''' + r""" A B / \ / \ B C Bl A @@ -75,33 +92,35 @@ def leftrotation(node): UB UB = unbalanced node - ''' - print("left rotation node:",node.getdata()) + """ + print("left rotation node:", node.getdata()) ret = node.getleft() node.setleft(ret.getright()) ret.setright(node) - h1 = my_max(getheight(node.getright()),getheight(node.getleft())) + 1 + h1 = my_max(getheight(node.getright()), getheight(node.getleft())) + 1 node.setheight(h1) - h2 = my_max(getheight(ret.getright()),getheight(ret.getleft())) + 1 + h2 = my_max(getheight(ret.getright()), getheight(ret.getleft())) + 1 ret.setheight(h2) return ret + def rightrotation(node): - ''' + """ a mirror symmetry rotation of the leftrotation - ''' - print("right rotation node:",node.getdata()) + """ + print("right rotation node:", node.getdata()) ret = node.getright() node.setright(ret.getleft()) ret.setleft(node) - h1 = my_max(getheight(node.getright()),getheight(node.getleft())) + 1 + h1 = my_max(getheight(node.getright()), getheight(node.getleft())) + 1 node.setheight(h1) - h2 = my_max(getheight(ret.getright()),getheight(ret.getleft())) + 1 + h2 = my_max(getheight(ret.getright()), getheight(ret.getleft())) + 1 ret.setheight(h2) return ret + def rlrotation(node): - r''' + r""" A A Br / \ / \ / \ B C RR Br C LR B A @@ -110,51 +129,60 @@ def rlrotation(node): \ / UB Bl RR = rightrotation LR = leftrotation - ''' + """ node.setleft(rightrotation(node.getleft())) return leftrotation(node) + def lrrotation(node): node.setright(leftrotation(node.getright())) return rightrotation(node) -def insert_node(node,data): +def insert_node(node, data): if node is None: return my_node(data) if data < node.getdata(): - node.setleft(insert_node(node.getleft(),data)) - if getheight(node.getleft()) - getheight(node.getright()) == 2: #an unbalance detected - if data < node.getleft().getdata(): #new node is the left child of the left child + node.setleft(insert_node(node.getleft(), data)) + if ( + getheight(node.getleft()) - getheight(node.getright()) == 2 + ): # an unbalance detected + if ( + data < node.getleft().getdata() + ): # new node is the left child of the left child node = leftrotation(node) else: - node = rlrotation(node) #new node is the right child of the left child + node = rlrotation(node) # new node is the right child of the left child else: - node.setright(insert_node(node.getright(),data)) + node.setright(insert_node(node.getright(), data)) if getheight(node.getright()) - getheight(node.getleft()) == 2: if data < node.getright().getdata(): node = lrrotation(node) else: node = rightrotation(node) - h1 = my_max(getheight(node.getright()),getheight(node.getleft())) + 1 + h1 = my_max(getheight(node.getright()), getheight(node.getleft())) + 1 node.setheight(h1) return node + def getRightMost(root): while root.getright() is not None: root = root.getright() return root.getdata() + + def getLeftMost(root): while root.getleft() is not None: root = root.getleft() return root.getdata() -def del_node(root,data): + +def del_node(root, data): if root.getdata() == data: if root.getleft() is not None and root.getright() is not None: temp_data = getLeftMost(root.getright()) root.setdata(temp_data) - root.setright(del_node(root.getright(),temp_data)) + root.setright(del_node(root.getright(), temp_data)) elif root.getleft() is not None: root = root.getleft() else: @@ -164,12 +192,12 @@ def del_node(root,data): print("No such data") return root else: - root.setleft(del_node(root.getleft(),data)) + root.setleft(del_node(root.getleft(), data)) elif root.getdata() < data: if root.getright() is None: return root else: - root.setright(del_node(root.getright(),data)) + root.setright(del_node(root.getright(), data)) if root is None: return root if getheight(root.getright()) - getheight(root.getleft()) == 2: @@ -182,27 +210,31 @@ def del_node(root,data): root = leftrotation(root) else: root = rlrotation(root) - height = my_max(getheight(root.getright()),getheight(root.getleft())) + 1 + height = my_max(getheight(root.getright()), getheight(root.getleft())) + 1 root.setheight(height) return root + class AVLtree: def __init__(self): self.root = None + def getheight(self): -# print("yyy") + # print("yyy") return getheight(self.root) - def insert(self,data): - print("insert:"+str(data)) - self.root = insert_node(self.root,data) - - def del_node(self,data): - print("delete:"+str(data)) + + def insert(self, data): + print("insert:" + str(data)) + self.root = insert_node(self.root, data) + + def del_node(self, data): + print("delete:" + str(data)) if self.root is None: print("Tree is empty!") return - self.root = del_node(self.root,data) - def traversale(self): #a level traversale, gives a more intuitive look on the tree + self.root = del_node(self.root, data) + + def traversale(self): # a level traversale, gives a more intuitive look on the tree q = my_queue() q.push(self.root) layer = self.getheight() @@ -211,21 +243,21 @@ def traversale(self): #a level traversale, gives a more intuitive look on the tr cnt = 0 while not q.isEmpty(): node = q.pop() - space = " "*int(math.pow(2,layer-1)) - print(space,end = "") + space = " " * int(math.pow(2, layer - 1)) + print(space, end="") if node is None: - print("*",end = "") + print("*", end="") q.push(None) q.push(None) else: - print(node.getdata(),end = "") + print(node.getdata(), end="") q.push(node.getleft()) q.push(node.getright()) - print(space,end = "") + print(space, end="") cnt = cnt + 1 for i in range(100): - if cnt == math.pow(2,i) - 1: - layer = layer -1 + if cnt == math.pow(2, i) - 1: + layer = layer - 1 if layer == 0: print() print("*************************************") @@ -235,11 +267,13 @@ def traversale(self): #a level traversale, gives a more intuitive look on the tr print() print("*************************************") return - + def test(self): getheight(None) print("****") self.getheight() + + if __name__ == "__main__": t = AVLtree() t.traversale() @@ -248,7 +282,7 @@ def test(self): for i in l: t.insert(i) t.traversale() - + random.shuffle(l) for i in l: t.del_node(i) diff --git a/binary_tree/basic_binary_tree.py b/data_structures/binary_tree/basic_binary_tree.py similarity index 67% rename from binary_tree/basic_binary_tree.py rename to data_structures/binary_tree/basic_binary_tree.py index 7c6240fb4dd4..6b7de7803704 100644 --- a/binary_tree/basic_binary_tree.py +++ b/data_structures/binary_tree/basic_binary_tree.py @@ -1,12 +1,13 @@ -class Node: # This is the Class Node with constructor that contains data variable to type data and left,right pointers. +class Node: # This is the Class Node with constructor that contains data variable to type data and left,right pointers. def __init__(self, data): self.data = data self.left = None self.right = None -def display(tree): #In Order traversal of the tree - if tree is None: +def display(tree): # In Order traversal of the tree + + if tree is None: return if tree.left is not None: @@ -19,7 +20,10 @@ def display(tree): #In Order traversal of the tree return -def depth_of_tree(tree): #This is the recursive function to find the depth of binary tree. + +def depth_of_tree( + tree +): # This is the recursive function to find the depth of binary tree. if tree is None: return 0 else: @@ -31,18 +35,20 @@ def depth_of_tree(tree): #This is the recursive function to find the depth of bi return 1 + depth_r_tree -def is_full_binary_tree(tree): # This functions returns that is it full binary tree or not? +def is_full_binary_tree( + tree +): # This functions returns that is it full binary tree or not? if tree is None: return True if (tree.left is None) and (tree.right is None): return True if (tree.left is not None) and (tree.right is not None): - return (is_full_binary_tree(tree.left) and is_full_binary_tree(tree.right)) + return is_full_binary_tree(tree.left) and is_full_binary_tree(tree.right) else: return False -def main(): # Main func for testing. +def main(): # Main func for testing. tree = Node(1) tree.left = Node(2) tree.right = Node(3) @@ -59,5 +65,5 @@ def main(): # Main func for testing. display(tree) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/data_structures/binary tree/binary_search_tree.py b/data_structures/binary_tree/binary_search_tree.py similarity index 62% rename from data_structures/binary tree/binary_search_tree.py rename to data_structures/binary_tree/binary_search_tree.py index cef5b55f245d..1e6c17112e81 100644 --- a/data_structures/binary tree/binary_search_tree.py +++ b/data_structures/binary_tree/binary_search_tree.py @@ -1,14 +1,14 @@ -''' +""" A binary search Tree -''' -from __future__ import print_function -class Node: +""" + +class Node: def __init__(self, label, parent): self.label = label self.left = None self.right = None - #Added in order to delete a node easier + # Added in order to delete a node easier self.parent = parent def getLabel(self): @@ -35,11 +35,12 @@ def getParent(self): def setParent(self, parent): self.parent = parent -class BinarySearchTree: +class BinarySearchTree: def __init__(self): self.root = None + # Insert a new node in Binary Search Tree with value label def insert(self, label): # Create a new Node new_node = Node(label, None) @@ -47,90 +48,90 @@ def insert(self, label): if self.empty(): self.root = new_node else: - #If Tree is not empty + # If Tree is not empty curr_node = self.root - #While we don't get to a leaf + # While we don't get to a leaf while curr_node is not None: - #We keep reference of the parent node + # We keep reference of the parent node parent_node = curr_node - #If node label is less than current node + # If node label is less than current node if new_node.getLabel() < curr_node.getLabel(): - #We go left + # We go left curr_node = curr_node.getLeft() else: - #Else we go right + # Else we go right curr_node = curr_node.getRight() - #We insert the new node in a leaf + # We insert the new node in a leaf if new_node.getLabel() < parent_node.getLabel(): parent_node.setLeft(new_node) else: parent_node.setRight(new_node) - #Set parent to the new node - new_node.setParent(parent_node) - + # Set parent to the new node + new_node.setParent(parent_node) + def delete(self, label): - if (not self.empty()): - #Look for the node with that label + if not self.empty(): + # Look for the node with that label node = self.getNode(label) - #If the node exists - if(node is not None): - #If it has no children - if(node.getLeft() is None and node.getRight() is None): + # If the node exists + if node is not None: + # If it has no children + if node.getLeft() is None and node.getRight() is None: self.__reassignNodes(node, None) node = None - #Has only right children - elif(node.getLeft() is None and node.getRight() is not None): + # Has only right children + elif node.getLeft() is None and node.getRight() is not None: self.__reassignNodes(node, node.getRight()) - #Has only left children - elif(node.getLeft() is not None and node.getRight() is None): + # Has only left children + elif node.getLeft() is not None and node.getRight() is None: self.__reassignNodes(node, node.getLeft()) - #Has two children + # Has two children else: - #Gets the max value of the left branch + # Gets the max value of the left branch tmpNode = self.getMax(node.getLeft()) - #Deletes the tmpNode + # Deletes the tmpNode self.delete(tmpNode.getLabel()) - #Assigns the value to the node to delete and keesp tree structure + # Assigns the value to the node to delete and keesp tree structure node.setLabel(tmpNode.getLabel()) - + def getNode(self, label): curr_node = None - #If the tree is not empty - if(not self.empty()): - #Get tree root + # If the tree is not empty + if not self.empty(): + # Get tree root curr_node = self.getRoot() - #While we don't find the node we look for - #I am using lazy evaluation here to avoid NoneType Attribute error + # While we don't find the node we look for + # I am using lazy evaluation here to avoid NoneType Attribute error while curr_node is not None and curr_node.getLabel() is not label: - #If node label is less than current node + # If node label is less than current node if label < curr_node.getLabel(): - #We go left + # We go left curr_node = curr_node.getLeft() else: - #Else we go right + # Else we go right curr_node = curr_node.getRight() return curr_node - def getMax(self, root = None): - if(root is not None): + def getMax(self, root=None): + if root is not None: curr_node = root else: - #We go deep on the right branch + # We go deep on the right branch curr_node = self.getRoot() - if(not self.empty()): - while(curr_node.getRight() is not None): + if not self.empty(): + while curr_node.getRight() is not None: curr_node = curr_node.getRight() return curr_node - def getMin(self, root = None): - if(root is not None): + def getMin(self, root=None): + if root is not None: curr_node = root else: - #We go deep on the left branch + # We go deep on the left branch curr_node = self.getRoot() - if(not self.empty()): + if not self.empty(): curr_node = self.getRoot() - while(curr_node.getLeft() is not None): + while curr_node.getLeft() is not None: curr_node = curr_node.getLeft() return curr_node @@ -151,34 +152,34 @@ def getRoot(self): return self.root def __isRightChildren(self, node): - if(node == node.getParent().getRight()): + if node == node.getParent().getRight(): return True return False def __reassignNodes(self, node, newChildren): - if(newChildren is not None): + if newChildren is not None: newChildren.setParent(node.getParent()) - if(node.getParent() is not None): - #If it is the Right Children - if(self.__isRightChildren(node)): + if node.getParent() is not None: + # If it is the Right Children + if self.__isRightChildren(node): node.getParent().setRight(newChildren) else: - #Else it is the left children + # Else it is the left children node.getParent().setLeft(newChildren) - #This function traversal the tree. By default it returns an - #In order traversal list. You can pass a function to traversal - #The tree as needed by client code - def traversalTree(self, traversalFunction = None, root = None): - if(traversalFunction is None): - #Returns a list of nodes in preOrder by default + # This function traversal the tree. By default it returns an + # In order traversal list. You can pass a function to traversal + # The tree as needed by client code + def traversalTree(self, traversalFunction=None, root=None): + if traversalFunction is None: + # Returns a list of nodes in preOrder by default return self.__InOrderTraversal(self.root) else: - #Returns a list of nodes in the order that the users wants to + # Returns a list of nodes in the order that the users wants to return traversalFunction(self.root) - #Returns an string of all the nodes labels in the list - #In Order Traversal + # Returns an string of all the nodes labels in the list + # In Order Traversal def __str__(self): list = self.__InOrderTraversal(self.root) str = "" @@ -186,6 +187,7 @@ def __str__(self): str = str + " " + x.getLabel().__str__() return str + def InPreOrder(curr_node): nodeList = [] if curr_node is not None: @@ -194,8 +196,9 @@ def InPreOrder(curr_node): nodeList = nodeList + InPreOrder(curr_node.getRight()) return nodeList + def testBinarySearchTree(): - r''' + r""" Example 8 / \ @@ -203,16 +206,16 @@ def testBinarySearchTree(): / \ \ 1 6 14 / \ / - 4 7 13 - ''' + 4 7 13 + """ - r''' + r""" Example After Deletion 7 / \ 1 4 - ''' + """ t = BinarySearchTree() t.insert(8) t.insert(3) @@ -224,23 +227,23 @@ def testBinarySearchTree(): t.insert(4) t.insert(7) - #Prints all the elements of the list in order traversal + # Prints all the elements of the list in order traversal print(t.__str__()) - if(t.getNode(6) is not None): + if t.getNode(6) is not None: print("The label 6 exists") else: print("The label 6 doesn't exist") - if(t.getNode(-1) is not None): + if t.getNode(-1) is not None: print("The label -1 exists") else: print("The label -1 doesn't exist") - - if(not t.empty()): + + if not t.empty(): print(("Max Value: ", t.getMax().getLabel())) print(("Min Value: ", t.getMin().getLabel())) - + t.delete(13) t.delete(10) t.delete(8) @@ -248,11 +251,12 @@ def testBinarySearchTree(): t.delete(6) t.delete(14) - #Gets all the elements of the tree In pre order - #And it prints them + # Gets all the elements of the tree In pre order + # And it prints them list = t.traversalTree(InPreOrder, t.root) for x in list: print(x) + if __name__ == "__main__": testBinarySearchTree() diff --git a/data_structures/binary_tree/fenwick_tree.py b/data_structures/binary_tree/fenwick_tree.py new file mode 100644 index 000000000000..54f0f07ac68d --- /dev/null +++ b/data_structures/binary_tree/fenwick_tree.py @@ -0,0 +1,28 @@ +class FenwickTree: + def __init__(self, SIZE): # create fenwick tree with size SIZE + self.Size = SIZE + self.ft = [0 for i in range(0, SIZE)] + + def update(self, i, val): # update data (adding) in index i in O(lg N) + while i < self.Size: + self.ft[i] += val + i += i & (-i) + + def query(self, i): # query cumulative data from index 0 to i in O(lg N) + ret = 0 + while i > 0: + ret += self.ft[i] + i -= i & (-i) + return ret + + +if __name__ == "__main__": + f = FenwickTree(100) + f.update(1, 20) + f.update(4, 4) + print(f.query(1)) + print(f.query(3)) + print(f.query(4)) + f.update(2, -5) + print(f.query(1)) + print(f.query(3)) diff --git a/data_structures/binary_tree/lazy_segment_tree.py b/data_structures/binary_tree/lazy_segment_tree.py new file mode 100644 index 000000000000..acd551b41b96 --- /dev/null +++ b/data_structures/binary_tree/lazy_segment_tree.py @@ -0,0 +1,94 @@ +import math + + +class SegmentTree: + def __init__(self, N): + self.N = N + self.st = [ + 0 for i in range(0, 4 * N) + ] # approximate the overall size of segment tree with array N + self.lazy = [0 for i in range(0, 4 * N)] # create array to store lazy update + self.flag = [0 for i in range(0, 4 * N)] # flag for lazy update + + def left(self, idx): + return idx * 2 + + def right(self, idx): + return idx * 2 + 1 + + def build(self, idx, l, r, A): + if l == r: + self.st[idx] = A[l - 1] + else: + mid = (l + r) // 2 + self.build(self.left(idx), l, mid, A) + self.build(self.right(idx), mid + 1, r, A) + self.st[idx] = max(self.st[self.left(idx)], self.st[self.right(idx)]) + + # update with O(lg N) (Normal segment tree without lazy update will take O(Nlg N) for each update) + def update( + self, idx, l, r, a, b, val + ): # update(1, 1, N, a, b, v) for update val v to [a,b] + if self.flag[idx] == True: + self.st[idx] = self.lazy[idx] + self.flag[idx] = False + if l != r: + self.lazy[self.left(idx)] = self.lazy[idx] + self.lazy[self.right(idx)] = self.lazy[idx] + self.flag[self.left(idx)] = True + self.flag[self.right(idx)] = True + + if r < a or l > b: + return True + if l >= a and r <= b: + self.st[idx] = val + if l != r: + self.lazy[self.left(idx)] = val + self.lazy[self.right(idx)] = val + self.flag[self.left(idx)] = True + self.flag[self.right(idx)] = True + return True + mid = (l + r) // 2 + self.update(self.left(idx), l, mid, a, b, val) + self.update(self.right(idx), mid + 1, r, a, b, val) + self.st[idx] = max(self.st[self.left(idx)], self.st[self.right(idx)]) + return True + + # query with O(lg N) + def query(self, idx, l, r, a, b): # query(1, 1, N, a, b) for query max of [a,b] + if self.flag[idx] == True: + self.st[idx] = self.lazy[idx] + self.flag[idx] = False + if l != r: + self.lazy[self.left(idx)] = self.lazy[idx] + self.lazy[self.right(idx)] = self.lazy[idx] + self.flag[self.left(idx)] = True + self.flag[self.right(idx)] = True + if r < a or l > b: + return -math.inf + if l >= a and r <= b: + return self.st[idx] + mid = (l + r) // 2 + q1 = self.query(self.left(idx), l, mid, a, b) + q2 = self.query(self.right(idx), mid + 1, r, a, b) + return max(q1, q2) + + def showData(self): + showList = [] + for i in range(1, N + 1): + showList += [self.query(1, 1, self.N, i, i)] + print(showList) + + +if __name__ == "__main__": + A = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8] + N = 15 + segt = SegmentTree(N) + segt.build(1, 1, N, A) + print(segt.query(1, 1, N, 4, 6)) + print(segt.query(1, 1, N, 7, 11)) + print(segt.query(1, 1, N, 7, 12)) + segt.update(1, 1, N, 1, 3, 111) + print(segt.query(1, 1, N, 1, 15)) + segt.update(1, 1, N, 7, 8, 235) + segt.showData() diff --git a/data_structures/LCA.py b/data_structures/binary_tree/lca.py similarity index 99% rename from data_structures/LCA.py rename to data_structures/binary_tree/lca.py index 9c9d8ca629c7..c18f1e944bab 100644 --- a/data_structures/LCA.py +++ b/data_structures/binary_tree/lca.py @@ -75,7 +75,7 @@ def main(): 10: [], 11: [], 12: [], - 13: [] + 13: [], } level, parent = bfs(level, parent, max_node, graph, 1) parent = creatSparse(max_node, parent) diff --git a/data_structures/binary_tree/non_recursive_segment_tree.py b/data_structures/binary_tree/non_recursive_segment_tree.py new file mode 100644 index 000000000000..877ee45b5baa --- /dev/null +++ b/data_structures/binary_tree/non_recursive_segment_tree.py @@ -0,0 +1,153 @@ +""" +A non-recursive Segment Tree implementation with range query and single element update, +works virtually with any list of the same type of elements with a "commutative" combiner. + +Explanation: +https://www.geeksforgeeks.org/iterative-segment-tree-range-minimum-query/ +https://www.geeksforgeeks.org/segment-tree-efficient-implementation/ + +>>> SegmentTree([1, 2, 3], lambda a, b: a + b).query(0, 2) +6 +>>> SegmentTree([3, 1, 2], min).query(0, 2) +1 +>>> SegmentTree([2, 3, 1], max).query(0, 2) +3 +>>> st = SegmentTree([1, 5, 7, -1, 6], lambda a, b: a + b) +>>> st.update(1, -1) +>>> st.update(2, 3) +>>> st.query(1, 2) +2 +>>> st.query(1, 1) +-1 +>>> st.update(4, 1) +>>> st.query(3, 4) +0 +>>> st = SegmentTree([[1, 2, 3], [3, 2, 1], [1, 1, 1]], lambda a, b: [a[i] + b[i] for i in range(len(a))]) +>>> st.query(0, 1) +[4, 4, 4] +>>> st.query(1, 2) +[4, 3, 2] +>>> st.update(1, [-1, -1, -1]) +>>> st.query(1, 2) +[0, 0, 0] +>>> st.query(0, 2) +[1, 2, 3] +""" +from typing import List, Callable, TypeVar + +T = TypeVar("T") + + +class SegmentTree: + def __init__(self, arr: List[T], fnc: Callable[[T, T], T]) -> None: + """ + Segment Tree constructor, it works just with commutative combiner. + :param arr: list of elements for the segment tree + :param fnc: commutative function for combine two elements + + >>> SegmentTree(['a', 'b', 'c'], lambda a, b: '{}{}'.format(a, b)).query(0, 2) + 'abc' + >>> SegmentTree([(1, 2), (2, 3), (3, 4)], lambda a, b: (a[0] + b[0], a[1] + b[1])).query(0, 2) + (6, 9) + """ + self.N = len(arr) + self.st = [None for _ in range(len(arr))] + arr + self.fn = fnc + self.build() + + def build(self) -> None: + for p in range(self.N - 1, 0, -1): + self.st[p] = self.fn(self.st[p * 2], self.st[p * 2 + 1]) + + def update(self, p: int, v: T) -> None: + """ + Update an element in log(N) time + :param p: position to be update + :param v: new value + + >>> st = SegmentTree([3, 1, 2, 4], min) + >>> st.query(0, 3) + 1 + >>> st.update(2, -1) + >>> st.query(0, 3) + -1 + """ + p += self.N + self.st[p] = v + while p > 1: + p = p // 2 + self.st[p] = self.fn(self.st[p * 2], self.st[p * 2 + 1]) + + def query(self, l: int, r: int) -> T: + """ + Get range query value in log(N) time + :param l: left element index + :param r: right element index + :return: element combined in the range [l, r] + + >>> st = SegmentTree([1, 2, 3, 4], lambda a, b: a + b) + >>> st.query(0, 2) + 6 + >>> st.query(1, 2) + 5 + >>> st.query(0, 3) + 10 + >>> st.query(2, 3) + 7 + """ + l, r = l + self.N, r + self.N + res = None + while l <= r: + if l % 2 == 1: + res = self.st[l] if res is None else self.fn(res, self.st[l]) + if r % 2 == 0: + res = self.st[r] if res is None else self.fn(res, self.st[r]) + l, r = (l + 1) // 2, (r - 1) // 2 + return res + + +if __name__ == "__main__": + from functools import reduce + + test_array = [1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12] + + test_updates = { + 0: 7, + 1: 2, + 2: 6, + 3: -14, + 4: 5, + 5: 4, + 6: 7, + 7: -10, + 8: 9, + 9: 10, + 10: 12, + 11: 1, + } + + min_segment_tree = SegmentTree(test_array, min) + max_segment_tree = SegmentTree(test_array, max) + sum_segment_tree = SegmentTree(test_array, lambda a, b: a + b) + + def test_all_segments(): + """ + Test all possible segments + """ + for i in range(len(test_array)): + for j in range(i, len(test_array)): + min_range = reduce(min, test_array[i : j + 1]) + max_range = reduce(max, test_array[i : j + 1]) + sum_range = reduce(lambda a, b: a + b, test_array[i : j + 1]) + assert min_range == min_segment_tree.query(i, j) + assert max_range == max_segment_tree.query(i, j) + assert sum_range == sum_segment_tree.query(i, j) + + test_all_segments() + + for index, value in test_updates.items(): + test_array[index] = value + min_segment_tree.update(index, value) + max_segment_tree.update(index, value) + sum_segment_tree.update(index, value) + test_all_segments() diff --git a/data_structures/binary_tree/red_black_tree.py b/data_structures/binary_tree/red_black_tree.py new file mode 100644 index 000000000000..908f13cd581e --- /dev/null +++ b/data_structures/binary_tree/red_black_tree.py @@ -0,0 +1,710 @@ +""" +python/black : true +flake8 : passed +""" + + +class RedBlackTree: + """ + A Red-Black tree, which is a self-balancing BST (binary search + tree). + This tree has similar performance to AVL trees, but the balancing is + less strict, so it will perform faster for writing/deleting nodes + and slower for reading in the average case, though, because they're + both balanced binary search trees, both will get the same asymptotic + perfomance. + To read more about them, https://en.wikipedia.org/wiki/Red–black_tree + Unless otherwise specified, all asymptotic runtimes are specified in + terms of the size of the tree. + """ + + def __init__(self, label=None, color=0, parent=None, left=None, right=None): + """Initialize a new Red-Black Tree node with the given values: + label: The value associated with this node + color: 0 if black, 1 if red + parent: The parent to this node + left: This node's left child + right: This node's right child + """ + self.label = label + self.parent = parent + self.left = left + self.right = right + self.color = color + + # Here are functions which are specific to red-black trees + + def rotate_left(self): + """Rotate the subtree rooted at this node to the left and + returns the new root to this subtree. + Perfoming one rotation can be done in O(1). + """ + parent = self.parent + right = self.right + self.right = right.left + if self.right: + self.right.parent = self + self.parent = right + right.left = self + if parent is not None: + if parent.left == self: + parent.left = right + else: + parent.right = right + right.parent = parent + return right + + def rotate_right(self): + """Rotate the subtree rooted at this node to the right and + returns the new root to this subtree. + Performing one rotation can be done in O(1). + """ + parent = self.parent + left = self.left + self.left = left.right + if self.left: + self.left.parent = self + self.parent = left + left.right = self + if parent is not None: + if parent.right is self: + parent.right = left + else: + parent.left = left + left.parent = parent + return left + + def insert(self, label): + """Inserts label into the subtree rooted at self, performs any + rotations necessary to maintain balance, and then returns the + new root to this subtree (likely self). + This is guaranteed to run in O(log(n)) time. + """ + if self.label is None: + # Only possible with an empty tree + self.label = label + return self + if self.label == label: + return self + elif self.label > label: + if self.left: + self.left.insert(label) + else: + self.left = RedBlackTree(label, 1, self) + self.left._insert_repair() + else: + if self.right: + self.right.insert(label) + else: + self.right = RedBlackTree(label, 1, self) + self.right._insert_repair() + return self.parent or self + + def _insert_repair(self): + """Repair the coloring from inserting into a tree.""" + if self.parent is None: + # This node is the root, so it just needs to be black + self.color = 0 + elif color(self.parent) == 0: + # If the parent is black, then it just needs to be red + self.color = 1 + else: + uncle = self.parent.sibling + if color(uncle) == 0: + if self.is_left() and self.parent.is_right(): + self.parent.rotate_right() + self.right._insert_repair() + elif self.is_right() and self.parent.is_left(): + self.parent.rotate_left() + self.left._insert_repair() + elif self.is_left(): + self.grandparent.rotate_right() + self.parent.color = 0 + self.parent.right.color = 1 + else: + self.grandparent.rotate_left() + self.parent.color = 0 + self.parent.left.color = 1 + else: + self.parent.color = 0 + uncle.color = 0 + self.grandparent.color = 1 + self.grandparent._insert_repair() + + def remove(self, label): + """Remove label from this tree.""" + if self.label == label: + if self.left and self.right: + # It's easier to balance a node with at most one child, + # so we replace this node with the greatest one less than + # it and remove that. + value = self.left.get_max() + self.label = value + self.left.remove(value) + else: + # This node has at most one non-None child, so we don't + # need to replace + child = self.left or self.right + if self.color == 1: + # This node is red, and its child is black + # The only way this happens to a node with one child + # is if both children are None leaves. + # We can just remove this node and call it a day. + if self.is_left(): + self.parent.left = None + else: + self.parent.right = None + else: + # The node is black + if child is None: + # This node and its child are black + if self.parent is None: + # The tree is now empty + return RedBlackTree(None) + else: + self._remove_repair() + if self.is_left(): + self.parent.left = None + else: + self.parent.right = None + self.parent = None + else: + # This node is black and its child is red + # Move the child node here and make it black + self.label = child.label + self.left = child.left + self.right = child.right + if self.left: + self.left.parent = self + if self.right: + self.right.parent = self + elif self.label > label: + if self.left: + self.left.remove(label) + else: + if self.right: + self.right.remove(label) + return self.parent or self + + def _remove_repair(self): + """Repair the coloring of the tree that may have been messed up.""" + if color(self.sibling) == 1: + self.sibling.color = 0 + self.parent.color = 1 + if self.is_left(): + self.parent.rotate_left() + else: + self.parent.rotate_right() + if ( + color(self.parent) == 0 + and color(self.sibling) == 0 + and color(self.sibling.left) == 0 + and color(self.sibling.right) == 0 + ): + self.sibling.color = 1 + self.parent._remove_repair() + return + if ( + color(self.parent) == 1 + and color(self.sibling) == 0 + and color(self.sibling.left) == 0 + and color(self.sibling.right) == 0 + ): + self.sibling.color = 1 + self.parent.color = 0 + return + if ( + self.is_left() + and color(self.sibling) == 0 + and color(self.sibling.right) == 0 + and color(self.sibling.left) == 1 + ): + self.sibling.rotate_right() + self.sibling.color = 0 + self.sibling.right.color = 1 + if ( + self.is_right() + and color(self.sibling) == 0 + and color(self.sibling.right) == 1 + and color(self.sibling.left) == 0 + ): + self.sibling.rotate_left() + self.sibling.color = 0 + self.sibling.left.color = 1 + if ( + self.is_left() + and color(self.sibling) == 0 + and color(self.sibling.right) == 1 + ): + self.parent.rotate_left() + self.grandparent.color = self.parent.color + self.parent.color = 0 + self.parent.sibling.color = 0 + if ( + self.is_right() + and color(self.sibling) == 0 + and color(self.sibling.left) == 1 + ): + self.parent.rotate_right() + self.grandparent.color = self.parent.color + self.parent.color = 0 + self.parent.sibling.color = 0 + + def check_color_properties(self): + """Check the coloring of the tree, and return True iff the tree + is colored in a way which matches these five properties: + (wording stolen from wikipedia article) + 1. Each node is either red or black. + 2. The root node is black. + 3. All leaves are black. + 4. If a node is red, then both its children are black. + 5. Every path from any node to all of its descendent NIL nodes + has the same number of black nodes. + This function runs in O(n) time, because properties 4 and 5 take + that long to check. + """ + # I assume property 1 to hold because there is nothing that can + # make the color be anything other than 0 or 1. + + # Property 2 + if self.color: + # The root was red + print("Property 2") + return False + + # Property 3 does not need to be checked, because None is assumed + # to be black and is all the leaves. + + # Property 4 + if not self.check_coloring(): + print("Property 4") + return False + + # Property 5 + if self.black_height() is None: + print("Property 5") + return False + # All properties were met + return True + + def check_coloring(self): + """A helper function to recursively check Property 4 of a + Red-Black Tree. See check_color_properties for more info. + """ + if self.color == 1: + if color(self.left) == 1 or color(self.right) == 1: + return False + if self.left and not self.left.check_coloring(): + return False + if self.right and not self.right.check_coloring(): + return False + return True + + def black_height(self): + """Returns the number of black nodes from this node to the + leaves of the tree, or None if there isn't one such value (the + tree is color incorrectly). + """ + if self is None: + # If we're already at a leaf, there is no path + return 1 + left = RedBlackTree.black_height(self.left) + right = RedBlackTree.black_height(self.right) + if left is None or right is None: + # There are issues with coloring below children nodes + return None + if left != right: + # The two children have unequal depths + return None + # Return the black depth of children, plus one if this node is + # black + return left + (1 - self.color) + + # Here are functions which are general to all binary search trees + + def __contains__(self, label): + """Search through the tree for label, returning True iff it is + found somewhere in the tree. + Guaranteed to run in O(log(n)) time. + """ + return self.search(label) is not None + + def search(self, label): + """Search through the tree for label, returning its node if + it's found, and None otherwise. + This method is guaranteed to run in O(log(n)) time. + """ + if self.label == label: + return self + elif label > self.label: + if self.right is None: + return None + else: + return self.right.search(label) + else: + if self.left is None: + return None + else: + return self.left.search(label) + + def floor(self, label): + """Returns the largest element in this tree which is at most label. + This method is guaranteed to run in O(log(n)) time.""" + if self.label == label: + return self.label + elif self.label > label: + if self.left: + return self.left.floor(label) + else: + return None + else: + if self.right: + attempt = self.right.floor(label) + if attempt is not None: + return attempt + return self.label + + def ceil(self, label): + """Returns the smallest element in this tree which is at least label. + This method is guaranteed to run in O(log(n)) time. + """ + if self.label == label: + return self.label + elif self.label < label: + if self.right: + return self.right.ceil(label) + else: + return None + else: + if self.left: + attempt = self.left.ceil(label) + if attempt is not None: + return attempt + return self.label + + def get_max(self): + """Returns the largest element in this tree. + This method is guaranteed to run in O(log(n)) time. + """ + if self.right: + # Go as far right as possible + return self.right.get_max() + else: + return self.label + + def get_min(self): + """Returns the smallest element in this tree. + This method is guaranteed to run in O(log(n)) time. + """ + if self.left: + # Go as far left as possible + return self.left.get_min() + else: + return self.label + + @property + def grandparent(self): + """Get the current node's grandparent, or None if it doesn't exist.""" + if self.parent is None: + return None + else: + return self.parent.parent + + @property + def sibling(self): + """Get the current node's sibling, or None if it doesn't exist.""" + if self.parent is None: + return None + elif self.parent.left is self: + return self.parent.right + else: + return self.parent.left + + def is_left(self): + """Returns true iff this node is the left child of its parent.""" + return self.parent and self.parent.left is self + + def is_right(self): + """Returns true iff this node is the right child of its parent.""" + return self.parent and self.parent.right is self + + def __bool__(self): + return True + + def __len__(self): + """ + Return the number of nodes in this tree. + """ + ln = 1 + if self.left: + ln += len(self.left) + if self.right: + ln += len(self.right) + return ln + + def preorder_traverse(self): + yield self.label + if self.left: + yield from self.left.preorder_traverse() + if self.right: + yield from self.right.preorder_traverse() + + def inorder_traverse(self): + if self.left: + yield from self.left.inorder_traverse() + yield self.label + if self.right: + yield from self.right.inorder_traverse() + + def postorder_traverse(self): + if self.left: + yield from self.left.postorder_traverse() + if self.right: + yield from self.right.postorder_traverse() + yield self.label + + def __repr__(self): + from pprint import pformat + + if self.left is None and self.right is None: + return "'%s %s'" % (self.label, (self.color and "red") or "blk") + return pformat( + { + "%s %s" + % (self.label, (self.color and "red") or "blk"): (self.left, self.right) + }, + indent=1, + ) + + def __eq__(self, other): + """Test if two trees are equal.""" + if self.label == other.label: + return self.left == other.left and self.right == other.right + else: + return False + + +def color(node): + """Returns the color of a node, allowing for None leaves.""" + if node is None: + return 0 + else: + return node.color + + +""" +Code for testing the various +functions of the red-black tree. +""" + + +def test_rotations(): + """Test that the rotate_left and rotate_right functions work.""" + # Make a tree to test on + tree = RedBlackTree(0) + tree.left = RedBlackTree(-10, parent=tree) + tree.right = RedBlackTree(10, parent=tree) + tree.left.left = RedBlackTree(-20, parent=tree.left) + tree.left.right = RedBlackTree(-5, parent=tree.left) + tree.right.left = RedBlackTree(5, parent=tree.right) + tree.right.right = RedBlackTree(20, parent=tree.right) + # Make the right rotation + left_rot = RedBlackTree(10) + left_rot.left = RedBlackTree(0, parent=left_rot) + left_rot.left.left = RedBlackTree(-10, parent=left_rot.left) + left_rot.left.right = RedBlackTree(5, parent=left_rot.left) + left_rot.left.left.left = RedBlackTree(-20, parent=left_rot.left.left) + left_rot.left.left.right = RedBlackTree(-5, parent=left_rot.left.left) + left_rot.right = RedBlackTree(20, parent=left_rot) + tree = tree.rotate_left() + if tree != left_rot: + return False + tree = tree.rotate_right() + tree = tree.rotate_right() + # Make the left rotation + right_rot = RedBlackTree(-10) + right_rot.left = RedBlackTree(-20, parent=right_rot) + right_rot.right = RedBlackTree(0, parent=right_rot) + right_rot.right.left = RedBlackTree(-5, parent=right_rot.right) + right_rot.right.right = RedBlackTree(10, parent=right_rot.right) + right_rot.right.right.left = RedBlackTree(5, parent=right_rot.right.right) + right_rot.right.right.right = RedBlackTree(20, parent=right_rot.right.right) + if tree != right_rot: + return False + return True + + +def test_insertion_speed(): + """Test that the tree balances inserts to O(log(n)) by doing a lot + of them. + """ + tree = RedBlackTree(-1) + for i in range(300000): + tree = tree.insert(i) + return True + + +def test_insert(): + """Test the insert() method of the tree correctly balances, colors, + and inserts. + """ + tree = RedBlackTree(0) + tree.insert(8) + tree.insert(-8) + tree.insert(4) + tree.insert(12) + tree.insert(10) + tree.insert(11) + ans = RedBlackTree(0, 0) + ans.left = RedBlackTree(-8, 0, ans) + ans.right = RedBlackTree(8, 1, ans) + ans.right.left = RedBlackTree(4, 0, ans.right) + ans.right.right = RedBlackTree(11, 0, ans.right) + ans.right.right.left = RedBlackTree(10, 1, ans.right.right) + ans.right.right.right = RedBlackTree(12, 1, ans.right.right) + return tree == ans + + +def test_insert_and_search(): + """Tests searching through the tree for values.""" + tree = RedBlackTree(0) + tree.insert(8) + tree.insert(-8) + tree.insert(4) + tree.insert(12) + tree.insert(10) + tree.insert(11) + if 5 in tree or -6 in tree or -10 in tree or 13 in tree: + # Found something not in there + return False + if not (11 in tree and 12 in tree and -8 in tree and 0 in tree): + # Didn't find something in there + return False + return True + + +def test_insert_delete(): + """Test the insert() and delete() method of the tree, verifying the + insertion and removal of elements, and the balancing of the tree. + """ + tree = RedBlackTree(0) + tree = tree.insert(-12) + tree = tree.insert(8) + tree = tree.insert(-8) + tree = tree.insert(15) + tree = tree.insert(4) + tree = tree.insert(12) + tree = tree.insert(10) + tree = tree.insert(9) + tree = tree.insert(11) + tree = tree.remove(15) + tree = tree.remove(-12) + tree = tree.remove(9) + if not tree.check_color_properties(): + return False + if list(tree.inorder_traverse()) != [-8, 0, 4, 8, 10, 11, 12]: + return False + return True + + +def test_floor_ceil(): + """Tests the floor and ceiling functions in the tree.""" + tree = RedBlackTree(0) + tree.insert(-16) + tree.insert(16) + tree.insert(8) + tree.insert(24) + tree.insert(20) + tree.insert(22) + tuples = [(-20, None, -16), (-10, -16, 0), (8, 8, 8), (50, 24, None)] + for val, floor, ceil in tuples: + if tree.floor(val) != floor or tree.ceil(val) != ceil: + return False + return True + + +def test_min_max(): + """Tests the min and max functions in the tree.""" + tree = RedBlackTree(0) + tree.insert(-16) + tree.insert(16) + tree.insert(8) + tree.insert(24) + tree.insert(20) + tree.insert(22) + if tree.get_max() != 22 or tree.get_min() != -16: + return False + return True + + +def test_tree_traversal(): + """Tests the three different tree traversal functions.""" + tree = RedBlackTree(0) + tree = tree.insert(-16) + tree.insert(16) + tree.insert(8) + tree.insert(24) + tree.insert(20) + tree.insert(22) + if list(tree.inorder_traverse()) != [-16, 0, 8, 16, 20, 22, 24]: + return False + if list(tree.preorder_traverse()) != [0, -16, 16, 8, 22, 20, 24]: + return False + if list(tree.postorder_traverse()) != [-16, 8, 20, 24, 22, 16, 0]: + return False + return True + + +def test_tree_chaining(): + """Tests the three different tree chaning functions.""" + tree = RedBlackTree(0) + tree = tree.insert(-16).insert(16).insert(8).insert(24).insert(20).insert(22) + if list(tree.inorder_traverse()) != [-16, 0, 8, 16, 20, 22, 24]: + return False + if list(tree.preorder_traverse()) != [0, -16, 16, 8, 22, 20, 24]: + return False + if list(tree.postorder_traverse()) != [-16, 8, 20, 24, 22, 16, 0]: + return False + return True + + +def print_results(msg: str, passes: bool) -> None: + print(str(msg), "works!" if passes else "doesn't work :(") + + +def pytests(): + assert test_rotations() + assert test_insert() + assert test_insert_and_search() + assert test_insert_delete() + assert test_floor_ceil() + assert test_tree_traversal() + assert test_tree_chaining() + + +def main(): + """ + >>> pytests() + """ + print_results("Rotating right and left", test_rotations()) + + print_results("Inserting", test_insert()) + + print_results("Searching", test_insert_and_search()) + + print_results("Deleting", test_insert_delete()) + + print_results("Floor and ceil", test_floor_ceil()) + + print_results("Tree traversal", test_tree_traversal()) + + print_results("Tree traversal", test_tree_chaining()) + + print("Testing tree balancing...") + print("This should only be a few seconds.") + test_insertion_speed() + print("Done!") + + +if __name__ == "__main__": + main() diff --git a/data_structures/binary tree/segment_tree.py b/data_structures/binary_tree/segment_tree.py similarity index 56% rename from data_structures/binary tree/segment_tree.py rename to data_structures/binary_tree/segment_tree.py index 001bf999f391..ad9476b4514b 100644 --- a/data_structures/binary tree/segment_tree.py +++ b/data_structures/binary_tree/segment_tree.py @@ -1,13 +1,14 @@ -from __future__ import print_function import math + class SegmentTree: - def __init__(self, A): self.N = len(A) - self.st = [0] * (4 * self.N) # approximate the overall size of segment tree with array N + self.st = [0] * ( + 4 * self.N + ) # approximate the overall size of segment tree with array N self.build(1, 0, self.N - 1) - + def left(self, idx): return idx * 2 @@ -21,51 +22,55 @@ def build(self, idx, l, r): mid = (l + r) // 2 self.build(self.left(idx), l, mid) self.build(self.right(idx), mid + 1, r) - self.st[idx] = max(self.st[self.left(idx)] , self.st[self.right(idx)]) - + self.st[idx] = max(self.st[self.left(idx)], self.st[self.right(idx)]) + def update(self, a, b, val): return self.update_recursive(1, 0, self.N - 1, a - 1, b - 1, val) - - def update_recursive(self, idx, l, r, a, b, val): # update(1, 1, N, a, b, v) for update val v to [a,b] + + def update_recursive( + self, idx, l, r, a, b, val + ): # update(1, 1, N, a, b, v) for update val v to [a,b] if r < a or l > b: return True - if l == r : + if l == r: self.st[idx] = val return True - mid = (l+r)//2 + mid = (l + r) // 2 self.update_recursive(self.left(idx), l, mid, a, b, val) - self.update_recursive(self.right(idx), mid+1, r, a, b, val) - self.st[idx] = max(self.st[self.left(idx)] , self.st[self.right(idx)]) + self.update_recursive(self.right(idx), mid + 1, r, a, b, val) + self.st[idx] = max(self.st[self.left(idx)], self.st[self.right(idx)]) return True def query(self, a, b): return self.query_recursive(1, 0, self.N - 1, a - 1, b - 1) - def query_recursive(self, idx, l, r, a, b): #query(1, 1, N, a, b) for query max of [a,b] + def query_recursive( + self, idx, l, r, a, b + ): # query(1, 1, N, a, b) for query max of [a,b] if r < a or l > b: return -math.inf if l >= a and r <= b: return self.st[idx] - mid = (l+r)//2 + mid = (l + r) // 2 q1 = self.query_recursive(self.left(idx), l, mid, a, b) q2 = self.query_recursive(self.right(idx), mid + 1, r, a, b) return max(q1, q2) def showData(self): showList = [] - for i in range(1,N+1): + for i in range(1, N + 1): showList += [self.query(i, i)] - print (showList) - + print(showList) + -if __name__ == '__main__': - A = [1,2,-4,7,3,-5,6,11,-20,9,14,15,5,2,-8] +if __name__ == "__main__": + A = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8] N = 15 segt = SegmentTree(A) - print (segt.query(4, 6)) - print (segt.query(7, 11)) - print (segt.query(7, 12)) - segt.update(1,3,111) - print (segt.query(1, 15)) - segt.update(7,8,235) + print(segt.query(4, 6)) + print(segt.query(7, 11)) + print(segt.query(7, 12)) + segt.update(1, 3, 111) + print(segt.query(1, 15)) + segt.update(7, 8, 235) segt.showData() diff --git a/data_structures/binary_tree/treap.py b/data_structures/binary_tree/treap.py new file mode 100644 index 000000000000..b603eec3ef3c --- /dev/null +++ b/data_structures/binary_tree/treap.py @@ -0,0 +1,182 @@ +from random import random +from typing import Tuple + + +class Node(object): + """ + Treap's node + Treap is a binary tree by value and heap by priority + """ + + def __init__(self, value: int = None): + self.value = value + self.prior = random() + self.left = None + self.right = None + + def __repr__(self): + from pprint import pformat + + if self.left is None and self.right is None: + return "'%s: %.5s'" % (self.value, self.prior) + else: + return pformat( + {"%s: %.5s" % (self.value, self.prior): (self.left, self.right)}, + indent=1, + ) + + def __str__(self): + value = str(self.value) + " " + left = str(self.left or "") + right = str(self.right or "") + return value + left + right + + +def split(root: Node, value: int) -> Tuple[Node, Node]: + """ + We split current tree into 2 trees with value: + + Left tree contains all values less than split value. + Right tree contains all values greater or equal, than split value + """ + if root is None: # None tree is split into 2 Nones + return (None, None) + elif root.value is None: + return (None, None) + else: + if value < root.value: + """ + Right tree's root will be current node. + Now we split(with the same value) current node's left son + Left tree: left part of that split + Right tree's left son: right part of that split + """ + left, root.left = split(root.left, value) + return (left, root) + else: + """ + Just symmetric to previous case + """ + root.right, right = split(root.right, value) + return (root, right) + + +def merge(left: Node, right: Node) -> Node: + """ + We merge 2 trees into one. + Note: all left tree's values must be less than all right tree's + """ + if (not left) or (not right): # If one node is None, return the other + return left or right + elif left.prior < right.prior: + """ + Left will be root because it has more priority + Now we need to merge left's right son and right tree + """ + left.right = merge(left.right, right) + return left + else: + """ + Symmetric as well + """ + right.left = merge(left, right.left) + return right + + +def insert(root: Node, value: int) -> Node: + """ + Insert element + + Split current tree with a value into left, right, + Insert new node into the middle + Merge left, node, right into root + """ + node = Node(value) + left, right = split(root, value) + return merge(merge(left, node), right) + + +def erase(root: Node, value: int) -> Node: + """ + Erase element + + Split all nodes with values less into left, + Split all nodes with values greater into right. + Merge left, right + """ + left, right = split(root, value - 1) + _, right = split(right, value) + return merge(left, right) + + +def inorder(root: Node): + """ + Just recursive print of a tree + """ + if not root: # None + return + else: + inorder(root.left) + print(root.value, end=" ") + inorder(root.right) + + +def interactTreap(root, args): + """ + Commands: + + value to add value into treap + - value to erase all nodes with value + + >>> root = interactTreap(None, "+1") + >>> inorder(root) + 1 + >>> root = interactTreap(root, "+3 +5 +17 +19 +2 +16 +4 +0") + >>> inorder(root) + 0 1 2 3 4 5 16 17 19 + >>> root = interactTreap(root, "+4 +4 +4") + >>> inorder(root) + 0 1 2 3 4 4 4 4 5 16 17 19 + >>> root = interactTreap(root, "-0") + >>> inorder(root) + 1 2 3 4 4 4 4 5 16 17 19 + >>> root = interactTreap(root, "-4") + >>> inorder(root) + 1 2 3 5 16 17 19 + >>> root = interactTreap(root, "=0") + Unknown command + """ + for arg in args.split(): + if arg[0] == "+": + root = insert(root, int(arg[1:])) + + elif arg[0] == "-": + root = erase(root, int(arg[1:])) + + else: + print("Unknown command") + + return root + + +def main(): + """After each command, program prints treap""" + root = None + print( + "enter numbers to creat a tree, + value to add value into treap, - value to erase all nodes with value. 'q' to quit. " + ) + + args = input() + while args != "q": + root = interactTreap(root, args) + print(root) + args = input() + + print("good by!") + + + +if __name__ == "__main__": + import doctest + + doctest.testmod() + main() diff --git a/data_structures/disjoint_set/disjoint_set.py b/data_structures/disjoint_set/disjoint_set.py new file mode 100644 index 000000000000..a93b89621c4a --- /dev/null +++ b/data_structures/disjoint_set/disjoint_set.py @@ -0,0 +1,79 @@ +""" + disjoint set + Reference: https://en.wikipedia.org/wiki/Disjoint-set_data_structure +""" + + +class Node: + def __init__(self, data): + self.data = data + + +def make_set(x): + """ + make x as a set. + """ + # rank is the distance from x to its' parent + # root's rank is 0 + x.rank = 0 + x.parent = x + + +def union_set(x, y): + """ + union two sets. + set with bigger rank should be parent, so that the + disjoint set tree will be more flat. + """ + x, y = find_set(x), find_set(y) + if x.rank > y.rank: + y.parent = x + else: + x.parent = y + if x.rank == y.rank: + y.rank += 1 + + +def find_set(x): + """ + return the parent of x + """ + if x != x.parent: + x.parent = find_set(x.parent) + return x.parent + + +def find_python_set(node: Node) -> set: + """ + Return a Python Standard Library set that contains i. + """ + sets = ({0, 1, 2}, {3, 4, 5}) + for s in sets: + if node.data in s: + return s + raise ValueError(f"{node.data} is not in {sets}") + + +def test_disjoint_set(): + """ + >>> test_disjoint_set() + """ + vertex = [Node(i) for i in range(6)] + for v in vertex: + make_set(v) + + union_set(vertex[0], vertex[1]) + union_set(vertex[1], vertex[2]) + union_set(vertex[3], vertex[4]) + union_set(vertex[3], vertex[5]) + + for node0 in vertex: + for node1 in vertex: + if find_python_set(node0).isdisjoint(find_python_set(node1)): + assert find_set(node0) != find_set(node1) + else: + assert find_set(node0) == find_set(node1) + + +if __name__ == "__main__": + test_disjoint_set() diff --git a/data_structures/hashing/__init__.py b/data_structures/hashing/__init__.py deleted file mode 100644 index b96ddd478458..000000000000 --- a/data_structures/hashing/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from .hash_table import HashTable - -class QuadraticProbing(HashTable): - - def __init__(self): - super(self.__class__, self).__init__() diff --git a/data_structures/hashing/double_hash.py b/data_structures/hashing/double_hash.py index 60098cda0ce1..6c3699cc9950 100644 --- a/data_structures/hashing/double_hash.py +++ b/data_structures/hashing/double_hash.py @@ -1,6 +1,6 @@ #!/usr/bin/env python3 -from .hash_table import HashTable +from hash_table import HashTable from number_theory.prime_numbers import next_prime, check_prime @@ -8,13 +8,17 @@ class DoubleHash(HashTable): """ Hash Table example with open addressing and Double Hash """ + def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def __hash_function_2(self, value, data): - next_prime_gt = next_prime(value % self.size_table) \ - if not check_prime(value % self.size_table) else value % self.size_table #gt = bigger than + next_prime_gt = ( + next_prime(value % self.size_table) + if not check_prime(value % self.size_table) + else value % self.size_table + ) # gt = bigger than return next_prime_gt - (data % next_prime_gt) def __hash_double_function(self, key, data, increment): @@ -25,9 +29,14 @@ def _colision_resolution(self, key, data=None): new_key = self.hash_function(data) while self.values[new_key] is not None and self.values[new_key] != key: - new_key = self.__hash_double_function(key, data, i) if \ - self.balanced_factor() >= self.lim_charge else None - if new_key is None: break - else: i += 1 + new_key = ( + self.__hash_double_function(key, data, i) + if self.balanced_factor() >= self.lim_charge + else None + ) + if new_key is None: + break + else: + i += 1 return new_key diff --git a/data_structures/hashing/hash_table.py b/data_structures/hashing/hash_table.py index f0de128d1ad1..ab473dc52324 100644 --- a/data_structures/hashing/hash_table.py +++ b/data_structures/hashing/hash_table.py @@ -19,8 +19,9 @@ def keys(self): return self._keys def balanced_factor(self): - return sum([1 for slot in self.values - if slot is not None]) / (self.size_table * self.charge_factor) + return sum([1 for slot in self.values if slot is not None]) / ( + self.size_table * self.charge_factor + ) def hash_function(self, key): return key % self.size_table @@ -46,8 +47,7 @@ def _set_value(self, key, data): def _colision_resolution(self, key, data=None): new_key = self.hash_function(key + 1) - while self.values[new_key] is not None \ - and self.values[new_key] != key: + while self.values[new_key] is not None and self.values[new_key] != key: if self.values.count(None) > 0: new_key = self.hash_function(new_key + 1) @@ -61,7 +61,7 @@ def rehashing(self): survivor_values = [value for value in self.values if value is not None] self.size_table = next_prime(self.size_table, factor=2) self._keys.clear() - self.values = [None] * self.size_table #hell's pointers D: don't DRY ;/ + self.values = [None] * self.size_table # hell's pointers D: don't DRY ;/ map(self.insert_data, survivor_values) def insert_data(self, data): @@ -80,5 +80,3 @@ def insert_data(self, data): else: self.rehashing() self.insert_data(data) - - diff --git a/data_structures/hashing/hash_table_with_linked_list.py b/data_structures/hashing/hash_table_with_linked_list.py index 9689e4fc9fcf..236985b69ac6 100644 --- a/data_structures/hashing/hash_table_with_linked_list.py +++ b/data_structures/hashing/hash_table_with_linked_list.py @@ -1,4 +1,4 @@ -from .hash_table import HashTable +from hash_table import HashTable from collections import deque @@ -7,18 +7,20 @@ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def _set_value(self, key, data): - self.values[key] = deque([]) if self.values[key] is None else self.values[key] + self.values[key] = deque([]) if self.values[key] is None else self.values[key] self.values[key].appendleft(data) self._keys[key] = self.values[key] def balanced_factor(self): - return sum([self.charge_factor - len(slot) for slot in self.values])\ - / self.size_table * self.charge_factor - + return ( + sum([self.charge_factor - len(slot) for slot in self.values]) + / self.size_table + * self.charge_factor + ) + def _colision_resolution(self, key, data=None): - if not (len(self.values[key]) == self.charge_factor - and self.values.count(None) == 0): + if not ( + len(self.values[key]) == self.charge_factor and self.values.count(None) == 0 + ): return key return super()._colision_resolution(key, data) - - diff --git a/data_structures/hashing/number_theory/prime_numbers.py b/data_structures/hashing/number_theory/prime_numbers.py index 8a521bc45758..2a966e0da7f2 100644 --- a/data_structures/hashing/number_theory/prime_numbers.py +++ b/data_structures/hashing/number_theory/prime_numbers.py @@ -5,25 +5,25 @@ def check_prime(number): - """ + """ it's not the best solution """ - special_non_primes = [0,1,2] - if number in special_non_primes[:2]: - return 2 - elif number == special_non_primes[-1]: - return 3 - - return all([number % i for i in range(2, number)]) + special_non_primes = [0, 1, 2] + if number in special_non_primes[:2]: + return 2 + elif number == special_non_primes[-1]: + return 3 + + return all([number % i for i in range(2, number)]) def next_prime(value, factor=1, **kwargs): value = factor * value first_value_val = value - + while not check_prime(value): value += 1 if not ("desc" in kwargs.keys() and kwargs["desc"] is True) else -1 - + if value == first_value_val: return next_prime(value + 1, **kwargs) return value diff --git a/data_structures/hashing/quadratic_probing.py b/data_structures/hashing/quadratic_probing.py index f7a9ac1ae347..ac966e1cd67e 100644 --- a/data_structures/hashing/quadratic_probing.py +++ b/data_structures/hashing/quadratic_probing.py @@ -1,24 +1,27 @@ #!/usr/bin/env python3 -from .hash_table import HashTable +from hash_table import HashTable class QuadraticProbing(HashTable): """ Basic Hash Table example with open addressing using Quadratic Probing """ + def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def _colision_resolution(self, key, data=None): i = 1 - new_key = self.hash_function(key + i*i) + new_key = self.hash_function(key + i * i) - while self.values[new_key] is not None \ - and self.values[new_key] != key: + while self.values[new_key] is not None and self.values[new_key] != key: i += 1 - new_key = self.hash_function(key + i*i) if not \ - self.balanced_factor() >= self.lim_charge else None + new_key = ( + self.hash_function(key + i * i) + if not self.balanced_factor() >= self.lim_charge + else None + ) if new_key is None: break diff --git a/data_structures/heap/binomial_heap.py b/data_structures/heap/binomial_heap.py new file mode 100644 index 000000000000..7f570f1c755b --- /dev/null +++ b/data_structures/heap/binomial_heap.py @@ -0,0 +1,401 @@ +""" +Binomial Heap +Reference: Advanced Data Structures, Peter Brass +""" + + +class Node: + """ + Node in a doubly-linked binomial tree, containing: + - value + - size of left subtree + - link to left, right and parent nodes + """ + + def __init__(self, val): + self.val = val + # Number of nodes in left subtree + self.left_tree_size = 0 + self.left = None + self.right = None + self.parent = None + + def mergeTrees(self, other): + """ + In-place merge of two binomial trees of equal size. + Returns the root of the resulting tree + """ + assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks" + + if self.val < other.val: + other.left = self.right + other.parent = None + if self.right: + self.right.parent = other + self.right = other + self.left_tree_size = self.left_tree_size * 2 + 1 + return self + else: + self.left = other.right + self.parent = None + if other.right: + other.right.parent = self + other.right = self + other.left_tree_size = other.left_tree_size * 2 + 1 + return other + + +class BinomialHeap: + r""" + Min-oriented priority queue implemented with the Binomial Heap data + structure implemented with the BinomialHeap class. It supports: + - Insert element in a heap with n elemnts: Guaranteed logn, amoratized 1 + - Merge (meld) heaps of size m and n: O(logn + logm) + - Delete Min: O(logn) + - Peek (return min without deleting it): O(1) + + Example: + + Create a random permutation of 30 integers to be inserted and 19 of them deleted + >>> import numpy as np + >>> permutation = np.random.permutation(list(range(30))) + + Create a Heap and insert the 30 integers + __init__() test + >>> first_heap = BinomialHeap() + + 30 inserts - insert() test + >>> for number in permutation: + ... first_heap.insert(number) + + Size test + >>> print(first_heap.size) + 30 + + Deleting - delete() test + >>> for i in range(25): + ... print(first_heap.deleteMin(), end=" ") + 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 + + Create a new Heap + >>> second_heap = BinomialHeap() + >>> vals = [17, 20, 31, 34] + >>> for value in vals: + ... second_heap.insert(value) + + + The heap should have the following structure: + + 17 + / \ + # 31 + / \ + 20 34 + / \ / \ + # # # # + + preOrder() test + >>> print(second_heap.preOrder()) + [(17, 0), ('#', 1), (31, 1), (20, 2), ('#', 3), ('#', 3), (34, 2), ('#', 3), ('#', 3)] + + printing Heap - __str__() test + >>> print(second_heap) + 17 + -# + -31 + --20 + ---# + ---# + --34 + ---# + ---# + + mergeHeaps() test + >>> merged = second_heap.mergeHeaps(first_heap) + >>> merged.peek() + 17 + + values in merged heap; (merge is inplace) + >>> while not first_heap.isEmpty(): + ... print(first_heap.deleteMin(), end=" ") + 17 20 25 26 27 28 29 31 34 + """ + + def __init__(self, bottom_root=None, min_node=None, heap_size=0): + self.size = heap_size + self.bottom_root = bottom_root + self.min_node = min_node + + def mergeHeaps(self, other): + """ + In-place merge of two binomial heaps. + Both of them become the resulting merged heap + """ + + # Empty heaps corner cases + if other.size == 0: + return + if self.size == 0: + self.size = other.size + self.bottom_root = other.bottom_root + self.min_node = other.min_node + return + # Update size + self.size = self.size + other.size + + # Update min.node + if self.min_node.val > other.min_node.val: + self.min_node = other.min_node + # Merge + + # Order roots by left_subtree_size + combined_roots_list = [] + i, j = self.bottom_root, other.bottom_root + while i or j: + if i and ((not j) or i.left_tree_size < j.left_tree_size): + combined_roots_list.append((i, True)) + i = i.parent + else: + combined_roots_list.append((j, False)) + j = j.parent + # Insert links between them + for i in range(len(combined_roots_list) - 1): + if combined_roots_list[i][1] != combined_roots_list[i + 1][1]: + combined_roots_list[i][0].parent = combined_roots_list[i + 1][0] + combined_roots_list[i + 1][0].left = combined_roots_list[i][0] + # Consecutively merge roots with same left_tree_size + i = combined_roots_list[0][0] + while i.parent: + if ( + (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent) + ) or ( + i.left_tree_size == i.parent.left_tree_size + and i.left_tree_size != i.parent.parent.left_tree_size + ): + + # Neighbouring Nodes + previous_node = i.left + next_node = i.parent.parent + + # Merging trees + i = i.mergeTrees(i.parent) + + # Updating links + i.left = previous_node + i.parent = next_node + if previous_node: + previous_node.parent = i + if next_node: + next_node.left = i + else: + i = i.parent + # Updating self.bottom_root + while i.left: + i = i.left + self.bottom_root = i + + # Update other + other.size = self.size + other.bottom_root = self.bottom_root + other.min_node = self.min_node + + # Return the merged heap + return self + + def insert(self, val): + """ + insert a value in the heap + """ + if self.size == 0: + self.bottom_root = Node(val) + self.size = 1 + self.min_node = self.bottom_root + else: + # Create new node + new_node = Node(val) + + # Update size + self.size += 1 + + # update min_node + if val < self.min_node.val: + self.min_node = new_node + # Put new_node as a bottom_root in heap + self.bottom_root.left = new_node + new_node.parent = self.bottom_root + self.bottom_root = new_node + + # Consecutively merge roots with same left_tree_size + while ( + self.bottom_root.parent + and self.bottom_root.left_tree_size + == self.bottom_root.parent.left_tree_size + ): + + # Next node + next_node = self.bottom_root.parent.parent + + # Merge + self.bottom_root = self.bottom_root.mergeTrees(self.bottom_root.parent) + + # Update Links + self.bottom_root.parent = next_node + self.bottom_root.left = None + if next_node: + next_node.left = self.bottom_root + + def peek(self): + """ + return min element without deleting it + """ + return self.min_node.val + + def isEmpty(self): + return self.size == 0 + + def deleteMin(self): + """ + delete min element and return it + """ + # assert not self.isEmpty(), "Empty Heap" + + # Save minimal value + min_value = self.min_node.val + + # Last element in heap corner case + if self.size == 1: + # Update size + self.size = 0 + + # Update bottom root + self.bottom_root = None + + # Update min_node + self.min_node = None + + return min_value + # No right subtree corner case + # The structure of the tree implies that this should be the bottom root + # and there is at least one other root + if self.min_node.right is None: + # Update size + self.size -= 1 + + # Update bottom root + self.bottom_root = self.bottom_root.parent + self.bottom_root.left = None + + # Update min_node + self.min_node = self.bottom_root + i = self.bottom_root.parent + while i: + if i.val < self.min_node.val: + self.min_node = i + i = i.parent + return min_value + # General case + # Find the BinomialHeap of the right subtree of min_node + bottom_of_new = self.min_node.right + bottom_of_new.parent = None + min_of_new = bottom_of_new + size_of_new = 1 + + # Size, min_node and bottom_root + while bottom_of_new.left: + size_of_new = size_of_new * 2 + 1 + bottom_of_new = bottom_of_new.left + if bottom_of_new.val < min_of_new.val: + min_of_new = bottom_of_new + # Corner case of single root on top left path + if (not self.min_node.left) and (not self.min_node.parent): + self.size = size_of_new + self.bottom_root = bottom_of_new + self.min_node = min_of_new + # print("Single root, multiple nodes case") + return min_value + # Remaining cases + # Construct heap of right subtree + newHeap = BinomialHeap( + bottom_root=bottom_of_new, min_node=min_of_new, heap_size=size_of_new + ) + + # Update size + self.size = self.size - 1 - size_of_new + + # Neighbour nodes + previous_node = self.min_node.left + next_node = self.min_node.parent + + # Initialize new bottom_root and min_node + self.min_node = previous_node or next_node + self.bottom_root = next_node + + # Update links of previous_node and search below for new min_node and + # bottom_root + if previous_node: + previous_node.parent = next_node + + # Update bottom_root and search for min_node below + self.bottom_root = previous_node + self.min_node = previous_node + while self.bottom_root.left: + self.bottom_root = self.bottom_root.left + if self.bottom_root.val < self.min_node.val: + self.min_node = self.bottom_root + if next_node: + next_node.left = previous_node + + # Search for new min_node above min_node + i = next_node + while i: + if i.val < self.min_node.val: + self.min_node = i + i = i.parent + # Merge heaps + self.mergeHeaps(newHeap) + + return min_value + + def preOrder(self): + """ + Returns the Pre-order representation of the heap including + values of nodes plus their level distance from the root; + Empty nodes appear as # + """ + # Find top root + top_root = self.bottom_root + while top_root.parent: + top_root = top_root.parent + # preorder + heap_preOrder = [] + self.__traversal(top_root, heap_preOrder) + return heap_preOrder + + def __traversal(self, curr_node, preorder, level=0): + """ + Pre-order traversal of nodes + """ + if curr_node: + preorder.append((curr_node.val, level)) + self.__traversal(curr_node.left, preorder, level + 1) + self.__traversal(curr_node.right, preorder, level + 1) + else: + preorder.append(("#", level)) + + def __str__(self): + """ + Overwriting str for a pre-order print of nodes in heap; + Performance is poor, so use only for small examples + """ + if self.isEmpty(): + return "" + preorder_heap = self.preOrder() + + return "\n".join(("-" * level + str(value)) for value, level in preorder_heap) + + +# Unit Tests +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/data_structures/heap/heap.py b/data_structures/heap/heap.py index 39778f725c3a..b020ab067cc8 100644 --- a/data_structures/heap/heap.py +++ b/data_structures/heap/heap.py @@ -1,91 +1,86 @@ #!/usr/bin/python -from __future__ import print_function, division - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - -#This heap class start from here. +# This heap class start from here. class Heap: - def __init__(self): #Default constructor of heap class. - self.h = [] - self.currsize = 0 + def __init__(self): # Default constructor of heap class. + self.h = [] + self.currsize = 0 - def leftChild(self,i): - if 2*i+1 < self.currsize: - return 2*i+1 - return None + def leftChild(self, i): + if 2 * i + 1 < self.currsize: + return 2 * i + 1 + return None - def rightChild(self,i): - if 2*i+2 < self.currsize: - return 2*i+2 - return None + def rightChild(self, i): + if 2 * i + 2 < self.currsize: + return 2 * i + 2 + return None - def maxHeapify(self,node): - if node < self.currsize: - m = node - lc = self.leftChild(node) - rc = self.rightChild(node) - if lc is not None and self.h[lc] > self.h[m]: - m = lc - if rc is not None and self.h[rc] > self.h[m]: - m = rc - if m!=node: - temp = self.h[node] - self.h[node] = self.h[m] - self.h[m] = temp - self.maxHeapify(m) + def maxHeapify(self, node): + if node < self.currsize: + m = node + lc = self.leftChild(node) + rc = self.rightChild(node) + if lc is not None and self.h[lc] > self.h[m]: + m = lc + if rc is not None and self.h[rc] > self.h[m]: + m = rc + if m != node: + temp = self.h[node] + self.h[node] = self.h[m] + self.h[m] = temp + self.maxHeapify(m) - def buildHeap(self,a): #This function is used to build the heap from the data container 'a'. - self.currsize = len(a) - self.h = list(a) - for i in range(self.currsize//2,-1,-1): - self.maxHeapify(i) + def buildHeap( + self, a + ): # This function is used to build the heap from the data container 'a'. + self.currsize = len(a) + self.h = list(a) + for i in range(self.currsize // 2, -1, -1): + self.maxHeapify(i) - def getMax(self): #This function is used to get maximum value from the heap. - if self.currsize >= 1: - me = self.h[0] - temp = self.h[0] - self.h[0] = self.h[self.currsize-1] - self.h[self.currsize-1] = temp - self.currsize -= 1 - self.maxHeapify(0) - return me - return None + def getMax(self): # This function is used to get maximum value from the heap. + if self.currsize >= 1: + me = self.h[0] + temp = self.h[0] + self.h[0] = self.h[self.currsize - 1] + self.h[self.currsize - 1] = temp + self.currsize -= 1 + self.maxHeapify(0) + return me + return None - def heapSort(self): #This function is used to sort the heap. - size = self.currsize - while self.currsize-1 >= 0: - temp = self.h[0] - self.h[0] = self.h[self.currsize-1] - self.h[self.currsize-1] = temp - self.currsize -= 1 - self.maxHeapify(0) - self.currsize = size + def heapSort(self): # This function is used to sort the heap. + size = self.currsize + while self.currsize - 1 >= 0: + temp = self.h[0] + self.h[0] = self.h[self.currsize - 1] + self.h[self.currsize - 1] = temp + self.currsize -= 1 + self.maxHeapify(0) + self.currsize = size - def insert(self,data): #This function is used to insert data in the heap. - self.h.append(data) - curr = self.currsize - self.currsize+=1 - while self.h[curr] > self.h[curr/2]: - temp = self.h[curr/2] - self.h[curr/2] = self.h[curr] - self.h[curr] = temp - curr = curr/2 + def insert(self, data): # This function is used to insert data in the heap. + self.h.append(data) + curr = self.currsize + self.currsize += 1 + while self.h[curr] > self.h[curr / 2]: + temp = self.h[curr / 2] + self.h[curr / 2] = self.h[curr] + self.h[curr] = temp + curr = curr / 2 - def display(self): #This function is used to print the heap. - print(self.h) + def display(self): # This function is used to print the heap. + print(self.h) -def main(): - l = list(map(int, raw_input().split())) - h = Heap() - h.buildHeap(l) - h.heapSort() - h.display() -if __name__=='__main__': - main() +def main(): + l = list(map(int, input().split())) + h = Heap() + h.buildHeap(l) + h.heapSort() + h.display() +if __name__ == "__main__": + main() diff --git a/data_structures/heap/min_heap.py b/data_structures/heap/min_heap.py new file mode 100644 index 000000000000..6184d83be774 --- /dev/null +++ b/data_structures/heap/min_heap.py @@ -0,0 +1,169 @@ +# Min head data structure +# with decrease key functionality - in O(log(n)) time + + +class Node: + def __init__(self, name, val): + self.name = name + self.val = val + + def __str__(self): + return f"{self.__class__.__name__}({self.name}, {self.val})" + + def __lt__(self, other): + return self.val < other.val + + +class MinHeap: + """ + >>> r = Node("R", -1) + >>> b = Node("B", 6) + >>> a = Node("A", 3) + >>> x = Node("X", 1) + >>> e = Node("E", 4) + >>> print(b) + Node(B, 6) + >>> myMinHeap = MinHeap([r, b, a, x, e]) + >>> myMinHeap.decrease_key(b, -17) + >>> print(b) + Node(B, -17) + >>> print(myMinHeap["B"]) + -17 + """ + + def __init__(self, array): + self.idx_of_element = {} + self.heap_dict = {} + self.heap = self.build_heap(array) + + def __getitem__(self, key): + return self.get_value(key) + + def get_parent_idx(self, idx): + return (idx - 1) // 2 + + def get_left_child_idx(self, idx): + return idx * 2 + 1 + + def get_right_child_idx(self, idx): + return idx * 2 + 2 + + def get_value(self, key): + return self.heap_dict[key] + + def build_heap(self, array): + lastIdx = len(array) - 1 + startFrom = self.get_parent_idx(lastIdx) + + for idx, i in enumerate(array): + self.idx_of_element[i] = idx + self.heap_dict[i.name] = i.val + + for i in range(startFrom, -1, -1): + self.sift_down(i, array) + return array + + # this is min-heapify method + def sift_down(self, idx, array): + while True: + l = self.get_left_child_idx(idx) + r = self.get_right_child_idx(idx) + + smallest = idx + if l < len(array) and array[l] < array[idx]: + smallest = l + if r < len(array) and array[r] < array[smallest]: + smallest = r + + if smallest != idx: + array[idx], array[smallest] = array[smallest], array[idx] + self.idx_of_element[array[idx]], self.idx_of_element[ + array[smallest] + ] = ( + self.idx_of_element[array[smallest]], + self.idx_of_element[array[idx]], + ) + idx = smallest + else: + break + + def sift_up(self, idx): + p = self.get_parent_idx(idx) + while p >= 0 and self.heap[p] > self.heap[idx]: + self.heap[p], self.heap[idx] = self.heap[idx], self.heap[p] + self.idx_of_element[self.heap[p]], self.idx_of_element[self.heap[idx]] = ( + self.idx_of_element[self.heap[idx]], + self.idx_of_element[self.heap[p]], + ) + idx = p + p = self.get_parent_idx(idx) + + def peek(self): + return self.heap[0] + + def remove(self): + self.heap[0], self.heap[-1] = self.heap[-1], self.heap[0] + self.idx_of_element[self.heap[0]], self.idx_of_element[self.heap[-1]] = ( + self.idx_of_element[self.heap[-1]], + self.idx_of_element[self.heap[0]], + ) + + x = self.heap.pop() + del self.idx_of_element[x] + self.sift_down(0, self.heap) + return x + + def insert(self, node): + self.heap.append(node) + self.idx_of_element[node] = len(self.heap) - 1 + self.heap_dict[node.name] = node.val + self.sift_up(len(self.heap) - 1) + + def is_empty(self): + return True if len(self.heap) == 0 else False + + def decrease_key(self, node, newValue): + assert ( + self.heap[self.idx_of_element[node]].val > newValue + ), "newValue must be less that current value" + node.val = newValue + self.heap_dict[node.name] = newValue + self.sift_up(self.idx_of_element[node]) + + +## USAGE + +r = Node("R", -1) +b = Node("B", 6) +a = Node("A", 3) +x = Node("X", 1) +e = Node("E", 4) + +# Use one of these two ways to generate Min-Heap + +# Generating Min-Heap from array +myMinHeap = MinHeap([r, b, a, x, e]) + +# Generating Min-Heap by Insert method +# myMinHeap.insert(a) +# myMinHeap.insert(b) +# myMinHeap.insert(x) +# myMinHeap.insert(r) +# myMinHeap.insert(e) + +# Before +print("Min Heap - before decrease key") +for i in myMinHeap.heap: + print(i) + +print("Min Heap - After decrease key of node [B -> -17]") +myMinHeap.decrease_key(b, -17) + +# After +for i in myMinHeap.heap: + print(i) + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/data_structures/linked_list/__init__.py b/data_structures/linked_list/__init__.py index 6d50f23c1f1a..a050adba42b2 100644 --- a/data_structures/linked_list/__init__.py +++ b/data_structures/linked_list/__init__.py @@ -3,6 +3,7 @@ def __init__(self, item, next): self.item = item self.next = next + class LinkedList: def __init__(self): self.head = None diff --git a/data_structures/linked_list/doubly_linked_list.py b/data_structures/linked_list/doubly_linked_list.py index 75b1f889dfc2..38fff867b416 100644 --- a/data_structures/linked_list/doubly_linked_list.py +++ b/data_structures/linked_list/doubly_linked_list.py @@ -1,77 +1,81 @@ -''' +""" - A linked list is similar to an array, it holds values. However, links in a linked list do not have indexes. - This is an example of a double ended, doubly linked list. - Each link references the next link and the previous one. - A Doubly Linked List (DLL) contains an extra pointer, typically called previous pointer, together with next pointer and data which are there in singly linked list. - - Advantages over SLL - IT can be traversed in both forward and backward direction.,Delete operation is more efficent''' -from __future__ import print_function + - Advantages over SLL - IT can be traversed in both forward and backward direction.,Delete operation is more efficent""" -class LinkedList: #making main class named linked list +class LinkedList: # making main class named linked list def __init__(self): self.head = None self.tail = None - + def insertHead(self, x): - newLink = Link(x) #Create a new link with a value attached to it - if(self.isEmpty() == True): #Set the first element added to be the tail + newLink = Link(x) # Create a new link with a value attached to it + if self.isEmpty() == True: # Set the first element added to be the tail self.tail = newLink else: - self.head.previous = newLink # newLink <-- currenthead(head) - newLink.next = self.head # newLink <--> currenthead(head) - self.head = newLink # newLink(head) <--> oldhead - + self.head.previous = newLink # newLink <-- currenthead(head) + newLink.next = self.head # newLink <--> currenthead(head) + self.head = newLink # newLink(head) <--> oldhead + def deleteHead(self): temp = self.head - self.head = self.head.next # oldHead <--> 2ndElement(head) - self.head.previous = None # oldHead --> 2ndElement(head) nothing pointing at it so the old head will be removed - if(self.head is None): - self.tail = None #if empty linked list + self.head = self.head.next # oldHead <--> 2ndElement(head) + self.head.previous = ( + None + ) # oldHead --> 2ndElement(head) nothing pointing at it so the old head will be removed + if self.head is None: + self.tail = None # if empty linked list return temp - + def insertTail(self, x): newLink = Link(x) - newLink.next = None # currentTail(tail) newLink --> - self.tail.next = newLink # currentTail(tail) --> newLink --> - newLink.previous = self.tail #currentTail(tail) <--> newLink --> - self.tail = newLink # oldTail <--> newLink(tail) --> - + newLink.next = None # currentTail(tail) newLink --> + self.tail.next = newLink # currentTail(tail) --> newLink --> + newLink.previous = self.tail # currentTail(tail) <--> newLink --> + self.tail = newLink # oldTail <--> newLink(tail) --> + def deleteTail(self): temp = self.tail - self.tail = self.tail.previous # 2ndLast(tail) <--> oldTail --> None - self.tail.next = None # 2ndlast(tail) --> None + self.tail = self.tail.previous # 2ndLast(tail) <--> oldTail --> None + self.tail.next = None # 2ndlast(tail) --> None return temp - + def delete(self, x): current = self.head - - while(current.value != x): # Find the position to delete + + while current.value != x: # Find the position to delete current = current.next - - if(current == self.head): + + if current == self.head: self.deleteHead() - - elif(current == self.tail): + + elif current == self.tail: self.deleteTail() - - else: #Before: 1 <--> 2(current) <--> 3 - current.previous.next = current.next # 1 --> 3 - current.next.previous = current.previous # 1 <--> 3 - - def isEmpty(self): #Will return True if the list is empty - return(self.head is None) - - def display(self): #Prints contents of the list + + else: # Before: 1 <--> 2(current) <--> 3 + current.previous.next = current.next # 1 --> 3 + current.next.previous = current.previous # 1 <--> 3 + + def isEmpty(self): # Will return True if the list is empty + return self.head is None + + def display(self): # Prints contents of the list current = self.head - while(current != None): + while current != None: current.displayLink() - current = current.next + current = current.next print() + class Link: - next = None #This points to the link in front of the new link - previous = None #This points to the link behind the new link + next = None # This points to the link in front of the new link + previous = None # This points to the link behind the new link + def __init__(self, x): self.value = x + def displayLink(self): print("{}".format(self.value), end=" ") diff --git a/data_structures/linked_list/is_Palindrome.py b/data_structures/linked_list/is_palindrome.py similarity index 100% rename from data_structures/linked_list/is_Palindrome.py rename to data_structures/linked_list/is_palindrome.py diff --git a/data_structures/linked_list/singly_linked_list.py b/data_structures/linked_list/singly_linked_list.py index 5ae97523b9a1..73b982316e76 100644 --- a/data_structures/linked_list/singly_linked_list.py +++ b/data_structures/linked_list/singly_linked_list.py @@ -1,6 +1,3 @@ -from __future__ import print_function - - class Node: # create a Node def __init__(self, data): self.data = data # given data @@ -9,52 +6,56 @@ def __init__(self, data): class Linked_List: def __init__(self): - self.Head = None # Initialize Head to None - + self.head = None # Initialize head to None + def insert_tail(self, data): - if(self.Head is None): self.insert_head(data) #If this is first node, call insert_head + if self.head is None: + self.insert_head(data) # If this is first node, call insert_head else: - temp = self.Head - while(temp.next != None): #traverse to last node + temp = self.head + while temp.next != None: # traverse to last node temp = temp.next - temp.next = Node(data) #create node & link to tail + temp.next = Node(data) # create node & link to tail def insert_head(self, data): - newNod = Node(data) # create a new node - if self.Head != None: - newNod.next = self.Head # link newNode to head - self.Head = newNod # make NewNode as Head + newNod = Node(data) # create a new node + if self.head != None: + newNod.next = self.head # link newNode to head + self.head = newNod # make NewNode as head def printList(self): # print every node data - tamp = self.Head - while tamp is not None: - print(tamp.data) - tamp = tamp.next + temp = self.head + while temp is not None: + print(temp.data) + temp = temp.next def delete_head(self): # delete from head - temp = self.Head - if self.Head != None: - self.Head = self.Head.next + temp = self.head + if self.head != None: + self.head = self.head.next temp.next = None return temp - + def delete_tail(self): # delete from tail - tamp = self.Head - if self.Head != None: - if(self.Head.next is None): # if Head is the only Node in the Linked List - self.Head = None + temp = self.head + if self.head != None: + if self.head.next is None: # if head is the only Node in the Linked List + self.head = None else: - while tamp.next.next is not None: # find the 2nd last element - tamp = tamp.next - tamp.next, tamp = None, tamp.next #(2nd last element).next = None and tamp = last element - return tamp + while temp.next.next is not None: # find the 2nd last element + temp = temp.next + temp.next, temp = ( + None, + temp.next, + ) # (2nd last element).next = None and temp = last element + return temp def isEmpty(self): - return self.Head is None # Return if Head is none + return self.head is None # Return if head is none def reverse(self): prev = None - current = self.Head + current = self.head while current: # Store the current node's next node. @@ -66,27 +67,28 @@ def reverse(self): # Make the current node the next node (to progress iteration) current = next_node # Return prev in order to put the head at the end - self.Head = prev + self.head = prev + def main(): A = Linked_List() - print("Inserting 1st at Head") - a1=input() + print("Inserting 1st at head") + a1 = input() A.insert_head(a1) - print("Inserting 2nd at Head") - a2=input() + print("Inserting 2nd at head") + a2 = input() A.insert_head(a2) print("\nPrint List : ") A.printList() print("\nInserting 1st at Tail") - a3=input() + a3 = input() A.insert_tail(a3) print("Inserting 2nd at Tail") - a4=input() + a4 = input() A.insert_tail(a4) print("\nPrint List : ") A.printList() - print("\nDelete Head") + print("\nDelete head") A.delete_head() print("Delete Tail") A.delete_tail() @@ -96,6 +98,7 @@ def main(): A.reverse() print("\nPrint List : ") A.printList() - -if __name__ == '__main__': - main() + + +if __name__ == "__main__": + main() diff --git a/data_structures/linked_list/swapNodes.py b/data_structures/linked_list/swap_nodes.py similarity index 90% rename from data_structures/linked_list/swapNodes.py rename to data_structures/linked_list/swap_nodes.py index ce2543bc46d8..a6a50091e3e0 100644 --- a/data_structures/linked_list/swapNodes.py +++ b/data_structures/linked_list/swap_nodes.py @@ -1,6 +1,6 @@ class Node: def __init__(self, data): - self.data = data; + self.data = data self.next = None @@ -14,13 +14,13 @@ def print_list(self): print(temp.data) temp = temp.next -# adding nodes + # adding nodes def push(self, new_data): new_node = Node(new_data) new_node.next = self.head self.head = new_node -# swapping nodes + # swapping nodes def swapNodes(self, d1, d2): prevD1 = None prevD2 = None @@ -53,11 +53,11 @@ def swapNodes(self, d1, d2): D1.next = D2.next D2.next = temp -# swapping code ends here +# swapping code ends here -if __name__ == '__main__': +if __name__ == "__main__": list = Linkedlist() list.push(5) list.push(4) @@ -70,6 +70,3 @@ def swapNodes(self, d1, d2): list.swapNodes(1, 4) print("After swapping") list.print_list() - - - diff --git a/data_structures/queue/deqeue.py b/data_structures/queue/deqeue.py deleted file mode 100644 index fdee64eb6ae0..000000000000 --- a/data_structures/queue/deqeue.py +++ /dev/null @@ -1,40 +0,0 @@ -from __future__ import print_function -# Python code to demonstrate working of -# extend(), extendleft(), rotate(), reverse() - -# importing "collections" for deque operations -import collections - -# initializing deque -de = collections.deque([1, 2, 3,]) - -# using extend() to add numbers to right end -# adds 4,5,6 to right end -de.extend([4,5,6]) - -# printing modified deque -print ("The deque after extending deque at end is : ") -print (de) - -# using extendleft() to add numbers to left end -# adds 7,8,9 to right end -de.extendleft([7,8,9]) - -# printing modified deque -print ("The deque after extending deque at beginning is : ") -print (de) - -# using rotate() to rotate the deque -# rotates by 3 to left -de.rotate(-3) - -# printing modified deque -print ("The deque after rotating deque is : ") -print (de) - -# using reverse() to reverse the deque -de.reverse() - -# printing modified deque -print ("The deque after reversing deque is : ") -print (de) diff --git a/data_structures/queue/double_ended_queue.py b/data_structures/queue/double_ended_queue.py new file mode 100644 index 000000000000..dd003b7c98ac --- /dev/null +++ b/data_structures/queue/double_ended_queue.py @@ -0,0 +1,57 @@ +# Python code to demonstrate working of +# extend(), extendleft(), rotate(), reverse() + +# importing "collections" for deque operations +import collections + +# initializing deque +de = collections.deque([1, 2, 3]) + +# using extend() to add numbers to right end +# adds 4,5,6 to right end +de.extend([4, 5, 6]) + +# printing modified deque +print("The deque after extending deque at end is : ") +print(de) + +# using extendleft() to add numbers to left end +# adds 7,8,9 to right end +de.extendleft([7, 8, 9]) + +# printing modified deque +print("The deque after extending deque at beginning is : ") +print(de) + +# using rotate() to rotate the deque +# rotates by 3 to left +de.rotate(-3) + +# printing modified deque +print("The deque after rotating deque is : ") +print(de) + +# using reverse() to reverse the deque +de.reverse() + +# printing modified deque +print("The deque after reversing deque is : ") +print(de) + +# get right-end value and eliminate +startValue = de.pop() + +print("The deque after popping value at end is : ") +print(de) + +# get left-end value and eliminate +endValue = de.popleft() + +print("The deque after popping value at start is : ") +print(de) + +# eliminate element searched by value +de.remove(5) + +print("The deque after eliminating element searched by value : ") +print(de) diff --git a/data_structures/queue/linked_queue.py b/data_structures/queue/linked_queue.py new file mode 100644 index 000000000000..614c60cd1ae2 --- /dev/null +++ b/data_structures/queue/linked_queue.py @@ -0,0 +1,74 @@ +""" A Queue using a Linked List like structure """ +from typing import Any, Optional + + +class Node: + def __init__(self, data: Any, next: Optional["Node"] = None): + self.data: Any = data + self.next: Optional["Node"] = next + + +class LinkedQueue: + """ + Linked List Queue implementing put (to end of queue), + get (from front of queue) and is_empty + + >>> queue = LinkedQueue() + >>> queue.is_empty() + True + >>> queue.put(5) + >>> queue.put(9) + >>> queue.put('python') + >>> queue.is_empty(); + False + >>> queue.get() + 5 + >>> queue.put('algorithms') + >>> queue.get() + 9 + >>> queue.get() + 'python' + >>> queue.get() + 'algorithms' + >>> queue.is_empty() + True + >>> queue.get() + Traceback (most recent call last): + ... + IndexError: get from empty queue + """ + + def __init__(self) -> None: + self.front: Optional[Node] = None + self.rear: Optional[Node] = None + + def is_empty(self) -> bool: + """ returns boolean describing if queue is empty """ + return self.front is None + + def put(self, item: Any) -> None: + """ append item to rear of queue """ + node: Node = Node(item) + if self.is_empty(): + # the queue contains just the single element + self.front = node + self.rear = node + else: + # not empty, so we add it to the rear of the queue + assert isinstance(self.rear, Node) + self.rear.next = node + self.rear = node + + def get(self) -> Any: + """ returns and removes item at front of queue """ + if self.is_empty(): + raise IndexError("get from empty queue") + else: + # "remove" element by having front point to the next one + assert isinstance(self.front, Node) + node: Node = self.front + self.front = node.next + if self.front is None: + self.rear = None + + return node.data diff --git a/data_structures/queue/queue_on_list.py b/data_structures/queue/queue_on_list.py index c8d0b41de5d5..bb44e08ad6c5 100644 --- a/data_structures/queue/queue_on_list.py +++ b/data_structures/queue/queue_on_list.py @@ -1,45 +1,52 @@ """Queue represented by a python list""" -class Queue(): + + +class Queue: def __init__(self): self.entries = [] self.length = 0 - self.front=0 + self.front = 0 def __str__(self): - printed = '<' + str(self.entries)[1:-1] + '>' + printed = "<" + str(self.entries)[1:-1] + ">" return printed """Enqueues {@code item} @param item item to enqueue""" + def put(self, item): self.entries.append(item) self.length = self.length + 1 - """Dequeues {@code item} @requirement: |self.length| > 0 @return dequeued item that was dequeued""" + def get(self): self.length = self.length - 1 dequeued = self.entries[self.front] - self.front-=1 - self.entries = self.entries[self.front:] + # self.front-=1 + # self.entries = self.entries[self.front:] + self.entries = self.entries[1:] return dequeued """Rotates the queue {@code rotation} times @param rotation number of times to rotate queue""" + def rotate(self, rotation): for i in range(rotation): self.put(self.get()) """Enqueues {@code item} @return item at front of self.entries""" + def front(self): return self.entries[0] """Returns the length of this.entries""" + def size(self): return self.length diff --git a/data_structures/queue/queue_on_pseudo_stack.py b/data_structures/queue/queue_on_pseudo_stack.py index b69fbcc988f7..7fa2fb2566af 100644 --- a/data_structures/queue/queue_on_pseudo_stack.py +++ b/data_structures/queue/queue_on_pseudo_stack.py @@ -1,16 +1,19 @@ """Queue represented by a pseudo stack (represented by a list with pop and append)""" -class Queue(): + + +class Queue: def __init__(self): self.stack = [] self.length = 0 def __str__(self): - printed = '<' + str(self.stack)[1:-1] + '>' + printed = "<" + str(self.stack)[1:-1] + ">" return printed """Enqueues {@code item} @param item item to enqueue""" + def put(self, item): self.stack.append(item) self.length = self.length + 1 @@ -19,17 +22,19 @@ def put(self, item): @requirement: |self.length| > 0 @return dequeued item that was dequeued""" + def get(self): self.rotate(1) - dequeued = self.stack[self.length-1] + dequeued = self.stack[self.length - 1] self.stack = self.stack[:-1] - self.rotate(self.length-1) - self.length = self.length -1 + self.rotate(self.length - 1) + self.length = self.length - 1 return dequeued """Rotates the queue {@code rotation} times @param rotation number of times to rotate queue""" + def rotate(self, rotation): for i in range(rotation): temp = self.stack[0] @@ -39,12 +44,14 @@ def rotate(self, rotation): """Reports item at the front of self @return item at front of self.stack""" + def front(self): front = self.get() self.put(front) - self.rotate(self.length-1) + self.rotate(self.length - 1) return front """Returns the length of this.stack""" + def size(self): return self.length diff --git a/data_structures/stacks/__init__.py b/data_structures/stacks/__init__.py index f7e92ae2d269..f6995cf98977 100644 --- a/data_structures/stacks/__init__.py +++ b/data_structures/stacks/__init__.py @@ -1,23 +1,22 @@ class Stack: + def __init__(self): + self.stack = [] + self.top = 0 - def __init__(self): - self.stack = [] - self.top = 0 + def is_empty(self): + return self.top == 0 - def is_empty(self): - return (self.top == 0) + def push(self, item): + if self.top < len(self.stack): + self.stack[self.top] = item + else: + self.stack.append(item) - def push(self, item): - if self.top < len(self.stack): - self.stack[self.top] = item - else: - self.stack.append(item) + self.top += 1 - self.top += 1 - - def pop(self): - if self.is_empty(): - return None - else: - self.top -= 1 - return self.stack[self.top] + def pop(self): + if self.is_empty(): + return None + else: + self.top -= 1 + return self.stack[self.top] diff --git a/data_structures/stacks/balanced_parentheses.py b/data_structures/stacks/balanced_parentheses.py index 3229d19c8621..7aacd5969277 100644 --- a/data_structures/stacks/balanced_parentheses.py +++ b/data_structures/stacks/balanced_parentheses.py @@ -1,25 +1,23 @@ -from __future__ import print_function -from __future__ import absolute_import -from stack import Stack +from .stack import Stack -__author__ = 'Omkar Pathak' +__author__ = "Omkar Pathak" def balanced_parentheses(parentheses): """ Use a stack to check if a string of parentheses is balanced.""" stack = Stack(len(parentheses)) for parenthesis in parentheses: - if parenthesis == '(': + if parenthesis == "(": stack.push(parenthesis) - elif parenthesis == ')': + elif parenthesis == ")": if stack.is_empty(): return False stack.pop() return stack.is_empty() -if __name__ == '__main__': - examples = ['((()))', '((())', '(()))'] - print('Balanced parentheses demonstration:\n') +if __name__ == "__main__": + examples = ["((()))", "((())", "(()))"] + print("Balanced parentheses demonstration:\n") for example in examples: - print(example + ': ' + str(balanced_parentheses(example))) + print(example + ": " + str(balanced_parentheses(example))) diff --git a/data_structures/stacks/infix_to_postfix_conversion.py b/data_structures/stacks/infix_to_postfix_conversion.py index 75211fed258d..61114402377a 100644 --- a/data_structures/stacks/infix_to_postfix_conversion.py +++ b/data_structures/stacks/infix_to_postfix_conversion.py @@ -1,10 +1,8 @@ -from __future__ import print_function -from __future__ import absolute_import import string -from .Stack import Stack +from .stack import Stack -__author__ = 'Omkar Pathak' +__author__ = "Omkar Pathak" def is_operand(char): @@ -17,9 +15,7 @@ def precedence(char): https://en.wikipedia.org/wiki/Order_of_operations """ - dictionary = {'+': 1, '-': 1, - '*': 2, '/': 2, - '^': 3} + dictionary = {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3} return dictionary.get(char, -1) @@ -36,29 +32,28 @@ def infix_to_postfix(expression): for char in expression: if is_operand(char): postfix.append(char) - elif char not in {'(', ')'}: - while (not stack.is_empty() - and precedence(char) <= precedence(stack.peek())): + elif char not in {"(", ")"}: + while not stack.is_empty() and precedence(char) <= precedence(stack.peek()): postfix.append(stack.pop()) stack.push(char) - elif char == '(': + elif char == "(": stack.push(char) - elif char == ')': - while not stack.is_empty() and stack.peek() != '(': + elif char == ")": + while not stack.is_empty() and stack.peek() != "(": postfix.append(stack.pop()) # Pop '(' from stack. If there is no '(', there is a mismatched # parentheses. - if stack.peek() != '(': - raise ValueError('Mismatched parentheses') + if stack.peek() != "(": + raise ValueError("Mismatched parentheses") stack.pop() while not stack.is_empty(): postfix.append(stack.pop()) - return ' '.join(postfix) + return " ".join(postfix) -if __name__ == '__main__': - expression = 'a+b*(c^d-e)^(f+g*h)-i' +if __name__ == "__main__": + expression = "a+b*(c^d-e)^(f+g*h)-i" - print('Infix to Postfix Notation demonstration:\n') - print('Infix notation: ' + expression) - print('Postfix notation: ' + infix_to_postfix(expression)) + print("Infix to Postfix Notation demonstration:\n") + print("Infix notation: " + expression) + print("Postfix notation: " + infix_to_postfix(expression)) diff --git a/data_structures/stacks/infix_to_prefix_conversion.py b/data_structures/stacks/infix_to_prefix_conversion.py index da5fc261fb9f..4f0e1ab8adfa 100644 --- a/data_structures/stacks/infix_to_prefix_conversion.py +++ b/data_structures/stacks/infix_to_prefix_conversion.py @@ -14,48 +14,82 @@ a+b^c (Infix) -> +a^bc (Prefix) """ + def infix_2_postfix(Infix): Stack = [] Postfix = [] - priority = {'^':3, '*':2, '/':2, '%':2, '+':1, '-':1} # Priority of each operator - print_width = len(Infix) if(len(Infix)>7) else 7 + priority = { + "^": 3, + "*": 2, + "/": 2, + "%": 2, + "+": 1, + "-": 1, + } # Priority of each operator + print_width = len(Infix) if (len(Infix) > 7) else 7 # Print table header for output - print('Symbol'.center(8), 'Stack'.center(print_width), 'Postfix'.center(print_width), sep = " | ") - print('-'*(print_width*3+7)) + print( + "Symbol".center(8), + "Stack".center(print_width), + "Postfix".center(print_width), + sep=" | ", + ) + print("-" * (print_width * 3 + 7)) for x in Infix: - if(x.isalpha() or x.isdigit()): Postfix.append(x) # if x is Alphabet / Digit, add it to Postfix - elif(x == '('): Stack.append(x) # if x is "(" push to Stack - elif(x == ')'): # if x is ")" pop stack until "(" is encountered - while(Stack[-1] != '('): - Postfix.append( Stack.pop() ) #Pop stack & add the content to Postfix + if x.isalpha() or x.isdigit(): + Postfix.append(x) # if x is Alphabet / Digit, add it to Postfix + elif x == "(": + Stack.append(x) # if x is "(" push to Stack + elif x == ")": # if x is ")" pop stack until "(" is encountered + while Stack[-1] != "(": + Postfix.append(Stack.pop()) # Pop stack & add the content to Postfix Stack.pop() else: - if(len(Stack)==0): Stack.append(x) #If stack is empty, push x to stack + if len(Stack) == 0: + Stack.append(x) # If stack is empty, push x to stack else: - while( len(Stack) > 0 and priority[x] <= priority[Stack[-1]]): # while priority of x is not greater than priority of element in the stack - Postfix.append( Stack.pop() ) # pop stack & add to Postfix - Stack.append(x) # push x to stack + while ( + len(Stack) > 0 and priority[x] <= priority[Stack[-1]] + ): # while priority of x is not greater than priority of element in the stack + Postfix.append(Stack.pop()) # pop stack & add to Postfix + Stack.append(x) # push x to stack + + print( + x.center(8), + ("".join(Stack)).ljust(print_width), + ("".join(Postfix)).ljust(print_width), + sep=" | ", + ) # Output in tabular format - print(x.center(8), (''.join(Stack)).ljust(print_width), (''.join(Postfix)).ljust(print_width), sep = " | ") # Output in tabular format + while len(Stack) > 0: # while stack is not empty + Postfix.append(Stack.pop()) # pop stack & add to Postfix + print( + " ".center(8), + ("".join(Stack)).ljust(print_width), + ("".join(Postfix)).ljust(print_width), + sep=" | ", + ) # Output in tabular format - while(len(Stack) > 0): # while stack is not empty - Postfix.append( Stack.pop() ) # pop stack & add to Postfix - print(' '.center(8), (''.join(Stack)).ljust(print_width), (''.join(Postfix)).ljust(print_width), sep = " | ") # Output in tabular format + return "".join(Postfix) # return Postfix as str - return "".join(Postfix) # return Postfix as str def infix_2_prefix(Infix): - Infix = list(Infix[::-1]) # reverse the infix equation - + Infix = list(Infix[::-1]) # reverse the infix equation + for i in range(len(Infix)): - if(Infix[i] == '('): Infix[i] = ')' # change "(" to ")" - elif(Infix[i] == ')'): Infix[i] = '(' # change ")" to "(" - - return (infix_2_postfix("".join(Infix)))[::-1] # call infix_2_postfix on Infix, return reverse of Postfix + if Infix[i] == "(": + Infix[i] = ")" # change "(" to ")" + elif Infix[i] == ")": + Infix[i] = "(" # change ")" to "(" + + return (infix_2_postfix("".join(Infix)))[ + ::-1 + ] # call infix_2_postfix on Infix, return reverse of Postfix + if __name__ == "__main__": - Infix = input("\nEnter an Infix Equation = ") #Input an Infix equation - Infix = "".join(Infix.split()) #Remove spaces from the input + Infix = input("\nEnter an Infix Equation = ") # Input an Infix equation + Infix = "".join(Infix.split()) # Remove spaces from the input print("\n\t", Infix, "(Infix) -> ", infix_2_prefix(Infix), "(Prefix)") diff --git a/data_structures/stacks/linked_stack.py b/data_structures/stacks/linked_stack.py new file mode 100644 index 000000000000..18ba87ddc221 --- /dev/null +++ b/data_structures/stacks/linked_stack.py @@ -0,0 +1,67 @@ +""" A Stack using a Linked List like structure """ +from typing import Any, Optional + + +class Node: + def __init__(self, data: Any, next: Optional["Node"] = None): + self.data: Any = data + self.next: Optional["Node"] = next + + +class LinkedStack: + """ + Linked List Stack implementing push (to top), + pop (from top) and is_empty + + >>> stack = LinkedStack() + >>> stack.is_empty() + True + >>> stack.push(5) + >>> stack.push(9) + >>> stack.push('python') + >>> stack.is_empty(); + False + >>> stack.pop() + 'python' + >>> stack.push('algorithms') + >>> stack.pop() + 'algorithms' + >>> stack.pop() + 9 + >>> stack.pop() + 5 + >>> stack.is_empty() + True + >>> stack.pop() + Traceback (most recent call last): + ... + IndexError: pop from empty stack + """ + + def __init__(self) -> None: + self.top: Optional[Node] = None + + def is_empty(self) -> bool: + """ returns boolean describing if stack is empty """ + return self.top is None + + def push(self, item: Any) -> None: + """ append item to top of stack """ + node: Node = Node(item) + if self.is_empty(): + self.top = node + else: + # each node points to the item "lower" in the stack + node.next = self.top + self.top = node + + def pop(self) -> Any: + """ returns and removes item at top of stack """ + if self.is_empty(): + raise IndexError("pop from empty stack") + else: + # "remove" element by having top point to the next one + assert isinstance(self.top, Node) + node: Node = self.top + self.top = node.next + return node.data diff --git a/data_structures/stacks/next.py b/data_structures/stacks/next.py deleted file mode 100644 index bca83339592c..000000000000 --- a/data_structures/stacks/next.py +++ /dev/null @@ -1,17 +0,0 @@ -from __future__ import print_function -# Function to print element and NGE pair for all elements of list -def printNGE(arr): - - for i in range(0, len(arr), 1): - - next = -1 - for j in range(i+1, len(arr), 1): - if arr[i] < arr[j]: - next = arr[j] - break - - print(str(arr[i]) + " -- " + str(next)) - -# Driver program to test above function -arr = [11,13,21,3] -printNGE(arr) diff --git a/data_structures/stacks/next_greater_element.py b/data_structures/stacks/next_greater_element.py new file mode 100644 index 000000000000..29a039b9698b --- /dev/null +++ b/data_structures/stacks/next_greater_element.py @@ -0,0 +1,24 @@ +def printNGE(arr): + """ + Function to print element and Next Greatest Element (NGE) pair for all elements of list + NGE - Maximum element present afterwards the current one which is also greater than current one + >>> printNGE([11,13,21,3]) + 11 -- 13 + 13 -- 21 + 21 -- -1 + 3 -- -1 + """ + for i in range(0, len(arr), 1): + + next = -1 + for j in range(i + 1, len(arr), 1): + if arr[i] < arr[j]: + next = arr[j] + break + + print(str(arr[i]) + " -- " + str(next)) + + +# Driver program to test above function +arr = [11, 13, 21, 3] +printNGE(arr) diff --git a/data_structures/stacks/postfix_evaluation.py b/data_structures/stacks/postfix_evaluation.py index 1786e71dd383..0f3d5c76d6a3 100644 --- a/data_structures/stacks/postfix_evaluation.py +++ b/data_structures/stacks/postfix_evaluation.py @@ -19,32 +19,52 @@ import operator as op + def Solve(Postfix): Stack = [] - Div = lambda x, y: int(x/y) # integer division operation - Opr = {'^':op.pow, '*':op.mul, '/':Div, '+':op.add, '-':op.sub} # operators & their respective operation + Div = lambda x, y: int(x / y) # integer division operation + Opr = { + "^": op.pow, + "*": op.mul, + "/": Div, + "+": op.add, + "-": op.sub, + } # operators & their respective operation # print table header - print('Symbol'.center(8), 'Action'.center(12), 'Stack', sep = " | ") - print('-'*(30+len(Postfix))) + print("Symbol".center(8), "Action".center(12), "Stack", sep=" | ") + print("-" * (30 + len(Postfix))) for x in Postfix: - if( x.isdigit() ): # if x in digit - Stack.append(x) # append x to stack - print(x.rjust(8), ('push('+x+')').ljust(12), ','.join(Stack), sep = " | ") # output in tabular format + if x.isdigit(): # if x in digit + Stack.append(x) # append x to stack + print( + x.rjust(8), ("push(" + x + ")").ljust(12), ",".join(Stack), sep=" | " + ) # output in tabular format else: - B = Stack.pop() # pop stack - print("".rjust(8), ('pop('+B+')').ljust(12), ','.join(Stack), sep = " | ") # output in tabular format + B = Stack.pop() # pop stack + print( + "".rjust(8), ("pop(" + B + ")").ljust(12), ",".join(Stack), sep=" | " + ) # output in tabular format - A = Stack.pop() # pop stack - print("".rjust(8), ('pop('+A+')').ljust(12), ','.join(Stack), sep = " | ") # output in tabular format + A = Stack.pop() # pop stack + print( + "".rjust(8), ("pop(" + A + ")").ljust(12), ",".join(Stack), sep=" | " + ) # output in tabular format - Stack.append( str(Opr[x](int(A), int(B))) ) # evaluate the 2 values poped from stack & push result to stack - print(x.rjust(8), ('push('+A+x+B+')').ljust(12), ','.join(Stack), sep = " | ") # output in tabular format + Stack.append( + str(Opr[x](int(A), int(B))) + ) # evaluate the 2 values poped from stack & push result to stack + print( + x.rjust(8), + ("push(" + A + x + B + ")").ljust(12), + ",".join(Stack), + sep=" | ", + ) # output in tabular format return int(Stack[0]) if __name__ == "__main__": - Postfix = input("\n\nEnter a Postfix Equation (space separated) = ").split(' ') + Postfix = input("\n\nEnter a Postfix Equation (space separated) = ").split(" ") print("\n\tResult = ", Solve(Postfix)) diff --git a/data_structures/stacks/stack.py b/data_structures/stacks/stack.py index 7f979d927d08..9f5b279710c6 100644 --- a/data_structures/stacks/stack.py +++ b/data_structures/stacks/stack.py @@ -1,5 +1,4 @@ -from __future__ import print_function -__author__ = 'Omkar Pathak' +__author__ = "Omkar Pathak" class Stack(object): @@ -33,7 +32,7 @@ def pop(self): if self.stack: return self.stack.pop() else: - raise IndexError('pop from an empty stack') + raise IndexError("pop from an empty stack") def peek(self): """ Peek at the top-most element of the stack.""" @@ -53,17 +52,17 @@ class StackOverflowError(BaseException): pass -if __name__ == '__main__': +if __name__ == "__main__": stack = Stack() for i in range(10): stack.push(i) - print('Stack demonstration:\n') - print('Initial stack: ' + str(stack)) - print('pop(): ' + str(stack.pop())) - print('After pop(), the stack is now: ' + str(stack)) - print('peek(): ' + str(stack.peek())) + print("Stack demonstration:\n") + print("Initial stack: " + str(stack)) + print("pop(): " + str(stack.pop())) + print("After pop(), the stack is now: " + str(stack)) + print("peek(): " + str(stack.peek())) stack.push(100) - print('After push(100), the stack is now: ' + str(stack)) - print('is_empty(): ' + str(stack.is_empty())) - print('size(): ' + str(stack.size())) + print("After push(100), the stack is now: " + str(stack)) + print("is_empty(): " + str(stack.is_empty())) + print("size(): " + str(stack.size())) diff --git a/data_structures/stacks/stock_span_problem.py b/data_structures/stacks/stock_span_problem.py index 9628864edd10..45cd6bae1282 100644 --- a/data_structures/stacks/stock_span_problem.py +++ b/data_structures/stacks/stock_span_problem.py @@ -1,52 +1,53 @@ -''' -The stock span problem is a financial problem where we have a series of n daily +""" +The stock span problem is a financial problem where we have a series of n daily price quotes for a stock and we need to calculate span of stock's price for all n days. -The span Si of the stock's price on a given day i is defined as the maximum -number of consecutive days just before the given day, for which the price of the stock +The span Si of the stock's price on a given day i is defined as the maximum +number of consecutive days just before the given day, for which the price of the stock on the current day is less than or equal to its price on the given day. -''' -from __future__ import print_function -def calculateSpan(price, S): - - n = len(price) - # Create a stack and push index of fist element to it - st = [] - st.append(0) - - # Span value of first element is always 1 - S[0] = 1 - - # Calculate span values for rest of the elements - for i in range(1, n): - - # Pop elements from stack whlie stack is not - # empty and top of stack is smaller than price[i] - while( len(st) > 0 and price[st[0]] <= price[i]): - st.pop() - - # If stack becomes empty, then price[i] is greater - # than all elements on left of it, i.e. price[0], - # price[1], ..price[i-1]. Else the price[i] is - # greater than elements after top of stack - S[i] = i+1 if len(st) <= 0 else (i - st[0]) - - # Push this element to stack - st.append(i) - - -# A utility function to print elements of array -def printArray(arr, n): - for i in range(0,n): - print (arr[i],end =" ") - - -# Driver program to test above function -price = [10, 4, 5, 90, 120, 80] -S = [0 for i in range(len(price)+1)] - -# Fill the span values in array S[] -calculateSpan(price, S) - -# Print the calculated span values -printArray(S, len(price)) +""" + + +def calculateSpan(price, S): + + n = len(price) + # Create a stack and push index of fist element to it + st = [] + st.append(0) + + # Span value of first element is always 1 + S[0] = 1 + + # Calculate span values for rest of the elements + for i in range(1, n): + + # Pop elements from stack whlie stack is not + # empty and top of stack is smaller than price[i] + while len(st) > 0 and price[st[0]] <= price[i]: + st.pop() + + # If stack becomes empty, then price[i] is greater + # than all elements on left of it, i.e. price[0], + # price[1], ..price[i-1]. Else the price[i] is + # greater than elements after top of stack + S[i] = i + 1 if len(st) <= 0 else (i - st[0]) + + # Push this element to stack + st.append(i) + + +# A utility function to print elements of array +def printArray(arr, n): + for i in range(0, n): + print(arr[i], end=" ") + + +# Driver program to test above function +price = [10, 4, 5, 90, 120, 80] +S = [0 for i in range(len(price) + 1)] + +# Fill the span values in array S[] +calculateSpan(price, S) + +# Print the calculated span values +printArray(S, len(price)) diff --git a/data_structures/trie/trie.py b/data_structures/trie/trie.py index b6234c6704c6..5a560b97c293 100644 --- a/data_structures/trie/trie.py +++ b/data_structures/trie/trie.py @@ -1,9 +1,8 @@ """ A Trie/Prefix Tree is a kind of search tree used to provide quick lookup of words/patterns in a set of words. A basic Trie however has O(n^2) space complexity -making it impractical in practice. It however provides O(max(search_string, length of longest word)) lookup -time making it an optimal approach when space is not an issue. - +making it impractical in practice. It however provides O(max(search_string, length of longest word)) +lookup time making it an optimal approach when space is not an issue. """ @@ -12,7 +11,7 @@ def __init__(self): self.nodes = dict() # Mapping from char to TrieNode self.is_leaf = False - def insert_many(self, words: [str]): # noqa: E999 This syntax is Python 3 only + def insert_many(self, words: [str]): """ Inserts a list of words into the Trie :param words: list of string words @@ -21,7 +20,7 @@ def insert_many(self, words: [str]): # noqa: E999 This syntax is Python 3 only for word in words: self.insert(word) - def insert(self, word: str): # noqa: E999 This syntax is Python 3 only + def insert(self, word: str): """ Inserts a word into the Trie :param word: word to be inserted @@ -34,7 +33,7 @@ def insert(self, word: str): # noqa: E999 This syntax is Python 3 only curr = curr.nodes[char] curr.is_leaf = True - def find(self, word: str) -> bool: # noqa: E999 This syntax is Python 3 only + def find(self, word: str) -> bool: """ Tries to find word in a Trie :param word: word to look for @@ -47,8 +46,36 @@ def find(self, word: str) -> bool: # noqa: E999 This syntax is Python 3 only curr = curr.nodes[char] return curr.is_leaf + def delete(self, word: str): + """ + Deletes a word in a Trie + :param word: word to delete + :return: None + """ -def print_words(node: TrieNode, word: str): # noqa: E999 This syntax is Python 3 only + def _delete(curr: TrieNode, word: str, index: int): + if index == len(word): + # If word does not exist + if not curr.is_leaf: + return False + curr.is_leaf = False + return len(curr.nodes) == 0 + char = word[index] + char_node = curr.nodes.get(char) + # If char not in current trie node + if not char_node: + return False + # Flag to check if node can be deleted + delete_curr = _delete(char_node, word, index + 1) + if delete_curr: + del curr.nodes[char] + return len(curr.nodes) == 0 + return delete_curr + + _delete(self, word, 0) + + +def print_words(node: TrieNode, word: str): """ Prints all the words in a Trie :param node: root node of Trie @@ -56,20 +83,45 @@ def print_words(node: TrieNode, word: str): # noqa: E999 This syntax is Python :return: None """ if node.is_leaf: - print(word, end=' ') + print(word, end=" ") for key, value in node.nodes.items(): print_words(value, word + key) -def test(): - words = ['banana', 'bananas', 'bandana', 'band', 'apple', 'all', 'beast'] +def test_trie(): + words = "banana bananas bandana band apple all beast".split() root = TrieNode() root.insert_many(words) - # print_words(root, '') - assert root.find('banana') - assert not root.find('bandanas') - assert not root.find('apps') - assert root.find('apple') + # print_words(root, "") + assert all(root.find(word) for word in words) + assert root.find("banana") + assert not root.find("bandanas") + assert not root.find("apps") + assert root.find("apple") + assert root.find("all") + root.delete("all") + assert not root.find("all") + root.delete("banana") + assert not root.find("banana") + assert root.find("bananas") + return True + + +def print_results(msg: str, passes: bool) -> None: + print(str(msg), "works!" if passes else "doesn't work :(") + + +def pytests(): + assert test_trie() + + +def main(): + """ + >>> pytests() + """ + print_results("Testing trie functionality", test_trie()) + -test() +if __name__ == "__main__": + main() diff --git a/data_structures/union_find/tests_union_find.py b/data_structures/union_find/tests_union_find.py deleted file mode 100644 index b0708778ddbd..000000000000 --- a/data_structures/union_find/tests_union_find.py +++ /dev/null @@ -1,78 +0,0 @@ -from __future__ import absolute_import -from .union_find import UnionFind -import unittest - - -class TestUnionFind(unittest.TestCase): - def test_init_with_valid_size(self): - uf = UnionFind(5) - self.assertEqual(uf.size, 5) - - def test_init_with_invalid_size(self): - with self.assertRaises(ValueError): - uf = UnionFind(0) - - with self.assertRaises(ValueError): - uf = UnionFind(-5) - - def test_union_with_valid_values(self): - uf = UnionFind(10) - - for i in range(11): - for j in range(11): - uf.union(i, j) - - def test_union_with_invalid_values(self): - uf = UnionFind(10) - - with self.assertRaises(ValueError): - uf.union(-1, 1) - - with self.assertRaises(ValueError): - uf.union(11, 1) - - def test_same_set_with_valid_values(self): - uf = UnionFind(10) - - for i in range(11): - for j in range(11): - if i == j: - self.assertTrue(uf.same_set(i, j)) - else: - self.assertFalse(uf.same_set(i, j)) - - uf.union(1, 2) - self.assertTrue(uf.same_set(1, 2)) - - uf.union(3, 4) - self.assertTrue(uf.same_set(3, 4)) - - self.assertFalse(uf.same_set(1, 3)) - self.assertFalse(uf.same_set(1, 4)) - self.assertFalse(uf.same_set(2, 3)) - self.assertFalse(uf.same_set(2, 4)) - - uf.union(1, 3) - self.assertTrue(uf.same_set(1, 3)) - self.assertTrue(uf.same_set(1, 4)) - self.assertTrue(uf.same_set(2, 3)) - self.assertTrue(uf.same_set(2, 4)) - - uf.union(4, 10) - self.assertTrue(uf.same_set(1, 10)) - self.assertTrue(uf.same_set(2, 10)) - self.assertTrue(uf.same_set(3, 10)) - self.assertTrue(uf.same_set(4, 10)) - - def test_same_set_with_invalid_values(self): - uf = UnionFind(10) - - with self.assertRaises(ValueError): - uf.same_set(-1, 1) - - with self.assertRaises(ValueError): - uf.same_set(11, 0) - - -if __name__ == '__main__': - unittest.main() diff --git a/data_structures/union_find/union_find.py b/data_structures/union_find/union_find.py deleted file mode 100644 index 40eea67ac944..000000000000 --- a/data_structures/union_find/union_find.py +++ /dev/null @@ -1,87 +0,0 @@ -class UnionFind(): - """ - https://en.wikipedia.org/wiki/Disjoint-set_data_structure - - The union-find is a disjoint-set data structure - - You can merge two sets and tell if one set belongs to - another one. - - It's used on the Kruskal Algorithm - (https://en.wikipedia.org/wiki/Kruskal%27s_algorithm) - - The elements are in range [0, size] - """ - def __init__(self, size): - if size <= 0: - raise ValueError("size should be greater than 0") - - self.size = size - - # The below plus 1 is because we are using elements - # in range [0, size]. It makes more sense. - - # Every set begins with only itself - self.root = [i for i in range(size+1)] - - # This is used for heuristic union by rank - self.weight = [0 for i in range(size+1)] - - def union(self, u, v): - """ - Union of the sets u and v. - Complexity: log(n). - Amortized complexity: < 5 (it's very fast). - """ - - self._validate_element_range(u, "u") - self._validate_element_range(v, "v") - - if u == v: - return - - # Using union by rank will guarantee the - # log(n) complexity - rootu = self._root(u) - rootv = self._root(v) - weight_u = self.weight[rootu] - weight_v = self.weight[rootv] - if weight_u >= weight_v: - self.root[rootv] = rootu - if weight_u == weight_v: - self.weight[rootu] += 1 - else: - self.root[rootu] = rootv - - def same_set(self, u, v): - """ - Return true if the elements u and v belongs to - the same set - """ - - self._validate_element_range(u, "u") - self._validate_element_range(v, "v") - - return self._root(u) == self._root(v) - - def _root(self, u): - """ - Get the element set root. - This uses the heuristic path compression - See wikipedia article for more details. - """ - - if u != self.root[u]: - self.root[u] = self._root(self.root[u]) - - return self.root[u] - - def _validate_element_range(self, u, element_name): - """ - Raises ValueError if element is not in range - """ - if u < 0 or u > self.size: - msg = ("element {0} with value {1} " - "should be in range [0~{2}]")\ - .format(element_name, u, self.size) - raise ValueError(msg) diff --git a/digital_image_processing/change_contrast.py b/digital_image_processing/change_contrast.py new file mode 100644 index 000000000000..76f1a3e1fcd8 --- /dev/null +++ b/digital_image_processing/change_contrast.py @@ -0,0 +1,35 @@ +""" +Changing contrast with PIL + +This algorithm is used in +https://noivce.pythonanywhere.com/ python web app. + +python/black: True +flake8 : True +""" + +from PIL import Image + + +def change_contrast(img: Image, level: float) -> Image: + """ + Function to change contrast + """ + factor = (259 * (level + 255)) / (255 * (259 - level)) + + def contrast(c: int) -> float: + """ + Fundamental Transformation/Operation that'll be performed on + every bit. + """ + return 128 + factor * (c - 128) + + return img.point(contrast) + + +if __name__ == "__main__": + # Load image + with Image.open("image_data/lena.jpg") as img: + # Change contrast to 170 + cont_img = change_contrast(img, 170) + cont_img.save("image_data/lena_high_contrast.png", format="png") diff --git a/data_structures/__init__.py b/digital_image_processing/edge_detection/__init__.py similarity index 100% rename from data_structures/__init__.py rename to digital_image_processing/edge_detection/__init__.py diff --git a/digital_image_processing/edge_detection/canny.py b/digital_image_processing/edge_detection/canny.py new file mode 100644 index 000000000000..6f98fee6308e --- /dev/null +++ b/digital_image_processing/edge_detection/canny.py @@ -0,0 +1,117 @@ +import cv2 +import numpy as np +from digital_image_processing.filters.convolve import img_convolve +from digital_image_processing.filters.sobel_filter import sobel_filter + +PI = 180 + + +def gen_gaussian_kernel(k_size, sigma): + center = k_size // 2 + x, y = np.mgrid[0 - center : k_size - center, 0 - center : k_size - center] + g = ( + 1 + / (2 * np.pi * sigma) + * np.exp(-(np.square(x) + np.square(y)) / (2 * np.square(sigma))) + ) + return g + + +def canny(image, threshold_low=15, threshold_high=30, weak=128, strong=255): + image_row, image_col = image.shape[0], image.shape[1] + # gaussian_filter + gaussian_out = img_convolve(image, gen_gaussian_kernel(9, sigma=1.4)) + # get the gradient and degree by sobel_filter + sobel_grad, sobel_theta = sobel_filter(gaussian_out) + gradient_direction = np.rad2deg(sobel_theta) + gradient_direction += PI + + dst = np.zeros((image_row, image_col)) + + """ + Non-maximum suppression. If the edge strength of the current pixel is the largest compared to the other pixels + in the mask with the same direction, the value will be preserved. Otherwise, the value will be suppressed. + """ + for row in range(1, image_row - 1): + for col in range(1, image_col - 1): + direction = gradient_direction[row, col] + + if ( + 0 <= direction < 22.5 + or 15 * PI / 8 <= direction <= 2 * PI + or 7 * PI / 8 <= direction <= 9 * PI / 8 + ): + W = sobel_grad[row, col - 1] + E = sobel_grad[row, col + 1] + if sobel_grad[row, col] >= W and sobel_grad[row, col] >= E: + dst[row, col] = sobel_grad[row, col] + + elif (PI / 8 <= direction < 3 * PI / 8) or ( + 9 * PI / 8 <= direction < 11 * PI / 8 + ): + SW = sobel_grad[row + 1, col - 1] + NE = sobel_grad[row - 1, col + 1] + if sobel_grad[row, col] >= SW and sobel_grad[row, col] >= NE: + dst[row, col] = sobel_grad[row, col] + + elif (3 * PI / 8 <= direction < 5 * PI / 8) or ( + 11 * PI / 8 <= direction < 13 * PI / 8 + ): + N = sobel_grad[row - 1, col] + S = sobel_grad[row + 1, col] + if sobel_grad[row, col] >= N and sobel_grad[row, col] >= S: + dst[row, col] = sobel_grad[row, col] + + elif (5 * PI / 8 <= direction < 7 * PI / 8) or ( + 13 * PI / 8 <= direction < 15 * PI / 8 + ): + NW = sobel_grad[row - 1, col - 1] + SE = sobel_grad[row + 1, col + 1] + if sobel_grad[row, col] >= NW and sobel_grad[row, col] >= SE: + dst[row, col] = sobel_grad[row, col] + + """ + High-Low threshold detection. If an edge pixel’s gradient value is higher than the high threshold + value, it is marked as a strong edge pixel. If an edge pixel’s gradient value is smaller than the high + threshold value and larger than the low threshold value, it is marked as a weak edge pixel. If an edge + pixel's value is smaller than the low threshold value, it will be suppressed. + """ + if dst[row, col] >= threshold_high: + dst[row, col] = strong + elif dst[row, col] <= threshold_low: + dst[row, col] = 0 + else: + dst[row, col] = weak + + """ + Edge tracking. Usually a weak edge pixel caused from true edges will be connected to a strong edge pixel while + noise responses are unconnected. As long as there is one strong edge pixel that is involved in its 8-connected + neighborhood, that weak edge point can be identified as one that should be preserved. + """ + for row in range(1, image_row): + for col in range(1, image_col): + if dst[row, col] == weak: + if 255 in ( + dst[row, col + 1], + dst[row, col - 1], + dst[row - 1, col], + dst[row + 1, col], + dst[row - 1, col - 1], + dst[row + 1, col - 1], + dst[row - 1, col + 1], + dst[row + 1, col + 1], + ): + dst[row, col] = strong + else: + dst[row, col] = 0 + + return dst + + +if __name__ == "__main__": + # read original image in gray mode + lena = cv2.imread(r"../image_data/lena.jpg", 0) + # canny edge detection + canny_dst = canny(lena) + cv2.imshow("canny", canny_dst) + cv2.waitKey(0) diff --git a/digital_image_processing/filters/convolve.py b/digital_image_processing/filters/convolve.py new file mode 100644 index 000000000000..ec500d940366 --- /dev/null +++ b/digital_image_processing/filters/convolve.py @@ -0,0 +1,49 @@ +# @Author : lightXu +# @File : convolve.py +# @Time : 2019/7/8 0008 下午 16:13 +from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey +from numpy import array, zeros, ravel, pad, dot, uint8 + + +def im2col(image, block_size): + rows, cols = image.shape + dst_height = cols - block_size[1] + 1 + dst_width = rows - block_size[0] + 1 + image_array = zeros((dst_height * dst_width, block_size[1] * block_size[0])) + row = 0 + for i in range(0, dst_height): + for j in range(0, dst_width): + window = ravel(image[i : i + block_size[0], j : j + block_size[1]]) + image_array[row, :] = window + row += 1 + + return image_array + + +def img_convolve(image, filter_kernel): + height, width = image.shape[0], image.shape[1] + k_size = filter_kernel.shape[0] + pad_size = k_size // 2 + # Pads image with the edge values of array. + image_tmp = pad(image, pad_size, mode="edge") + + # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows + image_array = im2col(image_tmp, (k_size, k_size)) + + # turn the kernel into shape(k*k, 1) + kernel_array = ravel(filter_kernel) + # reshape and get the dst image + dst = dot(image_array, kernel_array).reshape(height, width) + return dst + + +if __name__ == "__main__": + # read original image + img = imread(r"../image_data/lena.jpg") + # turn image in gray scale value + gray = cvtColor(img, COLOR_BGR2GRAY) + # Laplace operator + Laplace_kernel = array([[0, 1, 0], [1, -4, 1], [0, 1, 0]]) + out = img_convolve(gray, Laplace_kernel).astype(uint8) + imshow("Laplacian", out) + waitKey(0) diff --git a/digital_image_processing/filters/gaussian_filter.py b/digital_image_processing/filters/gaussian_filter.py new file mode 100644 index 000000000000..b800f0a7edc8 --- /dev/null +++ b/digital_image_processing/filters/gaussian_filter.py @@ -0,0 +1,53 @@ +""" +Implementation of gaussian filter algorithm +""" +from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey +from numpy import pi, mgrid, exp, square, zeros, ravel, dot, uint8 + + +def gen_gaussian_kernel(k_size, sigma): + center = k_size // 2 + x, y = mgrid[0 - center : k_size - center, 0 - center : k_size - center] + g = 1 / (2 * pi * sigma) * exp(-(square(x) + square(y)) / (2 * square(sigma))) + return g + + +def gaussian_filter(image, k_size, sigma): + height, width = image.shape[0], image.shape[1] + # dst image height and width + dst_height = height - k_size + 1 + dst_width = width - k_size + 1 + + # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows + image_array = zeros((dst_height * dst_width, k_size * k_size)) + row = 0 + for i in range(0, dst_height): + for j in range(0, dst_width): + window = ravel(image[i : i + k_size, j : j + k_size]) + image_array[row, :] = window + row += 1 + + # turn the kernel into shape(k*k, 1) + gaussian_kernel = gen_gaussian_kernel(k_size, sigma) + filter_array = ravel(gaussian_kernel) + + # reshape and get the dst image + dst = dot(image_array, filter_array).reshape(dst_height, dst_width).astype(uint8) + + return dst + + +if __name__ == "__main__": + # read original image + img = imread(r"../image_data/lena.jpg") + # turn image in gray scale value + gray = cvtColor(img, COLOR_BGR2GRAY) + + # get values with two different mask size + gaussian3x3 = gaussian_filter(gray, 3, sigma=1) + gaussian5x5 = gaussian_filter(gray, 5, sigma=0.8) + + # show result images + imshow("gaussian filter with 3x3 mask", gaussian3x3) + imshow("gaussian filter with 5x5 mask", gaussian5x5) + waitKey() diff --git a/digital_image_processing/filters/median_filter.py b/digital_image_processing/filters/median_filter.py index eea4295632a1..151ef8a55df1 100644 --- a/digital_image_processing/filters/median_filter.py +++ b/digital_image_processing/filters/median_filter.py @@ -15,20 +15,20 @@ def median_filter(gray_img, mask=3): # set image borders bd = int(mask / 2) # copy image size - median_img = zeros_like(gray) + median_img = zeros_like(gray_img) for i in range(bd, gray_img.shape[0] - bd): for j in range(bd, gray_img.shape[1] - bd): # get mask according with mask - kernel = ravel(gray_img[i - bd:i + bd + 1, j - bd:j + bd + 1]) + kernel = ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]) # calculate mask median median = sort(kernel)[int8(divide((multiply(mask, mask)), 2) + 1)] median_img[i, j] = median return median_img -if __name__ == '__main__': +if __name__ == "__main__": # read original image - img = imread('lena.jpg') + img = imread("../image_data/lena.jpg") # turn image in gray scale value gray = cvtColor(img, COLOR_BGR2GRAY) @@ -37,6 +37,6 @@ def median_filter(gray_img, mask=3): median5x5 = median_filter(gray, 5) # show result images - imshow('median filter with 3x3 mask', median3x3) - imshow('median filter with 5x5 mask', median5x5) + imshow("median filter with 3x3 mask", median3x3) + imshow("median filter with 5x5 mask", median5x5) waitKey(0) diff --git a/digital_image_processing/filters/sobel_filter.py b/digital_image_processing/filters/sobel_filter.py new file mode 100644 index 000000000000..822d49fe38a1 --- /dev/null +++ b/digital_image_processing/filters/sobel_filter.py @@ -0,0 +1,38 @@ +# @Author : lightXu +# @File : sobel_filter.py +# @Time : 2019/7/8 0008 下午 16:26 +import numpy as np +from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey +from digital_image_processing.filters.convolve import img_convolve + + +def sobel_filter(image): + kernel_x = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]) + kernel_y = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]) + + dst_x = np.abs(img_convolve(image, kernel_x)) + dst_y = np.abs(img_convolve(image, kernel_y)) + # modify the pix within [0, 255] + dst_x = dst_x * 255 / np.max(dst_x) + dst_y = dst_y * 255 / np.max(dst_y) + + dst_xy = np.sqrt((np.square(dst_x)) + (np.square(dst_y))) + dst_xy = dst_xy * 255 / np.max(dst_xy) + dst = dst_xy.astype(np.uint8) + + theta = np.arctan2(dst_y, dst_x) + return dst, theta + + +if __name__ == "__main__": + # read original image + img = imread("../image_data/lena.jpg") + # turn image in gray scale value + gray = cvtColor(img, COLOR_BGR2GRAY) + + sobel_grad, sobel_theta = sobel_filter(gray) + + # show result images + imshow("sobel filter", sobel_grad) + imshow("sobel theta", sobel_theta) + waitKey(0) diff --git a/digital_image_processing/image_data/lena.jpg b/digital_image_processing/image_data/lena.jpg new file mode 100644 index 000000000000..15c4d9764eff Binary files /dev/null and b/digital_image_processing/image_data/lena.jpg differ diff --git a/digital_image_processing/image_data/lena_small.jpg b/digital_image_processing/image_data/lena_small.jpg new file mode 100644 index 000000000000..b85144e9f65c Binary files /dev/null and b/digital_image_processing/image_data/lena_small.jpg differ diff --git a/data_structures/queue/__init__.py b/digital_image_processing/rotation/__init__.py similarity index 100% rename from data_structures/queue/__init__.py rename to digital_image_processing/rotation/__init__.py diff --git a/digital_image_processing/rotation/rotation.py b/digital_image_processing/rotation/rotation.py new file mode 100644 index 000000000000..37b45ca39897 --- /dev/null +++ b/digital_image_processing/rotation/rotation.py @@ -0,0 +1,52 @@ +from matplotlib import pyplot as plt +import numpy as np +import cv2 + + +def get_rotation( + img: np.array, pt1: np.float32, pt2: np.float32, rows: int, cols: int +) -> np.array: + """ + Get image rotation + :param img: np.array + :param pt1: 3x2 list + :param pt2: 3x2 list + :param rows: columns image shape + :param cols: rows image shape + :return: np.array + """ + matrix = cv2.getAffineTransform(pt1, pt2) + return cv2.warpAffine(img, matrix, (rows, cols)) + + +if __name__ == "__main__": + # read original image + image = cv2.imread("lena.jpg") + # turn image in gray scale value + gray_img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + # get image shape + img_rows, img_cols = gray_img.shape + + # set different points to rotate image + pts1 = np.float32([[50, 50], [200, 50], [50, 200]]) + pts2 = np.float32([[10, 100], [200, 50], [100, 250]]) + pts3 = np.float32([[50, 50], [150, 50], [120, 200]]) + pts4 = np.float32([[10, 100], [80, 50], [180, 250]]) + + # add all rotated images in a list + images = [ + gray_img, + get_rotation(gray_img, pts1, pts2, img_rows, img_cols), + get_rotation(gray_img, pts2, pts3, img_rows, img_cols), + get_rotation(gray_img, pts2, pts4, img_rows, img_cols), + ] + + # plot different image rotations + fig = plt.figure(1) + titles = ["Original", "Rotation 1", "Rotation 2", "Rotation 3"] + for i, image in enumerate(images): + plt.subplot(2, 2, i + 1), plt.imshow(image, "gray") + plt.title(titles[i]) + plt.axis("off") + plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95) + plt.show() diff --git a/digital_image_processing/test_digital_image_processing.py b/digital_image_processing/test_digital_image_processing.py new file mode 100644 index 000000000000..02c1a2d3a663 --- /dev/null +++ b/digital_image_processing/test_digital_image_processing.py @@ -0,0 +1,62 @@ +""" +PyTest's for Digital Image Processing +""" + +import digital_image_processing.edge_detection.canny as canny +import digital_image_processing.filters.gaussian_filter as gg +import digital_image_processing.filters.median_filter as med +import digital_image_processing.filters.sobel_filter as sob +import digital_image_processing.filters.convolve as conv +import digital_image_processing.change_contrast as cc +from cv2 import imread, cvtColor, COLOR_BGR2GRAY +from numpy import array, uint8 +from PIL import Image + +img = imread(r"digital_image_processing/image_data/lena_small.jpg") +gray = cvtColor(img, COLOR_BGR2GRAY) + +# Test: change_contrast() +def test_change_contrast(): + with Image.open("digital_image_processing/image_data/lena_small.jpg") as img: + # Work around assertion for response + assert str(cc.change_contrast(img, 110)).startswith( + " Divide and conquer +The points are sorted based on Xco-ords and +then based on Yco-ords separately. +And by applying divide and conquer approach, +minimum distance is obtained recursively. + +>> Closest points can lie on different sides of partition. +This case handled by forming a strip of points +whose Xco-ords distance is less than closest_pair_dis +from mid-point's Xco-ords. Points sorted based on Yco-ords +are used in this step to reduce sorting time. +Closest pair distance is found in the strip of points. (closest_in_strip) + +min(closest_pair_dis, closest_in_strip) would be the final answer. + +Time complexity: O(n * log n) +""" + + +def euclidean_distance_sqr(point1, point2): + """ + >>> euclidean_distance_sqr([1,2],[2,4]) + 5 + """ + return (point1[0] - point2[0]) ** 2 + (point1[1] - point2[1]) ** 2 + + +def column_based_sort(array, column=0): + """ + >>> column_based_sort([(5, 1), (4, 2), (3, 0)], 1) + [(3, 0), (5, 1), (4, 2)] + """ + return sorted(array, key=lambda x: x[column]) + + +def dis_between_closest_pair(points, points_counts, min_dis=float("inf")): + """ + brute force approach to find distance between closest pair points + + Parameters : + points, points_count, min_dis (list(tuple(int, int)), int, int) + + Returns : + min_dis (float): distance between closest pair of points + + >>> dis_between_closest_pair([[1,2],[2,4],[5,7],[8,9],[11,0]],5) + 5 + + """ + + for i in range(points_counts - 1): + for j in range(i + 1, points_counts): + current_dis = euclidean_distance_sqr(points[i], points[j]) + if current_dis < min_dis: + min_dis = current_dis + return min_dis + + +def dis_between_closest_in_strip(points, points_counts, min_dis=float("inf")): + """ + closest pair of points in strip + + Parameters : + points, points_count, min_dis (list(tuple(int, int)), int, int) + + Returns : + min_dis (float): distance btw closest pair of points in the strip (< min_dis) + + >>> dis_between_closest_in_strip([[1,2],[2,4],[5,7],[8,9],[11,0]],5) + 85 + """ + + for i in range(min(6, points_counts - 1), points_counts): + for j in range(max(0, i - 6), i): + current_dis = euclidean_distance_sqr(points[i], points[j]) + if current_dis < min_dis: + min_dis = current_dis + return min_dis + + +def closest_pair_of_points_sqr(points_sorted_on_x, points_sorted_on_y, points_counts): + """ divide and conquer approach + + Parameters : + points, points_count (list(tuple(int, int)), int) + + Returns : + (float): distance btw closest pair of points + + >>> closest_pair_of_points_sqr([(1, 2), (3, 4)], [(5, 6), (7, 8)], 2) + 8 + """ + + # base case + if points_counts <= 3: + return dis_between_closest_pair(points_sorted_on_x, points_counts) + + # recursion + mid = points_counts // 2 + closest_in_left = closest_pair_of_points_sqr( + points_sorted_on_x, points_sorted_on_y[:mid], mid + ) + closest_in_right = closest_pair_of_points_sqr( + points_sorted_on_y, points_sorted_on_y[mid:], points_counts - mid + ) + closest_pair_dis = min(closest_in_left, closest_in_right) + + """ + cross_strip contains the points, whose Xcoords are at a + distance(< closest_pair_dis) from mid's Xcoord + """ + + cross_strip = [] + for point in points_sorted_on_x: + if abs(point[0] - points_sorted_on_x[mid][0]) < closest_pair_dis: + cross_strip.append(point) + + closest_in_strip = dis_between_closest_in_strip( + cross_strip, len(cross_strip), closest_pair_dis + ) + return min(closest_pair_dis, closest_in_strip) + + +def closest_pair_of_points(points, points_counts): + """ + >>> closest_pair_of_points([(2, 3), (12, 30)], len([(2, 3), (12, 30)])) + 28.792360097775937 + """ + points_sorted_on_x = column_based_sort(points, column=0) + points_sorted_on_y = column_based_sort(points, column=1) + return ( + closest_pair_of_points_sqr( + points_sorted_on_x, points_sorted_on_y, points_counts + ) + ) ** 0.5 + + +if __name__ == "__main__": + points = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)] + print("Distance:", closest_pair_of_points(points, len(points))) diff --git a/divide_and_conquer/convex_hull.py b/divide_and_conquer/convex_hull.py new file mode 100644 index 000000000000..bd88256ab01c --- /dev/null +++ b/divide_and_conquer/convex_hull.py @@ -0,0 +1,447 @@ +from numbers import Number + +""" +The convex hull problem is problem of finding all the vertices of convex polygon, P of +a set of points in a plane such that all the points are either on the vertices of P or +inside P. TH convex hull problem has several applications in geometrical problems, +computer graphics and game development. + +Two algorithms have been implemented for the convex hull problem here. +1. A brute-force algorithm which runs in O(n^3) +2. A divide-and-conquer algorithm which runs in O(n log(n)) + +There are other several other algorithms for the convex hull problem +which have not been implemented here, yet. + +""" + + +class Point: + """ + Defines a 2-d point for use by all convex-hull algorithms. + + Parameters + ---------- + x: an int or a float, the x-coordinate of the 2-d point + y: an int or a float, the y-coordinate of the 2-d point + + Examples + -------- + >>> Point(1, 2) + (1, 2) + >>> Point("1", "2") + (1.0, 2.0) + >>> Point(1, 2) > Point(0, 1) + True + >>> Point(1, 1) == Point(1, 1) + True + >>> Point(-0.5, 1) == Point(0.5, 1) + False + >>> Point("pi", "e") + Traceback (most recent call last): + ... + ValueError: x and y must be both numeric types but got , instead + """ + + def __init__(self, x, y): + if not (isinstance(x, Number) and isinstance(y, Number)): + try: + x, y = float(x), float(y) + except ValueError as e: + e.args = ( + "x and y must be both numeric types " + "but got {}, {} instead".format(type(x), type(y)), + ) + raise + + self.x = x + self.y = y + + def __eq__(self, other): + return self.x == other.x and self.y == other.y + + def __ne__(self, other): + return not self == other + + def __gt__(self, other): + if self.x > other.x: + return True + elif self.x == other.x: + return self.y > other.y + return False + + def __lt__(self, other): + return not self > other + + def __ge__(self, other): + if self.x > other.x: + return True + elif self.x == other.x: + return self.y >= other.y + return False + + def __le__(self, other): + if self.x < other.x: + return True + elif self.x == other.x: + return self.y <= other.y + return False + + def __repr__(self): + return "({}, {})".format(self.x, self.y) + + def __hash__(self): + return hash(self.x) + + +def _construct_points(list_of_tuples): + """ + constructs a list of points from an array-like object of numbers + + Arguments + --------- + + list_of_tuples: array-like object of type numbers. Acceptable types so far + are lists, tuples and sets. + + Returns + -------- + points: a list where each item is of type Point. This contains only objects + which can be converted into a Point. + + Examples + ------- + >>> _construct_points([[1, 1], [2, -1], [0.3, 4]]) + [(1, 1), (2, -1), (0.3, 4)] + >>> _construct_points(([1, 1], [2, -1], [0.3, 4])) + [(1, 1), (2, -1), (0.3, 4)] + >>> _construct_points([(1, 1), (2, -1), (0.3, 4)]) + [(1, 1), (2, -1), (0.3, 4)] + >>> _construct_points([[1, 1], (2, -1), [0.3, 4]]) + [(1, 1), (2, -1), (0.3, 4)] + >>> _construct_points([1, 2]) + Ignoring deformed point 1. All points must have at least 2 coordinates. + Ignoring deformed point 2. All points must have at least 2 coordinates. + [] + >>> _construct_points([]) + [] + >>> _construct_points(None) + [] + """ + + points = [] + if list_of_tuples: + for p in list_of_tuples: + try: + points.append(Point(p[0], p[1])) + except (IndexError, TypeError): + print( + "Ignoring deformed point {}. All points" + " must have at least 2 coordinates.".format(p) + ) + return points + + +def _validate_input(points): + """ + validates an input instance before a convex-hull algorithms uses it + + Parameters + --------- + points: array-like, the 2d points to validate before using with + a convex-hull algorithm. The elements of points must be either lists, tuples or + Points. + + Returns + ------- + points: array_like, an iterable of all well-defined Points constructed passed in. + + + Exception + --------- + ValueError: if points is empty or None, or if a wrong data structure like a scalar is passed + + TypeError: if an iterable but non-indexable object (eg. dictionary) is passed. + The exception to this a set which we'll convert to a list before using + + + Examples + ------- + >>> _validate_input([[1, 2]]) + [(1, 2)] + >>> _validate_input([(1, 2)]) + [(1, 2)] + >>> _validate_input([Point(2, 1), Point(-1, 2)]) + [(2, 1), (-1, 2)] + >>> _validate_input([]) + Traceback (most recent call last): + ... + ValueError: Expecting a list of points but got [] + >>> _validate_input(1) + Traceback (most recent call last): + ... + ValueError: Expecting an iterable object but got an non-iterable type 1 + """ + + if not points: + raise ValueError("Expecting a list of points but got {}".format(points)) + + if isinstance(points, set): + points = list(points) + + try: + if hasattr(points, "__iter__") and not isinstance(points[0], Point): + if isinstance(points[0], (list, tuple)): + points = _construct_points(points) + else: + raise ValueError( + "Expecting an iterable of type Point, list or tuple. " + "Found objects of type {} instead".format(type(points[0])) + ) + elif not hasattr(points, "__iter__"): + raise ValueError( + "Expecting an iterable object " + "but got an non-iterable type {}".format(points) + ) + except TypeError as e: + print("Expecting an iterable of type Point, list or tuple.") + raise + + return points + + +def _det(a, b, c): + """ + Computes the sign perpendicular distance of a 2d point c from a line segment + ab. The sign indicates the direction of c relative to ab. + A Positive value means c is above ab (to the left), while a negative value + means c is below ab (to the right). 0 means all three points are on a straight line. + + As a side note, 0.5 * abs|det| is the area of triangle abc + + Parameters + ---------- + a: point, the point on the left end of line segment ab + b: point, the point on the right end of line segment ab + c: point, the point for which the direction and location is desired. + + Returns + -------- + det: float, abs(det) is the distance of c from ab. The sign + indicates which side of line segment ab c is. det is computed as + (a_xb_y + c_xa_y + b_xc_y) - (a_yb_x + c_ya_x + b_yc_x) + + Examples + ---------- + >>> _det(Point(1, 1), Point(1, 2), Point(1, 5)) + 0 + >>> _det(Point(0, 0), Point(10, 0), Point(0, 10)) + 100 + >>> _det(Point(0, 0), Point(10, 0), Point(0, -10)) + -100 + """ + + det = (a.x * b.y + b.x * c.y + c.x * a.y) - (a.y * b.x + b.y * c.x + c.y * a.x) + return det + + +def convex_hull_bf(points): + """ + Constructs the convex hull of a set of 2D points using a brute force algorithm. + The algorithm basically considers all combinations of points (i, j) and uses the + definition of convexity to determine whether (i, j) is part of the convex hull or not. + (i, j) is part of the convex hull if and only iff there are no points on both sides + of the line segment connecting the ij, and there is no point k such that k is on either end + of the ij. + + Runtime: O(n^3) - definitely horrible + + Parameters + --------- + points: array-like of object of Points, lists or tuples. + The set of 2d points for which the convex-hull is needed + + Returns + ------ + convex_set: list, the convex-hull of points sorted in non-decreasing order. + + See Also + -------- + convex_hull_recursive, + + Examples + --------- + >>> convex_hull_bf([[0, 0], [1, 0], [10, 1]]) + [(0, 0), (1, 0), (10, 1)] + >>> convex_hull_bf([[0, 0], [1, 0], [10, 0]]) + [(0, 0), (10, 0)] + >>> convex_hull_bf([[-1, 1],[-1, -1], [0, 0], [0.5, 0.5], [1, -1], [1, 1], [-0.75, 1]]) + [(-1, -1), (-1, 1), (1, -1), (1, 1)] + >>> convex_hull_bf([(0, 3), (2, 2), (1, 1), (2, 1), (3, 0), (0, 0), (3, 3), (2, -1), (2, -4), (1, -3)]) + [(0, 0), (0, 3), (1, -3), (2, -4), (3, 0), (3, 3)] + """ + + points = sorted(_validate_input(points)) + n = len(points) + convex_set = set() + + for i in range(n - 1): + for j in range(i + 1, n): + points_left_of_ij = points_right_of_ij = False + ij_part_of_convex_hull = True + for k in range(n): + if k != i and k != j: + det_k = _det(points[i], points[j], points[k]) + + if det_k > 0: + points_left_of_ij = True + elif det_k < 0: + points_right_of_ij = True + else: + # point[i], point[j], point[k] all lie on a straight line + # if point[k] is to the left of point[i] or it's to the + # right of point[j], then point[i], point[j] cannot be + # part of the convex hull of A + if points[k] < points[i] or points[k] > points[j]: + ij_part_of_convex_hull = False + break + + if points_left_of_ij and points_right_of_ij: + ij_part_of_convex_hull = False + break + + if ij_part_of_convex_hull: + convex_set.update([points[i], points[j]]) + + return sorted(convex_set) + + +def convex_hull_recursive(points): + """ + Constructs the convex hull of a set of 2D points using a divide-and-conquer strategy + The algorithm exploits the geometric properties of the problem by repeatedly partitioning + the set of points into smaller hulls, and finding the convex hull of these smaller hulls. + The union of the convex hull from smaller hulls is the solution to the convex hull of the larger problem. + + Parameter + --------- + points: array-like of object of Points, lists or tuples. + The set of 2d points for which the convex-hull is needed + + Runtime: O(n log n) + + Returns + ------- + convex_set: list, the convex-hull of points sorted in non-decreasing order. + + Examples + --------- + >>> convex_hull_recursive([[0, 0], [1, 0], [10, 1]]) + [(0, 0), (1, 0), (10, 1)] + >>> convex_hull_recursive([[0, 0], [1, 0], [10, 0]]) + [(0, 0), (10, 0)] + >>> convex_hull_recursive([[-1, 1],[-1, -1], [0, 0], [0.5, 0.5], [1, -1], [1, 1], [-0.75, 1]]) + [(-1, -1), (-1, 1), (1, -1), (1, 1)] + >>> convex_hull_recursive([(0, 3), (2, 2), (1, 1), (2, 1), (3, 0), (0, 0), (3, 3), (2, -1), (2, -4), (1, -3)]) + [(0, 0), (0, 3), (1, -3), (2, -4), (3, 0), (3, 3)] + + """ + points = sorted(_validate_input(points)) + n = len(points) + + # divide all the points into an upper hull and a lower hull + # the left most point and the right most point are definitely + # members of the convex hull by definition. + # use these two anchors to divide all the points into two hulls, + # an upper hull and a lower hull. + + # all points to the left (above) the line joining the extreme points belong to the upper hull + # all points to the right (below) the line joining the extreme points below to the lower hull + # ignore all points on the line joining the extreme points since they cannot be part of the + # convex hull + + left_most_point = points[0] + right_most_point = points[n - 1] + + convex_set = {left_most_point, right_most_point} + upperhull = [] + lowerhull = [] + + for i in range(1, n - 1): + det = _det(left_most_point, right_most_point, points[i]) + + if det > 0: + upperhull.append(points[i]) + elif det < 0: + lowerhull.append(points[i]) + + _construct_hull(upperhull, left_most_point, right_most_point, convex_set) + _construct_hull(lowerhull, right_most_point, left_most_point, convex_set) + + return sorted(convex_set) + + +def _construct_hull(points, left, right, convex_set): + """ + + Parameters + --------- + points: list or None, the hull of points from which to choose the next convex-hull point + left: Point, the point to the left of line segment joining left and right + right: The point to the right of the line segment joining left and right + convex_set: set, the current convex-hull. The state of convex-set gets updated by this function + + Note + ---- + For the line segment 'ab', 'a' is on the left and 'b' on the right. + but the reverse is true for the line segment 'ba'. + + Returns + ------- + Nothing, only updates the state of convex-set + """ + if points: + extreme_point = None + extreme_point_distance = float("-inf") + candidate_points = [] + + for p in points: + det = _det(left, right, p) + + if det > 0: + candidate_points.append(p) + + if det > extreme_point_distance: + extreme_point_distance = det + extreme_point = p + + if extreme_point: + _construct_hull(candidate_points, left, extreme_point, convex_set) + convex_set.add(extreme_point) + _construct_hull(candidate_points, extreme_point, right, convex_set) + + +def main(): + points = [ + (0, 3), + (2, 2), + (1, 1), + (2, 1), + (3, 0), + (0, 0), + (3, 3), + (2, -1), + (2, -4), + (1, -3), + ] + # the convex set of points is + # [(0, 0), (0, 3), (1, -3), (2, -4), (3, 0), (3, 3)] + results_recursive = convex_hull_recursive(points) + results_bf = convex_hull_bf(points) + assert results_bf == results_recursive + + print(results_bf) + + +if __name__ == "__main__": + main() diff --git a/divide_and_conquer/inversions.py b/divide_and_conquer/inversions.py new file mode 100644 index 000000000000..9bb656229321 --- /dev/null +++ b/divide_and_conquer/inversions.py @@ -0,0 +1,169 @@ +""" +Given an array-like data structure A[1..n], how many pairs +(i, j) for all 1 <= i < j <= n such that A[i] > A[j]? These pairs are +called inversions. Counting the number of such inversions in an array-like +object is the important. Among other things, counting inversions can help +us determine how close a given array is to being sorted + +In this implementation, I provide two algorithms, a divide-and-conquer +algorithm which runs in nlogn and the brute-force n^2 algorithm. + +""" + + +def count_inversions_bf(arr): + """ + Counts the number of inversions using a a naive brute-force algorithm + + Parameters + ---------- + arr: arr: array-like, the list containing the items for which the number + of inversions is desired. The elements of `arr` must be comparable. + + Returns + ------- + num_inversions: The total number of inversions in `arr` + + Examples + --------- + + >>> count_inversions_bf([1, 4, 2, 4, 1]) + 4 + >>> count_inversions_bf([1, 1, 2, 4, 4]) + 0 + >>> count_inversions_bf([]) + 0 + """ + + num_inversions = 0 + n = len(arr) + + for i in range(n - 1): + for j in range(i + 1, n): + if arr[i] > arr[j]: + num_inversions += 1 + + return num_inversions + + +def count_inversions_recursive(arr): + """ + Counts the number of inversions using a divide-and-conquer algorithm + + Parameters + ----------- + arr: array-like, the list containing the items for which the number + of inversions is desired. The elements of `arr` must be comparable. + + Returns + ------- + C: a sorted copy of `arr`. + num_inversions: int, the total number of inversions in 'arr' + + Examples + -------- + + >>> count_inversions_recursive([1, 4, 2, 4, 1]) + ([1, 1, 2, 4, 4], 4) + >>> count_inversions_recursive([1, 1, 2, 4, 4]) + ([1, 1, 2, 4, 4], 0) + >>> count_inversions_recursive([]) + ([], 0) + """ + if len(arr) <= 1: + return arr, 0 + else: + mid = len(arr) // 2 + P = arr[0:mid] + Q = arr[mid:] + + A, inversion_p = count_inversions_recursive(P) + B, inversions_q = count_inversions_recursive(Q) + C, cross_inversions = _count_cross_inversions(A, B) + + num_inversions = inversion_p + inversions_q + cross_inversions + return C, num_inversions + + +def _count_cross_inversions(P, Q): + """ + Counts the inversions across two sorted arrays. + And combine the two arrays into one sorted array + + For all 1<= i<=len(P) and for all 1 <= j <= len(Q), + if P[i] > Q[j], then (i, j) is a cross inversion + + Parameters + ---------- + P: array-like, sorted in non-decreasing order + Q: array-like, sorted in non-decreasing order + + Returns + ------ + R: array-like, a sorted array of the elements of `P` and `Q` + num_inversion: int, the number of inversions across `P` and `Q` + + Examples + -------- + + >>> _count_cross_inversions([1, 2, 3], [0, 2, 5]) + ([0, 1, 2, 2, 3, 5], 4) + >>> _count_cross_inversions([1, 2, 3], [3, 4, 5]) + ([1, 2, 3, 3, 4, 5], 0) + """ + + R = [] + i = j = num_inversion = 0 + while i < len(P) and j < len(Q): + if P[i] > Q[j]: + # if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P) + # These are all inversions. The claim emerges from the + # property that P is sorted. + num_inversion += len(P) - i + R.append(Q[j]) + j += 1 + else: + R.append(P[i]) + i += 1 + + if i < len(P): + R.extend(P[i:]) + else: + R.extend(Q[j:]) + + return R, num_inversion + + +def main(): + arr_1 = [10, 2, 1, 5, 5, 2, 11] + + # this arr has 8 inversions: + # (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2) + + num_inversions_bf = count_inversions_bf(arr_1) + _, num_inversions_recursive = count_inversions_recursive(arr_1) + + assert num_inversions_bf == num_inversions_recursive == 8 + + print("number of inversions = ", num_inversions_bf) + + # testing an array with zero inversion (a sorted arr_1) + + arr_1.sort() + num_inversions_bf = count_inversions_bf(arr_1) + _, num_inversions_recursive = count_inversions_recursive(arr_1) + + assert num_inversions_bf == num_inversions_recursive == 0 + print("number of inversions = ", num_inversions_bf) + + # an empty list should also have zero inversions + arr_1 = [] + num_inversions_bf = count_inversions_bf(arr_1) + _, num_inversions_recursive = count_inversions_recursive(arr_1) + + assert num_inversions_bf == num_inversions_recursive == 0 + print("number of inversions = ", num_inversions_bf) + + +if __name__ == "__main__": + main() diff --git a/divide_and_conquer/max_subarray_sum.py b/divide_and_conquer/max_subarray_sum.py new file mode 100644 index 000000000000..9e81c83649a6 --- /dev/null +++ b/divide_and_conquer/max_subarray_sum.py @@ -0,0 +1,76 @@ +""" +Given a array of length n, max_subarray_sum() finds +the maximum of sum of contiguous sub-array using divide and conquer method. + +Time complexity : O(n log n) + +Ref : INTRODUCTION TO ALGORITHMS THIRD EDITION +(section : 4, sub-section : 4.1, page : 70) + +""" + + +def max_sum_from_start(array): + """ This function finds the maximum contiguous sum of array from 0 index + + Parameters : + array (list[int]) : given array + + Returns : + max_sum (int) : maximum contiguous sum of array from 0 index + + """ + array_sum = 0 + max_sum = float("-inf") + for num in array: + array_sum += num + if array_sum > max_sum: + max_sum = array_sum + return max_sum + + +def max_cross_array_sum(array, left, mid, right): + """ This function finds the maximum contiguous sum of left and right arrays + + Parameters : + array, left, mid, right (list[int], int, int, int) + + Returns : + (int) : maximum of sum of contiguous sum of left and right arrays + + """ + + max_sum_of_left = max_sum_from_start(array[left : mid + 1][::-1]) + max_sum_of_right = max_sum_from_start(array[mid + 1 : right + 1]) + return max_sum_of_left + max_sum_of_right + + +def max_subarray_sum(array, left, right): + """ Maximum contiguous sub-array sum, using divide and conquer method + + Parameters : + array, left, right (list[int], int, int) : + given array, current left index and current right index + + Returns : + int : maximum of sum of contiguous sub-array + + """ + + # base case: array has only one element + if left == right: + return array[right] + + # Recursion + mid = (left + right) // 2 + left_half_sum = max_subarray_sum(array, left, mid) + right_half_sum = max_subarray_sum(array, mid + 1, right) + cross_sum = max_cross_array_sum(array, left, mid, right) + return max(left_half_sum, right_half_sum, cross_sum) + + +array = [-2, -5, 6, -2, -3, 1, 5, -6] +array_length = len(array) +print( + "Maximum sum of contiguous subarray:", max_subarray_sum(array, 0, array_length - 1) +) diff --git a/divide_and_conquer/mergesort.py b/divide_and_conquer/mergesort.py new file mode 100644 index 000000000000..d6693eb36a0a --- /dev/null +++ b/divide_and_conquer/mergesort.py @@ -0,0 +1,48 @@ +def merge(a, b, m, e): + l = a[b : m + 1] + r = a[m + 1 : e + 1] + k = b + i = 0 + j = 0 + while i < len(l) and j < len(r): + # change sign for Descending order + if l[i] < r[j]: + a[k] = l[i] + i += 1 + else: + a[k] = r[j] + j += 1 + k += 1 + while i < len(l): + a[k] = l[i] + i += 1 + k += 1 + while j < len(r): + a[k] = r[j] + j += 1 + k += 1 + return a + + +def mergesort(a, b, e): + """ + >>> mergesort([3,2,1],0,2) + [1, 2, 3] + >>> mergesort([3,2,1,0,1,2,3,5,4],0,8) + [0, 1, 1, 2, 2, 3, 3, 4, 5] + """ + if b < e: + m = (b + e) // 2 + # print("ms1",a,b,m) + mergesort(a, b, m) + # print("ms2",a,m+1,e) + mergesort(a, m + 1, e) + # print("m",a,b,m,e) + merge(a, b, m, e) + return a + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/dynamic_programming/FractionalKnapsack.py b/dynamic_programming/FractionalKnapsack.py deleted file mode 100644 index 74e85b4b4708..000000000000 --- a/dynamic_programming/FractionalKnapsack.py +++ /dev/null @@ -1,12 +0,0 @@ -from itertools import accumulate -from bisect import bisect - -def fracKnapsack(vl, wt, W, n): - - r = list(sorted(zip(vl,wt), key=lambda x:x[0]/x[1],reverse=True)) - vl , wt = [i[0] for i in r],[i[1] for i in r] - acc=list(accumulate(wt)) - k = bisect(acc,W) - return 0 if k == 0 else sum(vl[:k])+(W-acc[k-1])*(vl[k])/(wt[k]) if k!=n else sum(vl[:k]) - -print("%.0f"%fracKnapsack([60, 100, 120],[10, 20, 30],50,3)) diff --git a/dynamic_programming/abbreviation.py b/dynamic_programming/abbreviation.py index f4d07e402925..5432c24882e4 100644 --- a/dynamic_programming/abbreviation.py +++ b/dynamic_programming/abbreviation.py @@ -13,6 +13,12 @@ def abbr(a, b): + """ + >>> abbr("daBcd", "ABC") + True + >>> abbr("dBcd", "ABC") + False + """ n = len(a) m = len(b) dp = [[False for _ in range(m + 1)] for _ in range(n + 1)] @@ -28,4 +34,7 @@ def abbr(a, b): if __name__ == "__main__": - print(abbr("daBcd", "ABC")) # expect True + # print(abbr("daBcd", "ABC")) # expect True + import doctest + + doctest.testmod() diff --git a/dynamic_programming/bitmask.py b/dynamic_programming/bitmask.py index 213b22fe9051..5c1ed36cb42a 100644 --- a/dynamic_programming/bitmask.py +++ b/dynamic_programming/bitmask.py @@ -9,82 +9,82 @@ """ -from __future__ import print_function from collections import defaultdict class AssignmentUsingBitmask: - def __init__(self,task_performed,total): - - self.total_tasks = total #total no of tasks (N) - + def __init__(self, task_performed, total): + + self.total_tasks = total # total no of tasks (N) + # DP table will have a dimension of (2^M)*N # initially all values are set to -1 - self.dp = [[-1 for i in range(total+1)] for j in range(2**len(task_performed))] - - self.task = defaultdict(list) #stores the list of persons for each task - - #finalmask is used to check if all persons are included by setting all bits to 1 - self.finalmask = (1< self.total_tasks: return 0 - #if case already considered - if self.dp[mask][taskno]!=-1: + # if case already considered + if self.dp[mask][taskno] != -1: return self.dp[mask][taskno] # Number of ways when we dont this task in the arrangement - total_ways_util = self.CountWaysUtil(mask,taskno+1) + total_ways_util = self.CountWaysUtil(mask, taskno + 1) # now assign the tasks one by one to all possible persons and recursively assign for the remaining tasks. if taskno in self.task: for p in self.task[taskno]: - + # if p is already given a task - if mask & (1< int: + """ + LeetCdoe No.70: Climbing Stairs + Distinct ways to climb a n step staircase where + each time you can either climb 1 or 2 steps. + + Args: + n: number of steps of staircase + + Returns: + Distinct ways to climb a n step staircase + + Raises: + AssertionError: n not positive integer + + >>> climb_stairs(3) + 3 + >>> climb_stairs(1) + 1 + >>> climb_stairs(-7) # doctest: +ELLIPSIS + Traceback (most recent call last): + ... + AssertionError: n needs to be positive integer, your input -7 + """ + fmt = "n needs to be positive integer, your input {}" + assert isinstance(n, int) and n > 0, fmt.format(n) + if n == 1: + return 1 + dp = [0] * (n + 1) + dp[0], dp[1] = (1, 1) + for i in range(2, n + 1): + dp[i] = dp[i - 1] + dp[i - 2] + return dp[n] + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/dynamic_programming/coin_change.py b/dynamic_programming/coin_change.py index 74d86661f52d..a85d8e08dbb1 100644 --- a/dynamic_programming/coin_change.py +++ b/dynamic_programming/coin_change.py @@ -5,7 +5,6 @@ the given types of coins? https://www.hackerrank.com/challenges/coin-change/problem """ -from __future__ import print_function def dp_count(S, m, n): @@ -26,6 +25,6 @@ def dp_count(S, m, n): return table[n] -if __name__ == '__main__': +if __name__ == "__main__": print(dp_count([1, 2, 3], 3, 4)) # answer 4 print(dp_count([2, 5, 3, 6], 4, 10)) # answer 5 diff --git a/dynamic_programming/edit_distance.py b/dynamic_programming/edit_distance.py index 335e5196ed53..9df00eae96d7 100644 --- a/dynamic_programming/edit_distance.py +++ b/dynamic_programming/edit_distance.py @@ -7,7 +7,6 @@ The problem is : Given two strings A and B. Find the minimum number of operations to string B such that A = B. The permitted operations are removal, insertion, and substitution. """ -from __future__ import print_function class EditDistance: @@ -20,56 +19,85 @@ class EditDistance: def __init__(self): self.__prepare__() - def __prepare__(self, N = 0, M = 0): - self.dp = [[-1 for y in range(0,M)] for x in range(0,N)] + def __prepare__(self, N=0, M=0): + self.dp = [[-1 for y in range(0, M)] for x in range(0, N)] def __solveDP(self, x, y): - if (x==-1): - return y+1 - elif (y==-1): - return x+1 - elif (self.dp[x][y]>-1): + if x == -1: + return y + 1 + elif y == -1: + return x + 1 + elif self.dp[x][y] > -1: return self.dp[x][y] else: - if (self.A[x]==self.B[y]): - self.dp[x][y] = self.__solveDP(x-1,y-1) + if self.A[x] == self.B[y]: + self.dp[x][y] = self.__solveDP(x - 1, y - 1) else: - self.dp[x][y] = 1+min(self.__solveDP(x,y-1), self.__solveDP(x-1,y), self.__solveDP(x-1,y-1)) + self.dp[x][y] = 1 + min( + self.__solveDP(x, y - 1), + self.__solveDP(x - 1, y), + self.__solveDP(x - 1, y - 1), + ) return self.dp[x][y] def solve(self, A, B): - if isinstance(A,bytes): - A = A.decode('ascii') + if isinstance(A, bytes): + A = A.decode("ascii") - if isinstance(B,bytes): - B = B.decode('ascii') + if isinstance(B, bytes): + B = B.decode("ascii") self.A = str(A) self.B = str(B) self.__prepare__(len(A), len(B)) - return self.__solveDP(len(A)-1, len(B)-1) + return self.__solveDP(len(A) - 1, len(B) - 1) -if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - solver = EditDistance() +def min_distance_bottom_up(word1: str, word2: str) -> int: + """ + >>> min_distance_bottom_up("intention", "execution") + 5 + >>> min_distance_bottom_up("intention", "") + 9 + >>> min_distance_bottom_up("", "") + 0 + """ + m = len(word1) + n = len(word2) + dp = [[0 for _ in range(n + 1)] for _ in range(m + 1)] + for i in range(m + 1): + for j in range(n + 1): + + if i == 0: # first string is empty + dp[i][j] = j + elif j == 0: # second string is empty + dp[i][j] = i + elif ( + word1[i - 1] == word2[j - 1] + ): # last character of both substing is equal + dp[i][j] = dp[i - 1][j - 1] + else: + insert = dp[i][j - 1] + delete = dp[i - 1][j] + replace = dp[i - 1][j - 1] + dp[i][j] = 1 + min(insert, delete, replace) + return dp[m][n] + - print("****************** Testing Edit Distance DP Algorithm ******************") - print() +if __name__ == "__main__": + solver = EditDistance() - print("Enter the first string: ", end="") - S1 = raw_input().strip() + print("****************** Testing Edit Distance DP Algorithm ******************") + print() - print("Enter the second string: ", end="") - S2 = raw_input().strip() + S1 = input("Enter the first string: ").strip() + S2 = input("Enter the second string: ").strip() - print() - print("The minimum Edit Distance is: %d" % (solver.solve(S1, S2))) - print() - print("*************** End of Testing Edit Distance DP Algorithm ***************") + print() + print("The minimum Edit Distance is: %d" % (solver.solve(S1, S2))) + print("The minimum Edit Distance is: %d" % (min_distance_bottom_up(S1, S2))) + print() + print("*************** End of Testing Edit Distance DP Algorithm ***************") diff --git a/dynamic_programming/factorial.py b/dynamic_programming/factorial.py new file mode 100644 index 000000000000..0269014e7a18 --- /dev/null +++ b/dynamic_programming/factorial.py @@ -0,0 +1,38 @@ +# Factorial of a number using memoization +result = [-1] * 10 +result[0] = result[1] = 1 + + +def factorial(num): + """ + >>> factorial(7) + 5040 + >>> factorial(-1) + 'Number should not be negative.' + >>> [factorial(i) for i in range(5)] + [1, 1, 2, 6, 24] + """ + + if num < 0: + return "Number should not be negative." + if result[num] != -1: + return result[num] + else: + result[num] = num * factorial(num - 1) + # uncomment the following to see how recalculations are avoided + # print(result) + return result[num] + + +# factorial of num +# uncomment the following to see how recalculations are avoided +##result=[-1]*10 +##result[0]=result[1]=1 +##print(factorial(5)) +# print(factorial(3)) +# print(factorial(7)) + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/dynamic_programming/fastfibonacci.py b/dynamic_programming/fast_fibonacci.py similarity index 96% rename from dynamic_programming/fastfibonacci.py rename to dynamic_programming/fast_fibonacci.py index cbc118467b3c..47248078bd81 100644 --- a/dynamic_programming/fastfibonacci.py +++ b/dynamic_programming/fast_fibonacci.py @@ -5,7 +5,6 @@ This program calculates the nth Fibonacci number in O(log(n)). It's possible to calculate F(1000000) in less than a second. """ -from __future__ import print_function import sys diff --git a/dynamic_programming/fibonacci.py b/dynamic_programming/fibonacci.py index b453ce255853..923560b54d30 100644 --- a/dynamic_programming/fibonacci.py +++ b/dynamic_programming/fibonacci.py @@ -1,11 +1,9 @@ """ This is a pure Python implementation of Dynamic Programming solution to the fibonacci sequence problem. """ -from __future__ import print_function class Fibonacci: - def __init__(self, N=None): self.fib_array = [] if N: @@ -16,34 +14,42 @@ def __init__(self, N=None): self.fib_array.append(self.fib_array[i - 1] + self.fib_array[i - 2]) elif N == 0: self.fib_array.append(0) + print(self.fib_array) def get(self, sequence_no=None): + """ + >>> Fibonacci(5).get(3) + [0, 1, 1, 2, 3, 5] + [0, 1, 1, 2] + >>> Fibonacci(5).get(6) + [0, 1, 1, 2, 3, 5] + Out of bound. + >>> Fibonacci(5).get(-1) + [0, 1, 1, 2, 3, 5] + [] + """ if sequence_no != None: if sequence_no < len(self.fib_array): - return print(self.fib_array[:sequence_no + 1]) + return print(self.fib_array[: sequence_no + 1]) else: print("Out of bound.") else: print("Please specify a value") -if __name__ == '__main__': +if __name__ == "__main__": print("\n********* Fibonacci Series Using Dynamic Programming ************\n") - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - print("\n Enter the upper limit for the fibonacci sequence: ", end="") try: - N = eval(raw_input().strip()) + N = int(input().strip()) fib = Fibonacci(N) print( - "\n********* Enter different values to get the corresponding fibonacci sequence, enter any negative number to exit. ************\n") + "\n********* Enter different values to get the corresponding fibonacci " + "sequence, enter any negative number to exit. ************\n" + ) while True: - print("Enter value: ", end=" ") try: - i = eval(raw_input().strip()) + i = int(input("Enter value: ").strip()) if i < 0: print("\n********* Good Bye!! ************\n") break @@ -52,3 +58,7 @@ def get(self, sequence_no=None): print("\nInvalid input, please try again.") except NameError: print("\n********* Invalid input, good bye!! ************\n") + + import doctest + + doctest.testmod() diff --git a/dynamic_programming/floyd_warshall.py b/dynamic_programming/floyd_warshall.py index 038499ca03b6..a4b6c6a82568 100644 --- a/dynamic_programming/floyd_warshall.py +++ b/dynamic_programming/floyd_warshall.py @@ -1,37 +1,42 @@ import math + class Graph: - - def __init__(self, N = 0): # a graph with Node 0,1,...,N-1 + def __init__(self, N=0): # a graph with Node 0,1,...,N-1 self.N = N - self.W = [[math.inf for j in range(0,N)] for i in range(0,N)] # adjacency matrix for weight - self.dp = [[math.inf for j in range(0,N)] for i in range(0,N)] # dp[i][j] stores minimum distance from i to j + self.W = [ + [math.inf for j in range(0, N)] for i in range(0, N) + ] # adjacency matrix for weight + self.dp = [ + [math.inf for j in range(0, N)] for i in range(0, N) + ] # dp[i][j] stores minimum distance from i to j def addEdge(self, u, v, w): self.dp[u][v] = w def floyd_warshall(self): - for k in range(0,self.N): - for i in range(0,self.N): - for j in range(0,self.N): + for k in range(0, self.N): + for i in range(0, self.N): + for j in range(0, self.N): self.dp[i][j] = min(self.dp[i][j], self.dp[i][k] + self.dp[k][j]) def showMin(self, u, v): return self.dp[u][v] - -if __name__ == '__main__': + + +if __name__ == "__main__": graph = Graph(5) - graph.addEdge(0,2,9) - graph.addEdge(0,4,10) - graph.addEdge(1,3,5) - graph.addEdge(2,3,7) - graph.addEdge(3,0,10) - graph.addEdge(3,1,2) - graph.addEdge(3,2,1) - graph.addEdge(3,4,6) - graph.addEdge(4,1,3) - graph.addEdge(4,2,4) - graph.addEdge(4,3,9) + graph.addEdge(0, 2, 9) + graph.addEdge(0, 4, 10) + graph.addEdge(1, 3, 5) + graph.addEdge(2, 3, 7) + graph.addEdge(3, 0, 10) + graph.addEdge(3, 1, 2) + graph.addEdge(3, 2, 1) + graph.addEdge(3, 4, 6) + graph.addEdge(4, 1, 3) + graph.addEdge(4, 2, 4) + graph.addEdge(4, 3, 9) graph.floyd_warshall() - graph.showMin(1,4) - graph.showMin(0,3) + graph.showMin(1, 4) + graph.showMin(0, 3) diff --git a/dynamic_programming/fractional_knapsack.py b/dynamic_programming/fractional_knapsack.py new file mode 100644 index 000000000000..15210146bf66 --- /dev/null +++ b/dynamic_programming/fractional_knapsack.py @@ -0,0 +1,27 @@ +from itertools import accumulate +from bisect import bisect + + +def fracKnapsack(vl, wt, W, n): + """ + >>> fracKnapsack([60, 100, 120], [10, 20, 30], 50, 3) + 240.0 + """ + + r = list(sorted(zip(vl, wt), key=lambda x: x[0] / x[1], reverse=True)) + vl, wt = [i[0] for i in r], [i[1] for i in r] + acc = list(accumulate(wt)) + k = bisect(acc, W) + return ( + 0 + if k == 0 + else sum(vl[:k]) + (W - acc[k - 1]) * (vl[k]) / (wt[k]) + if k != n + else sum(vl[:k]) + ) + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/dynamic_programming/integer_partition.py b/dynamic_programming/integer_partition.py index 7b27afebaa6c..ec8c5bf62d7d 100644 --- a/dynamic_programming/integer_partition.py +++ b/dynamic_programming/integer_partition.py @@ -1,45 +1,36 @@ -from __future__ import print_function - -try: - xrange #Python 2 -except NameError: - xrange = range #Python 3 - -try: - raw_input #Python 2 -except NameError: - raw_input = input #Python 3 - -''' +""" The number of partitions of a number n into at least k parts equals the number of partitions into exactly k parts plus the number of partitions into at least k-1 parts. Subtracting 1 from each part of a partition of n into k parts gives a partition of n-k into k parts. These two facts together are used for this algorithm. -''' +""" + + def partition(m): - memo = [[0 for _ in xrange(m)] for _ in xrange(m+1)] - for i in xrange(m+1): - memo[i][0] = 1 + memo = [[0 for _ in range(m)] for _ in range(m + 1)] + for i in range(m + 1): + memo[i][0] = 1 + + for n in range(m + 1): + for k in range(1, m): + memo[n][k] += memo[n][k - 1] + if n - k > 0: + memo[n][k] += memo[n - k - 1][k] - for n in xrange(m+1): - for k in xrange(1, m): - memo[n][k] += memo[n][k-1] - if n-k > 0: - memo[n][k] += memo[n-k-1][k] + return memo[m][m - 1] - return memo[m][m-1] -if __name__ == '__main__': - import sys +if __name__ == "__main__": + import sys - if len(sys.argv) == 1: - try: - n = int(raw_input('Enter a number: ')) - print(partition(n)) - except ValueError: - print('Please enter a number.') - else: - try: - n = int(sys.argv[1]) - print(partition(n)) - except ValueError: - print('Please pass a number.') \ No newline at end of file + if len(sys.argv) == 1: + try: + n = int(input("Enter a number: ").strip()) + print(partition(n)) + except ValueError: + print("Please enter a number.") + else: + try: + n = int(sys.argv[1]) + print(partition(n)) + except ValueError: + print("Please pass a number.") diff --git a/dynamic_programming/k_means_clustering_tensorflow.py b/dynamic_programming/k_means_clustering_tensorflow.py index b6813c6a22b3..6b1eb628e5c3 100644 --- a/dynamic_programming/k_means_clustering_tensorflow.py +++ b/dynamic_programming/k_means_clustering_tensorflow.py @@ -14,24 +14,24 @@ def TFKMeansCluster(vectors, noofclusters): noofclusters = int(noofclusters) assert noofclusters < len(vectors) - #Find out the dimensionality + # Find out the dimensionality dim = len(vectors[0]) - #Will help select random centroids from among the available vectors + # Will help select random centroids from among the available vectors vector_indices = list(range(len(vectors))) shuffle(vector_indices) - #GRAPH OF COMPUTATION - #We initialize a new graph and set it as the default during each run - #of this algorithm. This ensures that as this function is called - #multiple times, the default graph doesn't keep getting crowded with - #unused ops and Variables from previous function calls. + # GRAPH OF COMPUTATION + # We initialize a new graph and set it as the default during each run + # of this algorithm. This ensures that as this function is called + # multiple times, the default graph doesn't keep getting crowded with + # unused ops and Variables from previous function calls. graph = tf.Graph() with graph.as_default(): - #SESSION OF COMPUTATION + # SESSION OF COMPUTATION sess = tf.Session() @@ -39,8 +39,9 @@ def TFKMeansCluster(vectors, noofclusters): ##First lets ensure we have a Variable vector for each centroid, ##initialized to one of the vectors from the available data points - centroids = [tf.Variable((vectors[vector_indices[i]])) - for i in range(noofclusters)] + centroids = [ + tf.Variable((vectors[vector_indices[i]])) for i in range(noofclusters) + ] ##These nodes will assign the centroid Variables the appropriate ##values centroid_value = tf.placeholder("float64", [dim]) @@ -56,26 +57,24 @@ def TFKMeansCluster(vectors, noofclusters): assignment_value = tf.placeholder("int32") cluster_assigns = [] for assignment in assignments: - cluster_assigns.append(tf.assign(assignment, - assignment_value)) + cluster_assigns.append(tf.assign(assignment, assignment_value)) ##Now lets construct the node that will compute the mean - #The placeholder for the input + # The placeholder for the input mean_input = tf.placeholder("float", [None, dim]) - #The Node/op takes the input and computes a mean along the 0th - #dimension, i.e. the list of input vectors + # The Node/op takes the input and computes a mean along the 0th + # dimension, i.e. the list of input vectors mean_op = tf.reduce_mean(mean_input, 0) ##Node for computing Euclidean distances - #Placeholders for input + # Placeholders for input v1 = tf.placeholder("float", [dim]) v2 = tf.placeholder("float", [dim]) - euclid_dist = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub( - v1, v2), 2))) + euclid_dist = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(v1, v2), 2))) ##This node will figure out which cluster to assign a vector to, ##based on Euclidean distances of the vector from the centroids. - #Placeholder for input + # Placeholder for input centroid_distances = tf.placeholder("float", [noofclusters]) cluster_assignment = tf.argmin(centroid_distances, 0) @@ -87,55 +86,62 @@ def TFKMeansCluster(vectors, noofclusters): ##will be included in the initialization. init_op = tf.initialize_all_variables() - #Initialize all variables + # Initialize all variables sess.run(init_op) ##CLUSTERING ITERATIONS - #Now perform the Expectation-Maximization steps of K-Means clustering - #iterations. To keep things simple, we will only do a set number of - #iterations, instead of using a Stopping Criterion. + # Now perform the Expectation-Maximization steps of K-Means clustering + # iterations. To keep things simple, we will only do a set number of + # iterations, instead of using a Stopping Criterion. noofiterations = 100 for iteration_n in range(noofiterations): ##EXPECTATION STEP ##Based on the centroid locations till last iteration, compute ##the _expected_ centroid assignments. - #Iterate over each vector + # Iterate over each vector for vector_n in range(len(vectors)): vect = vectors[vector_n] - #Compute Euclidean distance between this vector and each - #centroid. Remember that this list cannot be named + # Compute Euclidean distance between this vector and each + # centroid. Remember that this list cannot be named #'centroid_distances', since that is the input to the - #cluster assignment node. - distances = [sess.run(euclid_dist, feed_dict={ - v1: vect, v2: sess.run(centroid)}) - for centroid in centroids] - #Now use the cluster assignment node, with the distances - #as the input - assignment = sess.run(cluster_assignment, feed_dict = { - centroid_distances: distances}) - #Now assign the value to the appropriate state variable - sess.run(cluster_assigns[vector_n], feed_dict={ - assignment_value: assignment}) + # cluster assignment node. + distances = [ + sess.run(euclid_dist, feed_dict={v1: vect, v2: sess.run(centroid)}) + for centroid in centroids + ] + # Now use the cluster assignment node, with the distances + # as the input + assignment = sess.run( + cluster_assignment, feed_dict={centroid_distances: distances} + ) + # Now assign the value to the appropriate state variable + sess.run( + cluster_assigns[vector_n], feed_dict={assignment_value: assignment} + ) ##MAXIMIZATION STEP - #Based on the expected state computed from the Expectation Step, - #compute the locations of the centroids so as to maximize the - #overall objective of minimizing within-cluster Sum-of-Squares + # Based on the expected state computed from the Expectation Step, + # compute the locations of the centroids so as to maximize the + # overall objective of minimizing within-cluster Sum-of-Squares for cluster_n in range(noofclusters): - #Collect all the vectors assigned to this cluster - assigned_vects = [vectors[i] for i in range(len(vectors)) - if sess.run(assignments[i]) == cluster_n] - #Compute new centroid location - new_location = sess.run(mean_op, feed_dict={ - mean_input: array(assigned_vects)}) - #Assign value to appropriate variable - sess.run(cent_assigns[cluster_n], feed_dict={ - centroid_value: new_location}) - - #Return centroids and assignments + # Collect all the vectors assigned to this cluster + assigned_vects = [ + vectors[i] + for i in range(len(vectors)) + if sess.run(assignments[i]) == cluster_n + ] + # Compute new centroid location + new_location = sess.run( + mean_op, feed_dict={mean_input: array(assigned_vects)} + ) + # Assign value to appropriate variable + sess.run( + cent_assigns[cluster_n], feed_dict={centroid_value: new_location} + ) + + # Return centroids and assignments centroids = sess.run(centroids) assignments = sess.run(assignments) return centroids, assignments - diff --git a/dynamic_programming/knapsack.py b/dynamic_programming/knapsack.py index 27d1cfed799b..e71e3892e8cc 100644 --- a/dynamic_programming/knapsack.py +++ b/dynamic_programming/knapsack.py @@ -1,42 +1,150 @@ """ -Given weights and values of n items, put these items in a knapsack of capacity W to get the maximum total value in the knapsack. +Given weights and values of n items, put these items in a knapsack of + capacity W to get the maximum total value in the knapsack. + +Note that only the integer weights 0-1 knapsack problem is solvable + using dynamic programming. """ -def MF_knapsack(i,wt,val,j): - ''' + + +def MF_knapsack(i, wt, val, j): + """ This code involves the concept of memory functions. Here we solve the subproblems which are needed unlike the below example F is a 2D array with -1s filled up - ''' + """ global F # a global dp table for knapsack if F[i][j] < 0: if j < wt[i - 1]: - val = MF_knapsack(i - 1,wt,val,j) + val = MF_knapsack(i - 1, wt, val, j) else: - val = max(MF_knapsack(i - 1,wt,val,j),MF_knapsack(i - 1,wt,val,j - wt[i - 1]) + val[i - 1]) + val = max( + MF_knapsack(i - 1, wt, val, j), + MF_knapsack(i - 1, wt, val, j - wt[i - 1]) + val[i - 1], + ) F[i][j] = val return F[i][j] + def knapsack(W, wt, val, n): - dp = [[0 for i in range(W+1)]for j in range(n+1)] + dp = [[0 for i in range(W + 1)] for j in range(n + 1)] - for i in range(1,n+1): - for w in range(1,W+1): - if(wt[i-1]<=w): - dp[i][w] = max(val[i-1]+dp[i-1][w-wt[i-1]],dp[i-1][w]) + for i in range(1, n + 1): + for w in range(1, W + 1): + if wt[i - 1] <= w: + dp[i][w] = max(val[i - 1] + dp[i - 1][w - wt[i - 1]], dp[i - 1][w]) else: - dp[i][w] = dp[i-1][w] + dp[i][w] = dp[i - 1][w] + + return dp[n][W], dp + + +def knapsack_with_example_solution(W: int, wt: list, val: list): + """ + Solves the integer weights knapsack problem returns one of + the several possible optimal subsets. + + Parameters + --------- + + W: int, the total maximum weight for the given knapsack problem. + wt: list, the vector of weights for all items where wt[i] is the weight + of the ith item. + val: list, the vector of values for all items where val[i] is the value + of te ith item + + Returns + ------- + optimal_val: float, the optimal value for the given knapsack problem + example_optional_set: set, the indices of one of the optimal subsets + which gave rise to the optimal value. + + Examples + ------- + >>> knapsack_with_example_solution(10, [1, 3, 5, 2], [10, 20, 100, 22]) + (142, {2, 3, 4}) + >>> knapsack_with_example_solution(6, [4, 3, 2, 3], [3, 2, 4, 4]) + (8, {3, 4}) + >>> knapsack_with_example_solution(6, [4, 3, 2, 3], [3, 2, 4]) + Traceback (most recent call last): + ... + ValueError: The number of weights must be the same as the number of values. + But got 4 weights and 3 values + """ + if not (isinstance(wt, (list, tuple)) and isinstance(val, (list, tuple))): + raise ValueError( + "Both the weights and values vectors must be either lists or tuples" + ) + + num_items = len(wt) + if num_items != len(val): + raise ValueError( + "The number of weights must be the " + "same as the number of values.\nBut " + "got {} weights and {} values".format(num_items, len(val)) + ) + for i in range(num_items): + if not isinstance(wt[i], int): + raise TypeError( + "All weights must be integers but " + "got weight of type {} at index {}".format(type(wt[i]), i) + ) - return dp[n][w] + optimal_val, dp_table = knapsack(W, wt, val, num_items) + example_optional_set = set() + _construct_solution(dp_table, wt, num_items, W, example_optional_set) -if __name__ == '__main__': - ''' + return optimal_val, example_optional_set + + +def _construct_solution(dp: list, wt: list, i: int, j: int, optimal_set: set): + """ + Recursively reconstructs one of the optimal subsets given + a filled DP table and the vector of weights + + Parameters + --------- + + dp: list of list, the table of a solved integer weight dynamic programming problem + + wt: list or tuple, the vector of weights of the items + i: int, the index of the item under consideration + j: int, the current possible maximum weight + optimal_set: set, the optimal subset so far. This gets modified by the function. + + Returns + ------- + None + + """ + # for the current item i at a maximum weight j to be part of an optimal subset, + # the optimal value at (i, j) must be greater than the optimal value at (i-1, j). + # where i - 1 means considering only the previous items at the given maximum weight + if i > 0 and j > 0: + if dp[i - 1][j] == dp[i][j]: + _construct_solution(dp, wt, i - 1, j, optimal_set) + else: + optimal_set.add(i) + _construct_solution(dp, wt, i - 1, j - wt[i - 1], optimal_set) + + +if __name__ == "__main__": + """ Adding test case for knapsack - ''' - val = [3,2,4,4] - wt = [4,3,2,3] + """ + val = [3, 2, 4, 4] + wt = [4, 3, 2, 3] n = 4 w = 6 - F = [[0]*(w + 1)] + [[0] + [-1 for i in range(w + 1)] for j in range(n + 1)] - print(knapsack(w,wt,val,n)) - print(MF_knapsack(n,wt,val,w)) # switched the n and w - + F = [[0] * (w + 1)] + [[0] + [-1 for i in range(w + 1)] for j in range(n + 1)] + optimal_solution, _ = knapsack(w, wt, val, n) + print(optimal_solution) + print(MF_knapsack(n, wt, val, w)) # switched the n and w + + # testing the dynamic programming problem with example + # the optimal subset for the above example are items 3 and 4 + optimal_solution, optimal_subset = knapsack_with_example_solution(w, wt, val) + assert optimal_solution == 8 + assert optimal_subset == {3, 4} + print("optimal_value = ", optimal_solution) + print("An optimal subset corresponding to the optimal value", optimal_subset) diff --git a/dynamic_programming/longest_common_subsequence.py b/dynamic_programming/longest_common_subsequence.py index 0a4771cb2efd..a7206b221d96 100644 --- a/dynamic_programming/longest_common_subsequence.py +++ b/dynamic_programming/longest_common_subsequence.py @@ -1,37 +1,82 @@ """ LCS Problem Statement: Given two sequences, find the length of longest subsequence present in both of them. -A subsequence is a sequence that appears in the same relative order, but not necessarily continious. +A subsequence is a sequence that appears in the same relative order, but not necessarily continuous. Example:"abc", "abg" are subsequences of "abcdefgh". """ -from __future__ import print_function -try: - xrange # Python 2 -except NameError: - xrange = range # Python 3 -def lcs_dp(x, y): +def longest_common_subsequence(x: str, y: str): + """ + Finds the longest common subsequence between two strings. Also returns the + The subsequence found + + Parameters + ---------- + + x: str, one of the strings + y: str, the other string + + Returns + ------- + L[m][n]: int, the length of the longest subsequence. Also equal to len(seq) + Seq: str, the subsequence found + + >>> longest_common_subsequence("programming", "gaming") + (6, 'gaming') + >>> longest_common_subsequence("physics", "smartphone") + (2, 'ph') + >>> longest_common_subsequence("computer", "food") + (1, 'o') + """ # find the length of strings + + assert x is not None + assert y is not None + m = len(x) n = len(y) # declaring the array for storing the dp values - L = [[None] * (n + 1) for i in xrange(m + 1)] - seq = [] - - for i in range(m + 1): - for j in range(n + 1): - if i == 0 or j == 0: - L[i][j] = 0 - elif x[i - 1] == y[ j - 1]: - L[i][j] = L[i - 1][j - 1] + 1 - seq.append(x[i -1]) + L = [[0] * (n + 1) for _ in range(m + 1)] + + for i in range(1, m + 1): + for j in range(1, n + 1): + if x[i - 1] == y[j - 1]: + match = 1 else: - L[i][j] = max(L[i - 1][j], L[i][j - 1]) - # L[m][n] contains the length of LCS of X[0..n-1] & Y[0..m-1] + match = 0 + + L[i][j] = max(L[i - 1][j], L[i][j - 1], L[i - 1][j - 1] + match) + + seq = "" + i, j = m, n + while i > 0 and j > 0: + if x[i - 1] == y[j - 1]: + match = 1 + else: + match = 0 + + if L[i][j] == L[i - 1][j - 1] + match: + if match == 1: + seq = x[i - 1] + seq + i -= 1 + j -= 1 + elif L[i][j] == L[i - 1][j]: + i -= 1 + else: + j -= 1 + return L[m][n], seq -if __name__=='__main__': - x = 'AGGTAB' - y = 'GXTXAYB' - print(lcs_dp(x, y)) + +if __name__ == "__main__": + a = "AGGTAB" + b = "GXTXAYB" + expected_ln = 4 + expected_subseq = "GTAB" + + ln, subseq = longest_common_subsequence(a, b) + ## print("len =", ln, ", sub-sequence =", subseq) + import doctest + + doctest.testmod() diff --git a/dynamic_programming/longest_increasing_subsequence.py b/dynamic_programming/longest_increasing_subsequence.py index b6d165909e70..6d12f1c7caf0 100644 --- a/dynamic_programming/longest_increasing_subsequence.py +++ b/dynamic_programming/longest_increasing_subsequence.py @@ -1,42 +1,55 @@ -''' +""" Author : Mehdi ALAOUI This is a pure Python implementation of Dynamic Programming solution to the longest increasing subsequence of a given sequence. The problem is : -Given an ARRAY, to find the longest and increasing sub ARRAY in that given ARRAY and return it. +Given an array, to find the longest and increasing sub-array in that given array and return it. Example: [10, 22, 9, 33, 21, 50, 41, 60, 80] as input will return [10, 22, 33, 41, 60, 80] as output -''' -from __future__ import print_function +""" +from typing import List -def longestSub(ARRAY): #This function is recursive - - ARRAY_LENGTH = len(ARRAY) - if(ARRAY_LENGTH <= 1): #If the array contains only one element, we return it (it's the stop condition of recursion) - return ARRAY - #Else - PIVOT=ARRAY[0] - isFound=False - i=1 - LONGEST_SUB=[] - while(not isFound and i= ARRAY[i] ] - TEMPORARY_ARRAY = longestSub(TEMPORARY_ARRAY) - if ( len(TEMPORARY_ARRAY) > len(LONGEST_SUB) ): - LONGEST_SUB = TEMPORARY_ARRAY - else: - i+=1 - TEMPORARY_ARRAY = [ element for element in ARRAY[1:] if element >= PIVOT ] - TEMPORARY_ARRAY = [PIVOT] + longestSub(TEMPORARY_ARRAY) - if ( len(TEMPORARY_ARRAY) > len(LONGEST_SUB) ): - return TEMPORARY_ARRAY - else: - return LONGEST_SUB +def longest_subsequence(array: List[int]) -> List[int]: # This function is recursive + """ + Some examples + >>> longest_subsequence([10, 22, 9, 33, 21, 50, 41, 60, 80]) + [10, 22, 33, 41, 60, 80] + >>> longest_subsequence([4, 8, 7, 5, 1, 12, 2, 3, 9]) + [1, 2, 3, 9] + >>> longest_subsequence([9, 8, 7, 6, 5, 7]) + [8] + >>> longest_subsequence([1, 1, 1]) + [1, 1, 1] + """ + array_length = len(array) + if ( + array_length <= 1 + ): # If the array contains only one element, we return it (it's the stop condition of recursion) + return array + # Else + pivot = array[0] + isFound = False + i = 1 + longest_subseq = [] + while not isFound and i < array_length: + if array[i] < pivot: + isFound = True + temp_array = [element for element in array[i:] if element >= array[i]] + temp_array = longest_subsequence(temp_array) + if len(temp_array) > len(longest_subseq): + longest_subseq = temp_array + else: + i += 1 -#Some examples + temp_array = [element for element in array[1:] if element >= pivot] + temp_array = [pivot] + longest_subsequence(temp_array) + if len(temp_array) > len(longest_subseq): + return temp_array + else: + return longest_subseq + -print(longestSub([4,8,7,5,1,12,2,3,9])) -print(longestSub([9,8,7,6,5,7])) \ No newline at end of file +if __name__ == "__main__": + import doctest + doctest.testmod() diff --git a/dynamic_programming/longest_increasing_subsequence_O(nlogn).py b/dynamic_programming/longest_increasing_subsequence_O(nlogn).py deleted file mode 100644 index 21122a04d69f..000000000000 --- a/dynamic_programming/longest_increasing_subsequence_O(nlogn).py +++ /dev/null @@ -1,41 +0,0 @@ -from __future__ import print_function -############################# -# Author: Aravind Kashyap -# File: lis.py -# comments: This programme outputs the Longest Strictly Increasing Subsequence in O(NLogN) -# Where N is the Number of elements in the list -############################# -def CeilIndex(v,l,r,key): - while r-l > 1: - m = (l + r)/2 - if v[m] >= key: - r = m - else: - l = m - - return r - - -def LongestIncreasingSubsequenceLength(v): - if(len(v) == 0): - return 0 - - tail = [0]*len(v) - length = 1 - - tail[0] = v[0] - - for i in range(1,len(v)): - if v[i] < tail[0]: - tail[0] = v[i] - elif v[i] > tail[length-1]: - tail[length] = v[i] - length += 1 - else: - tail[CeilIndex(tail,-1,length-1,v[i])] = v[i] - - return length - - -v = [2, 5, 3, 7, 11, 8, 10, 13, 6] -print(LongestIncreasingSubsequenceLength(v)) diff --git a/dynamic_programming/longest_increasing_subsequence_o(nlogn).py b/dynamic_programming/longest_increasing_subsequence_o(nlogn).py new file mode 100644 index 000000000000..4b06e0d885f2 --- /dev/null +++ b/dynamic_programming/longest_increasing_subsequence_o(nlogn).py @@ -0,0 +1,52 @@ +############################# +# Author: Aravind Kashyap +# File: lis.py +# comments: This programme outputs the Longest Strictly Increasing Subsequence in O(NLogN) +# Where N is the Number of elements in the list +############################# +from typing import List + +def CeilIndex(v, l, r, key): + while r - l > 1: + m = (l + r) // 2 + if v[m] >= key: + r = m + else: + l = m + return r + + +def LongestIncreasingSubsequenceLength(v: List[int]) -> int: + """ + >>> LongestIncreasingSubsequenceLength([2, 5, 3, 7, 11, 8, 10, 13, 6]) + 6 + >>> LongestIncreasingSubsequenceLength([]) + 0 + >>> LongestIncreasingSubsequenceLength([0, 8, 4, 12, 2, 10, 6, 14, 1, 9, 5, 13, 3, 11, 7, 15]) + 6 + >>> LongestIncreasingSubsequenceLength([5, 4, 3, 2, 1]) + 1 + """ + if len(v) == 0: + return 0 + + tail = [0] * len(v) + length = 1 + + tail[0] = v[0] + + for i in range(1, len(v)): + if v[i] < tail[0]: + tail[0] = v[i] + elif v[i] > tail[length - 1]: + tail[length] = v[i] + length += 1 + else: + tail[CeilIndex(tail, -1, length - 1, v[i])] = v[i] + + return length + + +if __name__ == "__main__": + import doctest + doctest.testmod() diff --git a/dynamic_programming/longest_sub_array.py b/dynamic_programming/longest_sub_array.py index de2c88a8b525..65ce151c33d6 100644 --- a/dynamic_programming/longest_sub_array.py +++ b/dynamic_programming/longest_sub_array.py @@ -1,33 +1,32 @@ -''' +""" Auther : Yvonne This is a pure Python implementation of Dynamic Programming solution to the longest_sub_array problem. The problem is : Given an array, to find the longest and continuous sub array and get the max sum of the sub array in the given array. -''' -from __future__ import print_function +""" class SubArray: - def __init__(self, arr): # we need a list not a string, so do something to change the type - self.array = arr.split(',') + self.array = arr.split(",") print(("the input array is:", self.array)) def solve_sub_array(self): - rear = [int(self.array[0])]*len(self.array) - sum_value = [int(self.array[0])]*len(self.array) + rear = [int(self.array[0])] * len(self.array) + sum_value = [int(self.array[0])] * len(self.array) for i in range(1, len(self.array)): - sum_value[i] = max(int(self.array[i]) + sum_value[i-1], int(self.array[i])) - rear[i] = max(sum_value[i], rear[i-1]) - return rear[len(self.array)-1] + sum_value[i] = max( + int(self.array[i]) + sum_value[i - 1], int(self.array[i]) + ) + rear[i] = max(sum_value[i], rear[i - 1]) + return rear[len(self.array) - 1] -if __name__ == '__main__': +if __name__ == "__main__": whole_array = input("please input some numbers:") array = SubArray(whole_array) re = array.solve_sub_array() print(("the results is:", re)) - diff --git a/dynamic_programming/matrix_chain_order.py b/dynamic_programming/matrix_chain_order.py index b8234a65acbe..f88a9be8ac95 100644 --- a/dynamic_programming/matrix_chain_order.py +++ b/dynamic_programming/matrix_chain_order.py @@ -1,46 +1,54 @@ -from __future__ import print_function - import sys -''' + +""" Dynamic Programming Implementation of Matrix Chain Multiplication Time Complexity: O(n^3) Space Complexity: O(n^2) -''' +""" + + def MatrixChainOrder(array): - N=len(array) - Matrix=[[0 for x in range(N)] for x in range(N)] - Sol=[[0 for x in range(N)] for x in range(N)] + N = len(array) + Matrix = [[0 for x in range(N)] for x in range(N)] + Sol = [[0 for x in range(N)] for x in range(N)] - for ChainLength in range(2,N): - for a in range(1,N-ChainLength+1): - b = a+ChainLength-1 + for ChainLength in range(2, N): + for a in range(1, N - ChainLength + 1): + b = a + ChainLength - 1 Matrix[a][b] = sys.maxsize - for c in range(a , b): - cost = Matrix[a][c] + Matrix[c+1][b] + array[a-1]*array[c]*array[b] + for c in range(a, b): + cost = ( + Matrix[a][c] + Matrix[c + 1][b] + array[a - 1] * array[c] * array[b] + ) if cost < Matrix[a][b]: Matrix[a][b] = cost Sol[a][b] = c - return Matrix , Sol -#Print order of matrix with Ai as Matrix -def PrintOptimalSolution(OptimalSolution,i,j): - if i==j: - print("A" + str(i),end = " ") + return Matrix, Sol + + +# Print order of matrix with Ai as Matrix +def PrintOptimalSolution(OptimalSolution, i, j): + if i == j: + print("A" + str(i), end=" ") else: - print("(",end = " ") - PrintOptimalSolution(OptimalSolution,i,OptimalSolution[i][j]) - PrintOptimalSolution(OptimalSolution,OptimalSolution[i][j]+1,j) - print(")",end = " ") + print("(", end=" ") + PrintOptimalSolution(OptimalSolution, i, OptimalSolution[i][j]) + PrintOptimalSolution(OptimalSolution, OptimalSolution[i][j] + 1, j) + print(")", end=" ") + def main(): - array=[30,35,15,5,10,20,25] - n=len(array) - #Size of matrix created from above array will be + array = [30, 35, 15, 5, 10, 20, 25] + n = len(array) + # Size of matrix created from above array will be # 30*35 35*15 15*5 5*10 10*20 20*25 - Matrix , OptimalSolution = MatrixChainOrder(array) + Matrix, OptimalSolution = MatrixChainOrder(array) + + print("No. of Operation required: " + str((Matrix[1][n - 1]))) + PrintOptimalSolution(OptimalSolution, 1, n - 1) + - print("No. of Operation required: "+str((Matrix[1][n-1]))) - PrintOptimalSolution(OptimalSolution,1,n-1) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/dynamic_programming/max_sub_array.py b/dynamic_programming/max_sub_array.py index 5d48882427c0..f7c8209718ef 100644 --- a/dynamic_programming/max_sub_array.py +++ b/dynamic_programming/max_sub_array.py @@ -1,60 +1,92 @@ """ author : Mayank Kumar Jha (mk9440) """ -from __future__ import print_function - -import time -import matplotlib.pyplot as plt -from random import randint -def find_max_sub_array(A,low,high): - if low==high: - return low,high,A[low] - else : - mid=(low+high)//2 - left_low,left_high,left_sum=find_max_sub_array(A,low,mid) - right_low,right_high,right_sum=find_max_sub_array(A,mid+1,high) - cross_left,cross_right,cross_sum=find_max_cross_sum(A,low,mid,high) - if left_sum>=right_sum and left_sum>=cross_sum: - return left_low,left_high,left_sum - elif right_sum>=left_sum and right_sum>=cross_sum : - return right_low,right_high,right_sum +from typing import List + + +def find_max_sub_array(A, low, high): + if low == high: + return low, high, A[low] + else: + mid = (low + high) // 2 + left_low, left_high, left_sum = find_max_sub_array(A, low, mid) + right_low, right_high, right_sum = find_max_sub_array(A, mid + 1, high) + cross_left, cross_right, cross_sum = find_max_cross_sum(A, low, mid, high) + if left_sum >= right_sum and left_sum >= cross_sum: + return left_low, left_high, left_sum + elif right_sum >= left_sum and right_sum >= cross_sum: + return right_low, right_high, right_sum else: - return cross_left,cross_right,cross_sum - -def find_max_cross_sum(A,low,mid,high): - left_sum,max_left=-999999999,-1 - right_sum,max_right=-999999999,-1 - summ=0 - for i in range(mid,low-1,-1): - summ+=A[i] + return cross_left, cross_right, cross_sum + + +def find_max_cross_sum(A, low, mid, high): + left_sum, max_left = -999999999, -1 + right_sum, max_right = -999999999, -1 + summ = 0 + for i in range(mid, low - 1, -1): + summ += A[i] if summ > left_sum: - left_sum=summ - max_left=i - summ=0 - for i in range(mid+1,high+1): - summ+=A[i] + left_sum = summ + max_left = i + summ = 0 + for i in range(mid + 1, high + 1): + summ += A[i] if summ > right_sum: - right_sum=summ - max_right=i - return max_left,max_right,(left_sum+right_sum) - + right_sum = summ + max_right = i + return max_left, max_right, (left_sum + right_sum) -if __name__=='__main__': - inputs=[10,100,1000,10000,50000,100000,200000,300000,400000,500000] - tim=[] - for i in inputs: - li=[randint(1,i) for j in range(i)] - strt=time.time() - (find_max_sub_array(li,0,len(li)-1)) - end=time.time() - tim.append(end-strt) - print("No of Inputs Time Taken") - for i in range(len(inputs)): - print(inputs[i],'\t\t',tim[i]) - plt.plot(inputs,tim) - plt.xlabel("Number of Inputs");plt.ylabel("Time taken in seconds ") - plt.show() +def max_sub_array(nums: List[int]) -> int: + """ + Finds the contiguous subarray which has the largest sum and return its sum. + + >>> max_sub_array([-2, 1, -3, 4, -1, 2, 1, -5, 4]) + 6 + + An empty (sub)array has sum 0. + >>> max_sub_array([]) + 0 + + If all elements are negative, the largest subarray would be the empty array, + having the sum 0. + >>> max_sub_array([-1, -2, -3]) + 0 + >>> max_sub_array([5, -2, -3]) + 5 + >>> max_sub_array([31, -41, 59, 26, -53, 58, 97, -93, -23, 84]) + 187 + """ + best = 0 + current = 0 + for i in nums: + current += i + if current < 0: + current = 0 + best = max(best, current) + return best - +if __name__ == "__main__": + """ + A random simulation of this algorithm. + """ + import time + import matplotlib.pyplot as plt + from random import randint + inputs = [10, 100, 1000, 10000, 50000, 100000, 200000, 300000, 400000, 500000] + tim = [] + for i in inputs: + li = [randint(1, i) for j in range(i)] + strt = time.time() + (find_max_sub_array(li, 0, len(li) - 1)) + end = time.time() + tim.append(end - strt) + print("No of Inputs Time Taken") + for i in range(len(inputs)): + print(inputs[i], "\t\t", tim[i]) + plt.plot(inputs, tim) + plt.xlabel("Number of Inputs") + plt.ylabel("Time taken in seconds ") + plt.show() diff --git a/dynamic_programming/max_sum_contigous_subsequence.py b/dynamic_programming/max_sum_contigous_subsequence.py new file mode 100644 index 000000000000..2cbdb97a1759 --- /dev/null +++ b/dynamic_programming/max_sum_contigous_subsequence.py @@ -0,0 +1,20 @@ +def max_subarray_sum(nums: list) -> int: + """ + >>> max_subarray_sum([6 , 9, -1, 3, -7, -5, 10]) + 17 + """ + if not nums: + return 0 + n = len(nums) + s = [0] * n + res, s, s_pre = nums[0], nums[0], nums[0] + for i in range(1, n): + s = max(nums[i], s_pre + nums[i]) + s_pre = s + res = max(res, s) + return res + + +if __name__ == "__main__": + nums = [6, 9, -1, 3, -7, -5, 10] + print(max_subarray_sum(nums)) diff --git a/dynamic_programming/minimum_partition.py b/dynamic_programming/minimum_partition.py index 18aa1faa2fa6..d5750326fea4 100644 --- a/dynamic_programming/minimum_partition.py +++ b/dynamic_programming/minimum_partition.py @@ -1,28 +1,30 @@ """ Partition a set into two subsets such that the difference of subset sums is minimum """ + + def findMin(arr): n = len(arr) s = sum(arr) - dp = [[False for x in range(s+1)]for y in range(n+1)] + dp = [[False for x in range(s + 1)] for y in range(n + 1)] - for i in range(1, n+1): + for i in range(1, n + 1): dp[i][0] = True - for i in range(1, s+1): + for i in range(1, s + 1): dp[0][i] = False - for i in range(1, n+1): - for j in range(1, s+1): - dp[i][j]= dp[i][j-1] + for i in range(1, n + 1): + for j in range(1, s + 1): + dp[i][j] = dp[i][j - 1] - if (arr[i-1] <= j): - dp[i][j] = dp[i][j] or dp[i-1][j-arr[i-1]] + if arr[i - 1] <= j: + dp[i][j] = dp[i][j] or dp[i - 1][j - arr[i - 1]] - for j in range(int(s/2), -1, -1): + for j in range(int(s / 2), -1, -1): if dp[n][j] == True: - diff = s-2*j - break; + diff = s - 2 * j + break return diff diff --git a/dynamic_programming/rod_cutting.py b/dynamic_programming/rod_cutting.py index 34350cb8202b..3a1d55320d7b 100644 --- a/dynamic_programming/rod_cutting.py +++ b/dynamic_programming/rod_cutting.py @@ -1,58 +1,200 @@ -### PROBLEM ### -""" -We are given a rod of length n and we are given the array of prices, also of -length n. This array contains the price for selling a rod at a certain length. -For example, prices[5] shows the price we can sell a rod of length 5. -Generalising, prices[x] shows the price a rod of length x can be sold. -We are tasked to find the optimal solution to sell the given rod. """ +This module provides two implementations for the rod-cutting problem: +1. A naive recursive implementation which has an exponential runtime +2. Two dynamic programming implementations which have quadratic runtime -### SOLUTION ### -""" -Profit(n) = max(1>> naive_cut_rod_recursive(4, [1, 5, 8, 9]) + 10 + >>> naive_cut_rod_recursive(10, [1, 5, 8, 9, 10, 17, 17, 20, 24, 30]) + 30 + """ + + _enforce_args(n, prices) + if n == 0: + return 0 + max_revue = float("-inf") + for i in range(1, n + 1): + max_revue = max( + max_revue, prices[i - 1] + naive_cut_rod_recursive(n - i, prices) + ) + + return max_revue + + +def top_down_cut_rod(n: int, prices: list): + """ + Constructs a top-down dynamic programming solution for the rod-cutting problem + via memoization. This function serves as a wrapper for _top_down_cut_rod_recursive + + Runtime: O(n^2) + + Arguments + -------- + n: int, the length of the rod + prices: list, the prices for each piece of rod. ``p[i-i]`` is the + price for a rod of length ``i`` + + Note + ---- + For convenience and because Python's lists using 0-indexing, length(max_rev) = n + 1, + to accommodate for the revenue obtainable from a rod of length 0. + + Returns + ------- + The maximum revenue obtainable for a rod of length n given the list of prices for each piece. + + Examples + ------- + >>> top_down_cut_rod(4, [1, 5, 8, 9]) + 10 + >>> top_down_cut_rod(10, [1, 5, 8, 9, 10, 17, 17, 20, 24, 30]) + 30 + """ + _enforce_args(n, prices) + max_rev = [float("-inf") for _ in range(n + 1)] + return _top_down_cut_rod_recursive(n, prices, max_rev) + + +def _top_down_cut_rod_recursive(n: int, prices: list, max_rev: list): + """ + Constructs a top-down dynamic programming solution for the rod-cutting problem + via memoization. + + Runtime: O(n^2) + + Arguments + -------- + n: int, the length of the rod + prices: list, the prices for each piece of rod. ``p[i-i]`` is the + price for a rod of length ``i`` + max_rev: list, the computed maximum revenue for a piece of rod. + ``max_rev[i]`` is the maximum revenue obtainable for a rod of length ``i`` + + Returns + ------- + The maximum revenue obtainable for a rod of length n given the list of prices for each piece. + """ + if max_rev[n] >= 0: + return max_rev[n] + elif n == 0: + return 0 + else: + max_revenue = float("-inf") + for i in range(1, n + 1): + max_revenue = max( + max_revenue, + prices[i - 1] + _top_down_cut_rod_recursive(n - i, prices, max_rev), + ) + + max_rev[n] = max_revenue + + return max_rev[n] + + +def bottom_up_cut_rod(n: int, prices: list): + """ + Constructs a bottom-up dynamic programming solution for the rod-cutting problem + + Runtime: O(n^2) + + Arguments + ---------- + n: int, the maximum length of the rod. + prices: list, the prices for each piece of rod. ``p[i-i]`` is the + price for a rod of length ``i`` + + Returns + ------- + The maximum revenue obtainable from cutting a rod of length n given + the prices for each piece of rod p. + + Examples + ------- + >>> bottom_up_cut_rod(4, [1, 5, 8, 9]) + 10 + >>> bottom_up_cut_rod(10, [1, 5, 8, 9, 10, 17, 17, 20, 24, 30]) + 30 + """ + _enforce_args(n, prices) + + # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of length 0. + max_rev = [float("-inf") for _ in range(n + 1)] + max_rev[0] = 0 + + for i in range(1, n + 1): + max_revenue_i = max_rev[i] + for j in range(1, i + 1): + max_revenue_i = max(max_revenue_i, prices[j - 1] + max_rev[i - j]) + + max_rev[i] = max_revenue_i + + return max_rev[n] + + +def _enforce_args(n: int, prices: list): + """ + Basic checks on the arguments to the rod-cutting algorithms + + n: int, the length of the rod + prices: list, the price list for each piece of rod. + + Throws ValueError: + + if n is negative or there are fewer items in the price list than the length of the rod + """ + if n < 0: + raise ValueError(f"n must be greater than or equal to 0. Got n = {n}") + + if n > len(prices): + raise ValueError( + f"Each integral piece of rod must have a corresponding " + f"price. Got n = {n} but length of prices = {len(prices)}" + ) - for i in range(1,n): - if(solutions[i] == -1): - #We haven't calulated solution for length i yet. - #We know we sell the part of length i so we get prices[i]. - #We just need to know how to sell rod of length n-i - yesCut[i] = prices[i] + CutRod(n-i) - else: - #We have calculated solution for length i. - #We add the two prices. - yesCut[i] = prices[i] + solutions[n-i] - #We need to find the highest price in order to sell more efficiently. - #We have to choose between noCut and the prices in yesCut. - m = noCut #Initialize max to noCut - for i in range(n): - if(yesCut[i] > m): - m = yesCut[i] +def main(): + prices = [6, 10, 12, 15, 20, 23] + n = len(prices) - solutions[n] = m - return m + # the best revenue comes from cutting the rod into 6 pieces, each + # of length 1 resulting in a revenue of 6 * 6 = 36. + expected_max_revenue = 36 + max_rev_top_down = top_down_cut_rod(n, prices) + max_rev_bottom_up = bottom_up_cut_rod(n, prices) + max_rev_naive = naive_cut_rod_recursive(n, prices) + assert expected_max_revenue == max_rev_top_down + assert max_rev_top_down == max_rev_bottom_up + assert max_rev_bottom_up == max_rev_naive -### EXAMPLE ### -length = 5 -#The first price, 0, is for when we have no rod. -prices = [0, 1, 3, 7, 9, 11, 13, 17, 21, 21, 30] -solutions = [-1 for x in range(length+1)] -print(CutRod(length)) +if __name__ == "__main__": + main() diff --git a/dynamic_programming/subset_generation.py b/dynamic_programming/subset_generation.py new file mode 100644 index 000000000000..2cca97fc3cbc --- /dev/null +++ b/dynamic_programming/subset_generation.py @@ -0,0 +1,43 @@ +# python program to print all subset combination of n element in given set of r element . +# arr[] ---> Input Array +# data[] ---> Temporary array to store current combination +# start & end ---> Staring and Ending indexes in arr[] +# index ---> Current index in data[] +# r ---> Size of a combination to be printed +def combinationUtil(arr, n, r, index, data, i): + # Current combination is ready to be printed, + # print it + if index == r: + for j in range(r): + print(data[j], end=" ") + print(" ") + return + # When no more elements are there to put in data[] + if i >= n: + return + # current is included, put next at next + # location + data[index] = arr[i] + combinationUtil(arr, n, r, index + 1, data, i + 1) + # current is excluded, replace it with + # next (Note that i+1 is passed, but + # index is not changed) + combinationUtil(arr, n, r, index, data, i + 1) + # The main function that prints all combinations + # of size r in arr[] of size n. This function + # mainly uses combinationUtil() + + +def printcombination(arr, n, r): + # A temporary array to store all combination + # one by one + data = [0] * r + # Print all combination using temprary + # array 'data[]' + combinationUtil(arr, n, r, 0, data, 0) + + +# Driver function to check for above function +arr = [10, 20, 30, 40, 50] +printcombination(arr, len(arr), 3) +# This code is contributed by Ambuj sahu diff --git a/dynamic_programming/sum_of_subset.py b/dynamic_programming/sum_of_subset.py new file mode 100644 index 000000000000..9394d29dabc0 --- /dev/null +++ b/dynamic_programming/sum_of_subset.py @@ -0,0 +1,36 @@ +def isSumSubset(arr, arrLen, requiredSum): + """ + >>> isSumSubset([2, 4, 6, 8], 4, 5) + False + >>> isSumSubset([2, 4, 6, 8], 4, 14) + True + """ + # a subset value says 1 if that subset sum can be formed else 0 + # initially no subsets can be formed hence False/0 + subset = [[False for i in range(requiredSum + 1)] for i in range(arrLen + 1)] + + # for each arr value, a sum of zero(0) can be formed by not taking any element hence True/1 + for i in range(arrLen + 1): + subset[i][0] = True + + # sum is not zero and set is empty then false + for i in range(1, requiredSum + 1): + subset[0][i] = False + + for i in range(1, arrLen + 1): + for j in range(1, requiredSum + 1): + if arr[i - 1] > j: + subset[i][j] = subset[i - 1][j] + if arr[i - 1] <= j: + subset[i][j] = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]] + + # uncomment to print the subset + # for i in range(arrLen+1): + # print(subset[i]) + print(subset[arrLen][requiredSum]) + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/file_transfer/mytext.txt b/file_transfer/mytext.txt new file mode 100644 index 000000000000..54cfa7f766c7 --- /dev/null +++ b/file_transfer/mytext.txt @@ -0,0 +1,6 @@ +Hello +This is sample data +«küßî» +“ЌύБЇ” +😀😉 +😋 diff --git a/file_transfer/recieve_file.py b/file_transfer/recieve_file.py new file mode 100644 index 000000000000..cfba6ed88484 --- /dev/null +++ b/file_transfer/recieve_file.py @@ -0,0 +1,23 @@ +if __name__ == "__main__": + import socket # Import socket module + + sock = socket.socket() # Create a socket object + host = socket.gethostname() # Get local machine name + port = 12312 + + sock.connect((host, port)) + sock.send(b"Hello server!") + + with open("Received_file", "wb") as out_file: + print("File opened") + print("Receiving data...") + while True: + data = sock.recv(1024) + print(f"data={data}") + if not data: + break + out_file.write(data) # Write data to a file + + print("Successfully got the file") + sock.close() + print("Connection closed") diff --git a/file_transfer/send_file.py b/file_transfer/send_file.py new file mode 100644 index 000000000000..ebc075a30ad4 --- /dev/null +++ b/file_transfer/send_file.py @@ -0,0 +1,38 @@ +if __name__ == "__main__": + import socket # Import socket module + + ONE_CONNECTION_ONLY = ( + True + ) # Set this to False if you wish to continuously accept connections + + filename = "mytext.txt" + port = 12312 # Reserve a port for your service. + sock = socket.socket() # Create a socket object + host = socket.gethostname() # Get local machine name + sock.bind((host, port)) # Bind to the port + sock.listen(5) # Now wait for client connection. + + print("Server listening....") + + while True: + conn, addr = sock.accept() # Establish connection with client. + print(f"Got connection from {addr}") + data = conn.recv(1024) + print(f"Server received {data}") + + with open(filename, "rb") as in_file: + data = in_file.read(1024) + while data: + conn.send(data) + print(f"Sent {data!r}") + data = in_file.read(1024) + + print("Done sending") + conn.close() + if ( + ONE_CONNECTION_ONLY + ): # This is to make sure that the program doesn't hang while testing + break + + sock.shutdown(1) + sock.close() diff --git a/file_transfer_protocol/ftp_client_server.py b/file_transfer_protocol/ftp_client_server.py deleted file mode 100644 index 414c336dee9f..000000000000 --- a/file_transfer_protocol/ftp_client_server.py +++ /dev/null @@ -1,57 +0,0 @@ -# server - -import socket # Import socket module - -port = 60000 # Reserve a port for your service. -s = socket.socket() # Create a socket object -host = socket.gethostname() # Get local machine name -s.bind((host, port)) # Bind to the port -s.listen(5) # Now wait for client connection. - -print('Server listening....') - -while True: - conn, addr = s.accept() # Establish connection with client. - print('Got connection from', addr) - data = conn.recv(1024) - print('Server received', repr(data)) - - filename = 'mytext.txt' - with open(filename, 'rb') as f: - in_data = f.read(1024) - while in_data: - conn.send(in_data) - print('Sent ', repr(in_data)) - in_data = f.read(1024) - - print('Done sending') - conn.send('Thank you for connecting') - conn.close() - - -# client side server - -import socket # Import socket module - -s = socket.socket() # Create a socket object -host = socket.gethostname() # Get local machine name -port = 60000 # Reserve a port for your service. - -s.connect((host, port)) -s.send("Hello server!") - -with open('received_file', 'wb') as f: - print('file opened') - while True: - print('receiving data...') - data = s.recv(1024) - print('data=%s', (data)) - if not data: - break - # write data to a file - f.write(data) - -f.close() -print('Successfully get the file') -s.close() -print('connection closed') diff --git a/file_transfer_protocol/ftp_send_receive.py b/file_transfer_protocol/ftp_send_receive.py deleted file mode 100644 index 6a9819ef3f21..000000000000 --- a/file_transfer_protocol/ftp_send_receive.py +++ /dev/null @@ -1,36 +0,0 @@ -""" -File transfer protocol used to send and receive files using FTP server. -Use credentials to provide access to the FTP client - -Note: Do not use root username & password for security reasons -Create a seperate user and provide access to a home directory of the user -Use login id and password of the user created -cwd here stands for current working directory -""" - -from ftplib import FTP -ftp = FTP('xxx.xxx.x.x') # Enter the ip address or the domain name here -ftp.login(user='username', passwd='password') -ftp.cwd('/Enter the directory here/') - -""" -The file which will be received via the FTP server -Enter the location of the file where the file is received -""" - -def ReceiveFile(): - FileName = 'example.txt' """ Enter the location of the file """ - with open(FileName, 'wb') as LocalFile: - ftp.retrbinary('RETR ' + FileName, LocalFile.write, 1024) - ftp.quit() - -""" -The file which will be sent via the FTP server -The file send will be send to the current working directory -""" - -def SendFile(): - FileName = 'example.txt' """ Enter the name of the file """ - with open(FileName, 'rb') as LocalFile: - ftp.storbinary('STOR ' + FileName, LocalFile) - ftp.quit() diff --git a/fuzzy_logic/fuzzy_operations.py b/fuzzy_logic/fuzzy_operations.py new file mode 100644 index 000000000000..ba4a8a22a4d1 --- /dev/null +++ b/fuzzy_logic/fuzzy_operations.py @@ -0,0 +1,102 @@ +"""README, Author - Jigyasa Gandhi(mailto:jigsgandhi97@gmail.com) +Requirements: + - scikit-fuzzy + - numpy + - matplotlib +Python: + - 3.5 +""" +# Create universe of discourse in python using linspace () +import numpy as np + +X = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False) + +# Create two fuzzy sets by defining any membership function (trapmf(), gbellmf(),gaussmf(), etc). +import skfuzzy as fuzz + +abc1 = [0, 25, 50] +abc2 = [25, 50, 75] +young = fuzz.membership.trimf(X, abc1) +middle_aged = fuzz.membership.trimf(X, abc2) + +# Compute the different operations using inbuilt functions. +one = np.ones(75) +zero = np.zeros((75,)) +# 1. Union = max(µA(x), µB(x)) +union = fuzz.fuzzy_or(X, young, X, middle_aged)[1] +# 2. Intersection = min(µA(x), µB(x)) +intersection = fuzz.fuzzy_and(X, young, X, middle_aged)[1] +# 3. Complement (A) = (1- min(µA(x)) +complement_a = fuzz.fuzzy_not(young) +# 4. Difference (A/B) = min(µA(x),(1- µB(x))) +difference = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1] +# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))] +alg_sum = young + middle_aged - (young * middle_aged) +# 6. Algebraic Product = (µA(x) * µB(x)) +alg_product = young * middle_aged +# 7. Bounded Sum = min[1,(µA(x), µB(x))] +bdd_sum = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1] +# 8. Bounded difference = min[0,(µA(x), µB(x))] +bdd_difference = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1] + +# max-min composition +# max-product composition + + +# Plot each set A, set B and each operation result using plot() and subplot(). +import matplotlib.pyplot as plt + +plt.figure() + +plt.subplot(4, 3, 1) +plt.plot(X, young) +plt.title("Young") +plt.grid(True) + +plt.subplot(4, 3, 2) +plt.plot(X, middle_aged) +plt.title("Middle aged") +plt.grid(True) + +plt.subplot(4, 3, 3) +plt.plot(X, union) +plt.title("union") +plt.grid(True) + +plt.subplot(4, 3, 4) +plt.plot(X, intersection) +plt.title("intersection") +plt.grid(True) + +plt.subplot(4, 3, 5) +plt.plot(X, complement_a) +plt.title("complement_a") +plt.grid(True) + +plt.subplot(4, 3, 6) +plt.plot(X, difference) +plt.title("difference a/b") +plt.grid(True) + +plt.subplot(4, 3, 7) +plt.plot(X, alg_sum) +plt.title("alg_sum") +plt.grid(True) + +plt.subplot(4, 3, 8) +plt.plot(X, alg_product) +plt.title("alg_product") +plt.grid(True) + +plt.subplot(4, 3, 9) +plt.plot(X, bdd_sum) +plt.title("bdd_sum") +plt.grid(True) + +plt.subplot(4, 3, 10) +plt.plot(X, bdd_difference) +plt.title("bdd_difference") +plt.grid(True) + +plt.subplots_adjust(hspace=0.5) +plt.show() diff --git a/graphs/Directed and Undirected (Weighted) Graph.py b/graphs/Directed and Undirected (Weighted) Graph.py deleted file mode 100644 index a31a4a96d6d0..000000000000 --- a/graphs/Directed and Undirected (Weighted) Graph.py +++ /dev/null @@ -1,472 +0,0 @@ -from collections import deque -import random as rand -import math as math -import time - -# the dfault weight is 1 if not assigend but all the implementation is weighted - -class DirectedGraph: - def __init__(self): - self.graph = {} - - # adding vertices and edges - # adding the weight is optional - # handels repetition - def add_pair(self, u, v, w = 1): - if self.graph.get(u): - if self.graph[u].count([w,v]) == 0: - self.graph[u].append([w, v]) - else: - self.graph[u] = [[w, v]] - if not self.graph.get(v): - self.graph[v] = [] - - def all_nodes(self): - return list(self.graph) - - # handels if the input does not exist - def remove_pair(self, u, v): - if self.graph.get(u): - for _ in self.graph[u]: - if _[1] == v: - self.graph[u].remove(_) - - # if no destination is meant the defaut value is -1 - def dfs(self, s = -2, d = -1): - if s == d: - return [] - stack = [] - visited = [] - if s == -2: - s = list(self.graph.keys())[0] - stack.append(s) - visited.append(s) - ss = s - - while True: - # check if there is any non isolated nodes - if len(self.graph[s]) != 0: - ss = s - for __ in self.graph[s]: - if visited.count(__[1]) < 1: - if __[1] == d: - visited.append(d) - return visited - else: - stack.append(__[1]) - visited.append(__[1]) - ss =__[1] - break - - # check if all the children are visited - if s == ss : - stack.pop() - if len(stack) != 0: - s = stack[len(stack) - 1] - else: - s = ss - - # check if se have reached the starting point - if len(stack) == 0: - return visited - - # c is the count of nodes you want and if you leave it or pass -1 to the funtion the count - # will be random from 10 to 10000 - def fill_graph_randomly(self, c = -1): - if c == -1: - c = (math.floor(rand.random() * 10000)) + 10 - for _ in range(c): - # every vertex has max 100 edges - e = math.floor(rand.random() * 102) + 1 - for __ in range(e): - n = math.floor(rand.random() * (c)) + 1 - if n == _: - continue - self.add_pair(_, n, 1) - - def bfs(self, s = -2): - d = deque() - visited = [] - if s == -2: - s = list(self.graph.keys())[0] - d.append(s) - visited.append(s) - while d: - s = d.popleft() - if len(self.graph[s]) != 0: - for __ in self.graph[s]: - if visited.count(__[1]) < 1: - d.append(__[1]) - visited.append(__[1]) - return visited - def in_degree(self, u): - count = 0 - for _ in self.graph: - for __ in self.graph[_]: - if __[1] == u: - count += 1 - return count - - def out_degree(self, u): - return len(self.graph[u]) - - def topological_sort(self, s = -2): - stack = [] - visited = [] - if s == -2: - s = list(self.graph.keys())[0] - stack.append(s) - visited.append(s) - ss = s - sorted_nodes = [] - - while True: - # check if there is any non isolated nodes - if len(self.graph[s]) != 0: - ss = s - for __ in self.graph[s]: - if visited.count(__[1]) < 1: - stack.append(__[1]) - visited.append(__[1]) - ss =__[1] - break - - # check if all the children are visited - if s == ss : - sorted_nodes.append(stack.pop()) - if len(stack) != 0: - s = stack[len(stack) - 1] - else: - s = ss - - # check if se have reached the starting point - if len(stack) == 0: - return sorted_nodes - - def cycle_nodes(self): - stack = [] - visited = [] - s = list(self.graph.keys())[0] - stack.append(s) - visited.append(s) - parent = -2 - indirect_parents = [] - ss = s - on_the_way_back = False - anticipating_nodes = set() - - while True: - # check if there is any non isolated nodes - if len(self.graph[s]) != 0: - ss = s - for __ in self.graph[s]: - if visited.count(__[1]) > 0 and __[1] != parent and indirect_parents.count(__[1]) > 0 and not on_the_way_back: - l = len(stack) - 1 - while True and l >= 0: - if stack[l] == __[1]: - anticipating_nodes.add(__[1]) - break - else: - anticipating_nodes.add(stack[l]) - l -= 1 - if visited.count(__[1]) < 1: - stack.append(__[1]) - visited.append(__[1]) - ss =__[1] - break - - # check if all the children are visited - if s == ss : - stack.pop() - on_the_way_back = True - if len(stack) != 0: - s = stack[len(stack) - 1] - else: - on_the_way_back = False - indirect_parents.append(parent) - parent = s - s = ss - - # check if se have reached the starting point - if len(stack) == 0: - return list(anticipating_nodes) - - def has_cycle(self): - stack = [] - visited = [] - s = list(self.graph.keys())[0] - stack.append(s) - visited.append(s) - parent = -2 - indirect_parents = [] - ss = s - on_the_way_back = False - anticipating_nodes = set() - - while True: - # check if there is any non isolated nodes - if len(self.graph[s]) != 0: - ss = s - for __ in self.graph[s]: - if visited.count(__[1]) > 0 and __[1] != parent and indirect_parents.count(__[1]) > 0 and not on_the_way_back: - l = len(stack) - 1 - while True and l >= 0: - if stack[l] == __[1]: - anticipating_nodes.add(__[1]) - break - else: - return True - anticipating_nodes.add(stack[l]) - l -= 1 - if visited.count(__[1]) < 1: - stack.append(__[1]) - visited.append(__[1]) - ss =__[1] - break - - # check if all the children are visited - if s == ss : - stack.pop() - on_the_way_back = True - if len(stack) != 0: - s = stack[len(stack) - 1] - else: - on_the_way_back = False - indirect_parents.append(parent) - parent = s - s = ss - - # check if se have reached the starting point - if len(stack) == 0: - return False - - def dfs_time(self, s = -2, e = -1): - begin = time.time() - self.dfs(s,e) - end = time.time() - return end - begin - - def bfs_time(self, s = -2): - begin = time.time() - self.bfs(s) - end = time.time() - return end - begin - -class Graph: - def __init__(self): - self.graph = {} - - # adding vertices and edges - # adding the weight is optional - # handels repetition - def add_pair(self, u, v, w = 1): - # check if the u exists - if self.graph.get(u): - # if there already is a edge - if self.graph[u].count([w,v]) == 0: - self.graph[u].append([w, v]) - else: - # if u does not exist - self.graph[u] = [[w, v]] - # add the other way - if self.graph.get(v): - # if there already is a edge - if self.graph[v].count([w,u]) == 0: - self.graph[v].append([w, u]) - else: - # if u does not exist - self.graph[v] = [[w, u]] - - # handels if the input does not exist - def remove_pair(self, u, v): - if self.graph.get(u): - for _ in self.graph[u]: - if _[1] == v: - self.graph[u].remove(_) - # the other way round - if self.graph.get(v): - for _ in self.graph[v]: - if _[1] == u: - self.graph[v].remove(_) - - # if no destination is meant the defaut value is -1 - def dfs(self, s = -2, d = -1): - if s == d: - return [] - stack = [] - visited = [] - if s == -2: - s = list(self.graph.keys())[0] - stack.append(s) - visited.append(s) - ss = s - - while True: - # check if there is any non isolated nodes - if len(self.graph[s]) != 0: - ss = s - for __ in self.graph[s]: - if visited.count(__[1]) < 1: - if __[1] == d: - visited.append(d) - return visited - else: - stack.append(__[1]) - visited.append(__[1]) - ss =__[1] - break - - # check if all the children are visited - if s == ss : - stack.pop() - if len(stack) != 0: - s = stack[len(stack) - 1] - else: - s = ss - - # check if se have reached the starting point - if len(stack) == 0: - return visited - - # c is the count of nodes you want and if you leave it or pass -1 to the funtion the count - # will be random from 10 to 10000 - def fill_graph_randomly(self, c = -1): - if c == -1: - c = (math.floor(rand.random() * 10000)) + 10 - for _ in range(c): - # every vertex has max 100 edges - e = math.floor(rand.random() * 102) + 1 - for __ in range(e): - n = math.floor(rand.random() * (c)) + 1 - if n == _: - continue - self.add_pair(_, n, 1) - - def bfs(self, s = -2): - d = deque() - visited = [] - if s == -2: - s = list(self.graph.keys())[0] - d.append(s) - visited.append(s) - while d: - s = d.popleft() - if len(self.graph[s]) != 0: - for __ in self.graph[s]: - if visited.count(__[1]) < 1: - d.append(__[1]) - visited.append(__[1]) - return visited - def degree(self, u): - return len(self.graph[u]) - - def cycle_nodes(self): - stack = [] - visited = [] - s = list(self.graph.keys())[0] - stack.append(s) - visited.append(s) - parent = -2 - indirect_parents = [] - ss = s - on_the_way_back = False - anticipating_nodes = set() - - while True: - # check if there is any non isolated nodes - if len(self.graph[s]) != 0: - ss = s - for __ in self.graph[s]: - if visited.count(__[1]) > 0 and __[1] != parent and indirect_parents.count(__[1]) > 0 and not on_the_way_back: - l = len(stack) - 1 - while True and l >= 0: - if stack[l] == __[1]: - anticipating_nodes.add(__[1]) - break - else: - anticipating_nodes.add(stack[l]) - l -= 1 - if visited.count(__[1]) < 1: - stack.append(__[1]) - visited.append(__[1]) - ss =__[1] - break - - # check if all the children are visited - if s == ss : - stack.pop() - on_the_way_back = True - if len(stack) != 0: - s = stack[len(stack) - 1] - else: - on_the_way_back = False - indirect_parents.append(parent) - parent = s - s = ss - - # check if se have reached the starting point - if len(stack) == 0: - return list(anticipating_nodes) - - def has_cycle(self): - stack = [] - visited = [] - s = list(self.graph.keys())[0] - stack.append(s) - visited.append(s) - parent = -2 - indirect_parents = [] - ss = s - on_the_way_back = False - anticipating_nodes = set() - - while True: - # check if there is any non isolated nodes - if len(self.graph[s]) != 0: - ss = s - for __ in self.graph[s]: - if visited.count(__[1]) > 0 and __[1] != parent and indirect_parents.count(__[1]) > 0 and not on_the_way_back: - l = len(stack) - 1 - while True and l >= 0: - if stack[l] == __[1]: - anticipating_nodes.add(__[1]) - break - else: - return True - anticipating_nodes.add(stack[l]) - l -= 1 - if visited.count(__[1]) < 1: - stack.append(__[1]) - visited.append(__[1]) - ss =__[1] - break - - # check if all the children are visited - if s == ss : - stack.pop() - on_the_way_back = True - if len(stack) != 0: - s = stack[len(stack) - 1] - else: - on_the_way_back = False - indirect_parents.append(parent) - parent = s - s = ss - - # check if se have reached the starting point - if len(stack) == 0: - return False - def all_nodes(self): - return list(self.graph) - - def dfs_time(self, s = -2, e = -1): - begin = time.time() - self.dfs(s,e) - end = time.time() - return end - begin - - def bfs_time(self, s = -2): - begin = time.time() - self.bfs(s) - end = time.time() - return end - begin diff --git a/graphs/a_star.py b/graphs/a_star.py index 584222e6f62b..e1d17fc55434 100644 --- a/graphs/a_star.py +++ b/graphs/a_star.py @@ -1,44 +1,45 @@ -from __future__ import print_function +grid = [ + [0, 1, 0, 0, 0, 0], + [0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles + [0, 1, 0, 0, 0, 0], + [0, 1, 0, 0, 1, 0], + [0, 0, 0, 0, 1, 0], +] -grid = [[0, 1, 0, 0, 0, 0], - [0, 1, 0, 0, 0, 0],#0 are free path whereas 1's are obstacles - [0, 1, 0, 0, 0, 0], - [0, 1, 0, 0, 1, 0], - [0, 0, 0, 0, 1, 0]] - -''' +""" heuristic = [[9, 8, 7, 6, 5, 4], [8, 7, 6, 5, 4, 3], [7, 6, 5, 4, 3, 2], [6, 5, 4, 3, 2, 1], - [5, 4, 3, 2, 1, 0]]''' + [5, 4, 3, 2, 1, 0]]""" init = [0, 0] -goal = [len(grid)-1, len(grid[0])-1] #all coordinates are given in format [y,x] +goal = [len(grid) - 1, len(grid[0]) - 1] # all coordinates are given in format [y,x] cost = 1 -#the cost map which pushes the path closer to the goal +# the cost map which pushes the path closer to the goal heuristic = [[0 for row in range(len(grid[0]))] for col in range(len(grid))] -for i in range(len(grid)): - for j in range(len(grid[0])): +for i in range(len(grid)): + for j in range(len(grid[0])): heuristic[i][j] = abs(i - goal[0]) + abs(j - goal[1]) if grid[i][j] == 1: - heuristic[i][j] = 99 #added extra penalty in the heuristic map + heuristic[i][j] = 99 # added extra penalty in the heuristic map -#the actions we can take -delta = [[-1, 0 ], # go up - [ 0, -1], # go left - [ 1, 0 ], # go down - [ 0, 1 ]] # go right +# the actions we can take +delta = [[-1, 0], [0, -1], [1, 0], [0, 1]] # go up # go left # go down # go right -#function to search the path -def search(grid,init,goal,cost,heuristic): +# function to search the path +def search(grid, init, goal, cost, heuristic): - closed = [[0 for col in range(len(grid[0]))] for row in range(len(grid))]# the referrence grid + closed = [ + [0 for col in range(len(grid[0]))] for row in range(len(grid)) + ] # the referrence grid closed[init[0]][init[1]] = 1 - action = [[0 for col in range(len(grid[0]))] for row in range(len(grid))]#the action grid + action = [ + [0 for col in range(len(grid[0]))] for row in range(len(grid)) + ] # the action grid x = init[0] y = init[1] @@ -47,14 +48,14 @@ def search(grid,init,goal,cost,heuristic): cell = [[f, g, x, y]] found = False # flag that is set when search is complete - resign = False # flag set if we can't find expand + resign = False # flag set if we can't find expand while not found and not resign: if len(cell) == 0: resign = True return "FAIL" else: - cell.sort()#to choose the least costliest action so as to move closer to the goal + cell.sort() # to choose the least costliest action so as to move closer to the goal cell.reverse() next = cell.pop() x = next[2] @@ -62,14 +63,13 @@ def search(grid,init,goal,cost,heuristic): g = next[1] f = next[0] - if x == goal[0] and y == goal[1]: found = True else: - for i in range(len(delta)):#to try out different valid actions + for i in range(len(delta)): # to try out different valid actions x2 = x + delta[i][0] y2 = y + delta[i][1] - if x2 >= 0 and x2 < len(grid) and y2 >=0 and y2 < len(grid[0]): + if x2 >= 0 and x2 < len(grid) and y2 >= 0 and y2 < len(grid[0]): if closed[x2][y2] == 0 and grid[x2][y2] == 0: g2 = g + cost f2 = g2 + heuristic[x2][y2] @@ -79,7 +79,7 @@ def search(grid,init,goal,cost,heuristic): invpath = [] x = goal[0] y = goal[1] - invpath.append([x, y])#we get the reverse path from here + invpath.append([x, y]) # we get the reverse path from here while x != init[0] or y != init[1]: x2 = x - delta[action[x][y]][0] y2 = y - delta[action[x][y]][1] @@ -89,14 +89,14 @@ def search(grid,init,goal,cost,heuristic): path = [] for i in range(len(invpath)): - path.append(invpath[len(invpath) - 1 - i]) + path.append(invpath[len(invpath) - 1 - i]) print("ACTION MAP") for i in range(len(action)): print(action[i]) - + return path - -a = search(grid,init,goal,cost,heuristic) -for i in range(len(a)): - print(a[i]) + +a = search(grid, init, goal, cost, heuristic) +for i in range(len(a)): + print(a[i]) diff --git a/graphs/articulation_points.py b/graphs/articulation_points.py index 1173c4ea373c..3ecc829946e8 100644 --- a/graphs/articulation_points.py +++ b/graphs/articulation_points.py @@ -33,12 +33,23 @@ def dfs(root, at, parent, outEdgeCount): if not visited[i]: outEdgeCount = 0 outEdgeCount = dfs(i, i, -1, outEdgeCount) - isArt[i] = (outEdgeCount > 1) + isArt[i] = outEdgeCount > 1 for x in range(len(isArt)): if isArt[x] == True: print(x) + # Adjacency list of graph -l = {0:[1,2], 1:[0,2], 2:[0,1,3,5], 3:[2,4], 4:[3], 5:[2,6,8], 6:[5,7], 7:[6,8], 8:[5,7]} +l = { + 0: [1, 2], + 1: [0, 2], + 2: [0, 1, 3, 5], + 3: [2, 4], + 4: [3], + 5: [2, 6, 8], + 6: [5, 7], + 7: [6, 8], + 8: [5, 7], +} computeAP(l) diff --git a/graphs/basic_graphs.py b/graphs/basic_graphs.py index 3b3abeb1720d..161bc0c09d3b 100644 --- a/graphs/basic_graphs.py +++ b/graphs/basic_graphs.py @@ -1,51 +1,40 @@ -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - -try: - xrange # Python 2 -except NameError: - xrange = range # Python 3 - -# Accept No. of Nodes and edges -n, m = map(int, raw_input().split(" ")) - -# Initialising Dictionary of edges -g = {} -for i in xrange(n): - g[i + 1] = [] - -""" --------------------------------------------------------------------------------- - Accepting edges of Unweighted Directed Graphs --------------------------------------------------------------------------------- -""" -for _ in xrange(m): - x, y = map(int, raw_input().split(" ")) - g[x].append(y) - -""" --------------------------------------------------------------------------------- - Accepting edges of Unweighted Undirected Graphs --------------------------------------------------------------------------------- -""" -for _ in xrange(m): - x, y = map(int, raw_input().split(" ")) - g[x].append(y) - g[y].append(x) - -""" --------------------------------------------------------------------------------- - Accepting edges of Weighted Undirected Graphs --------------------------------------------------------------------------------- -""" -for _ in xrange(m): - x, y, r = map(int, raw_input().split(" ")) - g[x].append([y, r]) - g[y].append([x, r]) +if __name__ == "__main__": + # Accept No. of Nodes and edges + n, m = map(int, input().split(" ")) + + # Initialising Dictionary of edges + g = {} + for i in range(n): + g[i + 1] = [] + + """ + ---------------------------------------------------------------------------- + Accepting edges of Unweighted Directed Graphs + ---------------------------------------------------------------------------- + """ + for _ in range(m): + x, y = map(int, input().strip().split(" ")) + g[x].append(y) + + """ + ---------------------------------------------------------------------------- + Accepting edges of Unweighted Undirected Graphs + ---------------------------------------------------------------------------- + """ + for _ in range(m): + x, y = map(int, input().strip().split(" ")) + g[x].append(y) + g[y].append(x) + + """ + ---------------------------------------------------------------------------- + Accepting edges of Weighted Undirected Graphs + ---------------------------------------------------------------------------- + """ + for _ in range(m): + x, y, r = map(int, input().strip().split(" ")) + g[x].append([y, r]) + g[y].append([x, r]) """ -------------------------------------------------------------------------------- @@ -139,7 +128,9 @@ def dijk(G, s): from collections import deque -def topo(G, ind=None, Q=[1]): +def topo(G, ind=None, Q=None): + if Q is None: + Q = [1] if ind is None: ind = [0] * (len(G) + 1) # SInce oth Index is ignored for u in G: @@ -168,9 +159,10 @@ def topo(G, ind=None, Q=[1]): def adjm(): - n, a = raw_input(), [] - for i in xrange(n): - a.append(map(int, raw_input().split())) + n = input().strip() + a = [] + for i in range(n): + a.append(map(int, input().strip().split())) return a, n @@ -190,10 +182,10 @@ def adjm(): def floy(A_and_n): (A, n) = A_and_n dist = list(A) - path = [[0] * n for i in xrange(n)] - for k in xrange(n): - for i in xrange(n): - for j in xrange(n): + path = [[0] * n for i in range(n)] + for k in range(n): + for i in range(n): + for j in range(n): if dist[i][j] > dist[i][k] + dist[k][j]: dist[i][j] = dist[i][k] + dist[k][j] path[i][k] = k @@ -242,10 +234,10 @@ def prim(G, s): def edglist(): - n, m = map(int, raw_input().split(" ")) + n, m = map(int, input().split(" ")) l = [] - for i in xrange(m): - l.append(map(int, raw_input().split(' '))) + for i in range(m): + l.append(map(int, input().split(" "))) return l, n @@ -269,10 +261,10 @@ def krusk(E_and_n): break print(s) x = E.pop() - for i in xrange(len(s)): + for i in range(len(s)): if x[0] in s[i]: break - for j in xrange(len(s)): + for j in range(len(s)): if x[1] in s[j]: if i == j: break diff --git a/graphs/bellman_ford.py b/graphs/bellman_ford.py index 82db80546b94..b782a899fda9 100644 --- a/graphs/bellman_ford.py +++ b/graphs/bellman_ford.py @@ -1,54 +1,52 @@ -from __future__ import print_function - def printDist(dist, V): - print("\nVertex Distance") - for i in range(V): - if dist[i] != float('inf') : - print(i,"\t",int(dist[i]),end = "\t") - else: - print(i,"\t","INF",end="\t") - print() + print("\nVertex Distance") + for i in range(V): + if dist[i] != float("inf"): + print(i, "\t", int(dist[i]), end="\t") + else: + print(i, "\t", "INF", end="\t") + print() + def BellmanFord(graph, V, E, src): - mdist=[float('inf') for i in range(V)] - mdist[src] = 0.0 - - for i in range(V-1): - for j in range(V): - u = graph[j]["src"] - v = graph[j]["dst"] - w = graph[j]["weight"] - - if mdist[u] != float('inf') and mdist[u] + w < mdist[v]: - mdist[v] = mdist[u] + w - for j in range(V): - u = graph[j]["src"] - v = graph[j]["dst"] - w = graph[j]["weight"] - - if mdist[u] != float('inf') and mdist[u] + w < mdist[v]: - print("Negative cycle found. Solution not possible.") - return - - printDist(mdist, V) - - - -#MAIN -V = int(input("Enter number of vertices: ")) -E = int(input("Enter number of edges: ")) - -graph = [dict() for j in range(E)] - -for i in range(V): - graph[i][i] = 0.0 - -for i in range(E): - print("\nEdge ",i+1) - src = int(input("Enter source:")) - dst = int(input("Enter destination:")) - weight = float(input("Enter weight:")) - graph[i] = {"src": src,"dst": dst, "weight": weight} - -gsrc = int(input("\nEnter shortest path source:")) -BellmanFord(graph, V, E, gsrc) + mdist = [float("inf") for i in range(V)] + mdist[src] = 0.0 + + for i in range(V - 1): + for j in range(V): + u = graph[j]["src"] + v = graph[j]["dst"] + w = graph[j]["weight"] + + if mdist[u] != float("inf") and mdist[u] + w < mdist[v]: + mdist[v] = mdist[u] + w + for j in range(V): + u = graph[j]["src"] + v = graph[j]["dst"] + w = graph[j]["weight"] + + if mdist[u] != float("inf") and mdist[u] + w < mdist[v]: + print("Negative cycle found. Solution not possible.") + return + + printDist(mdist, V) + + +if __name__ == "__main__": + V = int(input("Enter number of vertices: ").strip()) + E = int(input("Enter number of edges: ").strip()) + + graph = [dict() for j in range(E)] + + for i in range(V): + graph[i][i] = 0.0 + + for i in range(E): + print("\nEdge ", i + 1) + src = int(input("Enter source:").strip()) + dst = int(input("Enter destination:").strip()) + weight = float(input("Enter weight:").strip()) + graph[i] = {"src": src, "dst": dst, "weight": weight} + + gsrc = int(input("\nEnter shortest path source:").strip()) + BellmanFord(graph, V, E, gsrc) diff --git a/graphs/BFS.py b/graphs/bfs.py similarity index 71% rename from graphs/BFS.py rename to graphs/bfs.py index bf9b572cec50..9d9b1ac037d9 100644 --- a/graphs/BFS.py +++ b/graphs/bfs.py @@ -1,6 +1,8 @@ -"""pseudo-code""" - """ +BFS. + +pseudo-code: + BFS(graph G, start vertex s): // all nodes initially unexplored mark s as explored @@ -14,10 +16,21 @@ """ -import collections +G = { + "A": ["B", "C"], + "B": ["A", "D", "E"], + "C": ["A", "F"], + "D": ["B"], + "E": ["B", "F"], + "F": ["C", "E"], +} def bfs(graph, start): + """ + >>> ''.join(sorted(bfs(G, 'A'))) + 'ABCDEF' + """ explored, queue = set(), [start] # collections.deque([start]) explored.add(start) while queue: @@ -29,11 +42,5 @@ def bfs(graph, start): return explored -G = {'A': ['B', 'C'], - 'B': ['A', 'D', 'E'], - 'C': ['A', 'F'], - 'D': ['B'], - 'E': ['B', 'F'], - 'F': ['C', 'E']} - -print(bfs(G, 'A')) +if __name__ == "__main__": + print(bfs(G, "A")) diff --git a/graphs/bfs_shortest_path.py b/graphs/bfs_shortest_path.py new file mode 100644 index 000000000000..ec82c13997e2 --- /dev/null +++ b/graphs/bfs_shortest_path.py @@ -0,0 +1,47 @@ +graph = { + "A": ["B", "C", "E"], + "B": ["A", "D", "E"], + "C": ["A", "F", "G"], + "D": ["B"], + "E": ["A", "B", "D"], + "F": ["C"], + "G": ["C"], +} + + +def bfs_shortest_path(graph, start, goal): + # keep track of explored nodes + explored = [] + # keep track of all the paths to be checked + queue = [[start]] + + # return path if start is goal + if start == goal: + return "That was easy! Start = goal" + + # keeps looping until all possible paths have been checked + while queue: + # pop the first path from the queue + path = queue.pop(0) + # get the last node from the path + node = path[-1] + if node not in explored: + neighbours = graph[node] + # go through all neighbour nodes, construct a new path and + # push it into the queue + for neighbour in neighbours: + new_path = list(path) + new_path.append(neighbour) + queue.append(new_path) + # return path if neighbour is goal + if neighbour == goal: + return new_path + + # mark node as explored + explored.append(node) + + # in case there's no path between the 2 nodes + return "So sorry, but a connecting path doesn't exist :(" + + +bfs_shortest_path(graph, "G", "D") # returns ['G', 'C', 'A', 'B', 'D'] diff --git a/graphs/breadth_first_search.py b/graphs/breadth_first_search.py index 3992e2d4d892..8516e60a59c4 100644 --- a/graphs/breadth_first_search.py +++ b/graphs/breadth_first_search.py @@ -3,17 +3,15 @@ """ Author: OMKAR PATHAK """ -from __future__ import print_function - -class Graph(): +class Graph: def __init__(self): self.vertex = {} # for printing the Graph vertexes def printGraph(self): for i in self.vertex.keys(): - print(i,' -> ', ' -> '.join([str(j) for j in self.vertex[i]])) + print(i, " -> ", " -> ".join([str(j) for j in self.vertex[i]])) # for adding the edge beween two vertexes def addEdge(self, fromVertex, toVertex): @@ -37,7 +35,7 @@ def BFS(self, startVertex): while queue: startVertex = queue.pop(0) - print(startVertex, end = ' ') + print(startVertex, end=" ") # mark all adjacent nodes as visited and print them for i in self.vertex[startVertex]: @@ -45,7 +43,8 @@ def BFS(self, startVertex): queue.append(i) visited[i] = True -if __name__ == '__main__': + +if __name__ == "__main__": g = Graph() g.addEdge(0, 1) g.addEdge(0, 2) @@ -55,7 +54,7 @@ def BFS(self, startVertex): g.addEdge(3, 3) g.printGraph() - print('BFS:') + print("BFS:") g.BFS(2) # OUTPUT: diff --git a/graphs/check_bipartite_graph_bfs.py b/graphs/check_bipartite_graph_bfs.py index 1b9c32c6ccc4..1ec3e3d1d45f 100644 --- a/graphs/check_bipartite_graph_bfs.py +++ b/graphs/check_bipartite_graph_bfs.py @@ -11,7 +11,7 @@ def checkBipartite(l): color = [-1] * len(l) def bfs(): - while(queue): + while queue: u = queue.pop(0) visited[u] = True @@ -38,6 +38,7 @@ def bfs(): return True + # Adjacency List of graph -l = {0:[1,3], 1:[0,2], 2:[1,3], 3:[0,2]} +l = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]} print(checkBipartite(l)) diff --git a/graphs/check_bipartite_graph_dfs.py b/graphs/check_bipartite_graph_dfs.py new file mode 100644 index 000000000000..6fe54a6723c5 --- /dev/null +++ b/graphs/check_bipartite_graph_dfs.py @@ -0,0 +1,33 @@ +# Check whether Graph is Bipartite or Not using DFS + +# A Bipartite Graph is a graph whose vertices can be divided into two independent sets, +# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex +# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V, +# or u belongs to V and v to U. We can also say that there is no edge that connects +# vertices of same set. +def check_bipartite_dfs(l): + visited = [False] * len(l) + color = [-1] * len(l) + + def dfs(v, c): + visited[v] = True + color[v] = c + for u in l[v]: + if not visited[u]: + dfs(u, 1 - c) + + for i in range(len(l)): + if not visited[i]: + dfs(i, 0) + + for i in range(len(l)): + for j in l[i]: + if color[i] == color[j]: + return False + + return True + + +# Adjacency list of graph +l = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []} +print(check_bipartite_dfs(l)) diff --git a/graphs/depth_first_search.py b/graphs/depth_first_search.py index 98faf61354f9..5347c2fbcfa3 100644 --- a/graphs/depth_first_search.py +++ b/graphs/depth_first_search.py @@ -2,10 +2,9 @@ # encoding=utf8 """ Author: OMKAR PATHAK """ -from __future__ import print_function -class Graph(): +class Graph: def __init__(self): self.vertex = {} @@ -13,7 +12,7 @@ def __init__(self): def printGraph(self): print(self.vertex) for i in self.vertex.keys(): - print(i,' -> ', ' -> '.join([str(j) for j in self.vertex[i]])) + print(i, " -> ", " -> ".join([str(j) for j in self.vertex[i]])) # for adding the edge beween two vertexes def addEdge(self, fromVertex, toVertex): @@ -37,14 +36,15 @@ def DFSRec(self, startVertex, visited): # mark start vertex as visited visited[startVertex] = True - print(startVertex, end = ' ') + print(startVertex, end=" ") # Recur for all the vertexes that are adjacent to this node for i in self.vertex.keys(): if visited[i] == False: self.DFSRec(i, visited) -if __name__ == '__main__': + +if __name__ == "__main__": g = Graph() g.addEdge(0, 1) g.addEdge(0, 2) @@ -54,7 +54,7 @@ def DFSRec(self, startVertex, visited): g.addEdge(3, 3) g.printGraph() - print('DFS:') + print("DFS:") g.DFS() # OUTPUT: @@ -63,4 +63,4 @@ def DFSRec(self, startVertex, visited): # 2  ->  0 -> 3 # 3  ->  3 # DFS: - # 0 1 2 3 + #  0 1 2 3 diff --git a/graphs/DFS.py b/graphs/dfs.py similarity index 68% rename from graphs/DFS.py rename to graphs/dfs.py index d3c34fabb7b3..f183eae73fef 100644 --- a/graphs/DFS.py +++ b/graphs/dfs.py @@ -16,21 +16,29 @@ def dfs(graph, start): to the node's children onto the iterator stack. When the iterator at the top of the stack terminates, we'll pop it off the stack.""" explored, stack = set(), [start] - explored.add(start) while stack: - v = stack.pop() # the only difference from BFS is to pop last element here instead of first one + v = ( + stack.pop() + ) # one difference from BFS is to pop last element here instead of first one + + if v in explored: + continue + + explored.add(v) + for w in graph[v]: if w not in explored: - explored.add(w) stack.append(w) return explored -G = {'A': ['B', 'C'], - 'B': ['A', 'D', 'E'], - 'C': ['A', 'F'], - 'D': ['B'], - 'E': ['B', 'F'], - 'F': ['C', 'E']} +G = { + "A": ["B", "C"], + "B": ["A", "D", "E"], + "C": ["A", "F"], + "D": ["B"], + "E": ["B", "F"], + "F": ["C", "E"], +} -print(dfs(G, 'A')) +print(dfs(G, "A")) diff --git a/graphs/dijkstra.py b/graphs/dijkstra.py index 6b08b28fcfd3..195f4e02d409 100644 --- a/graphs/dijkstra.py +++ b/graphs/dijkstra.py @@ -1,47 +1,119 @@ """pseudo-code""" """ -DIJKSTRA(graph G, start vertex s,destination vertex d): -// all nodes initially unexplored -let H = min heap data structure, initialized with 0 and s [here 0 indicates the distance from start vertex] -while H is non-empty: - remove the first node and cost of H, call it U and cost - if U is not explored - mark U as explored - if U is d: - return cost // total cost from start to destination vertex - for each edge(U, V): c=cost of edge(u,V) // for V in graph[U] - if V unexplored: - next=cost+c - add next,V to H (at the end) +DIJKSTRA(graph G, start vertex s, destination vertex d): + +//all nodes initially unexplored + +1 - let H = min heap data structure, initialized with 0 and s [here 0 indicates + the distance from start vertex s] +2 - while H is non-empty: +3 - remove the first node and cost of H, call it U and cost +4 - if U has been previously explored: +5 - go to the while loop, line 2 //Once a node is explored there is no need + to make it again +6 - mark U as explored +7 - if U is d: +8 - return cost // total cost from start to destination vertex +9 - for each edge(U, V): c=cost of edge(U,V) // for V in graph[U] +10 - if V explored: +11 - go to next V in line 9 +12 - total_cost = cost + c +13 - add (total_cost,V) to H + +You can think at cost as a distance where Dijkstra finds the shortest distance +between vertexes s and v in a graph G. The use of a min heap as H guarantees +that if a vertex has already been explored there will be no other path with +shortest distance, that happens because heapq.heappop will always return the +next vertex with the shortest distance, considering that the heap stores not +only the distance between previous vertex and current vertex but the entire +distance between each vertex that makes up the path from start vertex to target +vertex. """ + import heapq def dijkstra(graph, start, end): + """Return the cost of the shortest path between vertexes start and end. + + >>> dijkstra(G, "E", "C") + 6 + >>> dijkstra(G2, "E", "F") + 3 + >>> dijkstra(G3, "E", "F") + 3 + """ + heap = [(0, start)] # cost from start node,end node - visited = [] + visited = set() while heap: (cost, u) = heapq.heappop(heap) if u in visited: continue - visited.append(u) + visited.add(u) if u == end: return cost - for v, c in G[u]: + for v, c in graph[u]: if v in visited: continue next = cost + c heapq.heappush(heap, (next, v)) - return (-1, -1) + return -1 + + +G = { + "A": [["B", 2], ["C", 5]], + "B": [["A", 2], ["D", 3], ["E", 1], ["F", 1]], + "C": [["A", 5], ["F", 3]], + "D": [["B", 3]], + "E": [["B", 4], ["F", 3]], + "F": [["C", 3], ["E", 3]], +} + +r""" +Layout of G2: + +E -- 1 --> B -- 1 --> C -- 1 --> D -- 1 --> F + \ /\ + \ || + ----------------- 3 -------------------- +""" +G2 = { + "B": [["C", 1]], + "C": [["D", 1]], + "D": [["F", 1]], + "E": [["B", 1], ["F", 3]], + "F": [], +} + +r""" +Layout of G3: + +E -- 1 --> B -- 1 --> C -- 1 --> D -- 1 --> F + \ /\ + \ || + -------- 2 ---------> G ------- 1 ------ +""" +G3 = { + "B": [["C", 1]], + "C": [["D", 1]], + "D": [["F", 1]], + "E": [["B", 1], ["G", 2]], + "F": [], + "G": [["F", 1]], +} + +shortDistance = dijkstra(G, "E", "C") +print(shortDistance) # E -- 3 --> F -- 3 --> C == 6 + +shortDistance = dijkstra(G2, "E", "F") +print(shortDistance) # E -- 3 --> F == 3 +shortDistance = dijkstra(G3, "E", "F") +print(shortDistance) # E -- 2 --> G -- 1 --> F == 3 -G = {'A': [['B', 2], ['C', 5]], - 'B': [['A', 2], ['D', 3], ['E', 1]], - 'C': [['A', 5], ['F', 3]], - 'D': [['B', 3]], - 'E': [['B', 1], ['F', 3]], - 'F': [['C', 3], ['E', 3]]} +if __name__ == "__main__": + import doctest -shortDistance = dijkstra(G, 'E', 'C') -print(shortDistance) + doctest.testmod() diff --git a/graphs/dijkstra_2.py b/graphs/dijkstra_2.py index a6c340e8a68d..762884136e4a 100644 --- a/graphs/dijkstra_2.py +++ b/graphs/dijkstra_2.py @@ -1,57 +1,58 @@ -from __future__ import print_function - def printDist(dist, V): - print("\nVertex Distance") - for i in range(V): - if dist[i] != float('inf') : - print(i,"\t",int(dist[i]),end = "\t") - else: - print(i,"\t","INF",end="\t") - print() + print("\nVertex Distance") + for i in range(V): + if dist[i] != float("inf"): + print(i, "\t", int(dist[i]), end="\t") + else: + print(i, "\t", "INF", end="\t") + print() + def minDist(mdist, vset, V): - minVal = float('inf') - minInd = -1 - for i in range(V): - if (not vset[i]) and mdist[i] < minVal : - minInd = i - minVal = mdist[i] - return minInd + minVal = float("inf") + minInd = -1 + for i in range(V): + if (not vset[i]) and mdist[i] < minVal: + minInd = i + minVal = mdist[i] + return minInd + def Dijkstra(graph, V, src): - mdist=[float('inf') for i in range(V)] - vset = [False for i in range(V)] - mdist[src] = 0.0 - - for i in range(V-1): - u = minDist(mdist, vset, V) - vset[u] = True - - for v in range(V): - if (not vset[v]) and graph[u][v]!=float('inf') and mdist[u] + graph[u][v] < mdist[v]: - mdist[v] = mdist[u] + graph[u][v] - - - - printDist(mdist, V) - - - -#MAIN -V = int(input("Enter number of vertices: ")) -E = int(input("Enter number of edges: ")) - -graph = [[float('inf') for i in range(V)] for j in range(V)] - -for i in range(V): - graph[i][i] = 0.0 - -for i in range(E): - print("\nEdge ",i+1) - src = int(input("Enter source:")) - dst = int(input("Enter destination:")) - weight = float(input("Enter weight:")) - graph[src][dst] = weight - -gsrc = int(input("\nEnter shortest path source:")) -Dijkstra(graph, V, gsrc) + mdist = [float("inf") for i in range(V)] + vset = [False for i in range(V)] + mdist[src] = 0.0 + + for i in range(V - 1): + u = minDist(mdist, vset, V) + vset[u] = True + + for v in range(V): + if ( + (not vset[v]) + and graph[u][v] != float("inf") + and mdist[u] + graph[u][v] < mdist[v] + ): + mdist[v] = mdist[u] + graph[u][v] + + printDist(mdist, V) + + +if __name__ == "__main__": + V = int(input("Enter number of vertices: ").strip()) + E = int(input("Enter number of edges: ").strip()) + + graph = [[float("inf") for i in range(V)] for j in range(V)] + + for i in range(V): + graph[i][i] = 0.0 + + for i in range(E): + print("\nEdge ", i + 1) + src = int(input("Enter source:").strip()) + dst = int(input("Enter destination:").strip()) + weight = float(input("Enter weight:").strip()) + graph[src][dst] = weight + + gsrc = int(input("\nEnter shortest path source:").strip()) + Dijkstra(graph, V, gsrc) diff --git a/graphs/dijkstra_algorithm.py b/graphs/dijkstra_algorithm.py index 985c7f6c1301..9304a83148f3 100644 --- a/graphs/dijkstra_algorithm.py +++ b/graphs/dijkstra_algorithm.py @@ -2,9 +2,9 @@ # Author: Shubham Malik # References: https://en.wikipedia.org/wiki/Dijkstra%27s_algorithm -from __future__ import print_function import math import sys + # For storing the vertex set to retreive node with the lowest distance @@ -13,7 +13,7 @@ class PriorityQueue: def __init__(self): self.cur_size = 0 self.array = [] - self.pos = {} # To store the pos of node in array + self.pos = {} # To store the pos of node in array def isEmpty(self): return self.cur_size == 0 @@ -79,8 +79,8 @@ def decrease_key(self, tup, new_d): class Graph: def __init__(self, num): - self.adjList = {} # To store graph: u -> (v,w) - self.num_nodes = num # Number of nodes in graph + self.adjList = {} # To store graph: u -> (v,w) + self.num_nodes = num # Number of nodes in graph # To store the distance from source vertex self.dist = [0] * self.num_nodes self.par = [-1] * self.num_nodes # To store the path @@ -103,8 +103,11 @@ def add_edge(self, u, v, w): def show_graph(self): # u -> v(w) for u in self.adjList: - print(u, '->', ' -> '.join(str("{}({})".format(v, w)) - for v, w in self.adjList[u])) + print( + u, + "->", + " -> ".join(str("{}({})".format(v, w)) for v, w in self.adjList[u]), + ) def dijkstra(self, src): # Flush old junk values in par[] @@ -138,7 +141,7 @@ def dijkstra(self, src): def show_distances(self, src): print("Distance from node: {}".format(src)) for u in range(self.num_nodes): - print('Node {} has distance: {}'.format(u, self.dist[u])) + print("Node {} has distance: {}".format(u, self.dist[u])) def show_path(self, src, dest): # To show the shortest path from src to dest @@ -158,16 +161,16 @@ def show_path(self, src, dest): path.append(src) path.reverse() - print('----Path to reach {} from {}----'.format(dest, src)) + print("----Path to reach {} from {}----".format(dest, src)) for u in path: - print('{}'.format(u), end=' ') + print("{}".format(u), end=" ") if u != dest: - print('-> ', end='') + print("-> ", end="") - print('\nTotal cost of path: ', cost) + print("\nTotal cost of path: ", cost) -if __name__ == '__main__': +if __name__ == "__main__": graph = Graph(9) graph.add_edge(0, 1, 4) graph.add_edge(0, 7, 8) diff --git a/graphs/dinic.py b/graphs/dinic.py new file mode 100644 index 000000000000..4f5e81236984 --- /dev/null +++ b/graphs/dinic.py @@ -0,0 +1,94 @@ +INF = float("inf") + + +class Dinic: + def __init__(self, n): + self.lvl = [0] * n + self.ptr = [0] * n + self.q = [0] * n + self.adj = [[] for _ in range(n)] + + """ + Here we will add our edges containing with the following parameters: + vertex closest to source, vertex closest to sink and flow capacity + through that edge ... + """ + + def add_edge(self, a, b, c, rcap=0): + self.adj[a].append([b, len(self.adj[b]), c, 0]) + self.adj[b].append([a, len(self.adj[a]) - 1, rcap, 0]) + + # This is a sample depth first search to be used at max_flow + def depth_first_search(self, vertex, sink, flow): + if vertex == sink or not flow: + return flow + + for i in range(self.ptr[vertex], len(self.adj[vertex])): + e = self.adj[vertex][i] + if self.lvl[e[0]] == self.lvl[vertex] + 1: + p = self.depth_first_search(e[0], sink, min(flow, e[2] - e[3])) + if p: + self.adj[vertex][i][3] += p + self.adj[e[0]][e[1]][3] -= p + return p + self.ptr[vertex] = self.ptr[vertex] + 1 + return 0 + + # Here we calculate the flow that reaches the sink + def max_flow(self, source, sink): + flow, self.q[0] = 0, source + for l in range(31): # l = 30 maybe faster for random data + while True: + self.lvl, self.ptr = [0] * len(self.q), [0] * len(self.q) + qi, qe, self.lvl[source] = 0, 1, 1 + while qi < qe and not self.lvl[sink]: + v = self.q[qi] + qi += 1 + for e in self.adj[v]: + if not self.lvl[e[0]] and (e[2] - e[3]) >> (30 - l): + self.q[qe] = e[0] + qe += 1 + self.lvl[e[0]] = self.lvl[v] + 1 + + p = self.depth_first_search(source, sink, INF) + while p: + flow += p + p = self.depth_first_search(source, sink, INF) + + if not self.lvl[sink]: + break + + return flow + + +# Example to use + +""" +Will be a bipartite graph, than it has the vertices near the source(4) +and the vertices near the sink(4) +""" +# Here we make a graphs with 10 vertex(source and sink includes) +graph = Dinic(10) +source = 0 +sink = 9 +""" +Now we add the vertices next to the font in the font with 1 capacity in this edge +(source -> source vertices) +""" +for vertex in range(1, 5): + graph.add_edge(source, vertex, 1) +""" +We will do the same thing for the vertices near the sink, but from vertex to sink +(sink vertices -> sink) +""" +for vertex in range(5, 9): + graph.add_edge(vertex, sink, 1) +""" +Finally we add the verices near the sink to the vertices near the source. +(source vertices -> sink vertices) +""" +for vertex in range(1, 5): + graph.add_edge(vertex, vertex + 4, 1) + +# Now we can know that is the maximum flow(source -> sink) +print(graph.max_flow(source, sink)) diff --git a/graphs/directed_and_undirected_(weighted)_graph.py b/graphs/directed_and_undirected_(weighted)_graph.py new file mode 100644 index 000000000000..883a8a00c6b1 --- /dev/null +++ b/graphs/directed_and_undirected_(weighted)_graph.py @@ -0,0 +1,497 @@ +from collections import deque +import random as rand +import math as math +import time + +# the dfault weight is 1 if not assigend but all the implementation is weighted + + +class DirectedGraph: + def __init__(self): + self.graph = {} + + # adding vertices and edges + # adding the weight is optional + # handels repetition + def add_pair(self, u, v, w=1): + if self.graph.get(u): + if self.graph[u].count([w, v]) == 0: + self.graph[u].append([w, v]) + else: + self.graph[u] = [[w, v]] + if not self.graph.get(v): + self.graph[v] = [] + + def all_nodes(self): + return list(self.graph) + + # handels if the input does not exist + def remove_pair(self, u, v): + if self.graph.get(u): + for _ in self.graph[u]: + if _[1] == v: + self.graph[u].remove(_) + + # if no destination is meant the defaut value is -1 + def dfs(self, s=-2, d=-1): + if s == d: + return [] + stack = [] + visited = [] + if s == -2: + s = list(self.graph.keys())[0] + stack.append(s) + visited.append(s) + ss = s + + while True: + # check if there is any non isolated nodes + if len(self.graph[s]) != 0: + ss = s + for __ in self.graph[s]: + if visited.count(__[1]) < 1: + if __[1] == d: + visited.append(d) + return visited + else: + stack.append(__[1]) + visited.append(__[1]) + ss = __[1] + break + + # check if all the children are visited + if s == ss: + stack.pop() + if len(stack) != 0: + s = stack[len(stack) - 1] + else: + s = ss + + # check if se have reached the starting point + if len(stack) == 0: + return visited + + # c is the count of nodes you want and if you leave it or pass -1 to the funtion the count + # will be random from 10 to 10000 + def fill_graph_randomly(self, c=-1): + if c == -1: + c = (math.floor(rand.random() * 10000)) + 10 + for _ in range(c): + # every vertex has max 100 edges + e = math.floor(rand.random() * 102) + 1 + for __ in range(e): + n = math.floor(rand.random() * (c)) + 1 + if n == _: + continue + self.add_pair(_, n, 1) + + def bfs(self, s=-2): + d = deque() + visited = [] + if s == -2: + s = list(self.graph.keys())[0] + d.append(s) + visited.append(s) + while d: + s = d.popleft() + if len(self.graph[s]) != 0: + for __ in self.graph[s]: + if visited.count(__[1]) < 1: + d.append(__[1]) + visited.append(__[1]) + return visited + + def in_degree(self, u): + count = 0 + for _ in self.graph: + for __ in self.graph[_]: + if __[1] == u: + count += 1 + return count + + def out_degree(self, u): + return len(self.graph[u]) + + def topological_sort(self, s=-2): + stack = [] + visited = [] + if s == -2: + s = list(self.graph.keys())[0] + stack.append(s) + visited.append(s) + ss = s + sorted_nodes = [] + + while True: + # check if there is any non isolated nodes + if len(self.graph[s]) != 0: + ss = s + for __ in self.graph[s]: + if visited.count(__[1]) < 1: + stack.append(__[1]) + visited.append(__[1]) + ss = __[1] + break + + # check if all the children are visited + if s == ss: + sorted_nodes.append(stack.pop()) + if len(stack) != 0: + s = stack[len(stack) - 1] + else: + s = ss + + # check if se have reached the starting point + if len(stack) == 0: + return sorted_nodes + + def cycle_nodes(self): + stack = [] + visited = [] + s = list(self.graph.keys())[0] + stack.append(s) + visited.append(s) + parent = -2 + indirect_parents = [] + ss = s + on_the_way_back = False + anticipating_nodes = set() + + while True: + # check if there is any non isolated nodes + if len(self.graph[s]) != 0: + ss = s + for __ in self.graph[s]: + if ( + visited.count(__[1]) > 0 + and __[1] != parent + and indirect_parents.count(__[1]) > 0 + and not on_the_way_back + ): + l = len(stack) - 1 + while True and l >= 0: + if stack[l] == __[1]: + anticipating_nodes.add(__[1]) + break + else: + anticipating_nodes.add(stack[l]) + l -= 1 + if visited.count(__[1]) < 1: + stack.append(__[1]) + visited.append(__[1]) + ss = __[1] + break + + # check if all the children are visited + if s == ss: + stack.pop() + on_the_way_back = True + if len(stack) != 0: + s = stack[len(stack) - 1] + else: + on_the_way_back = False + indirect_parents.append(parent) + parent = s + s = ss + + # check if se have reached the starting point + if len(stack) == 0: + return list(anticipating_nodes) + + def has_cycle(self): + stack = [] + visited = [] + s = list(self.graph.keys())[0] + stack.append(s) + visited.append(s) + parent = -2 + indirect_parents = [] + ss = s + on_the_way_back = False + anticipating_nodes = set() + + while True: + # check if there is any non isolated nodes + if len(self.graph[s]) != 0: + ss = s + for __ in self.graph[s]: + if ( + visited.count(__[1]) > 0 + and __[1] != parent + and indirect_parents.count(__[1]) > 0 + and not on_the_way_back + ): + l = len(stack) - 1 + while True and l >= 0: + if stack[l] == __[1]: + anticipating_nodes.add(__[1]) + break + else: + return True + anticipating_nodes.add(stack[l]) + l -= 1 + if visited.count(__[1]) < 1: + stack.append(__[1]) + visited.append(__[1]) + ss = __[1] + break + + # check if all the children are visited + if s == ss: + stack.pop() + on_the_way_back = True + if len(stack) != 0: + s = stack[len(stack) - 1] + else: + on_the_way_back = False + indirect_parents.append(parent) + parent = s + s = ss + + # check if se have reached the starting point + if len(stack) == 0: + return False + + def dfs_time(self, s=-2, e=-1): + begin = time.time() + self.dfs(s, e) + end = time.time() + return end - begin + + def bfs_time(self, s=-2): + begin = time.time() + self.bfs(s) + end = time.time() + return end - begin + + +class Graph: + def __init__(self): + self.graph = {} + + # adding vertices and edges + # adding the weight is optional + # handels repetition + def add_pair(self, u, v, w=1): + # check if the u exists + if self.graph.get(u): + # if there already is a edge + if self.graph[u].count([w, v]) == 0: + self.graph[u].append([w, v]) + else: + # if u does not exist + self.graph[u] = [[w, v]] + # add the other way + if self.graph.get(v): + # if there already is a edge + if self.graph[v].count([w, u]) == 0: + self.graph[v].append([w, u]) + else: + # if u does not exist + self.graph[v] = [[w, u]] + + # handels if the input does not exist + def remove_pair(self, u, v): + if self.graph.get(u): + for _ in self.graph[u]: + if _[1] == v: + self.graph[u].remove(_) + # the other way round + if self.graph.get(v): + for _ in self.graph[v]: + if _[1] == u: + self.graph[v].remove(_) + + # if no destination is meant the defaut value is -1 + def dfs(self, s=-2, d=-1): + if s == d: + return [] + stack = [] + visited = [] + if s == -2: + s = list(self.graph.keys())[0] + stack.append(s) + visited.append(s) + ss = s + + while True: + # check if there is any non isolated nodes + if len(self.graph[s]) != 0: + ss = s + for __ in self.graph[s]: + if visited.count(__[1]) < 1: + if __[1] == d: + visited.append(d) + return visited + else: + stack.append(__[1]) + visited.append(__[1]) + ss = __[1] + break + + # check if all the children are visited + if s == ss: + stack.pop() + if len(stack) != 0: + s = stack[len(stack) - 1] + else: + s = ss + + # check if se have reached the starting point + if len(stack) == 0: + return visited + + # c is the count of nodes you want and if you leave it or pass -1 to the funtion the count + # will be random from 10 to 10000 + def fill_graph_randomly(self, c=-1): + if c == -1: + c = (math.floor(rand.random() * 10000)) + 10 + for _ in range(c): + # every vertex has max 100 edges + e = math.floor(rand.random() * 102) + 1 + for __ in range(e): + n = math.floor(rand.random() * (c)) + 1 + if n == _: + continue + self.add_pair(_, n, 1) + + def bfs(self, s=-2): + d = deque() + visited = [] + if s == -2: + s = list(self.graph.keys())[0] + d.append(s) + visited.append(s) + while d: + s = d.popleft() + if len(self.graph[s]) != 0: + for __ in self.graph[s]: + if visited.count(__[1]) < 1: + d.append(__[1]) + visited.append(__[1]) + return visited + + def degree(self, u): + return len(self.graph[u]) + + def cycle_nodes(self): + stack = [] + visited = [] + s = list(self.graph.keys())[0] + stack.append(s) + visited.append(s) + parent = -2 + indirect_parents = [] + ss = s + on_the_way_back = False + anticipating_nodes = set() + + while True: + # check if there is any non isolated nodes + if len(self.graph[s]) != 0: + ss = s + for __ in self.graph[s]: + if ( + visited.count(__[1]) > 0 + and __[1] != parent + and indirect_parents.count(__[1]) > 0 + and not on_the_way_back + ): + l = len(stack) - 1 + while True and l >= 0: + if stack[l] == __[1]: + anticipating_nodes.add(__[1]) + break + else: + anticipating_nodes.add(stack[l]) + l -= 1 + if visited.count(__[1]) < 1: + stack.append(__[1]) + visited.append(__[1]) + ss = __[1] + break + + # check if all the children are visited + if s == ss: + stack.pop() + on_the_way_back = True + if len(stack) != 0: + s = stack[len(stack) - 1] + else: + on_the_way_back = False + indirect_parents.append(parent) + parent = s + s = ss + + # check if se have reached the starting point + if len(stack) == 0: + return list(anticipating_nodes) + + def has_cycle(self): + stack = [] + visited = [] + s = list(self.graph.keys())[0] + stack.append(s) + visited.append(s) + parent = -2 + indirect_parents = [] + ss = s + on_the_way_back = False + anticipating_nodes = set() + + while True: + # check if there is any non isolated nodes + if len(self.graph[s]) != 0: + ss = s + for __ in self.graph[s]: + if ( + visited.count(__[1]) > 0 + and __[1] != parent + and indirect_parents.count(__[1]) > 0 + and not on_the_way_back + ): + l = len(stack) - 1 + while True and l >= 0: + if stack[l] == __[1]: + anticipating_nodes.add(__[1]) + break + else: + return True + anticipating_nodes.add(stack[l]) + l -= 1 + if visited.count(__[1]) < 1: + stack.append(__[1]) + visited.append(__[1]) + ss = __[1] + break + + # check if all the children are visited + if s == ss: + stack.pop() + on_the_way_back = True + if len(stack) != 0: + s = stack[len(stack) - 1] + else: + on_the_way_back = False + indirect_parents.append(parent) + parent = s + s = ss + + # check if se have reached the starting point + if len(stack) == 0: + return False + + def all_nodes(self): + return list(self.graph) + + def dfs_time(self, s=-2, e=-1): + begin = time.time() + self.dfs(s, e) + end = time.time() + return end - begin + + def bfs_time(self, s=-2): + begin = time.time() + self.bfs(s) + end = time.time() + return end - begin diff --git a/Graphs/edmonds_karp_Multiple_SourceAndSink.py b/graphs/edmonds_karp_multiple_source_and_sink.py similarity index 88% rename from Graphs/edmonds_karp_Multiple_SourceAndSink.py rename to graphs/edmonds_karp_multiple_source_and_sink.py index d231ac2c4cc3..6334f05c50bd 100644 --- a/Graphs/edmonds_karp_Multiple_SourceAndSink.py +++ b/graphs/edmonds_karp_multiple_source_and_sink.py @@ -28,14 +28,13 @@ def _normalizeGraph(self, sources, sinks): for i in sources: maxInputFlow += sum(self.graph[i]) - size = len(self.graph) + 1 for room in self.graph: room.insert(0, 0) self.graph.insert(0, [0] * size) for i in sources: self.graph[0][i + 1] = maxInputFlow - self.sourceIndex = 0 + self.sourceIndex = 0 size = len(self.graph) + 1 for room in self.graph: @@ -45,7 +44,6 @@ def _normalizeGraph(self, sources, sinks): self.graph[i + 1][size - 1] = maxInputFlow self.sinkIndex = size - 1 - def findMaximumFlow(self): if self.maximumFlowAlgorithm is None: raise Exception("You need to set maximum flow algorithm before.") @@ -80,7 +78,6 @@ def _algorithm(self): pass - class MaximumFlowAlgorithmExecutor(FlowNetworkAlgorithmExecutor): def __init__(self, flowNetwork): super(MaximumFlowAlgorithmExecutor, self).__init__(flowNetwork) @@ -93,6 +90,7 @@ def getMaximumFlow(self): return self.maximumFlow + class PushRelabelExecutor(MaximumFlowAlgorithmExecutor): def __init__(self, flowNetwork): super(PushRelabelExecutor, self).__init__(flowNetwork) @@ -112,8 +110,11 @@ def _algorithm(self): self.excesses[nextVertexIndex] += bandwidth # Relabel-to-front selection rule - verticesList = [i for i in range(self.verticesCount) - if i != self.sourceIndex and i != self.sinkIndex] + verticesList = [ + i + for i in range(self.verticesCount) + if i != self.sourceIndex and i != self.sinkIndex + ] # move through list i = 0 @@ -135,15 +136,21 @@ def processVertex(self, vertexIndex): while self.excesses[vertexIndex] > 0: for neighbourIndex in range(self.verticesCount): # if it's neighbour and current vertex is higher - if self.graph[vertexIndex][neighbourIndex] - self.preflow[vertexIndex][neighbourIndex] > 0\ - and self.heights[vertexIndex] > self.heights[neighbourIndex]: + if ( + self.graph[vertexIndex][neighbourIndex] + - self.preflow[vertexIndex][neighbourIndex] + > 0 + and self.heights[vertexIndex] > self.heights[neighbourIndex] + ): self.push(vertexIndex, neighbourIndex) self.relabel(vertexIndex) def push(self, fromIndex, toIndex): - preflowDelta = min(self.excesses[fromIndex], - self.graph[fromIndex][toIndex] - self.preflow[fromIndex][toIndex]) + preflowDelta = min( + self.excesses[fromIndex], + self.graph[fromIndex][toIndex] - self.preflow[fromIndex][toIndex], + ) self.preflow[fromIndex][toIndex] += preflowDelta self.preflow[toIndex][fromIndex] -= preflowDelta self.excesses[fromIndex] -= preflowDelta @@ -152,14 +159,18 @@ def push(self, fromIndex, toIndex): def relabel(self, vertexIndex): minHeight = None for toIndex in range(self.verticesCount): - if self.graph[vertexIndex][toIndex] - self.preflow[vertexIndex][toIndex] > 0: + if ( + self.graph[vertexIndex][toIndex] - self.preflow[vertexIndex][toIndex] + > 0 + ): if minHeight is None or self.heights[toIndex] < minHeight: minHeight = self.heights[toIndex] if minHeight is not None: self.heights[vertexIndex] = minHeight + 1 -if __name__ == '__main__': + +if __name__ == "__main__": entrances = [0] exits = [3] # graph = [ diff --git a/graphs/eulerian_path_and_circuit_for_undirected_graph.py b/graphs/eulerian_path_and_circuit_for_undirected_graph.py new file mode 100644 index 000000000000..a2e5cf4da26a --- /dev/null +++ b/graphs/eulerian_path_and_circuit_for_undirected_graph.py @@ -0,0 +1,71 @@ +# Eulerian Path is a path in graph that visits every edge exactly once. +# Eulerian Circuit is an Eulerian Path which starts and ends on the same +# vertex. +# time complexity is O(V+E) +# space complexity is O(VE) + + +# using dfs for finding eulerian path traversal +def dfs(u, graph, visited_edge, path=[]): + path = path + [u] + for v in graph[u]: + if visited_edge[u][v] == False: + visited_edge[u][v], visited_edge[v][u] = True, True + path = dfs(v, graph, visited_edge, path) + return path + + +# for checking in graph has euler path or circuit +def check_circuit_or_path(graph, max_node): + odd_degree_nodes = 0 + odd_node = -1 + for i in range(max_node): + if i not in graph.keys(): + continue + if len(graph[i]) % 2 == 1: + odd_degree_nodes += 1 + odd_node = i + if odd_degree_nodes == 0: + return 1, odd_node + if odd_degree_nodes == 2: + return 2, odd_node + return 3, odd_node + + +def check_euler(graph, max_node): + visited_edge = [[False for _ in range(max_node + 1)] for _ in range(max_node + 1)] + check, odd_node = check_circuit_or_path(graph, max_node) + if check == 3: + print("graph is not Eulerian") + print("no path") + return + start_node = 1 + if check == 2: + start_node = odd_node + print("graph has a Euler path") + if check == 1: + print("graph has a Euler cycle") + path = dfs(start_node, graph, visited_edge) + print(path) + + +def main(): + G1 = {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]} + G2 = {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]} + G3 = {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]} + G4 = {1: [2, 3], 2: [1, 3], 3: [1, 2]} + G5 = { + 1: [], + 2: [] + # all degree is zero + } + max_node = 10 + check_euler(G1, max_node) + check_euler(G2, max_node) + check_euler(G3, max_node) + check_euler(G4, max_node) + check_euler(G5, max_node) + + +if __name__ == "__main__": + main() diff --git a/graphs/even_tree.py b/graphs/even_tree.py index 9383ea9a13c1..c9aef6e7861f 100644 --- a/graphs/even_tree.py +++ b/graphs/even_tree.py @@ -12,7 +12,6 @@ Note: The tree input will be such that it can always be decomposed into components containing an even number of nodes. """ -from __future__ import print_function # pylint: disable=invalid-name from collections import defaultdict @@ -46,23 +45,13 @@ def even_tree(): dfs(1) -if __name__ == '__main__': +if __name__ == "__main__": n, m = 10, 9 tree = defaultdict(list) visited = {} cuts = [] count = 0 - edges = [ - (2, 1), - (3, 1), - (4, 3), - (5, 2), - (6, 1), - (7, 2), - (8, 6), - (9, 8), - (10, 8), - ] + edges = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)] for u, v in edges: tree[u].append(v) tree[v].append(u) diff --git a/graphs/finding_bridges.py b/graphs/finding_bridges.py index 56533dd48bde..e18a3bafa9c0 100644 --- a/graphs/finding_bridges.py +++ b/graphs/finding_bridges.py @@ -1,7 +1,7 @@ # Finding Bridges in Undirected Graph def computeBridges(l): id = 0 - n = len(l) # No of vertices in graph + n = len(l) # No of vertices in graph low = [0] * n visited = [False] * n @@ -23,9 +23,20 @@ def dfs(at, parent, bridges, id): bridges = [] for i in range(n): - if (not visited[i]): + if not visited[i]: dfs(i, -1, bridges, id) print(bridges) - -l = {0:[1,2], 1:[0,2], 2:[0,1,3,5], 3:[2,4], 4:[3], 5:[2,6,8], 6:[5,7], 7:[6,8], 8:[5,7]} + + +l = { + 0: [1, 2], + 1: [0, 2], + 2: [0, 1, 3, 5], + 3: [2, 4], + 4: [3], + 5: [2, 6, 8], + 6: [5, 7], + 7: [6, 8], + 8: [5, 7], +} computeBridges(l) diff --git a/graphs/floyd_warshall.py b/graphs/floyd_warshall.py deleted file mode 100644 index fae8b19b351a..000000000000 --- a/graphs/floyd_warshall.py +++ /dev/null @@ -1,48 +0,0 @@ -from __future__ import print_function - -def printDist(dist, V): - print("\nThe shortest path matrix using Floyd Warshall algorithm\n") - for i in range(V): - for j in range(V): - if dist[i][j] != float('inf') : - print(int(dist[i][j]),end = "\t") - else: - print("INF",end="\t") - print() - - - -def FloydWarshall(graph, V): - dist=[[float('inf') for i in range(V)] for j in range(V)] - - for i in range(V): - for j in range(V): - dist[i][j] = graph[i][j] - - for k in range(V): - for i in range(V): - for j in range(V): - if dist[i][k]!=float('inf') and dist[k][j]!=float('inf') and dist[i][k]+dist[k][j] < dist[i][j]: - dist[i][j] = dist[i][k] + dist[k][j] - - printDist(dist, V) - - - -#MAIN -V = int(input("Enter number of vertices: ")) -E = int(input("Enter number of edges: ")) - -graph = [[float('inf') for i in range(V)] for j in range(V)] - -for i in range(V): - graph[i][i] = 0.0 - -for i in range(E): - print("\nEdge ",i+1) - src = int(input("Enter source:")) - dst = int(input("Enter destination:")) - weight = float(input("Enter weight:")) - graph[src][dst] = weight - -FloydWarshall(graph, V) diff --git a/graphs/g_topological_sort.py b/graphs/g_topological_sort.py new file mode 100644 index 000000000000..1a2f4fa11d88 --- /dev/null +++ b/graphs/g_topological_sort.py @@ -0,0 +1,47 @@ +# Author: Phyllipe Bezerra (https://github.com/pmba) + +clothes = { + 0: "underwear", + 1: "pants", + 2: "belt", + 3: "suit", + 4: "shoe", + 5: "socks", + 6: "shirt", + 7: "tie", + 8: "clock", +} + +graph = [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []] + +visited = [0 for x in range(len(graph))] +stack = [] + + +def print_stack(stack, clothes): + order = 1 + while stack: + cur_clothe = stack.pop() + print(order, clothes[cur_clothe]) + order += 1 + + +def dfs(u, visited, graph): + visited[u] = 1 + for v in graph[u]: + if not visited[v]: + dfs(v, visited, graph) + + stack.append(u) + + +def top_sort(graph, visited): + for v in range(len(graph)): + if not visited[v]: + dfs(v, visited, graph) + + +if __name__ == "__main__": + top_sort(graph, visited) + print(stack) + print_stack(stack, clothes) diff --git a/graphs/graph.py b/graphs/graph.py deleted file mode 100644 index 9bd61559dcbf..000000000000 --- a/graphs/graph.py +++ /dev/null @@ -1,44 +0,0 @@ -#!/usr/bin/python -# encoding=utf8 - -from __future__ import print_function -# Author: OMKAR PATHAK - -# We can use Python's dictionary for constructing the graph - -class AdjacencyList(object): - def __init__(self): - self.List = {} - - def addEdge(self, fromVertex, toVertex): - # check if vertex is already present - if fromVertex in self.List.keys(): - self.List[fromVertex].append(toVertex) - else: - self.List[fromVertex] = [toVertex] - - def printList(self): - for i in self.List: - print((i,'->',' -> '.join([str(j) for j in self.List[i]]))) - -if __name__ == '__main__': - al = AdjacencyList() - al.addEdge(0, 1) - al.addEdge(0, 4) - al.addEdge(4, 1) - al.addEdge(4, 3) - al.addEdge(1, 0) - al.addEdge(1, 4) - al.addEdge(1, 3) - al.addEdge(1, 2) - al.addEdge(2, 3) - al.addEdge(3, 4) - - al.printList() - - # OUTPUT: - # 0 -> 1 -> 4 - # 1 -> 0 -> 4 -> 3 -> 2 - # 2 -> 3 - # 3 -> 4 - # 4 -> 1 -> 3 diff --git a/graphs/graph_list.py b/graphs/graph_list.py index d67bc96c4a81..4f0cbf15c033 100644 --- a/graphs/graph_list.py +++ b/graphs/graph_list.py @@ -1,31 +1,45 @@ -from __future__ import print_function - - -class Graph: - def __init__(self, vertex): - self.vertex = vertex - self.graph = [[0] for i in range(vertex)] - - def add_edge(self, u, v): - self.graph[u - 1].append(v - 1) - - def show(self): - for i in range(self.vertex): - print('%d: '% (i + 1), end=' ') - for j in self.graph[i]: - print('%d-> '% (j + 1), end=' ') - print(' ') - - - -g = Graph(100) - -g.add_edge(1,3) -g.add_edge(2,3) -g.add_edge(3,4) -g.add_edge(3,5) -g.add_edge(4,5) - - -g.show() - +#!/usr/bin/python +# encoding=utf8 + +# Author: OMKAR PATHAK + +# We can use Python's dictionary for constructing the graph. + + +class AdjacencyList(object): + def __init__(self): + self.List = {} + + def addEdge(self, fromVertex, toVertex): + # check if vertex is already present + if fromVertex in self.List.keys(): + self.List[fromVertex].append(toVertex) + else: + self.List[fromVertex] = [toVertex] + + def printList(self): + for i in self.List: + print((i, "->", " -> ".join([str(j) for j in self.List[i]]))) + + +if __name__ == "__main__": + al = AdjacencyList() + al.addEdge(0, 1) + al.addEdge(0, 4) + al.addEdge(4, 1) + al.addEdge(4, 3) + al.addEdge(1, 0) + al.addEdge(1, 4) + al.addEdge(1, 3) + al.addEdge(1, 2) + al.addEdge(2, 3) + al.addEdge(3, 4) + + al.printList() + + # OUTPUT: + # 0 -> 1 -> 4 + # 1 -> 0 -> 4 -> 3 -> 2 + # 2 -> 3 + # 3 -> 4 + # 4 -> 1 -> 3 diff --git a/graphs/graph_matrix.py b/graphs/graph_matrix.py index de25301d6dd1..987168426ba5 100644 --- a/graphs/graph_matrix.py +++ b/graphs/graph_matrix.py @@ -1,11 +1,7 @@ -from __future__ import print_function - - class Graph: - def __init__(self, vertex): self.vertex = vertex - self.graph = [[0] * vertex for i in range(vertex) ] + self.graph = [[0] * vertex for i in range(vertex)] def add_edge(self, u, v): self.graph[u - 1][v - 1] = 1 @@ -15,18 +11,15 @@ def show(self): for i in self.graph: for j in i: - print(j, end=' ') - print(' ') - - + print(j, end=" ") + print(" ") g = Graph(100) -g.add_edge(1,4) -g.add_edge(4,2) -g.add_edge(4,5) -g.add_edge(2,5) -g.add_edge(5,3) +g.add_edge(1, 4) +g.add_edge(4, 2) +g.add_edge(4, 5) +g.add_edge(2, 5) +g.add_edge(5, 3) g.show() - diff --git a/graphs/graphs_floyd_warshall.py b/graphs/graphs_floyd_warshall.py new file mode 100644 index 000000000000..5727a2f21d89 --- /dev/null +++ b/graphs/graphs_floyd_warshall.py @@ -0,0 +1,101 @@ +# floyd_warshall.py +""" + The problem is to find the shortest distance between all pairs of vertices in a weighted directed graph that can + have negative edge weights. +""" + + +def _print_dist(dist, v): + print("\nThe shortest path matrix using Floyd Warshall algorithm\n") + for i in range(v): + for j in range(v): + if dist[i][j] != float("inf"): + print(int(dist[i][j]), end="\t") + else: + print("INF", end="\t") + print() + + +def floyd_warshall(graph, v): + """ + :param graph: 2D array calculated from weight[edge[i, j]] + :type graph: List[List[float]] + :param v: number of vertices + :type v: int + :return: shortest distance between all vertex pairs + distance[u][v] will contain the shortest distance from vertex u to v. + + 1. For all edges from v to n, distance[i][j] = weight(edge(i, j)). + 3. The algorithm then performs distance[i][j] = min(distance[i][j], distance[i][k] + distance[k][j]) for each + possible pair i, j of vertices. + 4. The above is repeated for each vertex k in the graph. + 5. Whenever distance[i][j] is given a new minimum value, next vertex[i][j] is updated to the next vertex[i][k]. + """ + + dist = [[float("inf") for _ in range(v)] for _ in range(v)] + + for i in range(v): + for j in range(v): + dist[i][j] = graph[i][j] + + # check vertex k against all other vertices (i, j) + for k in range(v): + # looping through rows of graph array + for i in range(v): + # looping through columns of graph array + for j in range(v): + if ( + dist[i][k] != float("inf") + and dist[k][j] != float("inf") + and dist[i][k] + dist[k][j] < dist[i][j] + ): + dist[i][j] = dist[i][k] + dist[k][j] + + _print_dist(dist, v) + return dist, v + + +if __name__ == "__main__": + v = int(input("Enter number of vertices: ")) + e = int(input("Enter number of edges: ")) + + graph = [[float("inf") for i in range(v)] for j in range(v)] + + for i in range(v): + graph[i][i] = 0.0 + + # src and dst are indices that must be within the array size graph[e][v] + # failure to follow this will result in an error + for i in range(e): + print("\nEdge ", i + 1) + src = int(input("Enter source:")) + dst = int(input("Enter destination:")) + weight = float(input("Enter weight:")) + graph[src][dst] = weight + + floyd_warshall(graph, v) + + # Example Input + # Enter number of vertices: 3 + # Enter number of edges: 2 + + # # generated graph from vertex and edge inputs + # [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]] + # [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]] + + # specify source, destination and weight for edge #1 + # Edge 1 + # Enter source:1 + # Enter destination:2 + # Enter weight:2 + + # specify source, destination and weight for edge #2 + # Edge 2 + # Enter source:2 + # Enter destination:1 + # Enter weight:1 + + # # Expected Output from the vertice, edge and src, dst, weight inputs!! + # 0 INF INF + # INF 0 2 + # INF 1 0 diff --git a/graphs/kahns_algorithm_long.py b/graphs/kahns_algorithm_long.py index 453b5706f6da..0651040365d0 100644 --- a/graphs/kahns_algorithm_long.py +++ b/graphs/kahns_algorithm_long.py @@ -12,19 +12,20 @@ def longestDistance(l): if indegree[i] == 0: queue.append(i) - while(queue): + while queue: vertex = queue.pop(0) for x in l[vertex]: indegree[x] -= 1 if longDist[vertex] + 1 > longDist[x]: - longDist[x] = longDist[vertex] + 1 + longDist[x] = longDist[vertex] + 1 if indegree[x] == 0: queue.append(x) print(max(longDist)) + # Adjacency list of Graph -l = {0:[2,3,4], 1:[2,7], 2:[5], 3:[5,7], 4:[7], 5:[6], 6:[7], 7:[]} +l = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []} longestDistance(l) diff --git a/graphs/kahns_algorithm_topo.py b/graphs/kahns_algorithm_topo.py index 8c182c4e902c..d50bc9a43d19 100644 --- a/graphs/kahns_algorithm_topo.py +++ b/graphs/kahns_algorithm_topo.py @@ -13,7 +13,7 @@ def topologicalSort(l): if indegree[i] == 0: queue.append(i) - while(queue): + while queue: vertex = queue.pop(0) cnt += 1 topo.append(vertex) @@ -27,6 +27,7 @@ def topologicalSort(l): else: print(topo) + # Adjacency List of Graph -l = {0:[1,2], 1:[3], 2:[3], 3:[4,5], 4:[], 5:[]} +l = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []} topologicalSort(l) diff --git a/graphs/minimum_spanning_tree_kruskal.py b/graphs/minimum_spanning_tree_kruskal.py index 81d64f421a31..91b44f6508e7 100644 --- a/graphs/minimum_spanning_tree_kruskal.py +++ b/graphs/minimum_spanning_tree_kruskal.py @@ -1,32 +1,32 @@ -from __future__ import print_function -num_nodes, num_edges = list(map(int,input().split())) +if __name__ == "__main__": + num_nodes, num_edges = list(map(int, input().strip().split())) -edges = [] + edges = [] -for i in range(num_edges): - node1, node2, cost = list(map(int,input().split())) - edges.append((i,node1,node2,cost)) + for i in range(num_edges): + node1, node2, cost = list(map(int, input().strip().split())) + edges.append((i, node1, node2, cost)) -edges = sorted(edges, key=lambda edge: edge[3]) + edges = sorted(edges, key=lambda edge: edge[3]) -parent = [i for i in range(num_nodes)] + parent = list(range(num_nodes)) -def find_parent(i): - if(i != parent[i]): - parent[i] = find_parent(parent[i]) - return parent[i] + def find_parent(i): + if i != parent[i]: + parent[i] = find_parent(parent[i]) + return parent[i] -minimum_spanning_tree_cost = 0 -minimum_spanning_tree = [] + minimum_spanning_tree_cost = 0 + minimum_spanning_tree = [] -for edge in edges: - parent_a = find_parent(edge[1]) - parent_b = find_parent(edge[2]) - if(parent_a != parent_b): - minimum_spanning_tree_cost += edge[3] - minimum_spanning_tree.append(edge) - parent[parent_a] = parent_b + for edge in edges: + parent_a = find_parent(edge[1]) + parent_b = find_parent(edge[2]) + if parent_a != parent_b: + minimum_spanning_tree_cost += edge[3] + minimum_spanning_tree.append(edge) + parent[parent_a] = parent_b -print(minimum_spanning_tree_cost) -for edge in minimum_spanning_tree: - print(edge) + print(minimum_spanning_tree_cost) + for edge in minimum_spanning_tree: + print(edge) diff --git a/graphs/minimum_spanning_tree_prims.py b/graphs/minimum_spanning_tree_prims.py index 7b1ad0e743f7..216d6a3f56de 100644 --- a/graphs/minimum_spanning_tree_prims.py +++ b/graphs/minimum_spanning_tree_prims.py @@ -1,9 +1,11 @@ import sys from collections import defaultdict + def PrimsAlgorithm(l): nodePosition = [] + def getPosition(vertex): return nodePosition[vertex] @@ -36,11 +38,11 @@ def topToBottom(heap, start, size, positions): def bottomToTop(val, index, heap, position): temp = position[index] - while(index != 0): + while index != 0: if index % 2 == 0: - parent = int( (index-2) / 2 ) + parent = int((index - 2) / 2) else: - parent = int( (index-1) / 2 ) + parent = int((index - 1) / 2) if val < heap[parent]: heap[index] = heap[parent] @@ -69,9 +71,9 @@ def deleteMinimum(heap, positions): return temp visited = [0 for i in range(len(l))] - Nbr_TV = [-1 for i in range(len(l))] # Neighboring Tree Vertex of selected vertex + Nbr_TV = [-1 for i in range(len(l))] # Neighboring Tree Vertex of selected vertex # Minimum Distance of explored vertex with neighboring vertex of partial tree formed in graph - Distance_TV = [] # Heap of Distance of vertices from their neighboring vertex + Distance_TV = [] # Heap of Distance of vertices from their neighboring vertex Positions = [] for x in range(len(l)): @@ -84,8 +86,8 @@ def deleteMinimum(heap, positions): visited[0] = 1 Distance_TV[0] = sys.maxsize for x in l[0]: - Nbr_TV[ x[0] ] = 0 - Distance_TV[ x[0] ] = x[1] + Nbr_TV[x[0]] = 0 + Distance_TV[x[0]] = x[1] heapify(Distance_TV, Positions) for i in range(1, len(l)): @@ -94,18 +96,20 @@ def deleteMinimum(heap, positions): TreeEdges.append((Nbr_TV[vertex], vertex)) visited[vertex] = 1 for v in l[vertex]: - if visited[v[0]] == 0 and v[1] < Distance_TV[ getPosition(v[0]) ]: - Distance_TV[ getPosition(v[0]) ] = v[1] + if visited[v[0]] == 0 and v[1] < Distance_TV[getPosition(v[0])]: + Distance_TV[getPosition(v[0])] = v[1] bottomToTop(v[1], getPosition(v[0]), Distance_TV, Positions) - Nbr_TV[ v[0] ] = vertex + Nbr_TV[v[0]] = vertex return TreeEdges -# < --------- Prims Algorithm --------- > -n = int(input("Enter number of vertices: ")) -e = int(input("Enter number of edges: ")) -adjlist = defaultdict(list) -for x in range(e): - l = [int(x) for x in input().split()] - adjlist[l[0]].append([ l[1], l[2] ]) - adjlist[l[1]].append([ l[0], l[2] ]) -print(PrimsAlgorithm(adjlist)) + +if __name__ == "__main__": + # < --------- Prims Algorithm --------- > + n = int(input("Enter number of vertices: ").strip()) + e = int(input("Enter number of edges: ").strip()) + adjlist = defaultdict(list) + for x in range(e): + l = [int(x) for x in input().strip().split()] + adjlist[l[0]].append([l[1], l[2]]) + adjlist[l[1]].append([l[0], l[2]]) + print(PrimsAlgorithm(adjlist)) diff --git a/graphs/multi_hueristic_astar.py b/graphs/multi_hueristic_astar.py index 1acd098f327d..56cfc727d338 100644 --- a/graphs/multi_hueristic_astar.py +++ b/graphs/multi_hueristic_astar.py @@ -1,266 +1,322 @@ -from __future__ import print_function import heapq import numpy as np -try: - xrange # Python 2 -except NameError: - xrange = range # Python 3 - class PriorityQueue: - def __init__(self): - self.elements = [] - self.set = set() - - def minkey(self): - if not self.empty(): - return self.elements[0][0] - else: - return float('inf') - - def empty(self): - return len(self.elements) == 0 - - def put(self, item, priority): - if item not in self.set: - heapq.heappush(self.elements, (priority, item)) - self.set.add(item) - else: - # update - # print("update", item) - temp = [] - (pri, x) = heapq.heappop(self.elements) - while x != item: - temp.append((pri, x)) - (pri, x) = heapq.heappop(self.elements) - temp.append((priority, item)) - for (pro, xxx) in temp: - heapq.heappush(self.elements, (pro, xxx)) - - def remove_element(self, item): - if item in self.set: - self.set.remove(item) - temp = [] - (pro, x) = heapq.heappop(self.elements) - while x != item: - temp.append((pro, x)) - (pro, x) = heapq.heappop(self.elements) - for (prito, yyy) in temp: - heapq.heappush(self.elements, (prito, yyy)) - - def top_show(self): - return self.elements[0][1] - - def get(self): - (priority, item) = heapq.heappop(self.elements) - self.set.remove(item) - return (priority, item) + def __init__(self): + self.elements = [] + self.set = set() + + def minkey(self): + if not self.empty(): + return self.elements[0][0] + else: + return float("inf") + + def empty(self): + return len(self.elements) == 0 + + def put(self, item, priority): + if item not in self.set: + heapq.heappush(self.elements, (priority, item)) + self.set.add(item) + else: + # update + # print("update", item) + temp = [] + (pri, x) = heapq.heappop(self.elements) + while x != item: + temp.append((pri, x)) + (pri, x) = heapq.heappop(self.elements) + temp.append((priority, item)) + for (pro, xxx) in temp: + heapq.heappush(self.elements, (pro, xxx)) + + def remove_element(self, item): + if item in self.set: + self.set.remove(item) + temp = [] + (pro, x) = heapq.heappop(self.elements) + while x != item: + temp.append((pro, x)) + (pro, x) = heapq.heappop(self.elements) + for (prito, yyy) in temp: + heapq.heappush(self.elements, (prito, yyy)) + + def top_show(self): + return self.elements[0][1] + + def get(self): + (priority, item) = heapq.heappop(self.elements) + self.set.remove(item) + return (priority, item) + def consistent_hueristic(P, goal): - # euclidean distance - a = np.array(P) - b = np.array(goal) - return np.linalg.norm(a - b) + # euclidean distance + a = np.array(P) + b = np.array(goal) + return np.linalg.norm(a - b) + def hueristic_2(P, goal): - # integer division by time variable - return consistent_hueristic(P, goal) // t + # integer division by time variable + return consistent_hueristic(P, goal) // t + def hueristic_1(P, goal): - # manhattan distance - return abs(P[0] - goal[0]) + abs(P[1] - goal[1]) + # manhattan distance + return abs(P[0] - goal[0]) + abs(P[1] - goal[1]) + def key(start, i, goal, g_function): - ans = g_function[start] + W1 * hueristics[i](start, goal) - return ans - + ans = g_function[start] + W1 * hueristics[i](start, goal) + return ans + + def do_something(back_pointer, goal, start): - grid = np.chararray((n, n)) - for i in range(n): - for j in range(n): - grid[i][j] = '*' - - for i in range(n): - for j in range(n): - if (j, (n-1)-i) in blocks: - grid[i][j] = "#" - - grid[0][(n-1)] = "-" - x = back_pointer[goal] - while x != start: - (x_c, y_c) = x - # print(x) - grid[(n-1)-y_c][x_c] = "-" - x = back_pointer[x] - grid[(n-1)][0] = "-" - - - for i in xrange(n): - for j in range(n): - if (i, j) == (0, n-1): - print(grid[i][j], end=' ') - print("<-- End position", end=' ') - else: - print(grid[i][j], end=' ') - print() - print("^") - print("Start position") - print() - print("# is an obstacle") - print("- is the path taken by algorithm") - print("PATH TAKEN BY THE ALGORITHM IS:-") - x = back_pointer[goal] - while x != start: - print(x, end=' ') - x = back_pointer[x] - print(x) - quit() + grid = np.chararray((n, n)) + for i in range(n): + for j in range(n): + grid[i][j] = "*" + + for i in range(n): + for j in range(n): + if (j, (n - 1) - i) in blocks: + grid[i][j] = "#" + + grid[0][(n - 1)] = "-" + x = back_pointer[goal] + while x != start: + (x_c, y_c) = x + # print(x) + grid[(n - 1) - y_c][x_c] = "-" + x = back_pointer[x] + grid[(n - 1)][0] = "-" + + for i in range(n): + for j in range(n): + if (i, j) == (0, n - 1): + print(grid[i][j], end=" ") + print("<-- End position", end=" ") + else: + print(grid[i][j], end=" ") + print() + print("^") + print("Start position") + print() + print("# is an obstacle") + print("- is the path taken by algorithm") + print("PATH TAKEN BY THE ALGORITHM IS:-") + x = back_pointer[goal] + while x != start: + print(x, end=" ") + x = back_pointer[x] + print(x) + quit() + def valid(p): - if p[0] < 0 or p[0] > n-1: - return False - if p[1] < 0 or p[1] > n-1: - return False - return True - -def expand_state(s, j, visited, g_function, close_list_anchor, close_list_inad, open_list, back_pointer): - for itera in range(n_hueristic): - open_list[itera].remove_element(s) - # print("s", s) - # print("j", j) - (x, y) = s - left = (x-1, y) - right = (x+1, y) - up = (x, y+1) - down = (x, y-1) - - for neighbours in [left, right, up, down]: - if neighbours not in blocks: - if valid(neighbours) and neighbours not in visited: - # print("neighbour", neighbours) - visited.add(neighbours) - back_pointer[neighbours] = -1 - g_function[neighbours] = float('inf') - - if valid(neighbours) and g_function[neighbours] > g_function[s] + 1: - g_function[neighbours] = g_function[s] + 1 - back_pointer[neighbours] = s - if neighbours not in close_list_anchor: - open_list[0].put(neighbours, key(neighbours, 0, goal, g_function)) - if neighbours not in close_list_inad: - for var in range(1,n_hueristic): - if key(neighbours, var, goal, g_function) <= W2 * key(neighbours, 0, goal, g_function): - # print("why not plssssssssss") - open_list[j].put(neighbours, key(neighbours, var, goal, g_function)) - - - # print + if p[0] < 0 or p[0] > n - 1: + return False + if p[1] < 0 or p[1] > n - 1: + return False + return True + + +def expand_state( + s, + j, + visited, + g_function, + close_list_anchor, + close_list_inad, + open_list, + back_pointer, +): + for itera in range(n_hueristic): + open_list[itera].remove_element(s) + # print("s", s) + # print("j", j) + (x, y) = s + left = (x - 1, y) + right = (x + 1, y) + up = (x, y + 1) + down = (x, y - 1) + + for neighbours in [left, right, up, down]: + if neighbours not in blocks: + if valid(neighbours) and neighbours not in visited: + # print("neighbour", neighbours) + visited.add(neighbours) + back_pointer[neighbours] = -1 + g_function[neighbours] = float("inf") + + if valid(neighbours) and g_function[neighbours] > g_function[s] + 1: + g_function[neighbours] = g_function[s] + 1 + back_pointer[neighbours] = s + if neighbours not in close_list_anchor: + open_list[0].put(neighbours, key(neighbours, 0, goal, g_function)) + if neighbours not in close_list_inad: + for var in range(1, n_hueristic): + if key(neighbours, var, goal, g_function) <= W2 * key( + neighbours, 0, goal, g_function + ): + # print("why not plssssssssss") + open_list[j].put( + neighbours, key(neighbours, var, goal, g_function) + ) + + # print + def make_common_ground(): - some_list = [] - # block 1 - for x in range(1, 5): - for y in range(1, 6): - some_list.append((x, y)) - - # line - for x in range(15, 20): - some_list.append((x, 17)) - - # block 2 big - for x in range(10, 19): - for y in range(1, 15): - some_list.append((x, y)) - - # L block - for x in range(1, 4): - for y in range(12, 19): - some_list.append((x, y)) - for x in range(3, 13): - for y in range(16, 19): - some_list.append((x, y)) - return some_list + some_list = [] + # block 1 + for x in range(1, 5): + for y in range(1, 6): + some_list.append((x, y)) + + # line + for x in range(15, 20): + some_list.append((x, 17)) + + # block 2 big + for x in range(10, 19): + for y in range(1, 15): + some_list.append((x, y)) + + # L block + for x in range(1, 4): + for y in range(12, 19): + some_list.append((x, y)) + for x in range(3, 13): + for y in range(16, 19): + some_list.append((x, y)) + return some_list + hueristics = {0: consistent_hueristic, 1: hueristic_1, 2: hueristic_2} -blocks_blk = [(0, 1),(1, 1),(2, 1),(3, 1),(4, 1),(5, 1),(6, 1),(7, 1),(8, 1),(9, 1),(10, 1),(11, 1),(12, 1),(13, 1),(14, 1),(15, 1),(16, 1),(17, 1),(18, 1), (19, 1)] +blocks_blk = [ + (0, 1), + (1, 1), + (2, 1), + (3, 1), + (4, 1), + (5, 1), + (6, 1), + (7, 1), + (8, 1), + (9, 1), + (10, 1), + (11, 1), + (12, 1), + (13, 1), + (14, 1), + (15, 1), + (16, 1), + (17, 1), + (18, 1), + (19, 1), +] blocks_no = [] blocks_all = make_common_ground() - - blocks = blocks_blk # hyper parameters W1 = 1 W2 = 1 n = 20 -n_hueristic = 3 # one consistent and two other inconsistent +n_hueristic = 3 # one consistent and two other inconsistent # start and end destination start = (0, 0) -goal = (n-1, n-1) +goal = (n - 1, n - 1) t = 1 + + def multi_a_star(start, goal, n_hueristic): - g_function = {start: 0, goal: float('inf')} - back_pointer = {start:-1, goal:-1} - open_list = [] - visited = set() - - for i in range(n_hueristic): - open_list.append(PriorityQueue()) - open_list[i].put(start, key(start, i, goal, g_function)) - - close_list_anchor = [] - close_list_inad = [] - while open_list[0].minkey() < float('inf'): - for i in range(1, n_hueristic): - # print("i", i) - # print(open_list[0].minkey(), open_list[i].minkey()) - if open_list[i].minkey() <= W2 * open_list[0].minkey(): - global t - t += 1 - # print("less prio") - if g_function[goal] <= open_list[i].minkey(): - if g_function[goal] < float('inf'): - do_something(back_pointer, goal, start) - else: - _, get_s = open_list[i].top_show() - visited.add(get_s) - expand_state(get_s, i, visited, g_function, close_list_anchor, close_list_inad, open_list, back_pointer) - close_list_inad.append(get_s) - else: - # print("more prio") - if g_function[goal] <= open_list[0].minkey(): - if g_function[goal] < float('inf'): - do_something(back_pointer, goal, start) - else: - # print("hoolla") - get_s = open_list[0].top_show() - visited.add(get_s) - expand_state(get_s, 0, visited, g_function, close_list_anchor, close_list_inad, open_list, back_pointer) - close_list_anchor.append(get_s) - print("No path found to goal") - print() - for i in range(n-1,-1, -1): - for j in range(n): - if (j, i) in blocks: - print('#', end=' ') - elif (j, i) in back_pointer: - if (j, i) == (n-1, n-1): - print('*', end=' ') - else: - print('-', end=' ') - else: - print('*', end=' ') - if (j, i) == (n-1, n-1): - print('<-- End position', end=' ') - print() - print("^") - print("Start position") - print() - print("# is an obstacle") - print("- is the path taken by algorithm") -multi_a_star(start, goal, n_hueristic) + g_function = {start: 0, goal: float("inf")} + back_pointer = {start: -1, goal: -1} + open_list = [] + visited = set() + + for i in range(n_hueristic): + open_list.append(PriorityQueue()) + open_list[i].put(start, key(start, i, goal, g_function)) + + close_list_anchor = [] + close_list_inad = [] + while open_list[0].minkey() < float("inf"): + for i in range(1, n_hueristic): + # print("i", i) + # print(open_list[0].minkey(), open_list[i].minkey()) + if open_list[i].minkey() <= W2 * open_list[0].minkey(): + global t + t += 1 + # print("less prio") + if g_function[goal] <= open_list[i].minkey(): + if g_function[goal] < float("inf"): + do_something(back_pointer, goal, start) + else: + _, get_s = open_list[i].top_show() + visited.add(get_s) + expand_state( + get_s, + i, + visited, + g_function, + close_list_anchor, + close_list_inad, + open_list, + back_pointer, + ) + close_list_inad.append(get_s) + else: + # print("more prio") + if g_function[goal] <= open_list[0].minkey(): + if g_function[goal] < float("inf"): + do_something(back_pointer, goal, start) + else: + # print("hoolla") + get_s = open_list[0].top_show() + visited.add(get_s) + expand_state( + get_s, + 0, + visited, + g_function, + close_list_anchor, + close_list_inad, + open_list, + back_pointer, + ) + close_list_anchor.append(get_s) + print("No path found to goal") + print() + for i in range(n - 1, -1, -1): + for j in range(n): + if (j, i) in blocks: + print("#", end=" ") + elif (j, i) in back_pointer: + if (j, i) == (n - 1, n - 1): + print("*", end=" ") + else: + print("-", end=" ") + else: + print("*", end=" ") + if (j, i) == (n - 1, n - 1): + print("<-- End position", end=" ") + print() + print("^") + print("Start position") + print() + print("# is an obstacle") + print("- is the path taken by algorithm") + + +if __name__ == "__main__": + multi_a_star(start, goal, n_hueristic) diff --git a/graphs/page_rank.py b/graphs/page_rank.py new file mode 100644 index 000000000000..1e2c7d9aeb48 --- /dev/null +++ b/graphs/page_rank.py @@ -0,0 +1,72 @@ +""" +Author: https://github.com/bhushan-borole +""" +""" +The input graph for the algorithm is: + + A B C +A 0 1 1 +B 0 0 1 +C 1 0 0 + +""" + +graph = [[0, 1, 1], [0, 0, 1], [1, 0, 0]] + + +class Node: + def __init__(self, name): + self.name = name + self.inbound = [] + self.outbound = [] + + def add_inbound(self, node): + self.inbound.append(node) + + def add_outbound(self, node): + self.outbound.append(node) + + def __repr__(self): + return "Node {}: Inbound: {} ; Outbound: {}".format( + self.name, self.inbound, self.outbound + ) + + +def page_rank(nodes, limit=3, d=0.85): + ranks = {} + for node in nodes: + ranks[node.name] = 1 + + outbounds = {} + for node in nodes: + outbounds[node.name] = len(node.outbound) + + for i in range(limit): + print("======= Iteration {} =======".format(i + 1)) + for j, node in enumerate(nodes): + ranks[node.name] = (1 - d) + d * sum( + [ranks[ib] / outbounds[ib] for ib in node.inbound] + ) + print(ranks) + + +def main(): + names = list(input("Enter Names of the Nodes: ").split()) + + nodes = [Node(name) for name in names] + + for ri, row in enumerate(graph): + for ci, col in enumerate(row): + if col == 1: + nodes[ci].add_inbound(names[ri]) + nodes[ri].add_outbound(names[ci]) + + print("======= Nodes =======") + for node in nodes: + print(node) + + page_rank(nodes) + + +if __name__ == "__main__": + main() diff --git a/graphs/prim.py b/graphs/prim.py new file mode 100644 index 000000000000..336424d2c3c1 --- /dev/null +++ b/graphs/prim.py @@ -0,0 +1,79 @@ +""" +Prim's Algorithm. + +Determines the minimum spanning tree(MST) of a graph using the Prim's Algorithm + +Create a list to store x the vertices. +G = [vertex(n) for n in range(x)] + +For each vertex in G, add the neighbors: +G[x].addNeighbor(G[y]) +G[y].addNeighbor(G[x]) + +For each vertex in G, add the edges: +G[x].addEdge(G[y], w) +G[y].addEdge(G[x], w) + +To solve run: +MST = prim(G, G[0]) +""" + +import math + + +class vertex: + """Class Vertex.""" + + def __init__(self, id): + """ + Arguments: + id - input an id to identify the vertex + Attributes: + neighbors - a list of the vertices it is linked to + edges - a dict to store the edges's weight + """ + self.id = str(id) + self.key = None + self.pi = None + self.neighbors = [] + self.edges = {} # [vertex:distance] + + def __lt__(self, other): + """Comparison rule to < operator.""" + return self.key < other.key + + def __repr__(self): + """Return the vertex id.""" + return self.id + + def addNeighbor(self, vertex): + """Add a pointer to a vertex at neighbor's list.""" + self.neighbors.append(vertex) + + def addEdge(self, vertex, weight): + """Destination vertex and weight.""" + self.edges[vertex.id] = weight + + +def prim(graph, root): + """ + Prim's Algorithm. + Return a list with the edges of a Minimum Spanning Tree + prim(graph, graph[0]) + """ + A = [] + for u in graph: + u.key = math.inf + u.pi = None + root.key = 0 + Q = graph[:] + while Q: + u = min(Q) + Q.remove(u) + for v in u.neighbors: + if (v in Q) and (u.edges[v.id] < v.key): + v.pi = u + v.key = u.edges[v.id] + for i in range(1, len(graph)): + A.append([graph[i].id, graph[i].pi.id]) + return A diff --git a/graphs/scc_kosaraju.py b/graphs/scc_kosaraju.py index 1f13ebaba36b..573c1bf5e363 100644 --- a/graphs/scc_kosaraju.py +++ b/graphs/scc_kosaraju.py @@ -1,46 +1,51 @@ -from __future__ import print_function -# n - no of nodes, m - no of edges -n, m = list(map(int,input().split())) - -g = [[] for i in range(n)] #graph -r = [[] for i in range(n)] #reversed graph -# input graph data (edges) -for i in range(m): - u, v = list(map(int,input().split())) - g[u].append(v) - r[v].append(u) - -stack = [] -visit = [False]*n -scc = [] -component = [] - def dfs(u): global g, r, scc, component, visit, stack - if visit[u]: return + if visit[u]: + return visit[u] = True for v in g[u]: dfs(v) stack.append(u) + def dfs2(u): global g, r, scc, component, visit, stack - if visit[u]: return + if visit[u]: + return visit[u] = True component.append(u) for v in r[u]: dfs2(v) + def kosaraju(): global g, r, scc, component, visit, stack for i in range(n): dfs(i) - visit = [False]*n + visit = [False] * n for i in stack[::-1]: - if visit[i]: continue + if visit[i]: + continue component = [] dfs2(i) scc.append(component) return scc -print(kosaraju()) + +if __name__ == "__main__": + # n - no of nodes, m - no of edges + n, m = list(map(int, input().strip().split())) + + g = [[] for i in range(n)] # graph + r = [[] for i in range(n)] # reversed graph + # input graph data (edges) + for i in range(m): + u, v = list(map(int, input().strip().split())) + g[u].append(v) + r[v].append(u) + + stack = [] + visit = [False] * n + scc = [] + component = [] + print(kosaraju()) diff --git a/graphs/tarjans_scc.py b/graphs/tarjans_scc.py index 89754e593508..4b0a689ea3c0 100644 --- a/graphs/tarjans_scc.py +++ b/graphs/tarjans_scc.py @@ -36,9 +36,13 @@ def strong_connect(v, index, components): for w in g[v]: if index_of[w] == -1: index = strong_connect(w, index, components) - lowlink_of[v] = lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v] + lowlink_of[v] = ( + lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v] + ) elif on_stack[w]: - lowlink_of[v] = lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v] + lowlink_of[v] = ( + lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v] + ) if lowlink_of[v] == index_of[v]: component = [] @@ -67,7 +71,7 @@ def create_graph(n, edges): return g -if __name__ == '__main__': +if __name__ == "__main__": # Test n_vertices = 7 source = [0, 0, 1, 2, 3, 3, 4, 4, 6] diff --git a/hashes/chaos_machine.py b/hashes/chaos_machine.py index f0a305bfeade..8d3bbd4c0251 100644 --- a/hashes/chaos_machine.py +++ b/hashes/chaos_machine.py @@ -1,13 +1,9 @@ """example of simple chaos machine""" -from __future__ import print_function - -try: - input = raw_input # Python 2 -except NameError: - pass # Python 3 # Chaos Machine (K, t, m) -K = [0.33, 0.44, 0.55, 0.44, 0.33]; t = 3; m = 5 +K = [0.33, 0.44, 0.55, 0.44, 0.33] +t = 3 +m = 5 # Buffer Space (with Parameters Space) buffer_space, params_space = [], [] @@ -15,75 +11,73 @@ # Machine Time machine_time = 0 + def push(seed): - global buffer_space, params_space, machine_time, \ - K, m, t + global buffer_space, params_space, machine_time, K, m, t + + # Choosing Dynamical Systems (All) + for key, value in enumerate(buffer_space): + # Evolution Parameter + e = float(seed / value) - # Choosing Dynamical Systems (All) - for key, value in enumerate(buffer_space): - # Evolution Parameter - e = float(seed / value) + # Control Theory: Orbit Change + value = (buffer_space[(key + 1) % m] + e) % 1 - # Control Theory: Orbit Change - value = (buffer_space[(key + 1) % m] + e) % 1 + # Control Theory: Trajectory Change + r = (params_space[key] + e) % 1 + 3 - # Control Theory: Trajectory Change - r = (params_space[key] + e) % 1 + 3 + # Modification (Transition Function) - Jumps + buffer_space[key] = round(float(r * value * (1 - value)), 10) + params_space[key] = r # Saving to Parameters Space - # Modification (Transition Function) - Jumps - buffer_space[key] = \ - round(float(r * value * (1 - value)), 10) - params_space[key] = \ - r # Saving to Parameters Space + # Logistic Map + assert max(buffer_space) < 1 + assert max(params_space) < 4 - # Logistic Map - assert max(buffer_space) < 1 - assert max(params_space) < 4 + # Machine Time + machine_time += 1 - # Machine Time - machine_time += 1 def pull(): - global buffer_space, params_space, machine_time, \ - K, m, t + global buffer_space, params_space, machine_time, K, m, t - # PRNG (Xorshift by George Marsaglia) - def xorshift(X, Y): - X ^= Y >> 13 - Y ^= X << 17 - X ^= Y >> 5 - return X + # PRNG (Xorshift by George Marsaglia) + def xorshift(X, Y): + X ^= Y >> 13 + Y ^= X << 17 + X ^= Y >> 5 + return X - # Choosing Dynamical Systems (Increment) - key = machine_time % m + # Choosing Dynamical Systems (Increment) + key = machine_time % m - # Evolution (Time Length) - for i in range(0, t): - # Variables (Position + Parameters) - r = params_space[key] - value = buffer_space[key] + # Evolution (Time Length) + for i in range(0, t): + # Variables (Position + Parameters) + r = params_space[key] + value = buffer_space[key] - # Modification (Transition Function) - Flow - buffer_space[key] = \ - round(float(r * value * (1 - value)), 10) - params_space[key] = \ - (machine_time * 0.01 + r * 1.01) % 1 + 3 + # Modification (Transition Function) - Flow + buffer_space[key] = round(float(r * value * (1 - value)), 10) + params_space[key] = (machine_time * 0.01 + r * 1.01) % 1 + 3 - # Choosing Chaotic Data - X = int(buffer_space[(key + 2) % m] * (10 ** 10)) - Y = int(buffer_space[(key - 2) % m] * (10 ** 10)) + # Choosing Chaotic Data + X = int(buffer_space[(key + 2) % m] * (10 ** 10)) + Y = int(buffer_space[(key - 2) % m] * (10 ** 10)) - # Machine Time - machine_time += 1 + # Machine Time + machine_time += 1 + + return xorshift(X, Y) % 0xFFFFFFFF - return xorshift(X, Y) % 0xFFFFFFFF def reset(): - global buffer_space, params_space, machine_time, \ - K, m, t + global buffer_space, params_space, machine_time, K, m, t + + buffer_space = K + params_space = [0] * m + machine_time = 0 - buffer_space = K; params_space = [0] * m - machine_time = 0 ####################################### @@ -92,15 +86,17 @@ def reset(): # Pushing Data (Input) import random + message = random.sample(range(0xFFFFFFFF), 100) for chunk in message: - push(chunk) + push(chunk) -# for controlling +# for controlling inp = "" # Pulling Data (Output) while inp in ("e", "E"): - print("%s" % format(pull(), '#04x')) - print(buffer_space); print(params_space) - inp = input("(e)exit? ").strip() + print("%s" % format(pull(), "#04x")) + print(buffer_space) + print(params_space) + inp = input("(e)exit? ").strip() diff --git a/hashes/enigma_machine.py b/hashes/enigma_machine.py new file mode 100644 index 000000000000..5420bacc1409 --- /dev/null +++ b/hashes/enigma_machine.py @@ -0,0 +1,60 @@ +alphabets = [chr(i) for i in range(32, 126)] +gear_one = [i for i in range(len(alphabets))] +gear_two = [i for i in range(len(alphabets))] +gear_three = [i for i in range(len(alphabets))] +reflector = [i for i in reversed(range(len(alphabets)))] +code = [] +gear_one_pos = gear_two_pos = gear_three_pos = 0 + + +def rotator(): + global gear_one_pos + global gear_two_pos + global gear_three_pos + i = gear_one[0] + gear_one.append(i) + del gear_one[0] + gear_one_pos += 1 + if gear_one_pos % int(len(alphabets)) == 0: + i = gear_two[0] + gear_two.append(i) + del gear_two[0] + gear_two_pos += 1 + if gear_two_pos % int(len(alphabets)) == 0: + i = gear_three[0] + gear_three.append(i) + del gear_three[0] + gear_three_pos += 1 + + +def engine(input_character): + target = alphabets.index(input_character) + target = gear_one[target] + target = gear_two[target] + target = gear_three[target] + target = reflector[target] + target = gear_three.index(target) + target = gear_two.index(target) + target = gear_one.index(target) + code.append(alphabets[target]) + rotator() + + +if __name__ == "__main__": + decode = input("Type your message:\n") + decode = list(decode) + while True: + try: + token = int(input("Please set token:(must be only digits)\n")) + break + except Exception as error: + print(error) + for i in range(token): + rotator() + for i in decode: + engine(i) + print("\n" + "".join(code)) + print( + f"\nYour Token is {token} please write it down.\nIf you want to decode " + f"this message again you should input same digits as token!" + ) diff --git a/hashes/md5.py b/hashes/md5.py index d3f15510874e..85565533d175 100644 --- a/hashes/md5.py +++ b/hashes/md5.py @@ -1,155 +1,241 @@ -from __future__ import print_function import math + def rearrange(bitString32): - """[summary] - Regroups the given binary string. - - Arguments: - bitString32 {[string]} -- [32 bit binary] - - Raises: - ValueError -- [if the given string not are 32 bit binary string] - - Returns: - [string] -- [32 bit binary string] - """ - - if len(bitString32) != 32: - raise ValueError("Need length 32") - newString = "" - for i in [3,2,1,0]: - newString += bitString32[8*i:8*i+8] - return newString + """[summary] + Regroups the given binary string. + + Arguments: + bitString32 {[string]} -- [32 bit binary] + + Raises: + ValueError -- [if the given string not are 32 bit binary string] + + Returns: + [string] -- [32 bit binary string] + >>> rearrange('1234567890abcdfghijklmnopqrstuvw') + 'pqrstuvwhijklmno90abcdfg12345678' + """ + + if len(bitString32) != 32: + raise ValueError("Need length 32") + newString = "" + for i in [3, 2, 1, 0]: + newString += bitString32[8 * i : 8 * i + 8] + return newString + def reformatHex(i): - """[summary] - Converts the given integer into 8-digit hex number. + """[summary] + Converts the given integer into 8-digit hex number. + + Arguments: + i {[int]} -- [integer] + >>> reformatHex(666) + '9a020000' + """ - Arguments: - i {[int]} -- [integer] - """ + hexrep = format(i, "08x") + thing = "" + for i in [3, 2, 1, 0]: + thing += hexrep[2 * i : 2 * i + 2] + return thing - hexrep = format(i,'08x') - thing = "" - for i in [3,2,1,0]: - thing += hexrep[2*i:2*i+2] - return thing def pad(bitString): - """[summary] - Fills up the binary string to a 512 bit binary string - - Arguments: - bitString {[string]} -- [binary string] - - Returns: - [string] -- [binary string] - """ - - startLength = len(bitString) - bitString += '1' - while len(bitString) % 512 != 448: - bitString += '0' - lastPart = format(startLength,'064b') - bitString += rearrange(lastPart[32:]) + rearrange(lastPart[:32]) - return bitString + """[summary] + Fills up the binary string to a 512 bit binary string + + Arguments: + bitString {[string]} -- [binary string] + + Returns: + [string] -- [binary string] + """ + startLength = len(bitString) + bitString += "1" + while len(bitString) % 512 != 448: + bitString += "0" + lastPart = format(startLength, "064b") + bitString += rearrange(lastPart[32:]) + rearrange(lastPart[:32]) + return bitString + def getBlock(bitString): - """[summary] - Iterator: - Returns by each call a list of length 16 with the 32 bit - integer blocks. - - Arguments: - bitString {[string]} -- [binary string >= 512] - """ - - currPos = 0 - while currPos < len(bitString): - currPart = bitString[currPos:currPos+512] - mySplits = [] - for i in range(16): - mySplits.append(int(rearrange(currPart[32*i:32*i+32]),2)) - yield mySplits - currPos += 512 + """[summary] + Iterator: + Returns by each call a list of length 16 with the 32 bit + integer blocks. + + Arguments: + bitString {[string]} -- [binary string >= 512] + """ + + currPos = 0 + while currPos < len(bitString): + currPart = bitString[currPos : currPos + 512] + mySplits = [] + for i in range(16): + mySplits.append(int(rearrange(currPart[32 * i : 32 * i + 32]), 2)) + yield mySplits + currPos += 512 + def not32(i): - i_str = format(i,'032b') - new_str = '' - for c in i_str: - new_str += '1' if c=='0' else '0' - return int(new_str,2) + """ + >>> not32(34) + 4294967261 + """ + i_str = format(i, "032b") + new_str = "" + for c in i_str: + new_str += "1" if c == "0" else "0" + return int(new_str, 2) + + +def sum32(a, b): + """ -def sum32(a,b): - return (a + b) % 2**32 + """ + return (a + b) % 2 ** 32 + + +def leftrot32(i, s): + return (i << s) ^ (i >> (32 - s)) -def leftrot32(i,s): - return (i << s) ^ (i >> (32-s)) def md5me(testString): - """[summary] - Returns a 32-bit hash code of the string 'testString' - - Arguments: - testString {[string]} -- [message] - """ - - bs ='' - for i in testString: - bs += format(ord(i),'08b') - bs = pad(bs) - - tvals = [int(2**32 * abs(math.sin(i+1))) for i in range(64)] - - a0 = 0x67452301 - b0 = 0xefcdab89 - c0 = 0x98badcfe - d0 = 0x10325476 - - s = [7, 12, 17, 22, 7, 12, 17, 22, 7, 12, 17, 22, 7, 12, 17, 22, \ - 5, 9, 14, 20, 5, 9, 14, 20, 5, 9, 14, 20, 5, 9, 14, 20, \ - 4, 11, 16, 23, 4, 11, 16, 23, 4, 11, 16, 23, 4, 11, 16, 23, \ - 6, 10, 15, 21, 6, 10, 15, 21, 6, 10, 15, 21, 6, 10, 15, 21 ] - - for m in getBlock(bs): - A = a0 - B = b0 - C = c0 - D = d0 - for i in range(64): - if i <= 15: - #f = (B & C) | (not32(B) & D) - f = D ^ (B & (C ^ D)) - g = i - elif i<= 31: - #f = (D & B) | (not32(D) & C) - f = C ^ (D & (B ^ C)) - g = (5*i+1) % 16 - elif i <= 47: - f = B ^ C ^ D - g = (3*i+5) % 16 - else: - f = C ^ (B | not32(D)) - g = (7*i) % 16 - dtemp = D - D = C - C = B - B = sum32(B,leftrot32((A + f + tvals[i] + m[g]) % 2**32, s[i])) - A = dtemp - a0 = sum32(a0, A) - b0 = sum32(b0, B) - c0 = sum32(c0, C) - d0 = sum32(d0, D) - - digest = reformatHex(a0) + reformatHex(b0) + reformatHex(c0) + reformatHex(d0) - return digest + """[summary] + Returns a 32-bit hash code of the string 'testString' + + Arguments: + testString {[string]} -- [message] + """ + + bs = "" + for i in testString: + bs += format(ord(i), "08b") + bs = pad(bs) + + tvals = [int(2 ** 32 * abs(math.sin(i + 1))) for i in range(64)] + + a0 = 0x67452301 + b0 = 0xEFCDAB89 + c0 = 0x98BADCFE + d0 = 0x10325476 + + s = [ + 7, + 12, + 17, + 22, + 7, + 12, + 17, + 22, + 7, + 12, + 17, + 22, + 7, + 12, + 17, + 22, + 5, + 9, + 14, + 20, + 5, + 9, + 14, + 20, + 5, + 9, + 14, + 20, + 5, + 9, + 14, + 20, + 4, + 11, + 16, + 23, + 4, + 11, + 16, + 23, + 4, + 11, + 16, + 23, + 4, + 11, + 16, + 23, + 6, + 10, + 15, + 21, + 6, + 10, + 15, + 21, + 6, + 10, + 15, + 21, + 6, + 10, + 15, + 21, + ] + + for m in getBlock(bs): + A = a0 + B = b0 + C = c0 + D = d0 + for i in range(64): + if i <= 15: + # f = (B & C) | (not32(B) & D) + f = D ^ (B & (C ^ D)) + g = i + elif i <= 31: + # f = (D & B) | (not32(D) & C) + f = C ^ (D & (B ^ C)) + g = (5 * i + 1) % 16 + elif i <= 47: + f = B ^ C ^ D + g = (3 * i + 5) % 16 + else: + f = C ^ (B | not32(D)) + g = (7 * i) % 16 + dtemp = D + D = C + C = B + B = sum32(B, leftrot32((A + f + tvals[i] + m[g]) % 2 ** 32, s[i])) + A = dtemp + a0 = sum32(a0, A) + b0 = sum32(b0, B) + c0 = sum32(c0, C) + d0 = sum32(d0, D) + + digest = reformatHex(a0) + reformatHex(b0) + reformatHex(c0) + reformatHex(d0) + return digest + def test(): - assert md5me("") == "d41d8cd98f00b204e9800998ecf8427e" - assert md5me("The quick brown fox jumps over the lazy dog") == "9e107d9d372bb6826bd81d3542a419d6" - print("Success.") + assert md5me("") == "d41d8cd98f00b204e9800998ecf8427e" + assert ( + md5me("The quick brown fox jumps over the lazy dog") + == "9e107d9d372bb6826bd81d3542a419d6" + ) + print("Success.") if __name__ == "__main__": - test() + test() + import doctest + + doctest.testmod() diff --git a/hashes/sha1.py b/hashes/sha1.py index 4c78ad3a89e5..3bf27af27582 100644 --- a/hashes/sha1.py +++ b/hashes/sha1.py @@ -2,7 +2,7 @@ Demonstrates implementation of SHA1 Hash function in a Python class and gives utilities to find hash of string or hash of text from a file. Usage: python sha1.py --string "Hello World!!" - pyhton sha1.py --file "hello_world.txt" + python sha1.py --file "hello_world.txt" When run without any arguments, it prints the hash of the string "Hello World!! Welcome to Cryptography" Also contains a Test class to verify that the generated Hash is same as that returned by the hashlib library @@ -25,14 +25,17 @@ import argparse import struct -import hashlib #hashlib is only used inside the Test class +import hashlib # hashlib is only used inside the Test class import unittest class SHA1Hash: """ Class to contain the entire pipeline for SHA1 Hashing Algorithm + >>> SHA1Hash(bytes('Allan', 'utf-8')).final_hash() + '872af2d8ac3d8695387e7c804bf0e02c18df9e6e' """ + def __init__(self, data): """ Inititates the variables data and h. h is a list of 5 8-digit Hexadecimal @@ -47,32 +50,36 @@ def __init__(self, data): def rotate(n, b): """ Static method to be used inside other methods. Left rotates n by b. + >>> SHA1Hash('').rotate(12,2) + 48 """ - return ((n << b) | (n >> (32 - b))) & 0xffffffff + return ((n << b) | (n >> (32 - b))) & 0xFFFFFFFF def padding(self): """ Pads the input message with zeros so that padded_data has 64 bytes or 512 bits """ - padding = b'\x80' + b'\x00'*(63 - (len(self.data) + 8) % 64) - padded_data = self.data + padding + struct.pack('>Q', 8 * len(self.data)) + padding = b"\x80" + b"\x00" * (63 - (len(self.data) + 8) % 64) + padded_data = self.data + padding + struct.pack(">Q", 8 * len(self.data)) return padded_data def split_blocks(self): """ Returns a list of bytestrings each of length 64 """ - return [self.padded_data[i:i+64] for i in range(0, len(self.padded_data), 64)] + return [ + self.padded_data[i : i + 64] for i in range(0, len(self.padded_data), 64) + ] # @staticmethod def expand_block(self, block): """ Takes a bytestring-block of length 64, unpacks it to a list of integers and returns a - list of 80 integers pafter some bit operations + list of 80 integers after some bit operations """ - w = list(struct.unpack('>16L', block)) + [0] * 64 + w = list(struct.unpack(">16L", block)) + [0] * 64 for i in range(16, 80): - w[i] = self.rotate((w[i-3] ^ w[i-8] ^ w[i-14] ^ w[i-16]), 1) + w[i] = self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]), 1) return w def final_hash(self): @@ -102,22 +109,30 @@ def final_hash(self): elif 60 <= i < 80: f = b ^ c ^ d k = 0xCA62C1D6 - a, b, c, d, e = self.rotate(a, 5) + f + e + k + expanded_block[i] & 0xffffffff,\ - a, self.rotate(b, 30), c, d - self.h = self.h[0] + a & 0xffffffff,\ - self.h[1] + b & 0xffffffff,\ - self.h[2] + c & 0xffffffff,\ - self.h[3] + d & 0xffffffff,\ - self.h[4] + e & 0xffffffff - return '%08x%08x%08x%08x%08x' %tuple(self.h) + a, b, c, d, e = ( + self.rotate(a, 5) + f + e + k + expanded_block[i] & 0xFFFFFFFF, + a, + self.rotate(b, 30), + c, + d, + ) + self.h = ( + self.h[0] + a & 0xFFFFFFFF, + self.h[1] + b & 0xFFFFFFFF, + self.h[2] + c & 0xFFFFFFFF, + self.h[3] + d & 0xFFFFFFFF, + self.h[4] + e & 0xFFFFFFFF, + ) + return "%08x%08x%08x%08x%08x" % tuple(self.h) class SHA1HashTest(unittest.TestCase): """ Test class for the SHA1Hash class. Inherits the TestCase class from unittest """ + def testMatchHashes(self): - msg = bytes('Test String', 'utf-8') + msg = bytes("Test String", "utf-8") self.assertEqual(SHA1Hash(msg).final_hash(), hashlib.sha1(msg).hexdigest()) @@ -128,21 +143,27 @@ def main(): the test each time. """ # unittest.main() - parser = argparse.ArgumentParser(description='Process some strings or files') - parser.add_argument('--string', dest='input_string', - default='Hello World!! Welcome to Cryptography', - help='Hash the string') - parser.add_argument('--file', dest='input_file', help='Hash contents of a file') + parser = argparse.ArgumentParser(description="Process some strings or files") + parser.add_argument( + "--string", + dest="input_string", + default="Hello World!! Welcome to Cryptography", + help="Hash the string", + ) + parser.add_argument("--file", dest="input_file", help="Hash contents of a file") args = parser.parse_args() input_string = args.input_string - #In any case hash input should be a bytestring + # In any case hash input should be a bytestring if args.input_file: - with open(args.input_file, 'rb') as f: + with open(args.input_file, "rb") as f: hash_input = f.read() else: - hash_input = bytes(input_string, 'utf-8') + hash_input = bytes(input_string, "utf-8") print(SHA1Hash(hash_input).final_hash()) -if __name__ == '__main__': +if __name__ == "__main__": main() + import doctest + + doctest.testmod() diff --git a/images/Travis_CI_fail_1.png b/images/Travis_CI_fail_1.png new file mode 100644 index 000000000000..451e54e4844a Binary files /dev/null and b/images/Travis_CI_fail_1.png differ diff --git a/images/Travis_CI_fail_2.png b/images/Travis_CI_fail_2.png new file mode 100644 index 000000000000..caa406099da1 Binary files /dev/null and b/images/Travis_CI_fail_2.png differ diff --git a/linear_algebra_python/README.md b/linear_algebra/README.md similarity index 94% rename from linear_algebra_python/README.md rename to linear_algebra/README.md index 1e34d0bd7805..169cd074d396 100644 --- a/linear_algebra_python/README.md +++ b/linear_algebra/README.md @@ -6,7 +6,8 @@ This module contains some useful classes and functions for dealing with linear a ## Overview -- class Vector +### class Vector +- - This class represents a vector of arbitray size and operations on it. **Overview about the methods:** @@ -32,7 +33,8 @@ This module contains some useful classes and functions for dealing with linear a - function randomVector(N,a,b) - returns a random vector of size N, with random integer components between 'a' and 'b'. -- class Matrix +### class Matrix +- - This class represents a matrix of arbitrary size and operations on it. **Overview about the methods:** @@ -43,7 +45,8 @@ This module contains some useful classes and functions for dealing with linear a - changeComponent(x,y,value) : changes the specified component. - component(x,y) : returns the specified component. - width() : returns the width of the matrix - - height() : returns the height of the matrix + - height() : returns the height of the matrix + - determinate() : returns the determinate of the matrix if it is square - operator + : implements the matrix-addition. - operator - _ implements the matrix-subtraction diff --git a/linear_algebra_python/src/lib.py b/linear_algebra/src/lib.py similarity index 63% rename from linear_algebra_python/src/lib.py rename to linear_algebra/src/lib.py index 281991a93b2d..15d176cc6392 100644 --- a/linear_algebra_python/src/lib.py +++ b/linear_algebra/src/lib.py @@ -27,7 +27,7 @@ class Vector(object): """ - This class represents a vector of arbitray size. + This class represents a vector of arbitrary size. You need to give the vector components. Overview about the methods: @@ -37,7 +37,7 @@ class Vector(object): __str__() : toString method component(i : int): gets the i-th component (start by 0) __len__() : gets the size of the vector (number of components) - euclidLength() : returns the eulidean length of the vector. + euclidLength() : returns the euclidean length of the vector. operator + : vector addition operator - : vector subtraction operator * : scalar multiplication and dot product @@ -45,13 +45,17 @@ class Vector(object): changeComponent(pos,value) : changes the specified component. TODO: compare-operator """ - def __init__(self,components=[]): + + def __init__(self, components=None): """ input: components or nothing simple constructor for init the vector """ + if components is None: + components = [] self.__components = list(components) - def set(self,components): + + def set(self, components): """ input: new components changes the components of the vector. @@ -61,34 +65,39 @@ def set(self,components): self.__components = list(components) else: raise Exception("please give any vector") + def __str__(self): """ returns a string representation of the vector """ return "(" + ",".join(map(str, self.__components)) + ")" - def component(self,i): + + def component(self, i): """ input: index (start at 0) output: the i-th component of the vector. """ - if type(i) is int and -len(self.__components) <= i < len(self.__components) : + if type(i) is int and -len(self.__components) <= i < len(self.__components): return self.__components[i] else: raise Exception("index out of range") + def __len__(self): """ returns the size of the vector """ return len(self.__components) - def eulidLength(self): + + def euclidLength(self): """ - returns the eulidean length of the vector + returns the euclidean length of the vector """ summe = 0 for c in self.__components: - summe += c**2 + summe += c ** 2 return math.sqrt(summe) - def __add__(self,other): + + def __add__(self, other): """ input: other vector assumes: other vector has the same size @@ -100,83 +109,91 @@ def __add__(self,other): return Vector(result) else: raise Exception("must have the same size") - def __sub__(self,other): + + def __sub__(self, other): """ input: other vector assumes: other vector has the same size - returns a new vector that represents the differenz. + returns a new vector that represents the difference. """ size = len(self) if size == len(other): result = [self.__components[i] - other.component(i) for i in range(size)] - return result - else: # error case + return Vector(result) + else: # error case raise Exception("must have the same size") - def __mul__(self,other): + + def __mul__(self, other): """ mul implements the scalar multiplication and the dot-product """ - if isinstance(other,float) or isinstance(other,int): - ans = [c*other for c in self.__components] - return ans - elif (isinstance(other,Vector) and (len(self) == len(other))): + if isinstance(other, float) or isinstance(other, int): + ans = [c * other for c in self.__components] + return Vector(ans) + elif isinstance(other, Vector) and (len(self) == len(other)): size = len(self) summe = 0 for i in range(size): summe += self.__components[i] * other.component(i) return summe - else: # error case - raise Exception("invalide operand!") + else: # error case + raise Exception("invalid operand!") + def copy(self): """ copies this vector and returns it. """ return Vector(self.__components) - def changeComponent(self,pos,value): + + def changeComponent(self, pos, value): """ input: an index (pos) and a value changes the specified component (pos) with the 'value' """ - #precondition - assert (-len(self.__components) <= pos < len(self.__components)) + # precondition + assert -len(self.__components) <= pos < len(self.__components) self.__components[pos] = value - + + def zeroVector(dimension): """ returns a zero-vector of size 'dimension' - """ - #precondition - assert(isinstance(dimension,int)) - return Vector([0]*dimension) + """ + # precondition + assert isinstance(dimension, int) + return Vector([0] * dimension) -def unitBasisVector(dimension,pos): +def unitBasisVector(dimension, pos): """ returns a unit basis vector with a One at index 'pos' (indexing at 0) """ - #precondition - assert(isinstance(dimension,int) and (isinstance(pos,int))) - ans = [0]*dimension + # precondition + assert isinstance(dimension, int) and (isinstance(pos, int)) + ans = [0] * dimension ans[pos] = 1 return Vector(ans) - -def axpy(scalar,x,y): + +def axpy(scalar, x, y): """ input: a 'scalar' and two vectors 'x' and 'y' output: a vector computes the axpy operation """ # precondition - assert(isinstance(x,Vector) and (isinstance(y,Vector)) \ - and (isinstance(scalar,int) or isinstance(scalar,float))) - return (x*scalar + y) - + assert ( + isinstance(x, Vector) + and (isinstance(y, Vector)) + and (isinstance(scalar, int) or isinstance(scalar, float)) + ) + return x * scalar + y -def randomVector(N,a,b): + +def randomVector(N, a, b): """ input: size (N) of the vector. random range (a,b) @@ -184,7 +201,7 @@ def randomVector(N,a,b): random integer components between 'a' and 'b'. """ random.seed(None) - ans = [random.randint(a,b) for i in range(N)] + ans = [random.randint(a, b) for i in range(N)] return Vector(ans) @@ -205,14 +222,16 @@ class Matrix(object): operator + : implements the matrix-addition. operator - _ implements the matrix-subtraction """ - def __init__(self,matrix,w,h): + + def __init__(self, matrix, w, h): """ - simple constructor for initialzes + simple constructor for initializing the matrix with components. """ self.__matrix = matrix self.__width = w self.__height = h + def __str__(self): """ returns a string representation of this @@ -222,102 +241,140 @@ def __str__(self): for i in range(self.__height): ans += "|" for j in range(self.__width): - if j < self.__width -1: + if j < self.__width - 1: ans += str(self.__matrix[i][j]) + "," else: ans += str(self.__matrix[i][j]) + "|\n" return ans - def changeComponent(self,x,y, value): + + def changeComponent(self, x, y, value): """ changes the x-y component of this matrix """ - if x >= 0 and x < self.__height and y >= 0 and y < self.__width: + if 0 <= x < self.__height and 0 <= y < self.__width: self.__matrix[x][y] = value else: - raise Exception ("changeComponent: indices out of bounds") - def component(self,x,y): + raise Exception("changeComponent: indices out of bounds") + + def component(self, x, y): """ returns the specified (x,y) component """ - if x >= 0 and x < self.__height and y >= 0 and y < self.__width: + if 0 <= x < self.__height and 0 <= y < self.__width: return self.__matrix[x][y] else: - raise Exception ("changeComponent: indices out of bounds") + raise Exception("changeComponent: indices out of bounds") + def width(self): """ getter for the width """ return self.__width + def height(self): """ getter for the height """ return self.__height - def __mul__(self,other): + + def determinate(self) -> float: + """ + returns the determinate of an nxn matrix using Laplace expansion + """ + if self.__height == self.__width and self.__width >= 2: + total = 0 + if self.__width > 2: + for x in range(0, self.__width): + for y in range(0, self.__height): + total += ( + self.__matrix[x][y] + * (-1) ** (x + y) + * Matrix( + self.__matrix[0:x] + self.__matrix[x + 1 :], + self.__width - 1, + self.__height - 1, + ).determinate() + ) + else: + return ( + self.__matrix[0][0] * self.__matrix[1][1] + - self.__matrix[0][1] * self.__matrix[1][0] + ) + return total + else: + raise Exception("matrix is not square") + + def __mul__(self, other): """ implements the matrix-vector multiplication. implements the matrix-scalar multiplication """ - if isinstance(other, Vector): # vector-matrix - if (len(other) == self.__width): + if isinstance(other, Vector): # vector-matrix + if len(other) == self.__width: ans = zeroVector(self.__height) for i in range(self.__height): summe = 0 for j in range(self.__width): summe += other.component(j) * self.__matrix[i][j] - ans.changeComponent(i,summe) + ans.changeComponent(i, summe) summe = 0 return ans else: - raise Exception("vector must have the same size as the " + "number of columns of the matrix!") - elif isinstance(other,int) or isinstance(other,float): # matrix-scalar - matrix = [[self.__matrix[i][j] * other for j in range(self.__width)] for i in range(self.__height)] - return Matrix(matrix,self.__width,self.__height) - def __add__(self,other): + raise Exception( + "vector must have the same size as the " + + "number of columns of the matrix!" + ) + elif isinstance(other, int) or isinstance(other, float): # matrix-scalar + matrix = [ + [self.__matrix[i][j] * other for j in range(self.__width)] + for i in range(self.__height) + ] + return Matrix(matrix, self.__width, self.__height) + + def __add__(self, other): """ implements the matrix-addition. """ - if (self.__width == other.width() and self.__height == other.height()): + if self.__width == other.width() and self.__height == other.height(): matrix = [] for i in range(self.__height): row = [] for j in range(self.__width): - row.append(self.__matrix[i][j] + other.component(i,j)) + row.append(self.__matrix[i][j] + other.component(i, j)) matrix.append(row) - return Matrix(matrix,self.__width,self.__height) + return Matrix(matrix, self.__width, self.__height) else: raise Exception("matrix must have the same dimension!") - def __sub__(self,other): + + def __sub__(self, other): """ implements the matrix-subtraction. """ - if (self.__width == other.width() and self.__height == other.height()): + if self.__width == other.width() and self.__height == other.height(): matrix = [] for i in range(self.__height): row = [] for j in range(self.__width): - row.append(self.__matrix[i][j] - other.component(i,j)) + row.append(self.__matrix[i][j] - other.component(i, j)) matrix.append(row) - return Matrix(matrix,self.__width,self.__height) + return Matrix(matrix, self.__width, self.__height) else: raise Exception("matrix must have the same dimension!") - + def squareZeroMatrix(N): """ returns a square zero-matrix of dimension NxN """ - ans = [[0]*N for i in range(N)] - return Matrix(ans,N,N) - - -def randomMatrix(W,H,a,b): + ans = [[0] * N for i in range(N)] + return Matrix(ans, N, N) + + +def randomMatrix(W, H, a, b): """ returns a random matrix WxH with integer components between 'a' and 'b' """ random.seed(None) - matrix = [[random.randint(a,b) for j in range(W)] for i in range(H)] - return Matrix(matrix,W,H) - - + matrix = [[random.randint(a, b) for j in range(W)] for i in range(H)] + return Matrix(matrix, W, H) diff --git a/linear_algebra/src/polynom-for-points.py b/linear_algebra/src/polynom-for-points.py new file mode 100644 index 000000000000..c884416b6dad --- /dev/null +++ b/linear_algebra/src/polynom-for-points.py @@ -0,0 +1,130 @@ +def points_to_polynomial(coordinates): + """ + coordinates is a two dimensional matrix: [[x, y], [x, y], ...] + number of points you want to use + + >>> print(points_to_polynomial([])) + The program cannot work out a fitting polynomial. + >>> print(points_to_polynomial([[]])) + The program cannot work out a fitting polynomial. + >>> print(points_to_polynomial([[1, 0], [2, 0], [3, 0]])) + f(x)=x^2*0.0+x^1*-0.0+x^0*0.0 + >>> print(points_to_polynomial([[1, 1], [2, 1], [3, 1]])) + f(x)=x^2*0.0+x^1*-0.0+x^0*1.0 + >>> print(points_to_polynomial([[1, 3], [2, 3], [3, 3]])) + f(x)=x^2*0.0+x^1*-0.0+x^0*3.0 + >>> print(points_to_polynomial([[1, 1], [2, 2], [3, 3]])) + f(x)=x^2*0.0+x^1*1.0+x^0*0.0 + >>> print(points_to_polynomial([[1, 1], [2, 4], [3, 9]])) + f(x)=x^2*1.0+x^1*-0.0+x^0*0.0 + >>> print(points_to_polynomial([[1, 3], [2, 6], [3, 11]])) + f(x)=x^2*1.0+x^1*-0.0+x^0*2.0 + >>> print(points_to_polynomial([[1, -3], [2, -6], [3, -11]])) + f(x)=x^2*-1.0+x^1*-0.0+x^0*-2.0 + >>> print(points_to_polynomial([[1, 5], [2, 2], [3, 9]])) + f(x)=x^2*5.0+x^1*-18.0+x^0*18.0 + """ + try: + check = 1 + more_check = 0 + d = coordinates[0][0] + for j in range(len(coordinates)): + if j == 0: + continue + if d == coordinates[j][0]: + more_check += 1 + solved = "x=" + str(coordinates[j][0]) + if more_check == len(coordinates) - 1: + check = 2 + break + elif more_check > 0 and more_check != len(coordinates) - 1: + check = 3 + else: + check = 1 + + if len(coordinates) == 1 and coordinates[0][0] == 0: + check = 2 + solved = "x=0" + except Exception: + check = 3 + + x = len(coordinates) + + if check == 1: + count_of_line = 0 + matrix = [] + # put the x and x to the power values in a matrix + while count_of_line < x: + count_in_line = 0 + a = coordinates[count_of_line][0] + count_line = [] + while count_in_line < x: + count_line.append(a ** (x - (count_in_line + 1))) + count_in_line += 1 + matrix.append(count_line) + count_of_line += 1 + + count_of_line = 0 + # put the y values into a vector + vector = [] + while count_of_line < x: + count_in_line = 0 + vector.append(coordinates[count_of_line][1]) + count_of_line += 1 + + count = 0 + + while count < x: + zahlen = 0 + while zahlen < x: + if count == zahlen: + zahlen += 1 + if zahlen == x: + break + bruch = (matrix[zahlen][count]) / (matrix[count][count]) + for counting_columns, item in enumerate(matrix[count]): + # manipulating all the values in the matrix + matrix[zahlen][counting_columns] -= item * bruch + # manipulating the values in the vector + vector[zahlen] -= vector[count] * bruch + zahlen += 1 + count += 1 + + count = 0 + # make solutions + solution = [] + while count < x: + solution.append(vector[count] / matrix[count][count]) + count += 1 + + count = 0 + solved = "f(x)=" + + while count < x: + remove_e = str(solution[count]).split("E") + if len(remove_e) > 1: + solution[count] = remove_e[0] + "*10^" + remove_e[1] + solved += "x^" + str(x - (count + 1)) + "*" + str(solution[count]) + if count + 1 != x: + solved += "+" + count += 1 + + return solved + + elif check == 2: + return solved + else: + return "The program cannot work out a fitting polynomial." + + +if __name__ == "__main__": + print(points_to_polynomial([])) + print(points_to_polynomial([[]])) + print(points_to_polynomial([[1, 0], [2, 0], [3, 0]])) + print(points_to_polynomial([[1, 1], [2, 1], [3, 1]])) + print(points_to_polynomial([[1, 3], [2, 3], [3, 3]])) + print(points_to_polynomial([[1, 1], [2, 2], [3, 3]])) + print(points_to_polynomial([[1, 1], [2, 4], [3, 9]])) + print(points_to_polynomial([[1, 3], [2, 6], [3, 11]])) + print(points_to_polynomial([[1, -3], [2, -6], [3, -11]])) + print(points_to_polynomial([[1, 5], [2, 2], [3, 9]])) diff --git a/linear_algebra/src/test_linear_algebra.py b/linear_algebra/src/test_linear_algebra.py new file mode 100644 index 000000000000..f8e7db7de6cc --- /dev/null +++ b/linear_algebra/src/test_linear_algebra.py @@ -0,0 +1,161 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Feb 26 15:40:07 2018 + +@author: Christian Bender +@license: MIT-license + +This file contains the test-suite for the linear algebra library. +""" + +import unittest +from lib import Matrix, Vector, axpy, squareZeroMatrix, unitBasisVector, zeroVector + + +class Test(unittest.TestCase): + def test_component(self): + """ + test for method component + """ + x = Vector([1, 2, 3]) + self.assertEqual(x.component(0), 1) + self.assertEqual(x.component(2), 3) + try: + y = Vector() + self.assertTrue(False) + except: + self.assertTrue(True) + + def test_str(self): + """ + test for toString() method + """ + x = Vector([0, 0, 0, 0, 0, 1]) + self.assertEqual(str(x), "(0,0,0,0,0,1)") + + def test_size(self): + """ + test for size()-method + """ + x = Vector([1, 2, 3, 4]) + self.assertEqual(len(x), 4) + + def test_euclidLength(self): + """ + test for the eulidean length + """ + x = Vector([1, 2]) + self.assertAlmostEqual(x.euclidLength(), 2.236, 3) + + def test_add(self): + """ + test for + operator + """ + x = Vector([1, 2, 3]) + y = Vector([1, 1, 1]) + self.assertEqual((x + y).component(0), 2) + self.assertEqual((x + y).component(1), 3) + self.assertEqual((x + y).component(2), 4) + + def test_sub(self): + """ + test for - operator + """ + x = Vector([1, 2, 3]) + y = Vector([1, 1, 1]) + self.assertEqual((x - y).component(0), 0) + self.assertEqual((x - y).component(1), 1) + self.assertEqual((x - y).component(2), 2) + + def test_mul(self): + """ + test for * operator + """ + x = Vector([1, 2, 3]) + a = Vector([2, -1, 4]) # for test of dot-product + b = Vector([1, -2, -1]) + self.assertEqual(str(x * 3.0), "(3.0,6.0,9.0)") + self.assertEqual((a * b), 0) + + def test_zeroVector(self): + """ + test for the global function zeroVector(...) + """ + self.assertTrue(str(zeroVector(10)).count("0") == 10) + + def test_unitBasisVector(self): + """ + test for the global function unitBasisVector(...) + """ + self.assertEqual(str(unitBasisVector(3, 1)), "(0,1,0)") + + def test_axpy(self): + """ + test for the global function axpy(...) (operation) + """ + x = Vector([1, 2, 3]) + y = Vector([1, 0, 1]) + self.assertEqual(str(axpy(2, x, y)), "(3,4,7)") + + def test_copy(self): + """ + test for the copy()-method + """ + x = Vector([1, 0, 0, 0, 0, 0]) + y = x.copy() + self.assertEqual(str(x), str(y)) + + def test_changeComponent(self): + """ + test for the changeComponent(...)-method + """ + x = Vector([1, 0, 0]) + x.changeComponent(0, 0) + x.changeComponent(1, 1) + self.assertEqual(str(x), "(0,1,0)") + + def test_str_matrix(self): + A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3) + self.assertEqual("|1,2,3|\n|2,4,5|\n|6,7,8|\n", str(A)) + + def test_determinate(self): + """ + test for determinate() + """ + A = Matrix([[1, 1, 4, 5], [3, 3, 3, 2], [5, 1, 9, 0], [9, 7, 7, 9]], 4, 4) + self.assertEqual(-376, A.determinate()) + + def test__mul__matrix(self): + A = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]], 3, 3) + x = Vector([1, 2, 3]) + self.assertEqual("(14,32,50)", str(A * x)) + self.assertEqual("|2,4,6|\n|8,10,12|\n|14,16,18|\n", str(A * 2)) + + def test_changeComponent_matrix(self): + A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3) + A.changeComponent(0, 2, 5) + self.assertEqual("|1,2,5|\n|2,4,5|\n|6,7,8|\n", str(A)) + + def test_component_matrix(self): + A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3) + self.assertEqual(7, A.component(2, 1), 0.01) + + def test__add__matrix(self): + A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3) + B = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]], 3, 3) + self.assertEqual("|2,4,10|\n|4,8,10|\n|12,14,18|\n", str(A + B)) + + def test__sub__matrix(self): + A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]], 3, 3) + B = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]], 3, 3) + self.assertEqual("|0,0,-4|\n|0,0,0|\n|0,0,-2|\n", str(A - B)) + + def test_squareZeroMatrix(self): + self.assertEqual( + "|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|" + "\n|0,0,0,0,0|\n", + str(squareZeroMatrix(5)), + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/linear_algebra_python/src/tests.py b/linear_algebra_python/src/tests.py deleted file mode 100644 index a26eb92653e2..000000000000 --- a/linear_algebra_python/src/tests.py +++ /dev/null @@ -1,133 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Mon Feb 26 15:40:07 2018 - -@author: Christian Bender -@license: MIT-license - -This file contains the test-suite for the linear algebra library. -""" - -import unittest -from lib import * - -class Test(unittest.TestCase): - def test_component(self): - """ - test for method component - """ - x = Vector([1,2,3]) - self.assertEqual(x.component(0),1) - self.assertEqual(x.component(2),3) - try: - y = Vector() - self.assertTrue(False) - except: - self.assertTrue(True) - def test_str(self): - """ - test for toString() method - """ - x = Vector([0,0,0,0,0,1]) - self.assertEqual(str(x),"(0,0,0,0,0,1)") - def test_size(self): - """ - test for size()-method - """ - x = Vector([1,2,3,4]) - self.assertEqual(len(x),4) - def test_euclidLength(self): - """ - test for the eulidean length - """ - x = Vector([1,2]) - self.assertAlmostEqual(x.eulidLength(),2.236,3) - def test_add(self): - """ - test for + operator - """ - x = Vector([1,2,3]) - y = Vector([1,1,1]) - self.assertEqual((x+y).component(0),2) - self.assertEqual((x+y).component(1),3) - self.assertEqual((x+y).component(2),4) - def test_sub(self): - """ - test for - operator - """ - x = Vector([1,2,3]) - y = Vector([1,1,1]) - self.assertEqual((x-y).component(0),0) - self.assertEqual((x-y).component(1),1) - self.assertEqual((x-y).component(2),2) - def test_mul(self): - """ - test for * operator - """ - x = Vector([1,2,3]) - a = Vector([2,-1,4]) # for test of dot-product - b = Vector([1,-2,-1]) - self.assertEqual(str(x*3.0),"(3.0,6.0,9.0)") - self.assertEqual((a*b),0) - def test_zeroVector(self): - """ - test for the global function zeroVector(...) - """ - self.assertTrue(str(zeroVector(10)).count("0") == 10) - def test_unitBasisVector(self): - """ - test for the global function unitBasisVector(...) - """ - self.assertEqual(str(unitBasisVector(3,1)),"(0,1,0)") - def test_axpy(self): - """ - test for the global function axpy(...) (operation) - """ - x = Vector([1,2,3]) - y = Vector([1,0,1]) - self.assertEqual(str(axpy(2,x,y)),"(3,4,7)") - def test_copy(self): - """ - test for the copy()-method - """ - x = Vector([1,0,0,0,0,0]) - y = x.copy() - self.assertEqual(str(x),str(y)) - def test_changeComponent(self): - """ - test for the changeComponent(...)-method - """ - x = Vector([1,0,0]) - x.changeComponent(0,0) - x.changeComponent(1,1) - self.assertEqual(str(x),"(0,1,0)") - def test_str_matrix(self): - A = Matrix([[1,2,3],[2,4,5],[6,7,8]],3,3) - self.assertEqual("|1,2,3|\n|2,4,5|\n|6,7,8|\n",str(A)) - def test__mul__matrix(self): - A = Matrix([[1,2,3],[4,5,6],[7,8,9]],3,3) - x = Vector([1,2,3]) - self.assertEqual("(14,32,50)",str(A*x)) - self.assertEqual("|2,4,6|\n|8,10,12|\n|14,16,18|\n",str(A*2)) - def test_changeComponent_matrix(self): - A = Matrix([[1,2,3],[2,4,5],[6,7,8]],3,3) - A.changeComponent(0,2,5) - self.assertEqual("|1,2,5|\n|2,4,5|\n|6,7,8|\n",str(A)) - def test_component_matrix(self): - A = Matrix([[1,2,3],[2,4,5],[6,7,8]],3,3) - self.assertEqual(7,A.component(2,1),0.01) - def test__add__matrix(self): - A = Matrix([[1,2,3],[2,4,5],[6,7,8]],3,3) - B = Matrix([[1,2,7],[2,4,5],[6,7,10]],3,3) - self.assertEqual("|2,4,10|\n|4,8,10|\n|12,14,18|\n",str(A+B)) - def test__sub__matrix(self): - A = Matrix([[1,2,3],[2,4,5],[6,7,8]],3,3) - B = Matrix([[1,2,7],[2,4,5],[6,7,10]],3,3) - self.assertEqual("|0,0,-4|\n|0,0,0|\n|0,0,-2|\n",str(A-B)) - def test_squareZeroMatrix(self): - self.assertEqual('|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|' - +'\n|0,0,0,0,0|\n',str(squareZeroMatrix(5))) - - -if __name__ == "__main__": - unittest.main() diff --git a/machine_learning/NaiveBayes.ipynb b/machine_learning/NaiveBayes.ipynb deleted file mode 100644 index 5a427c5cb965..000000000000 --- a/machine_learning/NaiveBayes.ipynb +++ /dev/null @@ -1,1659 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from sklearn import datasets\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "iris = datasets.load_iris()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "df = pd.DataFrame(iris.data)\n", - "df.columns = [\"sl\", \"sw\", 'pl', 'pw']" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def abc(k, *val):\n", - " if k < val[0]:\n", - " return 0\n", - " else:\n", - " return 1" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 1\n", - "1 0\n", - "2 0\n", - "3 0\n", - "4 1\n", - "5 1\n", - "6 0\n", - "7 1\n", - "8 0\n", - "9 0\n", - "10 1\n", - "11 0\n", - "12 0\n", - "13 0\n", - "14 1\n", - "15 1\n", - "16 1\n", - "17 1\n", - "18 1\n", - "19 1\n", - "20 1\n", - "21 1\n", - "22 0\n", - "23 1\n", - "24 0\n", - "25 1\n", - "26 1\n", - "27 1\n", - "28 1\n", - "29 0\n", - " ..\n", - "120 1\n", - "121 1\n", - "122 1\n", - "123 1\n", - "124 1\n", - "125 1\n", - "126 1\n", - "127 1\n", - "128 1\n", - "129 1\n", - "130 1\n", - "131 1\n", - "132 1\n", - "133 1\n", - "134 1\n", - "135 1\n", - "136 1\n", - "137 1\n", - "138 1\n", - "139 1\n", - "140 1\n", - "141 1\n", - "142 1\n", - "143 1\n", - "144 1\n", - "145 1\n", - "146 1\n", - "147 1\n", - "148 1\n", - "149 1\n", - "Name: sl, dtype: int64" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.sl.apply(abc, args=(5,))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def label(val, *boundaries):\n", - " if (val < boundaries[0]):\n", - " return 'a'\n", - " elif (val < boundaries[1]):\n", - " return 'b'\n", - " elif (val < boundaries[2]):\n", - " return 'c'\n", - " else:\n", - " return 'd'\n", - "\n", - "def toLabel(df, old_feature_name):\n", - " second = df[old_feature_name].mean()\n", - " minimum = df[old_feature_name].min()\n", - " first = (minimum + second)/2\n", - " maximum = df[old_feature_name].max()\n", - " third = (maximum + second)/2\n", - " return df[old_feature_name].apply(label, args= (first, second, third))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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It will be removed in a future NumPy release.\n", - " from numpy.core.umath_tests import inner1d\n" - ] - } - ], - "source": [ - "# Importing the libraries\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.metrics import confusion_matrix\n", - "from matplotlib.colors import ListedColormap\n", - "from sklearn.ensemble import RandomForestClassifier" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Importing the dataset\n", - "dataset = pd.read_csv('Social_Network_Ads.csv')\n", - "X = dataset.iloc[:, [2, 3]].values\n", - "y = dataset.iloc[:, 4].values" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Splitting the dataset into the Training set and Test set\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Satyam\\AppData\\Roaming\\Python\\Python35\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n", - " warnings.warn(msg, DataConversionWarning)\n" - ] - } - ], - "source": [ - "# Feature Scaling\n", - "sc = StandardScaler()\n", - "X_train = sc.fit_transform(X_train)\n", - "X_test = sc.transform(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[63 5]\n", - " [ 3 29]]\n" - ] - } - ], - "source": [ - "# Fitting classifier to the Training set\n", - "# Create your classifier here\n", - "classifier = RandomForestClassifier(n_estimators=10,criterion='entropy',random_state=0)\n", - "classifier.fit(X_train,y_train)\n", - "# Predicting the Test set results\n", - "y_pred = classifier.predict(X_test)\n", - "\n", - "# Making the Confusion Matrix\n", - "cm = confusion_matrix(y_test, y_pred)\n", - "print(cm)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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UfCOTG/JOphvTROQaYAlwhIg8BnxCVeNWAjBGITaLGx0Eraai9LAOc24U91JD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\nF2GyjN4FfApvlTCI1z1P8X7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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Visualising the Training set results\n", - "X_set, y_set = X_train, y_train\n", - "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n", - " np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n", - "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n", - " alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n", - "plt.xlim(X1.min(), X1.max())\n", - "plt.ylim(X2.min(), X2.max())\n", - "for i, j in enumerate(np.unique(y_set)):\n", - " plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n", - " c = ListedColormap(('red', 'green'))(i), label = j)\n", - "plt.title('Random Forest Classifier (Training set)')\n", - "plt.xlabel('Age')\n", - "plt.ylabel('Estimated Salary')\n", - "plt.legend()\n", - "plt.show()\n", - "\n", - "# Visualising the Test set results\n", - "X_set, y_set = X_test, y_test\n", - "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n", - " np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n", - "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n", - " alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n", - "plt.xlim(X1.min(), X1.max())\n", - "plt.ylim(X2.min(), X2.max())\n", - "for i, j in enumerate(np.unique(y_set)):\n", - " plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n", - " c = ListedColormap(('red', 'green'))(i), label = j)\n", - "plt.title('Random Forest Classifier (Test set)')\n", - "plt.xlabel('Age')\n", - "plt.ylabel('Estimated Salary')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/machine_learning/Random Forest Classification/Social_Network_Ads.csv b/machine_learning/Random Forest Classification/Social_Network_Ads.csv deleted file mode 100644 index 4a53849c2baf..000000000000 --- a/machine_learning/Random Forest Classification/Social_Network_Ads.csv +++ /dev/null @@ -1,401 +0,0 @@ -User ID,Gender,Age,EstimatedSalary,Purchased -15624510,Male,19,19000,0 -15810944,Male,35,20000,0 -15668575,Female,26,43000,0 -15603246,Female,27,57000,0 -15804002,Male,19,76000,0 -15728773,Male,27,58000,0 -15598044,Female,27,84000,0 -15694829,Female,32,150000,1 -15600575,Male,25,33000,0 -15727311,Female,35,65000,0 -15570769,Female,26,80000,0 -15606274,Female,26,52000,0 -15746139,Male,20,86000,0 -15704987,Male,32,18000,0 -15628972,Male,18,82000,0 -15697686,Male,29,80000,0 -15733883,Male,47,25000,1 -15617482,Male,45,26000,1 -15704583,Male,46,28000,1 -15621083,Female,48,29000,1 -15649487,Male,45,22000,1 -15736760,Female,47,49000,1 -15714658,Male,48,41000,1 -15599081,Female,45,22000,1 -15705113,Male,46,23000,1 -15631159,Male,47,20000,1 -15792818,Male,49,28000,1 -15633531,Female,47,30000,1 -15744529,Male,29,43000,0 -15669656,Male,31,18000,0 -15581198,Male,31,74000,0 -15729054,Female,27,137000,1 -15573452,Female,21,16000,0 -15776733,Female,28,44000,0 -15724858,Male,27,90000,0 -15713144,Male,35,27000,0 -15690188,Female,33,28000,0 -15689425,Male,30,49000,0 -15671766,Female,26,72000,0 -15782806,Female,27,31000,0 -15764419,Female,27,17000,0 -15591915,Female,33,51000,0 -15772798,Male,35,108000,0 -15792008,Male,30,15000,0 -15715541,Female,28,84000,0 -15639277,Male,23,20000,0 -15798850,Male,25,79000,0 -15776348,Female,27,54000,0 -15727696,Male,30,135000,1 -15793813,Female,31,89000,0 -15694395,Female,24,32000,0 -15764195,Female,18,44000,0 -15744919,Female,29,83000,0 -15671655,Female,35,23000,0 -15654901,Female,27,58000,0 -15649136,Female,24,55000,0 -15775562,Female,23,48000,0 -15807481,Male,28,79000,0 -15642885,Male,22,18000,0 -15789109,Female,32,117000,0 -15814004,Male,27,20000,0 -15673619,Male,25,87000,0 -15595135,Female,23,66000,0 -15583681,Male,32,120000,1 -15605000,Female,59,83000,0 -15718071,Male,24,58000,0 -15679760,Male,24,19000,0 -15654574,Female,23,82000,0 -15577178,Female,22,63000,0 -15595324,Female,31,68000,0 -15756932,Male,25,80000,0 -15726358,Female,24,27000,0 -15595228,Female,20,23000,0 -15782530,Female,33,113000,0 -15592877,Male,32,18000,0 -15651983,Male,34,112000,1 -15746737,Male,18,52000,0 -15774179,Female,22,27000,0 -15667265,Female,28,87000,0 -15655123,Female,26,17000,0 -15595917,Male,30,80000,0 -15668385,Male,39,42000,0 -15709476,Male,20,49000,0 -15711218,Male,35,88000,0 -15798659,Female,30,62000,0 -15663939,Female,31,118000,1 -15694946,Male,24,55000,0 -15631912,Female,28,85000,0 -15768816,Male,26,81000,0 -15682268,Male,35,50000,0 -15684801,Male,22,81000,0 -15636428,Female,30,116000,0 -15809823,Male,26,15000,0 -15699284,Female,29,28000,0 -15786993,Female,29,83000,0 -15709441,Female,35,44000,0 -15710257,Female,35,25000,0 -15582492,Male,28,123000,1 -15575694,Male,35,73000,0 -15756820,Female,28,37000,0 -15766289,Male,27,88000,0 -15593014,Male,28,59000,0 -15584545,Female,32,86000,0 -15675949,Female,33,149000,1 -15672091,Female,19,21000,0 -15801658,Male,21,72000,0 -15706185,Female,26,35000,0 -15789863,Male,27,89000,0 -15720943,Male,26,86000,0 -15697997,Female,38,80000,0 -15665416,Female,39,71000,0 -15660200,Female,37,71000,0 -15619653,Male,38,61000,0 -15773447,Male,37,55000,0 -15739160,Male,42,80000,0 -15689237,Male,40,57000,0 -15679297,Male,35,75000,0 -15591433,Male,36,52000,0 -15642725,Male,40,59000,0 -15701962,Male,41,59000,0 -15811613,Female,36,75000,0 -15741049,Male,37,72000,0 -15724423,Female,40,75000,0 -15574305,Male,35,53000,0 -15678168,Female,41,51000,0 -15697020,Female,39,61000,0 -15610801,Male,42,65000,0 -15745232,Male,26,32000,0 -15722758,Male,30,17000,0 -15792102,Female,26,84000,0 -15675185,Male,31,58000,0 -15801247,Male,33,31000,0 -15725660,Male,30,87000,0 -15638963,Female,21,68000,0 -15800061,Female,28,55000,0 -15578006,Male,23,63000,0 -15668504,Female,20,82000,0 -15687491,Male,30,107000,1 -15610403,Female,28,59000,0 -15741094,Male,19,25000,0 -15807909,Male,19,85000,0 -15666141,Female,18,68000,0 -15617134,Male,35,59000,0 -15783029,Male,30,89000,0 -15622833,Female,34,25000,0 -15746422,Female,24,89000,0 -15750839,Female,27,96000,1 -15749130,Female,41,30000,0 -15779862,Male,29,61000,0 -15767871,Male,20,74000,0 -15679651,Female,26,15000,0 -15576219,Male,41,45000,0 -15699247,Male,31,76000,0 -15619087,Female,36,50000,0 -15605327,Male,40,47000,0 -15610140,Female,31,15000,0 -15791174,Male,46,59000,0 -15602373,Male,29,75000,0 -15762605,Male,26,30000,0 -15598840,Female,32,135000,1 -15744279,Male,32,100000,1 -15670619,Male,25,90000,0 -15599533,Female,37,33000,0 -15757837,Male,35,38000,0 -15697574,Female,33,69000,0 -15578738,Female,18,86000,0 -15762228,Female,22,55000,0 -15614827,Female,35,71000,0 -15789815,Male,29,148000,1 -15579781,Female,29,47000,0 -15587013,Male,21,88000,0 -15570932,Male,34,115000,0 -15794661,Female,26,118000,0 -15581654,Female,34,43000,0 -15644296,Female,34,72000,0 -15614420,Female,23,28000,0 -15609653,Female,35,47000,0 -15594577,Male,25,22000,0 -15584114,Male,24,23000,0 -15673367,Female,31,34000,0 -15685576,Male,26,16000,0 -15774727,Female,31,71000,0 -15694288,Female,32,117000,1 -15603319,Male,33,43000,0 -15759066,Female,33,60000,0 -15814816,Male,31,66000,0 -15724402,Female,20,82000,0 -15571059,Female,33,41000,0 -15674206,Male,35,72000,0 -15715160,Male,28,32000,0 -15730448,Male,24,84000,0 -15662067,Female,19,26000,0 -15779581,Male,29,43000,0 -15662901,Male,19,70000,0 -15689751,Male,28,89000,0 -15667742,Male,34,43000,0 -15738448,Female,30,79000,0 -15680243,Female,20,36000,0 -15745083,Male,26,80000,0 -15708228,Male,35,22000,0 -15628523,Male,35,39000,0 -15708196,Male,49,74000,0 -15735549,Female,39,134000,1 -15809347,Female,41,71000,0 -15660866,Female,58,101000,1 -15766609,Female,47,47000,0 -15654230,Female,55,130000,1 -15794566,Female,52,114000,0 -15800890,Female,40,142000,1 -15697424,Female,46,22000,0 -15724536,Female,48,96000,1 -15735878,Male,52,150000,1 -15707596,Female,59,42000,0 -15657163,Male,35,58000,0 -15622478,Male,47,43000,0 -15779529,Female,60,108000,1 -15636023,Male,49,65000,0 -15582066,Male,40,78000,0 -15666675,Female,46,96000,0 -15732987,Male,59,143000,1 -15789432,Female,41,80000,0 -15663161,Male,35,91000,1 -15694879,Male,37,144000,1 -15593715,Male,60,102000,1 -15575002,Female,35,60000,0 -15622171,Male,37,53000,0 -15795224,Female,36,126000,1 -15685346,Male,56,133000,1 -15691808,Female,40,72000,0 -15721007,Female,42,80000,1 -15794253,Female,35,147000,1 -15694453,Male,39,42000,0 -15813113,Male,40,107000,1 -15614187,Male,49,86000,1 -15619407,Female,38,112000,0 -15646227,Male,46,79000,1 -15660541,Male,40,57000,0 -15753874,Female,37,80000,0 -15617877,Female,46,82000,0 -15772073,Female,53,143000,1 -15701537,Male,42,149000,1 -15736228,Male,38,59000,0 -15780572,Female,50,88000,1 -15769596,Female,56,104000,1 -15586996,Female,41,72000,0 -15722061,Female,51,146000,1 -15638003,Female,35,50000,0 -15775590,Female,57,122000,1 -15730688,Male,41,52000,0 -15753102,Female,35,97000,1 -15810075,Female,44,39000,0 -15723373,Male,37,52000,0 -15795298,Female,48,134000,1 -15584320,Female,37,146000,1 -15724161,Female,50,44000,0 -15750056,Female,52,90000,1 -15609637,Female,41,72000,0 -15794493,Male,40,57000,0 -15569641,Female,58,95000,1 -15815236,Female,45,131000,1 -15811177,Female,35,77000,0 -15680587,Male,36,144000,1 -15672821,Female,55,125000,1 -15767681,Female,35,72000,0 -15600379,Male,48,90000,1 -15801336,Female,42,108000,1 -15721592,Male,40,75000,0 -15581282,Male,37,74000,0 -15746203,Female,47,144000,1 -15583137,Male,40,61000,0 -15680752,Female,43,133000,0 -15688172,Female,59,76000,1 -15791373,Male,60,42000,1 -15589449,Male,39,106000,1 -15692819,Female,57,26000,1 -15727467,Male,57,74000,1 -15734312,Male,38,71000,0 -15764604,Male,49,88000,1 -15613014,Female,52,38000,1 -15759684,Female,50,36000,1 -15609669,Female,59,88000,1 -15685536,Male,35,61000,0 -15750447,Male,37,70000,1 -15663249,Female,52,21000,1 -15638646,Male,48,141000,0 -15734161,Female,37,93000,1 -15631070,Female,37,62000,0 -15761950,Female,48,138000,1 -15649668,Male,41,79000,0 -15713912,Female,37,78000,1 -15586757,Male,39,134000,1 -15596522,Male,49,89000,1 -15625395,Male,55,39000,1 -15760570,Male,37,77000,0 -15566689,Female,35,57000,0 -15725794,Female,36,63000,0 -15673539,Male,42,73000,1 -15705298,Female,43,112000,1 -15675791,Male,45,79000,0 -15747043,Male,46,117000,1 -15736397,Female,58,38000,1 -15678201,Male,48,74000,1 -15720745,Female,37,137000,1 -15637593,Male,37,79000,1 -15598070,Female,40,60000,0 -15787550,Male,42,54000,0 -15603942,Female,51,134000,0 -15733973,Female,47,113000,1 -15596761,Male,36,125000,1 -15652400,Female,38,50000,0 -15717893,Female,42,70000,0 -15622585,Male,39,96000,1 -15733964,Female,38,50000,0 -15753861,Female,49,141000,1 -15747097,Female,39,79000,0 -15594762,Female,39,75000,1 -15667417,Female,54,104000,1 -15684861,Male,35,55000,0 -15742204,Male,45,32000,1 -15623502,Male,36,60000,0 -15774872,Female,52,138000,1 -15611191,Female,53,82000,1 -15674331,Male,41,52000,0 -15619465,Female,48,30000,1 -15575247,Female,48,131000,1 -15695679,Female,41,60000,0 -15713463,Male,41,72000,0 -15785170,Female,42,75000,0 -15796351,Male,36,118000,1 -15639576,Female,47,107000,1 -15693264,Male,38,51000,0 -15589715,Female,48,119000,1 -15769902,Male,42,65000,0 -15587177,Male,40,65000,0 -15814553,Male,57,60000,1 -15601550,Female,36,54000,0 -15664907,Male,58,144000,1 -15612465,Male,35,79000,0 -15810800,Female,38,55000,0 -15665760,Male,39,122000,1 -15588080,Female,53,104000,1 -15776844,Male,35,75000,0 -15717560,Female,38,65000,0 -15629739,Female,47,51000,1 -15729908,Male,47,105000,1 -15716781,Female,41,63000,0 -15646936,Male,53,72000,1 -15768151,Female,54,108000,1 -15579212,Male,39,77000,0 -15721835,Male,38,61000,0 -15800515,Female,38,113000,1 -15591279,Male,37,75000,0 -15587419,Female,42,90000,1 -15750335,Female,37,57000,0 -15699619,Male,36,99000,1 -15606472,Male,60,34000,1 -15778368,Male,54,70000,1 -15671387,Female,41,72000,0 -15573926,Male,40,71000,1 -15709183,Male,42,54000,0 -15577514,Male,43,129000,1 -15778830,Female,53,34000,1 -15768072,Female,47,50000,1 -15768293,Female,42,79000,0 -15654456,Male,42,104000,1 -15807525,Female,59,29000,1 -15574372,Female,58,47000,1 -15671249,Male,46,88000,1 -15779744,Male,38,71000,0 -15624755,Female,54,26000,1 -15611430,Female,60,46000,1 -15774744,Male,60,83000,1 -15629885,Female,39,73000,0 -15708791,Male,59,130000,1 -15793890,Female,37,80000,0 -15646091,Female,46,32000,1 -15596984,Female,46,74000,0 -15800215,Female,42,53000,0 -15577806,Male,41,87000,1 -15749381,Female,58,23000,1 -15683758,Male,42,64000,0 -15670615,Male,48,33000,1 -15715622,Female,44,139000,1 -15707634,Male,49,28000,1 -15806901,Female,57,33000,1 -15775335,Male,56,60000,1 -15724150,Female,49,39000,1 -15627220,Male,39,71000,0 -15672330,Male,47,34000,1 -15668521,Female,48,35000,1 -15807837,Male,48,33000,1 -15592570,Male,47,23000,1 -15748589,Female,45,45000,1 -15635893,Male,60,42000,1 -15757632,Female,39,59000,0 -15691863,Female,46,41000,1 -15706071,Male,51,23000,1 -15654296,Female,50,20000,1 -15755018,Male,36,33000,0 -15594041,Female,49,36000,1 \ No newline at end of file diff --git a/machine_learning/Random Forest Classification/random_forest_classification.py b/machine_learning/Random Forest Classification/random_forest_classification.py deleted file mode 100644 index d5dde4b13822..000000000000 --- a/machine_learning/Random Forest Classification/random_forest_classification.py +++ /dev/null @@ -1,69 +0,0 @@ -# Random Forest Classification - -# Importing the libraries -import numpy as np -import matplotlib.pyplot as plt -import pandas as pd - -# Importing the dataset -dataset = pd.read_csv('Social_Network_Ads.csv') -X = dataset.iloc[:, [2, 3]].values -y = dataset.iloc[:, 4].values - -# Splitting the dataset into the Training set and Test set -from sklearn.cross_validation import train_test_split -X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0) - -# Feature Scaling -from sklearn.preprocessing import StandardScaler -sc = StandardScaler() -X_train = sc.fit_transform(X_train) -X_test = sc.transform(X_test) - -# Fitting Random Forest Classification to the Training set -from sklearn.ensemble import RandomForestClassifier -classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0) -classifier.fit(X_train, y_train) - -# Predicting the Test set results -y_pred = classifier.predict(X_test) - -# Making the Confusion Matrix -from sklearn.metrics import confusion_matrix -cm = confusion_matrix(y_test, y_pred) - -# Visualising the Training set results -from matplotlib.colors import ListedColormap -X_set, y_set = X_train, y_train -X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01), - np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01)) -plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape), - alpha = 0.75, cmap = ListedColormap(('red', 'green'))) -plt.xlim(X1.min(), X1.max()) -plt.ylim(X2.min(), X2.max()) -for i, j in enumerate(np.unique(y_set)): - plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], - c = ListedColormap(('red', 'green'))(i), label = j) -plt.title('Random Forest Classification (Training set)') -plt.xlabel('Age') -plt.ylabel('Estimated Salary') -plt.legend() -plt.show() - -# Visualising the Test set results -from matplotlib.colors import ListedColormap -X_set, y_set = X_test, y_test -X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01), - np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01)) -plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape), - alpha = 0.75, cmap = ListedColormap(('red', 'green'))) -plt.xlim(X1.min(), X1.max()) -plt.ylim(X2.min(), X2.max()) -for i, j in enumerate(np.unique(y_set)): - plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], - c = ListedColormap(('red', 'green'))(i), label = j) -plt.title('Random Forest Classification (Test set)') -plt.xlabel('Age') -plt.ylabel('Estimated Salary') -plt.legend() -plt.show() \ No newline at end of file diff --git a/machine_learning/Random Forest Regression/Position_Salaries.csv b/machine_learning/Random Forest Regression/Position_Salaries.csv deleted file mode 100644 index 0c752c72a1d1..000000000000 --- a/machine_learning/Random Forest Regression/Position_Salaries.csv +++ /dev/null @@ -1,11 +0,0 @@ -Position,Level,Salary -Business Analyst,1,45000 -Junior Consultant,2,50000 -Senior Consultant,3,60000 -Manager,4,80000 -Country Manager,5,110000 -Region Manager,6,150000 -Partner,7,200000 -Senior Partner,8,300000 -C-level,9,500000 -CEO,10,1000000 \ No newline at end of file diff --git a/machine_learning/Random Forest Regression/Random Forest Regression.ipynb b/machine_learning/Random Forest Regression/Random Forest Regression.ipynb deleted file mode 100644 index 17f4d42bfb0d..000000000000 --- a/machine_learning/Random Forest Regression/Random Forest Regression.ipynb +++ /dev/null @@ -1,147 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Importing the libraries\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "from sklearn.ensemble import RandomForestRegressor" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Importing the dataset\n", - "dataset = pd.read_csv('Position_Salaries.csv')\n", - "X = dataset.iloc[:, 1:2].values\n", - "y = dataset.iloc[:, 2].values" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n", - " max_features='auto', max_leaf_nodes=None,\n", - " min_impurity_split=1e-07, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=300, n_jobs=1, oob_score=False, random_state=0,\n", - " verbose=0, warm_start=False)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Fitting Random Forest Regression to the dataset\n", - "regressor = RandomForestRegressor(n_estimators = 300, random_state = 0)\n", - "regressor.fit(X, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Predicting a new result\n", - "y_pred = regressor.predict(6.5)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 160333.33333333]\n" - ] - } - ], - "source": [ - "print(y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Visualising the Random Forest Regression results (higher resolution)\n", - "X_grid = np.arange(min(X), max(X), 0.01)\n", - "X_grid = X_grid.reshape((len(X_grid), 1))\n", - "plt.scatter(X, y, color = 'red')\n", - "plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')\n", - "plt.title('Truth or Bluff (Random Forest Regression)')\n", - "plt.xlabel('Position level')\n", - "plt.ylabel('Salary')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/machine_learning/Random Forest Regression/random_forest_regression.py b/machine_learning/Random Forest Regression/random_forest_regression.py deleted file mode 100644 index fce58b1fe283..000000000000 --- a/machine_learning/Random Forest Regression/random_forest_regression.py +++ /dev/null @@ -1,41 +0,0 @@ -# Random Forest Regression - -# Importing the libraries -import numpy as np -import matplotlib.pyplot as plt -import pandas as pd - -# Importing the dataset -dataset = pd.read_csv('Position_Salaries.csv') -X = dataset.iloc[:, 1:2].values -y = dataset.iloc[:, 2].values - -# Splitting the dataset into the Training set and Test set -"""from sklearn.cross_validation import train_test_split -X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)""" - -# Feature Scaling -"""from sklearn.preprocessing import StandardScaler -sc_X = StandardScaler() -X_train = sc_X.fit_transform(X_train) -X_test = sc_X.transform(X_test) -sc_y = StandardScaler() -y_train = sc_y.fit_transform(y_train)""" - -# Fitting Random Forest Regression to the dataset -from sklearn.ensemble import RandomForestRegressor -regressor = RandomForestRegressor(n_estimators = 10, random_state = 0) -regressor.fit(X, y) - -# Predicting a new result -y_pred = regressor.predict(6.5) - -# Visualising the Random Forest Regression results (higher resolution) -X_grid = np.arange(min(X), max(X), 0.01) -X_grid = X_grid.reshape((len(X_grid), 1)) -plt.scatter(X, y, color = 'red') -plt.plot(X_grid, regressor.predict(X_grid), color = 'blue') -plt.title('Truth or Bluff (Random Forest Regression)') -plt.xlabel('Position level') -plt.ylabel('Salary') -plt.show() \ No newline at end of file diff --git a/machine_learning/decision_tree.py b/machine_learning/decision_tree.py index 71849904ccf2..6b121c73f3b4 100644 --- a/machine_learning/decision_tree.py +++ b/machine_learning/decision_tree.py @@ -1,14 +1,13 @@ """ Implementation of a basic regression decision tree. Input data set: The input data set must be 1-dimensional with continuous labels. -Output: The decision tree maps a real number input to a real number output. +Output: The decision tree maps a real number input to a real number output. """ -from __future__ import print_function - import numpy as np + class Decision_Tree: - def __init__(self, depth = 5, min_leaf_size = 5): + def __init__(self, depth=5, min_leaf_size=5): self.depth = depth self.decision_boundary = 0 self.left = None @@ -19,9 +18,17 @@ def __init__(self, depth = 5, min_leaf_size = 5): def mean_squared_error(self, labels, prediction): """ mean_squared_error: - @param labels: a one dimensional numpy array + @param labels: a one dimensional numpy array @param prediction: a floating point value return value: mean_squared_error calculates the error if prediction is used to estimate the labels + >>> tester = Decision_Tree() + >>> test_labels = np.array([1,2,3,4,5,6,7,8,9,10]) + >>> test_prediction = np.float(6) + >>> assert tester.mean_squared_error(test_labels, test_prediction) == Test_Decision_Tree.helper_mean_squared_error_test(test_labels, test_prediction) + >>> test_labels = np.array([1,2,3]) + >>> test_prediction = np.float(2) + >>> assert tester.mean_squared_error(test_labels, test_prediction) == Test_Decision_Tree.helper_mean_squared_error_test(test_labels, test_prediction) + """ if labels.ndim != 1: print("Error: Input labels must be one dimensional") @@ -32,7 +39,7 @@ def train(self, X, y): """ train: @param X: a one dimensional numpy array - @param y: a one dimensional numpy array. + @param y: a one dimensional numpy array. The contents of y are the labels for the corresponding X values train does not have a return value @@ -60,8 +67,7 @@ def train(self, X, y): return best_split = 0 - min_error = self.mean_squared_error(X,np.mean(y)) * 2 - + min_error = self.mean_squared_error(X, np.mean(y)) * 2 """ loop over all possible splits for the decision tree. find the best split. @@ -88,8 +94,12 @@ def train(self, X, y): right_y = y[best_split:] self.decision_boundary = X[best_split] - self.left = Decision_Tree(depth = self.depth - 1, min_leaf_size = self.min_leaf_size) - self.right = Decision_Tree(depth = self.depth - 1, min_leaf_size = self.min_leaf_size) + self.left = Decision_Tree( + depth=self.depth - 1, min_leaf_size=self.min_leaf_size + ) + self.right = Decision_Tree( + depth=self.depth - 1, min_leaf_size=self.min_leaf_size + ) self.left.train(left_X, left_y) self.right.train(right_X, right_y) else: @@ -115,17 +125,37 @@ def predict(self, x): print("Error: Decision tree not yet trained") return None + +class Test_Decision_Tree: + """Decision Tres test class + """ + + @staticmethod + def helper_mean_squared_error_test(labels, prediction): + """ + helper_mean_squared_error_test: + @param labels: a one dimensional numpy array + @param prediction: a floating point value + return value: helper_mean_squared_error_test calculates the mean squared error + """ + squared_error_sum = np.float(0) + for label in labels: + squared_error_sum += (label - prediction) ** 2 + + return np.float(squared_error_sum / labels.size) + + def main(): """ In this demonstration we're generating a sample data set from the sin function in numpy. We then train a decision tree on the data set and use the decision tree to predict the label of 10 different test values. Then the mean squared error over this test is displayed. """ - X = np.arange(-1., 1., 0.005) + X = np.arange(-1.0, 1.0, 0.005) y = np.sin(X) - tree = Decision_Tree(depth = 10, min_leaf_size = 10) - tree.train(X,y) + tree = Decision_Tree(depth=10, min_leaf_size=10) + tree.train(X, y) test_cases = (np.random.rand(10) * 2) - 1 predictions = np.array([tree.predict(x) for x in test_cases]) @@ -135,6 +165,9 @@ def main(): print("Predictions: " + str(predictions)) print("Average error: " + str(avg_error)) - -if __name__ == '__main__': - main() \ No newline at end of file + +if __name__ == "__main__": + main() + import doctest + + doctest.testmod(name="mean_squarred_error", verbose=True) diff --git a/machine_learning/gradient_descent.py b/machine_learning/gradient_descent.py index 6387d4939205..811cc68467f9 100644 --- a/machine_learning/gradient_descent.py +++ b/machine_learning/gradient_descent.py @@ -1,25 +1,31 @@ """ Implementation of gradient descent algorithm for minimizing cost of a linear hypothesis function. """ -from __future__ import print_function, division import numpy # List of input, output pairs -train_data = (((5, 2, 3), 15), ((6, 5, 9), 25), - ((11, 12, 13), 41), ((1, 1, 1), 8), ((11, 12, 13), 41)) +train_data = ( + ((5, 2, 3), 15), + ((6, 5, 9), 25), + ((11, 12, 13), 41), + ((1, 1, 1), 8), + ((11, 12, 13), 41), +) test_data = (((515, 22, 13), 555), ((61, 35, 49), 150)) parameter_vector = [2, 4, 1, 5] m = len(train_data) LEARNING_RATE = 0.009 -def _error(example_no, data_set='train'): +def _error(example_no, data_set="train"): """ :param data_set: train data or test data :param example_no: example number whose error has to be checked :return: error in example pointed by example number. """ - return calculate_hypothesis_value(example_no, data_set) - output(example_no, data_set) + return calculate_hypothesis_value(example_no, data_set) - output( + example_no, data_set + ) def _hypothesis_value(data_input_tuple): @@ -33,7 +39,7 @@ def _hypothesis_value(data_input_tuple): """ hyp_val = 0 for i in range(len(parameter_vector) - 1): - hyp_val += data_input_tuple[i]*parameter_vector[i+1] + hyp_val += data_input_tuple[i] * parameter_vector[i + 1] hyp_val += parameter_vector[0] return hyp_val @@ -44,9 +50,9 @@ def output(example_no, data_set): :param example_no: example whose output is to be fetched :return: output for that example """ - if data_set == 'train': + if data_set == "train": return train_data[example_no][1] - elif data_set == 'test': + elif data_set == "test": return test_data[example_no][1] @@ -76,7 +82,7 @@ def summation_of_cost_derivative(index, end=m): if index == -1: summation_value += _error(i) else: - summation_value += _error(i)*train_data[i][0][index] + summation_value += _error(i) * train_data[i][0][index] return summation_value @@ -86,7 +92,7 @@ def get_cost_derivative(index): :return: derivative wrt to that index Note: If index is -1, this means we are calculating summation wrt to biased parameter. """ - cost_derivative_value = summation_of_cost_derivative(index, m)/m + cost_derivative_value = summation_of_cost_derivative(index, m) / m return cost_derivative_value @@ -100,11 +106,16 @@ def run_gradient_descent(): j += 1 temp_parameter_vector = [0, 0, 0, 0] for i in range(0, len(parameter_vector)): - cost_derivative = get_cost_derivative(i-1) - temp_parameter_vector[i] = parameter_vector[i] - \ - LEARNING_RATE*cost_derivative - if numpy.allclose(parameter_vector, temp_parameter_vector, - atol=absolute_error_limit, rtol=relative_error_limit): + cost_derivative = get_cost_derivative(i - 1) + temp_parameter_vector[i] = ( + parameter_vector[i] - LEARNING_RATE * cost_derivative + ) + if numpy.allclose( + parameter_vector, + temp_parameter_vector, + atol=absolute_error_limit, + rtol=relative_error_limit, + ): break parameter_vector = temp_parameter_vector print(("Number of iterations:", j)) @@ -112,11 +123,11 @@ def run_gradient_descent(): def test_gradient_descent(): for i in range(len(test_data)): - print(("Actual output value:", output(i, 'test'))) - print(("Hypothesis output:", calculate_hypothesis_value(i, 'test'))) + print(("Actual output value:", output(i, "test"))) + print(("Hypothesis output:", calculate_hypothesis_value(i, "test"))) -if __name__ == '__main__': +if __name__ == "__main__": run_gradient_descent() print("\nTesting gradient descent for a linear hypothesis function.\n") test_gradient_descent() diff --git a/machine_learning/k_means_clust.py b/machine_learning/k_means_clust.py index 368739a45fe9..4c643226b213 100644 --- a/machine_learning/k_means_clust.py +++ b/machine_learning/k_means_clust.py @@ -1,4 +1,4 @@ -'''README, Author - Anurag Kumar(mailto:anuragkumarak95@gmail.com) +"""README, Author - Anurag Kumar(mailto:anuragkumarak95@gmail.com) Requirements: - sklearn @@ -17,157 +17,183 @@ Usage: 1. define 'k' value, 'X' features array and 'hetrogeneity' empty list - + 2. create initial_centroids, initial_centroids = get_initial_centroids( - X, - k, + X, + k, seed=0 # seed value for initial centroid generation, None for randomness(default=None) ) 3. find centroids and clusters using kmeans function. - + centroids, cluster_assignment = kmeans( - X, - k, - initial_centroids, + X, + k, + initial_centroids, maxiter=400, - record_heterogeneity=heterogeneity, + record_heterogeneity=heterogeneity, verbose=True # whether to print logs in console or not.(default=False) ) - - + + 4. Plot the loss function, hetrogeneity values for every iteration saved in hetrogeneity list. plot_heterogeneity( - heterogeneity, + heterogeneity, k ) - + 5. Have fun.. - -''' -from __future__ import print_function + +""" from sklearn.metrics import pairwise_distances import numpy as np -TAG = 'K-MEANS-CLUST/ ' +TAG = "K-MEANS-CLUST/ " + def get_initial_centroids(data, k, seed=None): - '''Randomly choose k data points as initial centroids''' - if seed is not None: # useful for obtaining consistent results + """Randomly choose k data points as initial centroids""" + if seed is not None: # useful for obtaining consistent results np.random.seed(seed) - n = data.shape[0] # number of data points - + n = data.shape[0] # number of data points + # Pick K indices from range [0, N). rand_indices = np.random.randint(0, n, k) - + # Keep centroids as dense format, as many entries will be nonzero due to averaging. # As long as at least one document in a cluster contains a word, # it will carry a nonzero weight in the TF-IDF vector of the centroid. - centroids = data[rand_indices,:] - + centroids = data[rand_indices, :] + return centroids -def centroid_pairwise_dist(X,centroids): - return pairwise_distances(X,centroids,metric='euclidean') + +def centroid_pairwise_dist(X, centroids): + return pairwise_distances(X, centroids, metric="euclidean") + def assign_clusters(data, centroids): - + # Compute distances between each data point and the set of centroids: # Fill in the blank (RHS only) - distances_from_centroids = centroid_pairwise_dist(data,centroids) - + distances_from_centroids = centroid_pairwise_dist(data, centroids) + # Compute cluster assignments for each data point: # Fill in the blank (RHS only) - cluster_assignment = np.argmin(distances_from_centroids,axis=1) - + cluster_assignment = np.argmin(distances_from_centroids, axis=1) + return cluster_assignment + def revise_centroids(data, k, cluster_assignment): new_centroids = [] for i in range(k): # Select all data points that belong to cluster i. Fill in the blank (RHS only) - member_data_points = data[cluster_assignment==i] + member_data_points = data[cluster_assignment == i] # Compute the mean of the data points. Fill in the blank (RHS only) centroid = member_data_points.mean(axis=0) new_centroids.append(centroid) new_centroids = np.array(new_centroids) - + return new_centroids + def compute_heterogeneity(data, k, centroids, cluster_assignment): - + heterogeneity = 0.0 for i in range(k): - + # Select all data points that belong to cluster i. Fill in the blank (RHS only) - member_data_points = data[cluster_assignment==i, :] - - if member_data_points.shape[0] > 0: # check if i-th cluster is non-empty + member_data_points = data[cluster_assignment == i, :] + + if member_data_points.shape[0] > 0: # check if i-th cluster is non-empty # Compute distances from centroid to data points (RHS only) - distances = pairwise_distances(member_data_points, [centroids[i]], metric='euclidean') - squared_distances = distances**2 + distances = pairwise_distances( + member_data_points, [centroids[i]], metric="euclidean" + ) + squared_distances = distances ** 2 heterogeneity += np.sum(squared_distances) - + return heterogeneity + from matplotlib import pyplot as plt + + def plot_heterogeneity(heterogeneity, k): - plt.figure(figsize=(7,4)) + plt.figure(figsize=(7, 4)) plt.plot(heterogeneity, linewidth=4) - plt.xlabel('# Iterations') - plt.ylabel('Heterogeneity') - plt.title('Heterogeneity of clustering over time, K={0:d}'.format(k)) - plt.rcParams.update({'font.size': 16}) + plt.xlabel("# Iterations") + plt.ylabel("Heterogeneity") + plt.title("Heterogeneity of clustering over time, K={0:d}".format(k)) + plt.rcParams.update({"font.size": 16}) plt.show() -def kmeans(data, k, initial_centroids, maxiter=500, record_heterogeneity=None, verbose=False): - '''This function runs k-means on given data and initial set of centroids. + +def kmeans( + data, k, initial_centroids, maxiter=500, record_heterogeneity=None, verbose=False +): + """This function runs k-means on given data and initial set of centroids. maxiter: maximum number of iterations to run.(default=500) record_heterogeneity: (optional) a list, to store the history of heterogeneity as function of iterations if None, do not store the history. - verbose: if True, print how many data points changed their cluster labels in each iteration''' + verbose: if True, print how many data points changed their cluster labels in each iteration""" centroids = initial_centroids[:] prev_cluster_assignment = None - - for itr in range(maxiter): + + for itr in range(maxiter): if verbose: - print(itr, end='') - + print(itr, end="") + # 1. Make cluster assignments using nearest centroids - cluster_assignment = assign_clusters(data,centroids) - + cluster_assignment = assign_clusters(data, centroids) + # 2. Compute a new centroid for each of the k clusters, averaging all data points assigned to that cluster. - centroids = revise_centroids(data,k, cluster_assignment) - + centroids = revise_centroids(data, k, cluster_assignment) + # Check for convergence: if none of the assignments changed, stop - if prev_cluster_assignment is not None and \ - (prev_cluster_assignment==cluster_assignment).all(): + if ( + prev_cluster_assignment is not None + and (prev_cluster_assignment == cluster_assignment).all() + ): break - - # Print number of new assignments + + # Print number of new assignments if prev_cluster_assignment is not None: - num_changed = np.sum(prev_cluster_assignment!=cluster_assignment) + num_changed = np.sum(prev_cluster_assignment != cluster_assignment) if verbose: - print(' {0:5d} elements changed their cluster assignment.'.format(num_changed)) - + print( + " {0:5d} elements changed their cluster assignment.".format( + num_changed + ) + ) + # Record heterogeneity convergence metric if record_heterogeneity is not None: # YOUR CODE HERE - score = compute_heterogeneity(data,k,centroids,cluster_assignment) + score = compute_heterogeneity(data, k, centroids, cluster_assignment) record_heterogeneity.append(score) - + prev_cluster_assignment = cluster_assignment[:] - + return centroids, cluster_assignment + # Mock test below -if False: # change to true to run this test case. +if False: # change to true to run this test case. import sklearn.datasets as ds + dataset = ds.load_iris() k = 3 heterogeneity = [] - initial_centroids = get_initial_centroids(dataset['data'], k, seed=0) - centroids, cluster_assignment = kmeans(dataset['data'], k, initial_centroids, maxiter=400, - record_heterogeneity=heterogeneity, verbose=True) + initial_centroids = get_initial_centroids(dataset["data"], k, seed=0) + centroids, cluster_assignment = kmeans( + dataset["data"], + k, + initial_centroids, + maxiter=400, + record_heterogeneity=heterogeneity, + verbose=True, + ) plot_heterogeneity(heterogeneity, k) diff --git a/machine_learning/k_nearest_neighbours.py b/machine_learning/k_nearest_neighbours.py new file mode 100644 index 000000000000..a60b744bc65e --- /dev/null +++ b/machine_learning/k_nearest_neighbours.py @@ -0,0 +1,57 @@ +import numpy as np +from collections import Counter +from sklearn import datasets +from sklearn.model_selection import train_test_split + +data = datasets.load_iris() + +X = np.array(data["data"]) +y = np.array(data["target"]) +classes = data["target_names"] + +X_train, X_test, y_train, y_test = train_test_split(X, y) + + +def euclidean_distance(a, b): + """ + Gives the euclidean distance between two points + >>> euclidean_distance([0, 0], [3, 4]) + 5.0 + >>> euclidean_distance([1, 2, 3], [1, 8, 11]) + 10.0 + """ + return np.linalg.norm(np.array(a) - np.array(b)) + + +def classifier(train_data, train_target, classes, point, k=5): + """ + Classifies the point using the KNN algorithm + k closest points are found (ranked in ascending order of euclidean distance) + Params: + :train_data: Set of points that are classified into two or more classes + :train_target: List of classes in the order of train_data points + :classes: Labels of the classes + :point: The data point that needs to be classifed + + >>> X_train = [[0, 0], [1, 0], [0, 1], [0.5, 0.5], [3, 3], [2, 3], [3, 2]] + >>> y_train = [0, 0, 0, 0, 1, 1, 1] + >>> classes = ['A','B']; point = [1.2,1.2] + >>> classifier(X_train, y_train, classes,point) + 'A' + """ + data = zip(train_data, train_target) + # List of distances of all points from the point to be classified + distances = [] + for data_point in data: + distance = euclidean_distance(data_point[0], point) + distances.append((distance, data_point[1])) + # Choosing 'k' points with the least distances. + votes = [i[1] for i in sorted(distances)[:k]] + # Most commonly occuring class among them + # is the class into which the point is classified + result = Counter(votes).most_common(1)[0][0] + return classes[result] + + +if __name__ == "__main__": + print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4])) diff --git a/machine_learning/knn_sklearn.py b/machine_learning/knn_sklearn.py new file mode 100644 index 000000000000..a371e30f5403 --- /dev/null +++ b/machine_learning/knn_sklearn.py @@ -0,0 +1,31 @@ +from sklearn.model_selection import train_test_split +from sklearn.datasets import load_iris +from sklearn.neighbors import KNeighborsClassifier + +# Load iris file +iris = load_iris() +iris.keys() + + +print("Target names: \n {} ".format(iris.target_names)) +print("\n Features: \n {}".format(iris.feature_names)) + +# Train set e Test set +X_train, X_test, y_train, y_test = train_test_split( + iris["data"], iris["target"], random_state=4 +) + +# KNN + +knn = KNeighborsClassifier(n_neighbors=1) +knn.fit(X_train, y_train) + +# new array to test +X_new = [[1, 2, 1, 4], [2, 3, 4, 5]] + +prediction = knn.predict(X_new) + +print( + "\nNew array: \n {}" + "\n\nTarget Names Prediction: \n {}".format(X_new, iris["target_names"][prediction]) +) diff --git a/machine_learning/linear_regression.py b/machine_learning/linear_regression.py index 8c23f1f77908..b666feddccc7 100644 --- a/machine_learning/linear_regression.py +++ b/machine_learning/linear_regression.py @@ -1,14 +1,12 @@ """ Linear regression is the most basic type of regression commonly used for -predictive analysis. The idea is preety simple, we have a dataset and we have +predictive analysis. The idea is pretty simple, we have a dataset and we have a feature's associated with it. The Features should be choose very cautiously as they determine, how much our model will be able to make future predictions. We try to set these Feature weights, over many iterations, so that they best fits our dataset. In this particular code, i had used a CSGO dataset (ADR vs Rating). We try to best fit a line through dataset and estimate the parameters. """ -from __future__ import print_function - import requests import numpy as np @@ -18,21 +16,22 @@ def collect_dataset(): The dataset contains ADR vs Rating of a Player :return : dataset obtained from the link, as matrix """ - response = requests.get('https://raw.githubusercontent.com/yashLadha/' + - 'The_Math_of_Intelligence/master/Week1/ADRvs' + - 'Rating.csv') + response = requests.get( + "https://raw.githubusercontent.com/yashLadha/" + + "The_Math_of_Intelligence/master/Week1/ADRvs" + + "Rating.csv" + ) lines = response.text.splitlines() data = [] for item in lines: - item = item.split(',') + item = item.split(",") data.append(item) data.pop(0) # This is for removing the labels from the list dataset = np.matrix(data) return dataset -def run_steep_gradient_descent(data_x, data_y, - len_data, alpha, theta): +def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta): """ Run steep gradient descent and updates the Feature vector accordingly_ :param data_x : contains the dataset :param data_y : contains the output associated with each data-entry @@ -81,10 +80,9 @@ def run_linear_regression(data_x, data_y): theta = np.zeros((1, no_features)) for i in range(0, iterations): - theta = run_steep_gradient_descent(data_x, data_y, - len_data, alpha, theta) + theta = run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta) error = sum_of_square_error(data_x, data_y, len_data, theta) - print('At Iteration %d - Error is %.5f ' % (i + 1, error)) + print("At Iteration %d - Error is %.5f " % (i + 1, error)) return theta @@ -99,10 +97,10 @@ def main(): theta = run_linear_regression(data_x, data_y) len_result = theta.shape[1] - print('Resultant Feature vector : ') + print("Resultant Feature vector : ") for i in range(0, len_result): - print('%.5f' % (theta[0, i])) + print("%.5f" % (theta[0, i])) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/machine_learning/logistic_regression.py b/machine_learning/logistic_regression.py index 71952e792e81..f23d400ced55 100644 --- a/machine_learning/logistic_regression.py +++ b/machine_learning/logistic_regression.py @@ -9,8 +9,8 @@ # importing all the required libraries -''' Implementing logistic regression for classification problem - Helpful resources : 1.Coursera ML course 2.https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac''' +""" Implementing logistic regression for classification problem + Helpful resources : 1.Coursera ML course 2.https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac""" import numpy as np import matplotlib.pyplot as plt @@ -24,6 +24,7 @@ # sigmoid function or logistic function is used as a hypothesis function in classification problems + def sigmoid_function(z): return 1 / (1 + np.exp(-z)) @@ -32,70 +33,53 @@ def cost_function(h, y): return (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean() -# here alpha is the learning rate, X is the feature matrix,y is the target matrix +def log_likelihood(X, Y, weights): + scores = np.dot(X, weights) + return np.sum(Y * scores - np.log(1 + np.exp(scores))) + -def logistic_reg( - alpha, - X, - y, - max_iterations=70000, - ): - converged = False - iterations = 0 +# here alpha is the learning rate, X is the feature matrix,y is the target matrix +def logistic_reg(alpha, X, y, max_iterations=70000): theta = np.zeros(X.shape[1]) - while not converged: + for iterations in range(max_iterations): z = np.dot(X, theta) h = sigmoid_function(z) gradient = np.dot(X.T, h - y) / y.size - theta = theta - alpha * gradient - + theta = theta - alpha * gradient # updating the weights z = np.dot(X, theta) h = sigmoid_function(z) J = cost_function(h, y) - - iterations += 1 # update iterations - - if iterations == max_iterations: - print ('Maximum iterations exceeded!') - print ('Minimal cost function J=', J) - converged = True - + if iterations % 100 == 0: + print(f"loss: {J} \t") # printing the loss after every 100 iterations return theta # In[68]: -if __name__ == '__main__': +if __name__ == "__main__": iris = datasets.load_iris() X = iris.data[:, :2] y = (iris.target != 0) * 1 alpha = 0.1 theta = logistic_reg(alpha, X, y, max_iterations=70000) - print (theta) - + print("theta: ", theta) # printing the theta i.e our weights vector def predict_prob(X): - return sigmoid_function(np.dot(X, theta)) # predicting the value of probability from the logistic regression algorithm - + return sigmoid_function( + np.dot(X, theta) + ) # predicting the value of probability from the logistic regression algorithm plt.figure(figsize=(10, 6)) - plt.scatter(X[y == 0][:, 0], X[y == 0][:, 1], color='b', label='0') - plt.scatter(X[y == 1][:, 0], X[y == 1][:, 1], color='r', label='1') + plt.scatter(X[y == 0][:, 0], X[y == 0][:, 1], color="b", label="0") + plt.scatter(X[y == 1][:, 0], X[y == 1][:, 1], color="r", label="1") (x1_min, x1_max) = (X[:, 0].min(), X[:, 0].max()) (x2_min, x2_max) = (X[:, 1].min(), X[:, 1].max()) - (xx1, xx2) = np.meshgrid(np.linspace(x1_min, x1_max), - np.linspace(x2_min, x2_max)) + (xx1, xx2) = np.meshgrid(np.linspace(x1_min, x1_max), np.linspace(x2_min, x2_max)) grid = np.c_[xx1.ravel(), xx2.ravel()] probs = predict_prob(grid).reshape(xx1.shape) - plt.contour( - xx1, - xx2, - probs, - [0.5], - linewidths=1, - colors='black', - ) + plt.contour(xx1, xx2, probs, [0.5], linewidths=1, colors="black") plt.legend() + plt.show() diff --git a/machine_learning/perceptron.py b/machine_learning/perceptron.py deleted file mode 100644 index fe1032aff4af..000000000000 --- a/machine_learning/perceptron.py +++ /dev/null @@ -1,124 +0,0 @@ -''' - - Perceptron - w = w + N * (d(k) - y) * x(k) - - Using perceptron network for oil analysis, - with Measuring of 3 parameters that represent chemical characteristics we can classify the oil, in p1 or p2 - p1 = -1 - p2 = 1 - -''' -from __future__ import print_function - -import random - - -class Perceptron: - def __init__(self, sample, exit, learn_rate=0.01, epoch_number=1000, bias=-1): - self.sample = sample - self.exit = exit - self.learn_rate = learn_rate - self.epoch_number = epoch_number - self.bias = bias - self.number_sample = len(sample) - self.col_sample = len(sample[0]) - self.weight = [] - - def trannig(self): - for sample in self.sample: - sample.insert(0, self.bias) - - for i in range(self.col_sample): - self.weight.append(random.random()) - - self.weight.insert(0, self.bias) - - epoch_count = 0 - - while True: - erro = False - for i in range(self.number_sample): - u = 0 - for j in range(self.col_sample + 1): - u = u + self.weight[j] * self.sample[i][j] - y = self.sign(u) - if y != self.exit[i]: - - for j in range(self.col_sample + 1): - - self.weight[j] = self.weight[j] + self.learn_rate * (self.exit[i] - y) * self.sample[i][j] - erro = True - #print('Epoch: \n',epoch_count) - epoch_count = epoch_count + 1 - # if you want controle the epoch or just by erro - if erro == False: - print(('\nEpoch:\n',epoch_count)) - print('------------------------\n') - #if epoch_count > self.epoch_number or not erro: - break - - def sort(self, sample): - sample.insert(0, self.bias) - u = 0 - for i in range(self.col_sample + 1): - u = u + self.weight[i] * sample[i] - - y = self.sign(u) - - if y == -1: - print(('Sample: ', sample)) - print('classification: P1') - else: - print(('Sample: ', sample)) - print('classification: P2') - - def sign(self, u): - return 1 if u >= 0 else -1 - - -samples = [ - [-0.6508, 0.1097, 4.0009], - [-1.4492, 0.8896, 4.4005], - [2.0850, 0.6876, 12.0710], - [0.2626, 1.1476, 7.7985], - [0.6418, 1.0234, 7.0427], - [0.2569, 0.6730, 8.3265], - [1.1155, 0.6043, 7.4446], - [0.0914, 0.3399, 7.0677], - [0.0121, 0.5256, 4.6316], - [-0.0429, 0.4660, 5.4323], - [0.4340, 0.6870, 8.2287], - [0.2735, 1.0287, 7.1934], - [0.4839, 0.4851, 7.4850], - [0.4089, -0.1267, 5.5019], - [1.4391, 0.1614, 8.5843], - [-0.9115, -0.1973, 2.1962], - [0.3654, 1.0475, 7.4858], - [0.2144, 0.7515, 7.1699], - [0.2013, 1.0014, 6.5489], - [0.6483, 0.2183, 5.8991], - [-0.1147, 0.2242, 7.2435], - [-0.7970, 0.8795, 3.8762], - [-1.0625, 0.6366, 2.4707], - [0.5307, 0.1285, 5.6883], - [-1.2200, 0.7777, 1.7252], - [0.3957, 0.1076, 5.6623], - [-0.1013, 0.5989, 7.1812], - [2.4482, 0.9455, 11.2095], - [2.0149, 0.6192, 10.9263], - [0.2012, 0.2611, 5.4631] - -] - -exit = [-1, -1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1] - -network = Perceptron(sample=samples, exit = exit, learn_rate=0.01, epoch_number=1000, bias=-1) - -network.trannig() - -while True: - sample = [] - for i in range(3): - sample.insert(i, float(input('value: '))) - network.sort(sample) diff --git a/machine_learning/polymonial_regression.py b/machine_learning/polymonial_regression.py new file mode 100644 index 000000000000..0d9db0f7578a --- /dev/null +++ b/machine_learning/polymonial_regression.py @@ -0,0 +1,43 @@ +import matplotlib.pyplot as plt +import pandas as pd + +# Importing the dataset +dataset = pd.read_csv( + "https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/position_salaries.csv" +) +X = dataset.iloc[:, 1:2].values +y = dataset.iloc[:, 2].values + + +# Splitting the dataset into the Training set and Test set +from sklearn.model_selection import train_test_split + +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) + + +# Fitting Polynomial Regression to the dataset +from sklearn.preprocessing import PolynomialFeatures +from sklearn.linear_model import LinearRegression + +poly_reg = PolynomialFeatures(degree=4) +X_poly = poly_reg.fit_transform(X) +pol_reg = LinearRegression() +pol_reg.fit(X_poly, y) + + +# Visualizing the Polymonial Regression results +def viz_polymonial(): + plt.scatter(X, y, color="red") + plt.plot(X, pol_reg.predict(poly_reg.fit_transform(X)), color="blue") + plt.title("Truth or Bluff (Linear Regression)") + plt.xlabel("Position level") + plt.ylabel("Salary") + plt.show() + return + + +viz_polymonial() + +# Predicting a new result with Polymonial Regression +pol_reg.predict(poly_reg.fit_transform([[5.5]])) +# output should be 132148.43750003 diff --git a/machine_learning/reuters_one_vs_rest_classifier.ipynb b/machine_learning/reuters_one_vs_rest_classifier.ipynb deleted file mode 100644 index 968130a6053a..000000000000 --- a/machine_learning/reuters_one_vs_rest_classifier.ipynb +++ /dev/null @@ -1,405 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " import nltk\n", - "except ModuleNotFoundError:\n", - " !pip install nltk" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "## This code downloads the required packages.\n", - "## You can run `nltk.download('all')` to download everything.\n", - "\n", - "nltk_packages = [\n", - " (\"reuters\", \"corpora/reuters.zip\")\n", - "]\n", - "\n", - "for pid, fid in nltk_packages:\n", - " try:\n", - " nltk.data.find(fid)\n", - " except LookupError:\n", - " nltk.download(pid)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up corpus" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from nltk.corpus import reuters" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up train/test data" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "train_documents, train_categories = zip(*[(reuters.raw(i), reuters.categories(i)) for i in reuters.fileids() if i.startswith('training/')])\n", - "test_documents, test_categories = zip(*[(reuters.raw(i), reuters.categories(i)) for i in reuters.fileids() if i.startswith('test/')])" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "all_categories = sorted(list(set(reuters.categories())))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following cell defines a function **tokenize** that performs following actions:\n", - "- Receive a document as an argument to the function\n", - "- Tokenize the document using `nltk.word_tokenize()`\n", - "- Use `PorterStemmer` provided by the `nltk` to remove morphological affixes from each token\n", - "- Append stemmed token to an already defined list `stems`\n", - "- Return the list `stems`" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from nltk.stem.porter import PorterStemmer\n", - "def tokenize(text):\n", - " tokens = nltk.word_tokenize(text)\n", - " stems = []\n", - " for item in tokens:\n", - " stems.append(PorterStemmer().stem(item))\n", - " return stems" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To begin, I first used TF-IDF for feature selection on both train as well as test data using `TfidfVectorizer`.\n", - "\n", - "But first, What `TfidfVectorizer` actually does?\n", - "- `TfidfVectorizer` converts a collection of raw documents to a matrix of **TF-IDF** features.\n", - "\n", - "**TF-IDF**?\n", - "- TFIDF (abbreviation of the term *frequency–inverse document frequency*) is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus. [tf–idf](https://en.wikipedia.org/wiki/Tf%E2%80%93idf)\n", - "\n", - "**Why `TfidfVectorizer`**?\n", - "- `TfidfVectorizer` scale down the impact of tokens that occur very frequently (e.g., “a”, “the”, and “of”) in a given corpus. [Feature Extraction and Transformation](https://spark.apache.org/docs/latest/mllib-feature-extraction.html#tf-idf)\n", - "\n", - "I gave following two arguments to `TfidfVectorizer`:\n", - "- tokenizer: `tokenize` function\n", - "- stop_words\n", - "\n", - "Then I used `fit_transform` and `transform` on the train and test documents repectively.\n", - "\n", - "**Why `fit_transform` for training data while `transform` for test data**?\n", - "\n", - "To avoid data leakage during cross-validation, imputer computes the statistic on the train data during the `fit`, **stores it** and uses the same on the test data, during the `transform`. This also prevents the test data from appearing in `fit` operation." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.feature_extraction.text import TfidfVectorizer\n", - "\n", - "vectorizer = TfidfVectorizer(tokenizer = tokenize, stop_words = 'english')\n", - "\n", - "vectorised_train_documents = vectorizer.fit_transform(train_documents)\n", - "vectorised_test_documents = vectorizer.transform(test_documents)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the **efficient implementation** of machine learning algorithms, many machine learning algorithms **requires all input variables and output variables to be numeric**. This means that categorical data must be converted to a numerical form.\n", - "\n", - "For this purpose, I used `MultiLabelBinarizer` from `sklearn.preprocessing`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.preprocessing import MultiLabelBinarizer\n", - "\n", - "mlb = MultiLabelBinarizer()\n", - "train_labels = mlb.fit_transform(train_categories)\n", - "test_labels = mlb.transform(test_categories)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, To **train** the classifier, I used `LinearSVC` in combination with the `OneVsRestClassifier` function in the scikit-learn package.\n", - "\n", - "The strategy of `OneVsRestClassifier` is of **fitting one classifier per label** and the `OneVsRestClassifier` can efficiently do this task and also outputs are easy to interpret. Since each label is represented by **one and only one classifier**, it is possible to gain knowledge about the label by inspecting its corresponding classifier. [OneVsRestClassifier](http://scikit-learn.org/stable/modules/multiclass.html#one-vs-the-rest)\n", - "\n", - "The reason I combined `LinearSVC` with `OneVsRestClassifier` is because `LinearSVC` supports **Multi-class**, while we want to perform **Multi-label** classification." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "from sklearn.multiclass import OneVsRestClassifier\n", - "from sklearn.svm import LinearSVC\n", - "\n", - "classifier = OneVsRestClassifier(LinearSVC())\n", - "classifier.fit(vectorised_train_documents, train_labels)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After fitting the classifier, I decided to use `cross_val_score` to **measure score** of the classifier by **cross validation** on the training data. But the only problem was, I wanted to **shuffle** data to use with `cross_val_score`, but it does not support shuffle argument.\n", - "\n", - "So, I decided to use `KFold` with `cross_val_score` as `KFold` supports shuffling the data.\n", - "\n", - "I also enabled `random_state`, because `random_state` will guarantee the same output in each run. By setting the `random_state`, it is guaranteed that the pseudorandom number generator will generate the same sequence of random integers each time, which in turn will affect the split.\n", - "\n", - "Why **42**?\n", - "- [Why '42' is the preferred number when indicating something random?](https://softwareengineering.stackexchange.com/questions/507/why-42-is-the-preferred-number-when-indicating-something-random)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "from sklearn.model_selection import KFold, cross_val_score\n", - "\n", - "kf = KFold(n_splits=10, random_state = 42, shuffle = True)\n", - "scores = cross_val_score(classifier, vectorised_train_documents, train_labels, cv = kf)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cross-validation scores: [0.83655084 0.86743887 0.8043758 0.83011583 0.83655084 0.81724582\n", - " 0.82754183 0.8030888 0.80694981 0.82731959]\n", - "Cross-validation accuracy: 0.8257 (+/- 0.0368)\n" - ] - } - ], - "source": [ - "print('Cross-validation scores:', scores)\n", - "print('Cross-validation accuracy: {:.4f} (+/- {:.4f})'.format(scores.mean(), scores.std() * 2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the end, I used different methods (`accuracy_score`, `precision_score`, `recall_score`, `f1_score` and `confusion_matrix`) provided by scikit-learn **to evaluate** the classifier. (both *Macro-* and *Micro-averages*)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n", - "\n", - "predictions = classifier.predict(vectorised_test_documents)\n", - "\n", - "accuracy = accuracy_score(test_labels, predictions)\n", - "\n", - "macro_precision = precision_score(test_labels, predictions, average='macro')\n", - "macro_recall = recall_score(test_labels, predictions, average='macro')\n", - "macro_f1 = f1_score(test_labels, predictions, average='macro')\n", - "\n", - "micro_precision = precision_score(test_labels, predictions, average='micro')\n", - "micro_recall = recall_score(test_labels, predictions, average='micro')\n", - "micro_f1 = f1_score(test_labels, predictions, average='micro')\n", - "\n", - "cm = confusion_matrix(test_labels.argmax(axis = 1), predictions.argmax(axis = 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy: 0.8099\n", - "Precision:\n", - "- Macro: 0.6076\n", - "- Micro: 0.9471\n", - "Recall:\n", - "- Macro: 0.3708\n", - "- Micro: 0.7981\n", - "F1-measure:\n", - "- Macro: 0.4410\n", - "- Micro: 0.8662\n" - ] - } - ], - "source": [ - "print(\"Accuracy: {:.4f}\\nPrecision:\\n- Macro: {:.4f}\\n- Micro: {:.4f}\\nRecall:\\n- Macro: {:.4f}\\n- Micro: {:.4f}\\nF1-measure:\\n- Macro: {:.4f}\\n- Micro: {:.4f}\".format(accuracy, macro_precision, micro_precision, macro_recall, micro_recall, macro_f1, micro_f1))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In below cell, I used `matplotlib.pyplot` to **plot the confusion matrix** (of first *few results only* to keep the readings readable) using `heatmap` of `seaborn`." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import seaborn as sb\n", - "import pandas as pd\n", - "\n", - "cm_plt = pd.DataFrame(cm[:73])\n", - "\n", - "plt.figure(figsize = (25, 25))\n", - "ax = plt.axes()\n", - "\n", - "sb.heatmap(cm_plt, annot=True)\n", - "\n", - "ax.xaxis.set_ticks_position('top')\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pipeline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, I took the data from [Coconut - Wikipedia](https://en.wikipedia.org/wiki/Coconut) to check if the classifier is able to **correctly** predict the label(s) or not.\n", - "\n", - "And here is the output:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Example labels: [('coconut', 'oilseed')]\n" - ] - } - ], - "source": [ - "example_text = '''The coconut tree (Cocos nucifera) is a member of the family Arecaceae (palm family) and the only species of the genus Cocos.\n", - "The term coconut can refer to the whole coconut palm or the seed, or the fruit, which, botanically, is a drupe, not a nut.\n", - "The spelling cocoanut is an archaic form of the word.\n", - "The term is derived from the 16th-century Portuguese and Spanish word coco meaning \"head\" or \"skull\", from the three indentations on the coconut shell that resemble facial features.\n", - "Coconuts are known for their versatility ranging from food to cosmetics.\n", - "They form a regular part of the diets of many people in the tropics and subtropics.\n", - "Coconuts are distinct from other fruits for their endosperm containing a large quantity of water (also called \"milk\"), and when immature, may be harvested for the potable coconut water.\n", - "When mature, they can be used as seed nuts or processed for oil, charcoal from the hard shell, and coir from the fibrous husk.\n", - "When dried, the coconut flesh is called copra.\n", - "The oil and milk derived from it are commonly used in cooking and frying, as well as in soaps and cosmetics.\n", - "The husks and leaves can be used as material to make a variety of products for furnishing and decorating.\n", - "The coconut also has cultural and religious significance in certain societies, particularly in India, where it is used in Hindu rituals.'''\n", - "\n", - "example_preds = classifier.predict(vectorizer.transform([example_text]))\n", - "example_labels = mlb.inverse_transform(example_preds)\n", - "print(\"Example labels: {}\".format(example_labels))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/machine_learning/scoring_functions.py b/machine_learning/scoring_functions.py index a2d97b09ded2..5c84f7026e74 100755 --- a/machine_learning/scoring_functions.py +++ b/machine_learning/scoring_functions.py @@ -14,8 +14,18 @@ and types of data """ -#Mean Absolute Error +# Mean Absolute Error def mae(predict, actual): + """ + Examples(rounded for precision): + >>> actual = [1,2,3];predict = [1,4,3] + >>> np.around(mae(predict,actual),decimals = 2) + 0.67 + + >>> actual = [1,1,1];predict = [1,1,1] + >>> mae(predict,actual) + 0.0 + """ predict = np.array(predict) actual = np.array(actual) @@ -24,8 +34,19 @@ def mae(predict, actual): return score -#Mean Squared Error + +# Mean Squared Error def mse(predict, actual): + """ + Examples(rounded for precision): + >>> actual = [1,2,3];predict = [1,4,3] + >>> np.around(mse(predict,actual),decimals = 2) + 1.33 + + >>> actual = [1,1,1];predict = [1,1,1] + >>> mse(predict,actual) + 0.0 + """ predict = np.array(predict) actual = np.array(actual) @@ -35,8 +56,19 @@ def mse(predict, actual): score = square_diff.mean() return score -#Root Mean Squared Error + +# Root Mean Squared Error def rmse(predict, actual): + """ + Examples(rounded for precision): + >>> actual = [1,2,3];predict = [1,4,3] + >>> np.around(rmse(predict,actual),decimals = 2) + 1.15 + + >>> actual = [1,1,1];predict = [1,1,1] + >>> rmse(predict,actual) + 0.0 + """ predict = np.array(predict) actual = np.array(actual) @@ -46,13 +78,24 @@ def rmse(predict, actual): score = np.sqrt(mean_square_diff) return score -#Root Mean Square Logarithmic Error + +# Root Mean Square Logarithmic Error def rmsle(predict, actual): + """ + Examples(rounded for precision): + >>> actual = [10,10,30];predict = [10,2,30] + >>> np.around(rmsle(predict,actual),decimals = 2) + 0.75 + + >>> actual = [1,1,1];predict = [1,1,1] + >>> rmsle(predict,actual) + 0.0 + """ predict = np.array(predict) actual = np.array(actual) - log_predict = np.log(predict+1) - log_actual = np.log(actual+1) + log_predict = np.log(predict + 1) + log_actual = np.log(actual + 1) difference = log_predict - log_actual square_diff = np.square(difference) @@ -62,17 +105,32 @@ def rmsle(predict, actual): return score -#Mean Bias Deviation + +# Mean Bias Deviation def mbd(predict, actual): + """ + This value is Negative, if the model underpredicts, + positive, if it overpredicts. + + Example(rounded for precision): + + Here the model overpredicts + >>> actual = [1,2,3];predict = [2,3,4] + >>> np.around(mbd(predict,actual),decimals = 2) + 50.0 + + Here the model underpredicts + >>> actual = [1,2,3];predict = [0,1,1] + >>> np.around(mbd(predict,actual),decimals = 2) + -66.67 + """ predict = np.array(predict) actual = np.array(actual) difference = predict - actual - numerator = np.sum(difference) / len(predict) - denumerator = np.sum(actual) / len(predict) - print(numerator) - print(denumerator) - + numerator = np.sum(difference) / len(predict) + denumerator = np.sum(actual) / len(predict) + # print(numerator, denumerator) score = float(numerator) / denumerator * 100 return score diff --git a/machine_learning/sequential_minimum_optimization.py b/machine_learning/sequential_minimum_optimization.py new file mode 100644 index 000000000000..1d4e4a276bc1 --- /dev/null +++ b/machine_learning/sequential_minimum_optimization.py @@ -0,0 +1,627 @@ +# coding: utf-8 +""" + Implementation of sequential minimal optimization(SMO) for support vector machines(SVM). + + Sequential minimal optimization (SMO) is an algorithm for solving the quadratic programming (QP) problem + that arises during the training of support vector machines. + It was invented by John Platt in 1998. + +Input: + 0: type: numpy.ndarray. + 1: first column of ndarray must be tags of samples, must be 1 or -1. + 2: rows of ndarray represent samples. + +Usage: + Command: + python3 sequential_minimum_optimization.py + Code: + from sequential_minimum_optimization import SmoSVM, Kernel + + kernel = Kernel(kernel='poly', degree=3., coef0=1., gamma=0.5) + init_alphas = np.zeros(train.shape[0]) + SVM = SmoSVM(train=train, alpha_list=init_alphas, kernel_func=kernel, cost=0.4, b=0.0, tolerance=0.001) + SVM.fit() + predict = SVM.predict(test_samples) + +Reference: + https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/smo-book.pdf + https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-98-14.pdf + http://web.cs.iastate.edu/~honavar/smo-svm.pdf +""" + +from __future__ import division + +import os +import sys +import urllib.request + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from sklearn.datasets import make_blobs, make_circles +from sklearn.preprocessing import StandardScaler + +CANCER_DATASET_URL = "http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data" + + +class SmoSVM(object): + def __init__( + self, + train, + kernel_func, + alpha_list=None, + cost=0.4, + b=0.0, + tolerance=0.001, + auto_norm=True, + ): + self._init = True + self._auto_norm = auto_norm + self._c = np.float64(cost) + self._b = np.float64(b) + self._tol = np.float64(tolerance) if tolerance > 0.0001 else np.float64(0.001) + + self.tags = train[:, 0] + self.samples = self._norm(train[:, 1:]) if self._auto_norm else train[:, 1:] + self.alphas = alpha_list if alpha_list is not None else np.zeros(train.shape[0]) + self.Kernel = kernel_func + + self._eps = 0.001 + self._all_samples = list(range(self.length)) + self._K_matrix = self._calculate_k_matrix() + self._error = np.zeros(self.length) + self._unbound = [] + + self.choose_alpha = self._choose_alphas() + + # Calculate alphas using SMO algorithsm + def fit(self): + K = self._k + state = None + while True: + + # 1: Find alpha1, alpha2 + try: + i1, i2 = self.choose_alpha.send(state) + state = None + except StopIteration: + print("Optimization done!\r\nEvery sample satisfy the KKT condition!") + break + + # 2: calculate new alpha2 and new alpha1 + y1, y2 = self.tags[i1], self.tags[i2] + a1, a2 = self.alphas[i1].copy(), self.alphas[i2].copy() + e1, e2 = self._e(i1), self._e(i2) + args = (i1, i2, a1, a2, e1, e2, y1, y2) + a1_new, a2_new = self._get_new_alpha(*args) + if not a1_new and not a2_new: + state = False + continue + self.alphas[i1], self.alphas[i2] = a1_new, a2_new + + # 3: update threshold(b) + b1_new = np.float64( + -e1 + - y1 * K(i1, i1) * (a1_new - a1) + - y2 * K(i2, i1) * (a2_new - a2) + + self._b + ) + b2_new = np.float64( + -e2 + - y2 * K(i2, i2) * (a2_new - a2) + - y1 * K(i1, i2) * (a1_new - a1) + + self._b + ) + if 0.0 < a1_new < self._c: + b = b1_new + if 0.0 < a2_new < self._c: + b = b2_new + if not (np.float64(0) < a2_new < self._c) and not ( + np.float64(0) < a1_new < self._c + ): + b = (b1_new + b2_new) / 2.0 + b_old = self._b + self._b = b + + # 4: update error value,here we only calculate those non-bound samples' error + self._unbound = [i for i in self._all_samples if self._is_unbound(i)] + for s in self.unbound: + if s == i1 or s == i2: + continue + self._error[s] += ( + y1 * (a1_new - a1) * K(i1, s) + + y2 * (a2_new - a2) * K(i2, s) + + (self._b - b_old) + ) + + # if i1 or i2 is non-bound,update there error value to zero + if self._is_unbound(i1): + self._error[i1] = 0 + if self._is_unbound(i2): + self._error[i2] = 0 + + # Predict test samles + def predict(self, test_samples, classify=True): + + if test_samples.shape[1] > self.samples.shape[1]: + raise ValueError( + "Test samples' feature length does not equal to that of train samples" + ) + + if self._auto_norm: + test_samples = self._norm(test_samples) + + results = [] + for test_sample in test_samples: + result = self._predict(test_sample) + if classify: + results.append(1 if result > 0 else -1) + else: + results.append(result) + return np.array(results) + + # Check if alpha violate KKT condition + def _check_obey_kkt(self, index): + alphas = self.alphas + tol = self._tol + r = self._e(index) * self.tags[index] + c = self._c + + return (r < -tol and alphas[index] < c) or (r > tol and alphas[index] > 0.0) + + # Get value calculated from kernel function + def _k(self, i1, i2): + # for test samples,use Kernel function + if isinstance(i2, np.ndarray): + return self.Kernel(self.samples[i1], i2) + # for train samples,Kernel values have been saved in matrix + else: + return self._K_matrix[i1, i2] + + # Get sample's error + def _e(self, index): + """ + Two cases: + 1:Sample[index] is non-bound,Fetch error from list: _error + 2:sample[index] is bound,Use predicted value deduct true value: g(xi) - yi + + """ + # get from error data + if self._is_unbound(index): + return self._error[index] + # get by g(xi) - yi + else: + gx = np.dot(self.alphas * self.tags, self._K_matrix[:, index]) + self._b + yi = self.tags[index] + return gx - yi + + # Calculate Kernel matrix of all possible i1,i2 ,saving time + def _calculate_k_matrix(self): + k_matrix = np.zeros([self.length, self.length]) + for i in self._all_samples: + for j in self._all_samples: + k_matrix[i, j] = np.float64( + self.Kernel(self.samples[i, :], self.samples[j, :]) + ) + return k_matrix + + # Predict test sample's tag + def _predict(self, sample): + k = self._k + predicted_value = ( + np.sum( + [ + self.alphas[i1] * self.tags[i1] * k(i1, sample) + for i1 in self._all_samples + ] + ) + + self._b + ) + return predicted_value + + # Choose alpha1 and alpha2 + def _choose_alphas(self): + locis = yield from self._choose_a1() + if not locis: + return + return locis + + def _choose_a1(self): + """ + Choose first alpha ;steps: + 1:Fisrt loop over all sample + 2:Second loop over all non-bound samples till all non-bound samples does not voilate kkt condition. + 3:Repeat this two process endlessly,till all samples does not voilate kkt condition samples after first loop. + """ + while True: + all_not_obey = True + # all sample + print("scanning all sample!") + for i1 in [i for i in self._all_samples if self._check_obey_kkt(i)]: + all_not_obey = False + yield from self._choose_a2(i1) + + # non-bound sample + print("scanning non-bound sample!") + while True: + not_obey = True + for i1 in [ + i + for i in self._all_samples + if self._check_obey_kkt(i) and self._is_unbound(i) + ]: + not_obey = False + yield from self._choose_a2(i1) + if not_obey: + print("all non-bound samples fit the KKT condition!") + break + if all_not_obey: + print("all samples fit the KKT condition! Optimization done!") + break + return False + + def _choose_a2(self, i1): + """ + Choose the second alpha by using heuristic algorithm ;steps: + 1:Choosed alpha2 which get the maximum step size (|E1 - E2|). + 2:Start in a random point,loop over all non-bound samples till alpha1 and alpha2 are optimized. + 3:Start in a random point,loop over all samples till alpha1 and alpha2 are optimized. + """ + self._unbound = [i for i in self._all_samples if self._is_unbound(i)] + + if len(self.unbound) > 0: + tmp_error = self._error.copy().tolist() + tmp_error_dict = { + index: value + for index, value in enumerate(tmp_error) + if self._is_unbound(index) + } + if self._e(i1) >= 0: + i2 = min(tmp_error_dict, key=lambda index: tmp_error_dict[index]) + else: + i2 = max(tmp_error_dict, key=lambda index: tmp_error_dict[index]) + cmd = yield i1, i2 + if cmd is None: + return + + for i2 in np.roll(self.unbound, np.random.choice(self.length)): + cmd = yield i1, i2 + if cmd is None: + return + + for i2 in np.roll(self._all_samples, np.random.choice(self.length)): + cmd = yield i1, i2 + if cmd is None: + return + + # Get the new alpha2 and new alpha1 + def _get_new_alpha(self, i1, i2, a1, a2, e1, e2, y1, y2): + K = self._k + if i1 == i2: + return None, None + + # calculate L and H which bound the new alpha2 + s = y1 * y2 + if s == -1: + L, H = max(0.0, a2 - a1), min(self._c, self._c + a2 - a1) + else: + L, H = max(0.0, a2 + a1 - self._c), min(self._c, a2 + a1) + if L == H: + return None, None + + # calculate eta + k11 = K(i1, i1) + k22 = K(i2, i2) + k12 = K(i1, i2) + eta = k11 + k22 - 2.0 * k12 + + # select the new alpha2 which could get the minimal objectives + if eta > 0.0: + a2_new_unc = a2 + (y2 * (e1 - e2)) / eta + # a2_new has a boundry + if a2_new_unc >= H: + a2_new = H + elif a2_new_unc <= L: + a2_new = L + else: + a2_new = a2_new_unc + else: + b = self._b + l1 = a1 + s * (a2 - L) + h1 = a1 + s * (a2 - H) + + # way 1 + f1 = y1 * (e1 + b) - a1 * K(i1, i1) - s * a2 * K(i1, i2) + f2 = y2 * (e2 + b) - a2 * K(i2, i2) - s * a1 * K(i1, i2) + ol = ( + l1 * f1 + + L * f2 + + 1 / 2 * l1 ** 2 * K(i1, i1) + + 1 / 2 * L ** 2 * K(i2, i2) + + s * L * l1 * K(i1, i2) + ) + oh = ( + h1 * f1 + + H * f2 + + 1 / 2 * h1 ** 2 * K(i1, i1) + + 1 / 2 * H ** 2 * K(i2, i2) + + s * H * h1 * K(i1, i2) + ) + """ + # way 2 + Use objective function check which alpha2 new could get the minimal objectives + + """ + if ol < (oh - self._eps): + a2_new = L + elif ol > oh + self._eps: + a2_new = H + else: + a2_new = a2 + + # a1_new has a boundry too + a1_new = a1 + s * (a2 - a2_new) + if a1_new < 0: + a2_new += s * a1_new + a1_new = 0 + if a1_new > self._c: + a2_new += s * (a1_new - self._c) + a1_new = self._c + + return a1_new, a2_new + + # Normalise data using min_max way + def _norm(self, data): + if self._init: + self._min = np.min(data, axis=0) + self._max = np.max(data, axis=0) + self._init = False + return (data - self._min) / (self._max - self._min) + else: + return (data - self._min) / (self._max - self._min) + + def _is_unbound(self, index): + if 0.0 < self.alphas[index] < self._c: + return True + else: + return False + + def _is_support(self, index): + if self.alphas[index] > 0: + return True + else: + return False + + @property + def unbound(self): + return self._unbound + + @property + def support(self): + return [i for i in range(self.length) if self._is_support(i)] + + @property + def length(self): + return self.samples.shape[0] + + +class Kernel(object): + def __init__(self, kernel, degree=1.0, coef0=0.0, gamma=1.0): + self.degree = np.float64(degree) + self.coef0 = np.float64(coef0) + self.gamma = np.float64(gamma) + self._kernel_name = kernel + self._kernel = self._get_kernel(kernel_name=kernel) + self._check() + + def _polynomial(self, v1, v2): + return (self.gamma * np.inner(v1, v2) + self.coef0) ** self.degree + + def _linear(self, v1, v2): + return np.inner(v1, v2) + self.coef0 + + def _rbf(self, v1, v2): + return np.exp(-1 * (self.gamma * np.linalg.norm(v1 - v2) ** 2)) + + def _check(self): + if self._kernel == self._rbf: + if self.gamma < 0: + raise ValueError("gamma value must greater than 0") + + def _get_kernel(self, kernel_name): + maps = {"linear": self._linear, "poly": self._polynomial, "rbf": self._rbf} + return maps[kernel_name] + + def __call__(self, v1, v2): + return self._kernel(v1, v2) + + def __repr__(self): + return self._kernel_name + + +def count_time(func): + def call_func(*args, **kwargs): + import time + + start_time = time.time() + func(*args, **kwargs) + end_time = time.time() + print("smo algorithm cost {} seconds".format(end_time - start_time)) + + return call_func + + +@count_time +def test_cancel_data(): + print("Hello!\r\nStart test svm by smo algorithm!") + # 0: download dataset and load into pandas' dataframe + if not os.path.exists(r"cancel_data.csv"): + request = urllib.request.Request( + CANCER_DATASET_URL, + headers={"User-Agent": "Mozilla/4.0 (compatible; MSIE 5.5; Windows NT)"}, + ) + response = urllib.request.urlopen(request) + content = response.read().decode("utf-8") + with open(r"cancel_data.csv", "w") as f: + f.write(content) + + data = pd.read_csv(r"cancel_data.csv", header=None) + + # 1: pre-processing data + del data[data.columns.tolist()[0]] + data = data.dropna(axis=0) + data = data.replace({"M": np.float64(1), "B": np.float64(-1)}) + samples = np.array(data)[:, :] + + # 2: deviding data into train_data data and test_data data + train_data, test_data = samples[:328, :], samples[328:, :] + test_tags, test_samples = test_data[:, 0], test_data[:, 1:] + + # 3: choose kernel function,and set initial alphas to zero(optional) + mykernel = Kernel(kernel="rbf", degree=5, coef0=1, gamma=0.5) + al = np.zeros(train_data.shape[0]) + + # 4: calculating best alphas using SMO algorithm and predict test_data samples + mysvm = SmoSVM( + train=train_data, + kernel_func=mykernel, + alpha_list=al, + cost=0.4, + b=0.0, + tolerance=0.001, + ) + mysvm.fit() + predict = mysvm.predict(test_samples) + + # 5: check accuracy + score = 0 + test_num = test_tags.shape[0] + for i in range(test_tags.shape[0]): + if test_tags[i] == predict[i]: + score += 1 + print( + "\r\nall: {}\r\nright: {}\r\nfalse: {}".format( + test_num, score, test_num - score + ) + ) + print("Rough Accuracy: {}".format(score / test_tags.shape[0])) + + +def test_demonstration(): + # change stdout + print("\r\nStart plot,please wait!!!") + sys.stdout = open(os.devnull, "w") + + ax1 = plt.subplot2grid((2, 2), (0, 0)) + ax2 = plt.subplot2grid((2, 2), (0, 1)) + ax3 = plt.subplot2grid((2, 2), (1, 0)) + ax4 = plt.subplot2grid((2, 2), (1, 1)) + ax1.set_title("linear svm,cost:0.1") + test_linear_kernel(ax1, cost=0.1) + ax2.set_title("linear svm,cost:500") + test_linear_kernel(ax2, cost=500) + ax3.set_title("rbf kernel svm,cost:0.1") + test_rbf_kernel(ax3, cost=0.1) + ax4.set_title("rbf kernel svm,cost:500") + test_rbf_kernel(ax4, cost=500) + + sys.stdout = sys.__stdout__ + print("Plot done!!!") + + +def test_linear_kernel(ax, cost): + train_x, train_y = make_blobs( + n_samples=500, centers=2, n_features=2, random_state=1 + ) + train_y[train_y == 0] = -1 + scaler = StandardScaler() + train_x_scaled = scaler.fit_transform(train_x, train_y) + train_data = np.hstack((train_y.reshape(500, 1), train_x_scaled)) + mykernel = Kernel(kernel="linear", degree=5, coef0=1, gamma=0.5) + mysvm = SmoSVM( + train=train_data, + kernel_func=mykernel, + cost=cost, + tolerance=0.001, + auto_norm=False, + ) + mysvm.fit() + plot_partition_boundary(mysvm, train_data, ax=ax) + + +def test_rbf_kernel(ax, cost): + train_x, train_y = make_circles( + n_samples=500, noise=0.1, factor=0.1, random_state=1 + ) + train_y[train_y == 0] = -1 + scaler = StandardScaler() + train_x_scaled = scaler.fit_transform(train_x, train_y) + train_data = np.hstack((train_y.reshape(500, 1), train_x_scaled)) + mykernel = Kernel(kernel="rbf", degree=5, coef0=1, gamma=0.5) + mysvm = SmoSVM( + train=train_data, + kernel_func=mykernel, + cost=cost, + tolerance=0.001, + auto_norm=False, + ) + mysvm.fit() + plot_partition_boundary(mysvm, train_data, ax=ax) + + +def plot_partition_boundary( + model, train_data, ax, resolution=100, colors=("b", "k", "r") +): + """ + We can not get the optimum w of our kernel svm model which is different from linear svm. + For this reason, we generate randomly destributed points with high desity and prediced values of these points are + calculated by using our tained model. Then we could use this prediced values to draw contour map. + And this contour map can represent svm's partition boundary. + + """ + train_data_x = train_data[:, 1] + train_data_y = train_data[:, 2] + train_data_tags = train_data[:, 0] + xrange = np.linspace(train_data_x.min(), train_data_x.max(), resolution) + yrange = np.linspace(train_data_y.min(), train_data_y.max(), resolution) + test_samples = np.array([(x, y) for x in xrange for y in yrange]).reshape( + resolution * resolution, 2 + ) + + test_tags = model.predict(test_samples, classify=False) + grid = test_tags.reshape((len(xrange), len(yrange))) + + # Plot contour map which represents the partition boundary + ax.contour( + xrange, + yrange, + np.mat(grid).T, + levels=(-1, 0, 1), + linestyles=("--", "-", "--"), + linewidths=(1, 1, 1), + colors=colors, + ) + # Plot all train samples + ax.scatter( + train_data_x, + train_data_y, + c=train_data_tags, + cmap=plt.cm.Dark2, + lw=0, + alpha=0.5, + ) + + # Plot support vectors + support = model.support + ax.scatter( + train_data_x[support], + train_data_y[support], + c=train_data_tags[support], + cmap=plt.cm.Dark2, + ) + + +if __name__ == "__main__": + test_cancel_data() + test_demonstration() + plt.show() diff --git a/machine_learning/support_vector_machines.py b/machine_learning/support_vector_machines.py new file mode 100644 index 000000000000..92fa814c998f --- /dev/null +++ b/machine_learning/support_vector_machines.py @@ -0,0 +1,54 @@ +from sklearn.datasets import load_iris +from sklearn import svm +from sklearn.model_selection import train_test_split +import doctest + +# different functions implementing different types of SVM's +def NuSVC(train_x, train_y): + svc_NuSVC = svm.NuSVC() + svc_NuSVC.fit(train_x, train_y) + return svc_NuSVC + + +def Linearsvc(train_x, train_y): + svc_linear = svm.LinearSVC() + svc_linear.fit(train_x, train_y) + return svc_linear + + +def SVC(train_x, train_y): + # svm.SVC(C=1.0, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True, probability=False,tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, random_state=None) + # various parameters like "kernal","gamma","C" can effectively tuned for a given machine learning model. + SVC = svm.SVC(gamma="auto") + SVC.fit(train_x, train_y) + return SVC + + +def test(X_new): + """ + 3 test cases to be passed + an array containing the sepal length (cm), sepal width (cm),petal length (cm),petal width (cm) + based on which the target name will be predicted + >>> test([1,2,1,4]) + 'virginica' + >>> test([5, 2, 4, 1]) + 'versicolor' + >>> test([6,3,4,1]) + 'versicolor' + + """ + iris = load_iris() + # splitting the dataset to test and train + train_x, test_x, train_y, test_y = train_test_split( + iris["data"], iris["target"], random_state=4 + ) + # any of the 3 types of SVM can be used + # current_model=SVC(train_x, train_y) + # current_model=NuSVC(train_x, train_y) + current_model = Linearsvc(train_x, train_y) + prediction = current_model.predict([X_new]) + return iris["target_names"][prediction][0] + + +if __name__ == "__main__": + doctest.testmod() diff --git a/maths/3n+1.py b/maths/3n+1.py index 6424fe0d8f15..f6fe77b2b3fe 100644 --- a/maths/3n+1.py +++ b/maths/3n+1.py @@ -1,19 +1,149 @@ -def main(): - def n31(a):# a = initial number - c = 0 - l = [a] - while a != 1: - if a % 2 == 0:#if even divide it by 2 - a = a // 2 - elif a % 2 == 1:#if odd 3n+1 - a = 3*a +1 - c += 1#counter - l += [a] - - return l , c - print(n31(43)) - print(n31(98)[0][-1])# = a - print("It took {0} steps.".format(n31(13)[1]))#optional finish - -if __name__ == '__main__': - main() +from typing import Tuple, List + + +def n31(a: int) -> Tuple[List[int], int]: + """ + Returns the Collatz sequence and its length of any postiver integer. + >>> n31(4) + ([4, 2, 1], 3) + """ + + if not isinstance(a, int): + raise TypeError("Must be int, not {0}".format(type(a).__name__)) + if a < 1: + raise ValueError("Given integer must be greater than 1, not {0}".format(a)) + + path = [a] + while a != 1: + if a % 2 == 0: + a = a // 2 + else: + a = 3 * a + 1 + path += [a] + return path, len(path) + + +def test_n31(): + """ + >>> test_n31() + """ + assert n31(4) == ([4, 2, 1], 3) + assert n31(11) == ([11, 34, 17, 52, 26, 13, 40, 20, 10, 5, 16, 8, 4, 2, 1], 15) + assert n31(31) == ( + [ + 31, + 94, + 47, + 142, + 71, + 214, + 107, + 322, + 161, + 484, + 242, + 121, + 364, + 182, + 91, + 274, + 137, + 412, + 206, + 103, + 310, + 155, + 466, + 233, + 700, + 350, + 175, + 526, + 263, + 790, + 395, + 1186, + 593, + 1780, + 890, + 445, + 1336, + 668, + 334, + 167, + 502, + 251, + 754, + 377, + 1132, + 566, + 283, + 850, + 425, + 1276, + 638, + 319, + 958, + 479, + 1438, + 719, + 2158, + 1079, + 3238, + 1619, + 4858, + 2429, + 7288, + 3644, + 1822, + 911, + 2734, + 1367, + 4102, + 2051, + 6154, + 3077, + 9232, + 4616, + 2308, + 1154, + 577, + 1732, + 866, + 433, + 1300, + 650, + 325, + 976, + 488, + 244, + 122, + 61, + 184, + 92, + 46, + 23, + 70, + 35, + 106, + 53, + 160, + 80, + 40, + 20, + 10, + 5, + 16, + 8, + 4, + 2, + 1, + ], + 107, + ) + + +if __name__ == "__main__": + num = 4 + path, length = n31(num) + print(f"The Collatz sequence of {num} took {length} steps. \nPath: {path}") diff --git a/maths/FindMax.py b/maths/FindMax.py deleted file mode 100644 index 0ce49a68c348..000000000000 --- a/maths/FindMax.py +++ /dev/null @@ -1,14 +0,0 @@ -# NguyenU - -def find_max(nums): - max = nums[0] - for x in nums: - if x > max: - max = x - print(max) - -def main(): - find_max([2, 4, 9, 7, 19, 94, 5]) - -if __name__ == '__main__': - main() diff --git a/maths/FindMin.py b/maths/FindMin.py deleted file mode 100644 index 86207984e3da..000000000000 --- a/maths/FindMin.py +++ /dev/null @@ -1,12 +0,0 @@ -def main(): - def findMin(x): - minNum = x[0] - for i in x: - if minNum > i: - minNum = i - return minNum - - print(findMin([0,1,2,3,4,5,-3,24,-56])) # = -56 - -if __name__ == '__main__': - main() diff --git a/maths/Hanoi.py b/maths/Hanoi.py deleted file mode 100644 index dd04d0fa58d8..000000000000 --- a/maths/Hanoi.py +++ /dev/null @@ -1,24 +0,0 @@ -# @author willx75 -# Tower of Hanoi recursion game algorithm is a game, it consists of three rods and a number of disks of different sizes, which can slide onto any rod - -import logging - -log = logging.getLogger() -logging.basicConfig(level=logging.DEBUG) - - -def Tower_Of_Hanoi(n, source, dest, by, mouvement): - if n == 0: - return n - elif n == 1: - mouvement += 1 - # no print statement (you could make it an optional flag for printing logs) - logging.debug('Move the plate from', source, 'to', dest) - return mouvement - else: - - mouvement = mouvement + Tower_Of_Hanoi(n-1, source, by, dest, 0) - logging.debug('Move the plate from', source, 'to', dest) - - mouvement = mouvement + 1 + Tower_Of_Hanoi(n-1, by, dest, source, 0) - return mouvement diff --git a/maths/PrimeCheck.py b/maths/PrimeCheck.py deleted file mode 100644 index e0c51d77a038..000000000000 --- a/maths/PrimeCheck.py +++ /dev/null @@ -1,13 +0,0 @@ -import math -def primeCheck(number): - if number % 2 == 0 and number > 2: - return False - return all(number % i for i in range(3, int(math.sqrt(number)) + 1, 2)) - -def main(): - print(primeCheck(37)) - print(primeCheck(100)) - print(primeCheck(77)) - -if __name__ == '__main__': - main() diff --git a/maths/__init__.py b/maths/__init__.py new file mode 100644 index 000000000000..8b137891791f --- /dev/null +++ b/maths/__init__.py @@ -0,0 +1 @@ + diff --git a/maths/abs.py b/maths/abs.py index 6d0596478d5f..68c99a1d51d8 100644 --- a/maths/abs.py +++ b/maths/abs.py @@ -1,18 +1,28 @@ -def absVal(num): +"""Absolute Value.""" + + +def abs_val(num): """ - Function to fins absolute value of numbers. - >>absVal(-5) - 5 - >>absVal(0) + Find the absolute value of a number. + + >>> abs_val(-5.1) + 5.1 + >>> abs_val(-5) == abs_val(5) + True + >>> abs_val(0) 0 """ - if num < 0: - return -num - else: - return num + return -num if num < 0 else num + + +def test_abs_val(): + """ + >>> test_abs_val() + """ + assert 0 == abs_val(0) + assert 34 == abs_val(34) + assert 100000000000 == abs_val(-100000000000) -def main(): - print(absVal(-34)) # = 34 -if __name__ == '__main__': - main() +if __name__ == "__main__": + print(abs_val(-34)) # --> 34 diff --git a/maths/absMax.py b/maths/absMax.py deleted file mode 100644 index 7ff9e4d3ca09..000000000000 --- a/maths/absMax.py +++ /dev/null @@ -1,25 +0,0 @@ -def absMax(x): - """ - #>>>absMax([0,5,1,11]) - 11 - >>absMax([3,-10,-2]) - -10 - """ - j =x[0] - for i in x: - if abs(i) > abs(j): - j = i - return j - - -def main(): - a = [1,2,-11] - print(absMax(a)) # = -11 - - -if __name__ == '__main__': - main() - -""" -print abs Max -""" diff --git a/maths/absMin.py b/maths/absMin.py deleted file mode 100644 index 67d510551907..000000000000 --- a/maths/absMin.py +++ /dev/null @@ -1,20 +0,0 @@ -from Maths.abs import absVal -def absMin(x): - """ - # >>>absMin([0,5,1,11]) - 0 - # >>absMin([3,-10,-2]) - -2 - """ - j = x[0] - for i in x: - if absVal(i) < absVal(j): - j = i - return j - -def main(): - a = [-3,-1,2,-11] - print(absMin(a)) # = -1 - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/maths/abs_max.py b/maths/abs_max.py new file mode 100644 index 000000000000..554e27f6ee66 --- /dev/null +++ b/maths/abs_max.py @@ -0,0 +1,35 @@ +from typing import List + + +def abs_max(x: List[int]) -> int: + """ + >>> abs_max([0,5,1,11]) + 11 + >>> abs_max([3,-10,-2]) + -10 + """ + j = x[0] + for i in x: + if abs(i) > abs(j): + j = i + return j + + +def abs_max_sort(x): + """ + >>> abs_max_sort([0,5,1,11]) + 11 + >>> abs_max_sort([3,-10,-2]) + -10 + """ + return sorted(x, key=abs)[-1] + + +def main(): + a = [1, 2, -11] + assert abs_max(a) == -11 + assert abs_max_sort(a) == -11 + + +if __name__ == "__main__": + main() diff --git a/maths/abs_min.py b/maths/abs_min.py new file mode 100644 index 000000000000..eb84de37ce23 --- /dev/null +++ b/maths/abs_min.py @@ -0,0 +1,24 @@ +from .abs import abs_val + + +def absMin(x): + """ + >>> absMin([0,5,1,11]) + 0 + >>> absMin([3,-10,-2]) + -2 + """ + j = x[0] + for i in x: + if abs_val(i) < abs_val(j): + j = i + return j + + +def main(): + a = [-3, -1, 2, -11] + print(absMin(a)) # = -1 + + +if __name__ == "__main__": + main() diff --git a/maths/average.py b/maths/average.py deleted file mode 100644 index dc70836b5e83..000000000000 --- a/maths/average.py +++ /dev/null @@ -1,14 +0,0 @@ -def average(nums): - sum = 0 - n = 0 - for x in nums: - sum += x - n += 1 - avg = sum / n - print(avg) - -def main(): - average([2, 4, 6, 8, 20, 50, 70]) - -if __name__ == '__main__': - main() diff --git a/maths/average_mean.py b/maths/average_mean.py new file mode 100644 index 000000000000..4beca1f741a0 --- /dev/null +++ b/maths/average_mean.py @@ -0,0 +1,20 @@ +"""Find mean of a list of numbers.""" + + +def average(nums): + """Find mean of a list of numbers.""" + return sum(nums) / len(nums) + + +def test_average(): + """ + >>> test_average() + """ + assert 12.0 == average([3, 6, 9, 12, 15, 18, 21]) + assert 20 == average([5, 10, 15, 20, 25, 30, 35]) + assert 4.5 == average([1, 2, 3, 4, 5, 6, 7, 8]) + + +if __name__ == "__main__": + """Call average module to find mean of a specific list of numbers.""" + print(average([2, 4, 6, 8, 20, 50, 70])) diff --git a/maths/average_median.py b/maths/average_median.py new file mode 100644 index 000000000000..ccb250d7718c --- /dev/null +++ b/maths/average_median.py @@ -0,0 +1,36 @@ +def median(nums): + """ + Find median of a list of numbers. + + >>> median([0]) + 0 + >>> median([4,1,3,2]) + 2.5 + + Args: + nums: List of nums + + Returns: + Median. + """ + sorted_list = sorted(nums) + med = None + if len(sorted_list) % 2 == 0: + mid_index_1 = len(sorted_list) // 2 + mid_index_2 = (len(sorted_list) // 2) - 1 + med = (sorted_list[mid_index_1] + sorted_list[mid_index_2]) / float(2) + else: + mid_index = (len(sorted_list) - 1) // 2 + med = sorted_list[mid_index] + return med + + +def main(): + print("Odd number of numbers:") + print(median([2, 4, 6, 8, 20, 50, 70])) + print("Even number of numbers:") + print(median([2, 4, 6, 8, 20, 50])) + + +if __name__ == "__main__": + main() diff --git a/maths/average_mode.py b/maths/average_mode.py new file mode 100644 index 000000000000..c1a4b3521448 --- /dev/null +++ b/maths/average_mode.py @@ -0,0 +1,31 @@ +import statistics + + +def mode(input_list): # Defining function "mode." + """This function returns the mode(Mode as in the measures of + central tendency) of the input data. + + The input list may contain any Datastructure or any Datatype. + + >>> input_list = [2, 3, 4, 5, 3, 4, 2, 5, 2, 2, 4, 2, 2, 2] + >>> mode(input_list) + 2 + >>> input_list = [2, 3, 4, 5, 3, 4, 2, 5, 2, 2, 4, 2, 2, 2] + >>> mode(input_list) == statistics.mode(input_list) + True + """ + # Copying inputlist to check with the index number later. + check_list = input_list.copy() + result = list() # Empty list to store the counts of elements in input_list + for x in input_list: + result.append(input_list.count(x)) + input_list.remove(x) + y = max(result) # Gets the maximum value in the result list. + # Returns the value with the maximum number of repetitions. + return check_list[result.index(y)] + + +if __name__ == "__main__": + data = [2, 3, 4, 5, 3, 4, 2, 5, 2, 2, 4, 2, 2, 2] + print(mode(data)) + print(statistics.mode(data)) diff --git a/maths/basic_maths.py b/maths/basic_maths.py index 6e8c919a001d..5dbfd250d308 100644 --- a/maths/basic_maths.py +++ b/maths/basic_maths.py @@ -1,74 +1,80 @@ +"""Implementation of Basic Math in Python.""" import math -def primeFactors(n): + +def prime_factors(n: int) -> list: + """Find Prime Factors. + >>> prime_factors(100) + [2, 2, 5, 5] + """ pf = [] while n % 2 == 0: pf.append(2) n = int(n / 2) - - for i in range(3, int(math.sqrt(n))+1, 2): + for i in range(3, int(math.sqrt(n)) + 1, 2): while n % i == 0: pf.append(i) n = int(n / i) - if n > 2: pf.append(n) - return pf -def numberOfDivisors(n): + +def number_of_divisors(n: int) -> int: + """Calculate Number of Divisors of an Integer. + >>> number_of_divisors(100) + 9 + """ div = 1 - temp = 1 while n % 2 == 0: temp += 1 n = int(n / 2) - div = div * (temp) - - for i in range(3, int(math.sqrt(n))+1, 2): + div *= temp + for i in range(3, int(math.sqrt(n)) + 1, 2): temp = 1 while n % i == 0: temp += 1 n = int(n / i) - div = div * (temp) - + div *= temp return div -def sumOfDivisors(n): + +def sum_of_divisors(n: int) -> int: + """Calculate Sum of Divisors. + >>> sum_of_divisors(100) + 217 + """ s = 1 - temp = 1 while n % 2 == 0: temp += 1 n = int(n / 2) if temp > 1: - s *= (2**temp - 1) / (2 - 1) - - for i in range(3, int(math.sqrt(n))+1, 2): + s *= (2 ** temp - 1) / (2 - 1) + for i in range(3, int(math.sqrt(n)) + 1, 2): temp = 1 while n % i == 0: temp += 1 n = int(n / i) if temp > 1: - s *= (i**temp - 1) / (i - 1) - - return s + s *= (i ** temp - 1) / (i - 1) + return int(s) -def eulerPhi(n): - l = primeFactors(n) - l = set(l) + +def euler_phi(n: int) -> int: + """Calculte Euler's Phi Function. + >>> euler_phi(100) + 40 + """ s = n - for x in l: - s *= (x - 1)/x - return s + for x in set(prime_factors(n)): + s *= (x - 1) / x + return int(s) + -def main(): - print(primeFactors(100)) - print(numberOfDivisors(100)) - print(sumOfDivisors(100)) - print(eulerPhi(100)) - -if __name__ == '__main__': - main() - - \ No newline at end of file +if __name__ == "__main__": + print(prime_factors(100)) + print(number_of_divisors(100)) + print(sum_of_divisors(100)) + print(euler_phi(100)) diff --git a/maths/binary_exponentiation.py b/maths/binary_exponentiation.py new file mode 100644 index 000000000000..57c4b8686f5c --- /dev/null +++ b/maths/binary_exponentiation.py @@ -0,0 +1,28 @@ +"""Binary Exponentiation.""" + +# Author : Junth Basnet +# Time Complexity : O(logn) + + +def binary_exponentiation(a, n): + + if n == 0: + return 1 + + elif n % 2 == 1: + return binary_exponentiation(a, n - 1) * a + + else: + b = binary_exponentiation(a, n / 2) + return b * b + + +if __name__ == "__main__": + try: + BASE = int(input("Enter Base : ").strip()) + POWER = int(input("Enter Power : ").strip()) + except ValueError: + print("Invalid literal for integer") + + RESULT = binary_exponentiation(BASE, POWER) + print("{}^({}) : {}".format(BASE, POWER, RESULT)) diff --git a/maths/binomial_coefficient.py b/maths/binomial_coefficient.py new file mode 100644 index 000000000000..4def041492f3 --- /dev/null +++ b/maths/binomial_coefficient.py @@ -0,0 +1,20 @@ +def binomial_coefficient(n, r): + """ + Find binomial coefficient using pascals triangle. + + >>> binomial_coefficient(10, 5) + 252 + """ + C = [0 for i in range(r + 1)] + # nc0 = 1 + C[0] = 1 + for i in range(1, n + 1): + # to compute current row from previous row. + j = min(i, r) + while j > 0: + C[j] += C[j - 1] + j -= 1 + return C[r] + + +print(binomial_coefficient(n=10, r=5)) diff --git a/maths/ceil.py b/maths/ceil.py new file mode 100644 index 000000000000..3e46f1474dcf --- /dev/null +++ b/maths/ceil.py @@ -0,0 +1,18 @@ +def ceil(x) -> int: + """ + Return the ceiling of x as an Integral. + + :param x: the number + :return: the smallest integer >= x. + + >>> import math + >>> all(ceil(n) == math.ceil(n) for n in (1, -1, 0, -0, 1.1, -1.1, 1.0, -1.0, 1_000_000_000)) + True + """ + return x if isinstance(x, int) or x - int(x) == 0 else int(x + 1) if x > 0 else int(x) + + +if __name__ == '__main__': + import doctest + + doctest.testmod() diff --git a/maths/collatz_sequence.py b/maths/collatz_sequence.py new file mode 100644 index 000000000000..a5f044a62b18 --- /dev/null +++ b/maths/collatz_sequence.py @@ -0,0 +1,29 @@ +def collatz_sequence(n): + """ + Collatz conjecture: start with any positive integer n.Next term is obtained from the previous term as follows: + if the previous term is even, the next term is one half of the previous term. + If the previous term is odd, the next term is 3 times the previous term plus 1. + The conjecture states the sequence will always reach 1 regaardless of starting value n. + Example: + >>> collatz_sequence(43) + [43, 130, 65, 196, 98, 49, 148, 74, 37, 112, 56, 28, 14, 7, 22, 11, 34, 17, 52, 26, 13, 40, 20, 10, 5, 16, 8, 4, 2, 1] + """ + sequence = [n] + while n != 1: + if n % 2 == 0: # even number condition + n //= 2 + else: + n = 3 * n + 1 + sequence.append(n) + return sequence + + +def main(): + n = 43 + sequence = collatz_sequence(n) + print(sequence) + print("collatz sequence from %d took %d steps." % (n, len(sequence))) + + +if __name__ == "__main__": + main() diff --git a/maths/explicit_euler.py b/maths/explicit_euler.py new file mode 100644 index 000000000000..8a43d71fb432 --- /dev/null +++ b/maths/explicit_euler.py @@ -0,0 +1,40 @@ +import numpy as np + + +def explicit_euler(ode_func, y0, x0, stepsize, x_end): + """ + Calculate numeric solution at each step to an ODE using Euler's Method + + https://en.wikipedia.org/wiki/Euler_method + + Arguments: + ode_func -- The ode as a function of x and y + y0 -- the initial value for y + x0 -- the initial value for x + stepsize -- the increment value for x + x_end -- the end value for x + + >>> # the exact solution is math.exp(x) + >>> def f(x, y): + ... return y + >>> y0 = 1 + >>> y = explicit_euler(f, y0, 0.0, 0.01, 5) + >>> y[-1] + 144.77277243257308 + """ + N = int(np.ceil((x_end - x0) / stepsize)) + y = np.zeros((N + 1,)) + y[0] = y0 + x = x0 + + for k in range(N): + y[k + 1] = y[k] + stepsize * ode_func(x, y[k]) + x += stepsize + + return y + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/maths/extended_euclidean_algorithm.py b/maths/extended_euclidean_algorithm.py index f5a3cc88e474..fe81bcfaf71d 100644 --- a/maths/extended_euclidean_algorithm.py +++ b/maths/extended_euclidean_algorithm.py @@ -1,20 +1,38 @@ +""" +Extended Euclidean Algorithm. + +Finds 2 numbers a and b such that it satisfies +the equation am + bn = gcd(m, n) (a.k.a Bezout's Identity) +""" + # @Author: S. Sharma # @Date: 2019-02-25T12:08:53-06:00 # @Email: silentcat@protonmail.com -# @Last modified by: silentcat -# @Last modified time: 2019-02-26T07:07:38-06:00 +# @Last modified by: PatOnTheBack +# @Last modified time: 2019-07-05 import sys -# Finds 2 numbers a and b such that it satisfies -# the equation am + bn = gcd(m, n) (a.k.a Bezout's Identity) + def extended_euclidean_algorithm(m, n): - a = 0; aprime = 1; b = 1; bprime = 0 - q = 0; r = 0 + """ + Extended Euclidean Algorithm. + + Finds 2 numbers a and b such that it satisfies + the equation am + bn = gcd(m, n) (a.k.a Bezout's Identity) + """ + a = 0 + a_prime = 1 + b = 1 + b_prime = 0 + q = 0 + r = 0 if m > n: - c = m; d = n + c = m + d = n else: - c = n; d = m + c = n + d = m while True: q = int(c / d) @@ -24,28 +42,31 @@ def extended_euclidean_algorithm(m, n): c = d d = r - t = aprime - aprime = a - a = t - q*a + t = a_prime + a_prime = a + a = t - q * a - t = bprime - bprime = b - b = t - q*b + t = b_prime + b_prime = b + b = t - q * b pair = None if m > n: - pair = (a,b) + pair = (a, b) else: - pair = (b,a) + pair = (b, a) return pair + def main(): + """Call Extended Euclidean Algorithm.""" if len(sys.argv) < 3: - print('2 integer arguments required') + print("2 integer arguments required") exit(1) m = int(sys.argv[1]) n = int(sys.argv[2]) print(extended_euclidean_algorithm(m, n)) -if __name__ == '__main__': + +if __name__ == "__main__": main() diff --git a/maths/factorial_python.py b/maths/factorial_python.py index 376983e08dab..b9adfdbaeaff 100644 --- a/maths/factorial_python.py +++ b/maths/factorial_python.py @@ -1,19 +1,34 @@ -# Python program to find the factorial of a number provided by the user. +def factorial(input_number: int) -> int: + """ + Calculate the factorial of specified number -# change the value for a different result -num = 10 + >>> factorial(1) + 1 + >>> factorial(6) + 720 + >>> factorial(0) + 1 + >>> factorial(-1) + Traceback (most recent call last): + ... + ValueError: factorial() not defined for negative values + >>> factorial(0.1) + Traceback (most recent call last): + ... + ValueError: factorial() only accepts integral values + """ -# uncomment to take input from the user -#num = int(input("Enter a number: ")) + if input_number < 0: + raise ValueError("factorial() not defined for negative values") + if not isinstance(input_number, int): + raise ValueError("factorial() only accepts integral values") + result = 1 + for i in range(1, input_number): + result = result * (i + 1) + return result -factorial = 1 -# check if the number is negative, positive or zero -if num < 0: - print("Sorry, factorial does not exist for negative numbers") -elif num == 0: - print("The factorial of 0 is 1") -else: - for i in range(1,num + 1): - factorial = factorial*i - print("The factorial of",num,"is",factorial) +if __name__ == '__main__': + import doctest + + doctest.testmod() diff --git a/maths/factorial_recursive.py b/maths/factorial_recursive.py index 41391a2718f6..013560b28b42 100644 --- a/maths/factorial_recursive.py +++ b/maths/factorial_recursive.py @@ -1,13 +1,30 @@ -def fact(n): - """ - Return 1, if n is 1 or below, - otherwise, return n * fact(n-1). - """ - return 1 if n <= 1 else n * fact(n-1) - -""" -Shown factorial for i, -where i ranges from 1 to 20. -""" -for i in range(1,21): - print(i, ": ", fact(i), sep='') +def factorial(n: int) -> int: + """ + Calculate the factorial of specified number + + >>> factorial(1) + 1 + >>> factorial(6) + 720 + >>> factorial(0) + 1 + >>> factorial(-1) + Traceback (most recent call last): + ... + ValueError: factorial() not defined for negative values + >>> factorial(0.1) + Traceback (most recent call last): + ... + ValueError: factorial() only accepts integral values + """ + if n < 0: + raise ValueError("factorial() not defined for negative values") + if not isinstance(n, int): + raise ValueError("factorial() only accepts integral values") + return 1 if n == 0 or n == 1 else n * factorial(n - 1) + + +if __name__ == '__main__': + import doctest + + doctest.testmod() diff --git a/maths/factors.py b/maths/factors.py new file mode 100644 index 000000000000..e2fdc4063a13 --- /dev/null +++ b/maths/factors.py @@ -0,0 +1,18 @@ +def factors_of_a_number(num: int) -> list: + """ + >>> factors_of_a_number(1) + [1] + >>> factors_of_a_number(5) + [1, 5] + >>> factors_of_a_number(24) + [1, 2, 3, 4, 6, 8, 12, 24] + >>> factors_of_a_number(-24) + [] + """ + return [i for i in range(1, num + 1) if num % i == 0] + + +if __name__ == "__main__": + num = int(input("Enter a number to find its factors: ")) + factors = factors_of_a_number(num) + print(f"{num} has {len(factors)} factors: {', '.join(str(f) for f in factors)}") diff --git a/maths/fermat_little_theorem.py b/maths/fermat_little_theorem.py new file mode 100644 index 000000000000..24d558115795 --- /dev/null +++ b/maths/fermat_little_theorem.py @@ -0,0 +1,30 @@ +# Python program to show the usage of Fermat's little theorem in a division +# According to Fermat's little theorem, (a / b) mod p always equals a * (b ^ (p - 2)) mod p +# Here we assume that p is a prime number, b divides a, and p doesn't divide b +# Wikipedia reference: https://en.wikipedia.org/wiki/Fermat%27s_little_theorem + + +def binary_exponentiation(a, n, mod): + + if n == 0: + return 1 + + elif n % 2 == 1: + return (binary_exponentiation(a, n - 1, mod) * a) % mod + + else: + b = binary_exponentiation(a, n / 2, mod) + return (b * b) % mod + + +# a prime number +p = 701 + +a = 1000000000 +b = 10 + +# using binary exponentiation function, O(log(p)): +print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p) + +# using Python operators: +print((a / b) % p == (a * b ** (p - 2)) % p) diff --git a/maths/fibonacci.py b/maths/fibonacci.py new file mode 100644 index 000000000000..5ba9f6636364 --- /dev/null +++ b/maths/fibonacci.py @@ -0,0 +1,127 @@ +# fibonacci.py +""" +1. Calculates the iterative fibonacci sequence + +2. Calculates the fibonacci sequence with a formula + an = [ Phin - (phi)n ]/Sqrt[5] + reference-->Su, Francis E., et al. "Fibonacci Number Formula." Math Fun Facts. +""" +import math +import functools +import time +from decimal import getcontext, Decimal + +getcontext().prec = 100 + + +def timer_decorator(func): + @functools.wraps(func) + def timer_wrapper(*args, **kwargs): + start = time.time() + func(*args, **kwargs) + end = time.time() + if int(end - start) > 0: + print(f"Run time for {func.__name__}: {(end - start):0.2f}s") + else: + print(f"Run time for {func.__name__}: {(end - start)*1000:0.2f}ms") + return func(*args, **kwargs) + + return timer_wrapper + + +# define Python user-defined exceptions +class Error(Exception): + """Base class for other exceptions""" + + +class ValueTooLargeError(Error): + """Raised when the input value is too large""" + + +class ValueTooSmallError(Error): + """Raised when the input value is not greater than one""" + + +class ValueLessThanZero(Error): + """Raised when the input value is less than zero""" + + +def _check_number_input(n, min_thresh, max_thresh=None): + """ + :param n: single integer + :type n: int + :param min_thresh: min threshold, single integer + :type min_thresh: int + :param max_thresh: max threshold, single integer + :type max_thresh: int + :return: boolean + """ + try: + if n >= min_thresh and max_thresh is None: + return True + elif min_thresh <= n <= max_thresh: + return True + elif n < 0: + raise ValueLessThanZero + elif n < min_thresh: + raise ValueTooSmallError + elif n > max_thresh: + raise ValueTooLargeError + except ValueLessThanZero: + print("Incorrect Input: number must not be less than 0") + except ValueTooSmallError: + print( + f"Incorrect Input: input number must be > {min_thresh} for the recursive calculation" + ) + except ValueTooLargeError: + print( + f"Incorrect Input: input number must be < {max_thresh} for the recursive calculation" + ) + return False + + +@timer_decorator +def fib_iterative(n): + """ + :param n: calculate Fibonacci to the nth integer + :type n:int + :return: Fibonacci sequence as a list + """ + n = int(n) + if _check_number_input(n, 2): + seq_out = [0, 1] + a, b = 0, 1 + for _ in range(n - len(seq_out)): + a, b = b, a + b + seq_out.append(b) + return seq_out + + +@timer_decorator +def fib_formula(n): + """ + :param n: calculate Fibonacci to the nth integer + :type n:int + :return: Fibonacci sequence as a list + """ + seq_out = [0, 1] + n = int(n) + if _check_number_input(n, 2, 1000000): + sqrt = Decimal(math.sqrt(5)) + phi_1 = Decimal(1 + sqrt) / Decimal(2) + phi_2 = Decimal(1 - sqrt) / Decimal(2) + for i in range(2, n): + temp_out = ((phi_1 ** Decimal(i)) - (phi_2 ** Decimal(i))) * ( + Decimal(sqrt) ** Decimal(-1) + ) + seq_out.append(int(temp_out)) + return seq_out + + +if __name__ == "__main__": + num = 20 + # print(f'{fib_recursive(num)}\n') + # print(f'{fib_iterative(num)}\n') + # print(f'{fib_formula(num)}\n') + fib_iterative(num) + fib_formula(num) diff --git a/maths/fibonacci_sequence_recursion.py b/maths/fibonacci_sequence_recursion.py index 9190e7fc7a40..91619600d5b4 100644 --- a/maths/fibonacci_sequence_recursion.py +++ b/maths/fibonacci_sequence_recursion.py @@ -1,21 +1,22 @@ # Fibonacci Sequence Using Recursion + def recur_fibo(n): - if n <= 1: - return n - else: - (recur_fibo(n-1) + recur_fibo(n-2)) + """ + >>> [recur_fibo(i) for i in range(12)] + [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89] + """ + return n if n <= 1 else recur_fibo(n - 1) + recur_fibo(n - 2) -def isPositiveInteger(limit): - return limit >= 0 def main(): limit = int(input("How many terms to include in fibonacci series: ")) - if isPositiveInteger(limit): - print("The first {limit} terms of the fibonacci series are as follows:") + if limit > 0: + print(f"The first {limit} terms of the fibonacci series are as follows:") print([recur_fibo(n) for n in range(limit)]) else: print("Please enter a positive integer: ") -if __name__ == '__main__': + +if __name__ == "__main__": main() diff --git a/maths/find_lcm.py b/maths/find_lcm.py deleted file mode 100644 index 126242699ab7..000000000000 --- a/maths/find_lcm.py +++ /dev/null @@ -1,18 +0,0 @@ -def find_lcm(num_1, num_2): - max = num_1 if num_1 > num_2 else num_2 - lcm = max - while (True): - if ((lcm % num_1 == 0) and (lcm % num_2 == 0)): - break - lcm += max - return lcm - - -def main(): - num_1 = 12 - num_2 = 76 - print(find_lcm(num_1, num_2)) - - -if __name__ == '__main__': - main() diff --git a/maths/find_max.py b/maths/find_max.py new file mode 100644 index 000000000000..4d92e37eb2e1 --- /dev/null +++ b/maths/find_max.py @@ -0,0 +1,25 @@ +# NguyenU + + +def find_max(nums): + """ + >>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]): + ... find_max(nums) == max(nums) + True + True + True + True + """ + max_num = nums[0] + for x in nums: + if x > max_num: + max_num = x + return max_num + + +def main(): + print(find_max([2, 4, 9, 7, 19, 94, 5])) # 94 + + +if __name__ == "__main__": + main() diff --git a/maths/find_max_recursion.py b/maths/find_max_recursion.py new file mode 100644 index 000000000000..fc10ecf3757a --- /dev/null +++ b/maths/find_max_recursion.py @@ -0,0 +1,25 @@ +# Divide and Conquer algorithm +def find_max(nums, left, right): + """ + find max value in list + :param nums: contains elements + :param left: index of first element + :param right: index of last element + :return: max in nums + + >>> nums = [1, 3, 5, 7, 9, 2, 4, 6, 8, 10] + >>> find_max(nums, 0, len(nums) - 1) == max(nums) + True + """ + if left == right: + return nums[left] + mid = (left + right) >> 1 # the middle + left_max = find_max(nums, left, mid) # find max in range[left, mid] + right_max = find_max(nums, mid + 1, right) # find max in range[mid + 1, right] + + return left_max if left_max >= right_max else right_max + + +if __name__ == "__main__": + nums = [1, 3, 5, 7, 9, 2, 4, 6, 8, 10] + assert find_max(nums, 0, len(nums) - 1) == 10 diff --git a/maths/find_min.py b/maths/find_min.py new file mode 100644 index 000000000000..4d721ce82194 --- /dev/null +++ b/maths/find_min.py @@ -0,0 +1,26 @@ +def find_min(nums): + """ + Find Minimum Number in a List + :param nums: contains elements + :return: max number in list + + >>> for nums in ([3, 2, 1], [-3, -2, -1], [3, -3, 0], [3.0, 3.1, 2.9]): + ... find_min(nums) == min(nums) + True + True + True + True + """ + min_num = nums[0] + for num in nums: + if min_num > num: + min_num = num + return min_num + + +def main(): + assert find_min([0, 1, 2, 3, 4, 5, -3, 24, -56]) == -56 + + +if __name__ == "__main__": + main() diff --git a/maths/find_min_recursion.py b/maths/find_min_recursion.py new file mode 100644 index 000000000000..4488967cc57a --- /dev/null +++ b/maths/find_min_recursion.py @@ -0,0 +1,25 @@ +# Divide and Conquer algorithm +def find_min(nums, left, right): + """ + find min value in list + :param nums: contains elements + :param left: index of first element + :param right: index of last element + :return: min in nums + + >>> nums = [1, 3, 5, 7, 9, 2, 4, 6, 8, 10] + >>> find_min(nums, 0, len(nums) - 1) == min(nums) + True + """ + if left == right: + return nums[left] + mid = (left + right) >> 1 # the middle + left_min = find_min(nums, left, mid) # find min in range[left, mid] + right_min = find_min(nums, mid + 1, right) # find min in range[mid + 1, right] + + return left_min if left_min <= right_min else right_min + + +if __name__ == "__main__": + nums = [1, 3, 5, 7, 9, 2, 4, 6, 8, 10] + assert find_min(nums, 0, len(nums) - 1) == 1 diff --git a/maths/floor.py b/maths/floor.py new file mode 100644 index 000000000000..a9b680b37b97 --- /dev/null +++ b/maths/floor.py @@ -0,0 +1,18 @@ +def floor(x) -> int: + """ + Return the floor of x as an Integral. + + :param x: the number + :return: the largest integer <= x. + + >>> import math + >>> all(floor(n) == math.floor(n) for n in (1, -1, 0, -0, 1.1, -1.1, 1.0, -1.0, 1_000_000_000)) + True + """ + return x if isinstance(x, int) or x - int(x) == 0 else int(x) if x > 0 else int(x - 1) + + +if __name__ == '__main__': + import doctest + + doctest.testmod() diff --git a/maths/gaussian.py b/maths/gaussian.py new file mode 100644 index 000000000000..e5f55dfaffd1 --- /dev/null +++ b/maths/gaussian.py @@ -0,0 +1,59 @@ +""" +Reference: https://en.wikipedia.org/wiki/Gaussian_function + +python/black : True +python : 3.7.3 + +""" +from numpy import pi, sqrt, exp + + +def gaussian(x, mu: float = 0.0, sigma: float = 1.0) -> int: + """ + >>> gaussian(1) + 0.24197072451914337 + + >>> gaussian(24) + 3.342714441794458e-126 + + Supports NumPy Arrays + Use numpy.meshgrid with this to generate gaussian blur on images. + >>> import numpy as np + >>> x = np.arange(15) + >>> gaussian(x) + array([3.98942280e-01, 2.41970725e-01, 5.39909665e-02, 4.43184841e-03, + 1.33830226e-04, 1.48671951e-06, 6.07588285e-09, 9.13472041e-12, + 5.05227108e-15, 1.02797736e-18, 7.69459863e-23, 2.11881925e-27, + 2.14638374e-32, 7.99882776e-38, 1.09660656e-43]) + + >>> gaussian(15) + 5.530709549844416e-50 + + >>> gaussian([1,2, 'string']) + Traceback (most recent call last): + ... + TypeError: unsupported operand type(s) for -: 'list' and 'float' + + >>> gaussian('hello world') + Traceback (most recent call last): + ... + TypeError: unsupported operand type(s) for -: 'str' and 'float' + + >>> gaussian(10**234) # doctest: +IGNORE_EXCEPTION_DETAIL + Traceback (most recent call last): + ... + OverflowError: (34, 'Result too large') + + >>> gaussian(10**-326) + 0.3989422804014327 + + >>> gaussian(2523, mu=234234, sigma=3425) + 0.0 + """ + return 1 / sqrt(2 * pi * sigma ** 2) * exp(-(x - mu) ** 2 / 2 * sigma ** 2) + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/maths/greater_common_divisor.py b/maths/greater_common_divisor.py deleted file mode 100644 index 15adaca1fb8d..000000000000 --- a/maths/greater_common_divisor.py +++ /dev/null @@ -1,15 +0,0 @@ -# Greater Common Divisor - https://en.wikipedia.org/wiki/Greatest_common_divisor -def gcd(a, b): - return b if a == 0 else gcd(b % a, a) - -def main(): - try: - nums = input("Enter two Integers separated by comma (,): ").split(',') - num1 = int(nums[0]); num2 = int(nums[1]) - except (IndexError, UnboundLocalError, ValueError): - print("Wrong Input") - print(f"gcd({num1}, {num2}) = {gcd(num1, num2)}") - -if __name__ == '__main__': - main() - diff --git a/maths/greatest_common_divisor.py b/maths/greatest_common_divisor.py new file mode 100644 index 000000000000..21c427d5b227 --- /dev/null +++ b/maths/greatest_common_divisor.py @@ -0,0 +1,61 @@ +""" +Greatest Common Divisor. + +Wikipedia reference: https://en.wikipedia.org/wiki/Greatest_common_divisor +""" + + +def greatest_common_divisor(a, b): + """ + Calculate Greatest Common Divisor (GCD). + >>> greatest_common_divisor(24, 40) + 8 + >>> greatest_common_divisor(1, 1) + 1 + >>> greatest_common_divisor(1, 800) + 1 + >>> greatest_common_divisor(11, 37) + 1 + >>> greatest_common_divisor(3, 5) + 1 + >>> greatest_common_divisor(16, 4) + 4 + """ + return b if a == 0 else greatest_common_divisor(b % a, a) + + +""" +Below method is more memory efficient because it does not use the stack (chunk of memory). +While above method is good, uses more memory for huge numbers because of the recursive calls +required to calculate the greatest common divisor. +""" + + +def gcd_by_iterative(x, y): + """ + >>> gcd_by_iterative(24, 40) + 8 + >>> greatest_common_divisor(24, 40) == gcd_by_iterative(24, 40) + True + """ + while y: # --> when y=0 then loop will terminate and return x as final GCD. + x, y = y, x % y + return x + + +def main(): + """Call Greatest Common Divisor function.""" + try: + nums = input("Enter two integers separated by comma (,): ").split(",") + num_1 = int(nums[0]) + num_2 = int(nums[1]) + print( + f"greatest_common_divisor({num_1}, {num_2}) = {greatest_common_divisor(num_1, num_2)}" + ) + print(f"By iterative gcd({num_1}, {num_2}) = {gcd_by_iterative(num_1, num_2)}") + except (IndexError, UnboundLocalError, ValueError): + print("Wrong input") + + +if __name__ == "__main__": + main() diff --git a/maths/hardy_ramanujanalgo.py b/maths/hardy_ramanujanalgo.py new file mode 100644 index 000000000000..bb31a1be49fb --- /dev/null +++ b/maths/hardy_ramanujanalgo.py @@ -0,0 +1,45 @@ +# This theorem states that the number of prime factors of n +# will be approximately log(log(n)) for most natural numbers n + +import math + + +def exactPrimeFactorCount(n): + """ + >>> exactPrimeFactorCount(51242183) + 3 + """ + count = 0 + if n % 2 == 0: + count += 1 + while n % 2 == 0: + n = int(n / 2) + # the n input value must be odd so that + # we can skip one element (ie i += 2) + + i = 3 + + while i <= int(math.sqrt(n)): + if n % i == 0: + count += 1 + while n % i == 0: + n = int(n / i) + i = i + 2 + + # this condition checks the prime + # number n is greater than 2 + + if n > 2: + count += 1 + return count + + +if __name__ == "__main__": + n = 51242183 + print(f"The number of distinct prime factors is/are {exactPrimeFactorCount(n)}") + print("The value of log(log(n)) is {0:.4f}".format(math.log(math.log(n)))) + + """ + The number of distinct prime factors is/are 3 + The value of log(log(n)) is 2.8765 + """ diff --git a/maths/images/gaussian.png b/maths/images/gaussian.png new file mode 100644 index 000000000000..eb007c7e21b2 Binary files /dev/null and b/maths/images/gaussian.png differ diff --git a/maths/is_square_free.py b/maths/is_square_free.py new file mode 100644 index 000000000000..acc13fa5f833 --- /dev/null +++ b/maths/is_square_free.py @@ -0,0 +1,39 @@ +""" +References: wikipedia:square free number +python/black : True +flake8 : True +""" +from typing import List + + +def is_square_free(factors: List[int]) -> bool: + """ + # doctest: +NORMALIZE_WHITESPACE + This functions takes a list of prime factors as input. + returns True if the factors are square free. + >>> is_square_free([1, 1, 2, 3, 4]) + False + + These are wrong but should return some value + it simply checks for repition in the numbers. + >>> is_square_free([1, 3, 4, 'sd', 0.0]) + True + + >>> is_square_free([1, 0.5, 2, 0.0]) + True + >>> is_square_free([1, 2, 2, 5]) + False + >>> is_square_free('asd') + True + >>> is_square_free(24) + Traceback (most recent call last): + ... + TypeError: 'int' object is not iterable + """ + return len(set(factors)) == len(factors) + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/maths/jaccard_similarity.py b/maths/jaccard_similarity.py new file mode 100644 index 000000000000..4f24d308f340 --- /dev/null +++ b/maths/jaccard_similarity.py @@ -0,0 +1,80 @@ +""" +The Jaccard similarity coefficient is a commonly used indicator of the +similarity between two sets. Let U be a set and A and B be subsets of U, +then the Jaccard index/similarity is defined to be the ratio of the number +of elements of their intersection and the number of elements of their union. + +Inspired from Wikipedia and +the book Mining of Massive Datasets [MMDS 2nd Edition, Chapter 3] + +https://en.wikipedia.org/wiki/Jaccard_index +https://mmds.org + +Jaccard similarity is widely used with MinHashing. +""" + + +def jaccard_similariy(setA, setB, alternativeUnion=False): + """ + Finds the jaccard similarity between two sets. + Essentially, its intersection over union. + + The alternative way to calculate this is to take union as sum of the + number of items in the two sets. This will lead to jaccard similarity + of a set with itself be 1/2 instead of 1. [MMDS 2nd Edition, Page 77] + + Parameters: + :setA (set,list,tuple): A non-empty set/list + :setB (set,list,tuple): A non-empty set/list + :alternativeUnion (boolean): If True, use sum of number of + items as union + + Output: + (float) The jaccard similarity between the two sets. + + Examples: + >>> setA = {'a', 'b', 'c', 'd', 'e'} + >>> setB = {'c', 'd', 'e', 'f', 'h', 'i'} + >>> jaccard_similariy(setA,setB) + 0.375 + + >>> jaccard_similariy(setA,setA) + 1.0 + + >>> jaccard_similariy(setA,setA,True) + 0.5 + + >>> setA = ['a', 'b', 'c', 'd', 'e'] + >>> setB = ('c', 'd', 'e', 'f', 'h', 'i') + >>> jaccard_similariy(setA,setB) + 0.375 + """ + + if isinstance(setA, set) and isinstance(setB, set): + + intersection = len(setA.intersection(setB)) + + if alternativeUnion: + union = len(setA) + len(setB) + else: + union = len(setA.union(setB)) + + return intersection / union + + if isinstance(setA, (list, tuple)) and isinstance(setB, (list, tuple)): + + intersection = [element for element in setA if element in setB] + + if alternativeUnion: + union = len(setA) + len(setB) + else: + union = setA + [element for element in setB if element not in setA] + + return len(intersection) / len(union) + + +if __name__ == "__main__": + + setA = {"a", "b", "c", "d", "e"} + setB = {"c", "d", "e", "f", "h", "i"} + print(jaccard_similariy(setA, setB)) diff --git a/maths/karatsuba.py b/maths/karatsuba.py new file mode 100644 index 000000000000..df29c77a5cf2 --- /dev/null +++ b/maths/karatsuba.py @@ -0,0 +1,32 @@ +""" Multiply two numbers using Karatsuba algorithm """ + + +def karatsuba(a, b): + """ + >>> karatsuba(15463, 23489) == 15463 * 23489 + True + >>> karatsuba(3, 9) == 3 * 9 + True + """ + if len(str(a)) == 1 or len(str(b)) == 1: + return a * b + else: + m1 = max(len(str(a)), len(str(b))) + m2 = m1 // 2 + + a1, a2 = divmod(a, 10 ** m2) + b1, b2 = divmod(b, 10 ** m2) + + x = karatsuba(a2, b2) + y = karatsuba((a1 + a2), (b1 + b2)) + z = karatsuba(a1, b1) + + return (z * 10 ** (2 * m2)) + ((y - z - x) * 10 ** (m2)) + (x) + + +def main(): + print(karatsuba(15463, 23489)) + + +if __name__ == "__main__": + main() diff --git a/maths/kth_lexicographic_permutation.py b/maths/kth_lexicographic_permutation.py new file mode 100644 index 000000000000..1820be7274e3 --- /dev/null +++ b/maths/kth_lexicographic_permutation.py @@ -0,0 +1,40 @@ +def kthPermutation(k, n): + """ + Finds k'th lexicographic permutation (in increasing order) of + 0,1,2,...n-1 in O(n^2) time. + + Examples: + First permutation is always 0,1,2,...n + >>> kthPermutation(0,5) + [0, 1, 2, 3, 4] + + The order of permutation of 0,1,2,3 is [0,1,2,3], [0,1,3,2], [0,2,1,3], + [0,2,3,1], [0,3,1,2], [0,3,2,1], [1,0,2,3], [1,0,3,2], [1,2,0,3], + [1,2,3,0], [1,3,0,2] + >>> kthPermutation(10,4) + [1, 3, 0, 2] + """ + # Factorails from 1! to (n-1)! + factorials = [1] + for i in range(2, n): + factorials.append(factorials[-1] * i) + assert 0 <= k < factorials[-1] * n, "k out of bounds" + + permutation = [] + elements = list(range(n)) + + # Find permutation + while factorials: + factorial = factorials.pop() + number, k = divmod(k, factorial) + permutation.append(elements[number]) + elements.remove(elements[number]) + permutation.append(elements[0]) + + return permutation + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/maths/largest_of_very_large_numbers.py b/maths/largest_of_very_large_numbers.py new file mode 100644 index 000000000000..d2dc0af18126 --- /dev/null +++ b/maths/largest_of_very_large_numbers.py @@ -0,0 +1,35 @@ +# Author: Abhijeeth S + +import math + + +def res(x, y): + if 0 not in (x, y): + # We use the relation x^y = y*log10(x), where 10 is the base. + return y * math.log10(x) + else: + if x == 0: # 0 raised to any number is 0 + return 0 + elif y == 0: + return 1 # any number raised to 0 is 1 + + +if __name__ == "__main__": # Main function + # Read two numbers from input and typecast them to int using map function. + # Here x is the base and y is the power. + prompt = "Enter the base and the power separated by a comma: " + x1, y1 = map(int, input(prompt).split(",")) + x2, y2 = map(int, input(prompt).split(",")) + + # We find the log of each number, using the function res(), which takes two + # arguments. + res1 = res(x1, y1) + res2 = res(x2, y2) + + # We check for the largest number + if res1 > res2: + print("Largest number is", x1, "^", y1) + elif res2 > res1: + print("Largest number is", x2, "^", y2) + else: + print("Both are equal") diff --git a/maths/least_common_multiple.py b/maths/least_common_multiple.py new file mode 100644 index 000000000000..863744e182b6 --- /dev/null +++ b/maths/least_common_multiple.py @@ -0,0 +1,44 @@ +import unittest + + +def find_lcm(first_num: int, second_num: int) -> int: + """Find the least common multiple of two numbers. + + Learn more: https://en.wikipedia.org/wiki/Least_common_multiple + + >>> find_lcm(5,2) + 10 + >>> find_lcm(12,76) + 228 + """ + max_num = first_num if first_num >= second_num else second_num + common_mult = max_num + while (common_mult % first_num > 0) or (common_mult % second_num > 0): + common_mult += max_num + return common_mult + + +class TestLeastCommonMultiple(unittest.TestCase): + + test_inputs = [ + (10, 20), + (13, 15), + (4, 31), + (10, 42), + (43, 34), + (5, 12), + (12, 25), + (10, 25), + (6, 9), + ] + expected_results = [20, 195, 124, 210, 1462, 60, 300, 50, 18] + + def test_lcm_function(self): + for i, (first_num, second_num) in enumerate(self.test_inputs): + actual_result = find_lcm(first_num, second_num) + with self.subTest(i=i): + self.assertEqual(actual_result, self.expected_results[i]) + + +if __name__ == "__main__": + unittest.main() diff --git a/maths/lucas_lehmer_primality_test.py b/maths/lucas_lehmer_primality_test.py new file mode 100644 index 000000000000..44e41ba58d93 --- /dev/null +++ b/maths/lucas_lehmer_primality_test.py @@ -0,0 +1,42 @@ +# -*- coding: utf-8 -*- +""" + In mathematics, the Lucas–Lehmer test (LLT) is a primality test for Mersenne numbers. + https://en.wikipedia.org/wiki/Lucas%E2%80%93Lehmer_primality_test + + A Mersenne number is a number that is one less than a power of two. + That is M_p = 2^p - 1 + https://en.wikipedia.org/wiki/Mersenne_prime + + The Lucas–Lehmer test is the primality test used by the + Great Internet Mersenne Prime Search (GIMPS) to locate large primes. +""" + + +# Primality test 2^p - 1 +# Return true if 2^p - 1 is prime +def lucas_lehmer_test(p: int) -> bool: + """ + >>> lucas_lehmer_test(p=7) + True + + >>> lucas_lehmer_test(p=11) + False + + # M_11 = 2^11 - 1 = 2047 = 23 * 89 + """ + + if p < 2: + raise ValueError("p should not be less than 2!") + elif p == 2: + return True + + s = 4 + M = (1 << p) - 1 + for i in range(p - 2): + s = ((s * s) - 2) % M + return s == 0 + + +if __name__ == "__main__": + print(lucas_lehmer_test(7)) + print(lucas_lehmer_test(11)) diff --git a/maths/lucas_series.py b/maths/lucas_series.py new file mode 100644 index 000000000000..22ad893a6567 --- /dev/null +++ b/maths/lucas_series.py @@ -0,0 +1,22 @@ +# Lucas Sequence Using Recursion + + +def recur_luc(n): + """ + >>> recur_luc(1) + 1 + >>> recur_luc(0) + 2 + """ + if n == 1: + return n + if n == 0: + return 2 + return recur_luc(n - 1) + recur_luc(n - 2) + + +if __name__ == "__main__": + limit = int(input("How many terms to include in Lucas series:")) + print("Lucas series:") + for i in range(limit): + print(recur_luc(i)) diff --git a/maths/matrix_exponentiation.py b/maths/matrix_exponentiation.py new file mode 100644 index 000000000000..c20292735a92 --- /dev/null +++ b/maths/matrix_exponentiation.py @@ -0,0 +1,100 @@ +"""Matrix Exponentiation""" + +import timeit + +""" +Matrix Exponentiation is a technique to solve linear recurrences in logarithmic time. +You read more about it here: +http://zobayer.blogspot.com/2010/11/matrix-exponentiation.html +https://www.hackerearth.com/practice/notes/matrix-exponentiation-1/ +""" + + +class Matrix(object): + def __init__(self, arg): + if isinstance(arg, list): # Initialzes a matrix identical to the one provided. + self.t = arg + self.n = len(arg) + else: # Initializes a square matrix of the given size and set the values to zero. + self.n = arg + self.t = [[0 for _ in range(self.n)] for _ in range(self.n)] + + def __mul__(self, b): + matrix = Matrix(self.n) + for i in range(self.n): + for j in range(self.n): + for k in range(self.n): + matrix.t[i][j] += self.t[i][k] * b.t[k][j] + return matrix + + +def modular_exponentiation(a, b): + matrix = Matrix([[1, 0], [0, 1]]) + while b > 0: + if b & 1: + matrix *= a + a *= a + b >>= 1 + return matrix + + +def fibonacci_with_matrix_exponentiation(n, f1, f2): + # Trivial Cases + if n == 1: + return f1 + elif n == 2: + return f2 + matrix = Matrix([[1, 1], [1, 0]]) + matrix = modular_exponentiation(matrix, n - 2) + return f2 * matrix.t[0][0] + f1 * matrix.t[0][1] + + +def simple_fibonacci(n, f1, f2): + # Trival Cases + if n == 1: + return f1 + elif n == 2: + return f2 + + fn_1 = f1 + fn_2 = f2 + n -= 2 + + while n > 0: + fn_1, fn_2 = fn_1 + fn_2, fn_1 + n -= 1 + + return fn_1 + + +def matrix_exponentiation_time(): + setup = """ +from random import randint +from __main__ import fibonacci_with_matrix_exponentiation +""" + code = "fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)" + exec_time = timeit.timeit(setup=setup, stmt=code, number=100) + print("With matrix exponentiation the average execution time is ", exec_time / 100) + return exec_time + + +def simple_fibonacci_time(): + setup = """ +from random import randint +from __main__ import simple_fibonacci +""" + code = "simple_fibonacci(randint(1,70000), 1, 1)" + exec_time = timeit.timeit(setup=setup, stmt=code, number=100) + print( + "Without matrix exponentiation the average execution time is ", exec_time / 100 + ) + return exec_time + + +def main(): + matrix_exponentiation_time() + simple_fibonacci_time() + + +if __name__ == "__main__": + main() diff --git a/maths/mobius_function.py b/maths/mobius_function.py new file mode 100644 index 000000000000..15fb3d4380f4 --- /dev/null +++ b/maths/mobius_function.py @@ -0,0 +1,43 @@ +""" +Refrences: https://en.wikipedia.org/wiki/M%C3%B6bius_function +References: wikipedia:square free number +python/black : True +flake8 : True +""" + +from maths.prime_factors import prime_factors +from maths.is_square_free import is_square_free + + +def mobius(n: int) -> int: + """ + Mobius function + >>> mobius(24) + 0 + >>> mobius(-1) + 1 + >>> mobius('asd') + Traceback (most recent call last): + ... + TypeError: '<=' not supported between instances of 'int' and 'str' + >>> mobius(10**400) + 0 + >>> mobius(10**-400) + 1 + >>> mobius(-1424) + 1 + >>> mobius([1, '2', 2.0]) + Traceback (most recent call last): + ... + TypeError: '<=' not supported between instances of 'int' and 'list' + """ + factors = prime_factors(n) + if is_square_free(factors): + return -1 if len(factors) % 2 else 1 + return 0 + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/maths/modular_exponential.py b/maths/modular_exponential.py index b3f4c00bd5d8..8715e17147ff 100644 --- a/maths/modular_exponential.py +++ b/maths/modular_exponential.py @@ -1,20 +1,25 @@ -def modularExponential(base, power, mod): - if power < 0: - return -1 - base %= mod - result = 1 +"""Modular Exponential.""" - while power > 0: - if power & 1: - result = (result * base) % mod - power = power >> 1 - base = (base * base) % mod - return result + +def modular_exponential(base, power, mod): + """Calculate Modular Exponential.""" + if power < 0: + return -1 + base %= mod + result = 1 + + while power > 0: + if power & 1: + result = (result * base) % mod + power = power >> 1 + base = (base * base) % mod + return result def main(): - print(modularExponential(3, 200, 13)) + """Call Modular Exponential Function.""" + print(modular_exponential(3, 200, 13)) -if __name__ == '__main__': - main() +if __name__ == "__main__": + main() diff --git a/maths/newton_raphson.py b/maths/newton_raphson.py index c08bcedc9a4d..093cc4438416 100644 --- a/maths/newton_raphson.py +++ b/maths/newton_raphson.py @@ -1,4 +1,4 @@ -''' +""" Author: P Shreyas Shetty Implementation of Newton-Raphson method for solving equations of kind f(x) = 0. It is an iterative method where solution is found by the expression @@ -6,45 +6,50 @@ If no solution exists, then either the solution will not be found when iteration limit is reached or the gradient f'(x[n]) approaches zero. In both cases, exception is raised. If iteration limit is reached, try increasing maxiter. - ''' + """ import math as m + def calc_derivative(f, a, h=0.001): - ''' + """ Calculates derivative at point a for function f using finite difference method - ''' - return (f(a+h)-f(a-h))/(2*h) + """ + return (f(a + h) - f(a - h)) / (2 * h) + + +def newton_raphson(f, x0=0, maxiter=100, step=0.0001, maxerror=1e-6, logsteps=False): -def newton_raphson(f, x0=0, maxiter=100, step=0.0001, maxerror=1e-6,logsteps=False): - - a = x0 #set the initial guess + a = x0 # set the initial guess steps = [a] error = abs(f(a)) - f1 = lambda x:calc_derivative(f, x, h=step) #Derivative of f(x) + f1 = lambda x: calc_derivative(f, x, h=step) # Derivative of f(x) for _ in range(maxiter): if f1(a) == 0: raise ValueError("No converging solution found") - a = a - f(a)/f1(a) #Calculate the next estimate + a = a - f(a) / f1(a) # Calculate the next estimate if logsteps: steps.append(a) - error = abs(f(a)) if error < maxerror: break else: - raise ValueError("Itheration limit reached, no converging solution found") + raise ValueError("Iteration limit reached, no converging solution found") if logsteps: - #If logstep is true, then log intermediate steps + # If logstep is true, then log intermediate steps return a, error, steps return a, error - -if __name__ == '__main__': + + +if __name__ == "__main__": import matplotlib.pyplot as plt - f = lambda x:m.tanh(x)**2-m.exp(3*x) - solution, error, steps = newton_raphson(f, x0=10, maxiter=1000, step=1e-6, logsteps=True) + + f = lambda x: m.tanh(x) ** 2 - m.exp(3 * x) + solution, error, steps = newton_raphson( + f, x0=10, maxiter=1000, step=1e-6, logsteps=True + ) plt.plot([abs(f(x)) for x in steps]) plt.xlabel("step") plt.ylabel("error") plt.show() - print("solution = {%f}, error = {%f}" % (solution, error)) \ No newline at end of file + print("solution = {%f}, error = {%f}" % (solution, error)) diff --git a/maths/perfect_square.py b/maths/perfect_square.py new file mode 100644 index 000000000000..9b868c5de98a --- /dev/null +++ b/maths/perfect_square.py @@ -0,0 +1,27 @@ +import math + + +def perfect_square(num: int) -> bool: + """ + Check if a number is perfect square number or not + :param num: the number to be checked + :return: True if number is square number, otherwise False + + >>> perfect_square(9) + True + >>> perfect_square(16) + True + >>> perfect_square(1) + True + >>> perfect_square(0) + True + >>> perfect_square(10) + False + """ + return math.sqrt(num) * math.sqrt(num) == num + + +if __name__ == '__main__': + import doctest + + doctest.testmod() diff --git a/maths/polynomial_evaluation.py b/maths/polynomial_evaluation.py new file mode 100644 index 000000000000..d2394f398c36 --- /dev/null +++ b/maths/polynomial_evaluation.py @@ -0,0 +1,53 @@ +from typing import Sequence + + +def evaluate_poly(poly: Sequence[float], x: float) -> float: + """Evaluate a polynomial f(x) at specified point x and return the value. + + Arguments: + poly -- the coeffiecients of a polynomial as an iterable in order of + ascending degree + x -- the point at which to evaluate the polynomial + + >>> evaluate_poly((0.0, 0.0, 5.0, 9.3, 7.0), 10.0) + 79800.0 + """ + return sum(c * (x ** i) for i, c in enumerate(poly)) + + +def horner(poly: Sequence[float], x: float) -> float: + """Evaluate a polynomial at specified point using Horner's method. + + In terms of computational complexity, Horner's method is an efficient method + of evaluating a polynomial. It avoids the use of expensive exponentiation, + and instead uses only multiplication and addition to evaluate the polynomial + in O(n), where n is the degree of the polynomial. + + https://en.wikipedia.org/wiki/Horner's_method + + Arguments: + poly -- the coeffiecients of a polynomial as an iterable in order of + ascending degree + x -- the point at which to evaluate the polynomial + + >>> horner((0.0, 0.0, 5.0, 9.3, 7.0), 10.0) + 79800.0 + """ + result = 0.0 + for coeff in reversed(poly): + result = result * x + coeff + return result + + +if __name__ == "__main__": + """ + Example: + >>> poly = (0.0, 0.0, 5.0, 9.3, 7.0) # f(x) = 7.0x^4 + 9.3x^3 + 5.0x^2 + >>> x = -13.0 + >>> print(evaluate_poly(poly, x)) # f(-13) = 7.0(-13)^4 + 9.3(-13)^3 + 5.0(-13)^2 = 180339.9 + 180339.9 + """ + poly = (0.0, 0.0, 5.0, 9.3, 7.0) + x = 10.0 + print(evaluate_poly(poly, x)) + print(horner(poly, x)) diff --git a/maths/prime_check.py b/maths/prime_check.py new file mode 100644 index 000000000000..e60281228fda --- /dev/null +++ b/maths/prime_check.py @@ -0,0 +1,58 @@ +"""Prime Check.""" + +import math +import unittest + + +def prime_check(number): + """ + Check to See if a Number is Prime. + + A number is prime if it has exactly two dividers: 1 and itself. + """ + if number < 2: + # Negatives, 0 and 1 are not primes + return False + if number < 4: + # 2 and 3 are primes + return True + if number % 2 == 0: + # Even values are not primes + return False + + # Except 2, all primes are odd. If any odd value divide + # the number, then that number is not prime. + odd_numbers = range(3, int(math.sqrt(number)) + 1, 2) + return not any(number % i == 0 for i in odd_numbers) + + +class Test(unittest.TestCase): + def test_primes(self): + self.assertTrue(prime_check(2)) + self.assertTrue(prime_check(3)) + self.assertTrue(prime_check(5)) + self.assertTrue(prime_check(7)) + self.assertTrue(prime_check(11)) + self.assertTrue(prime_check(13)) + self.assertTrue(prime_check(17)) + self.assertTrue(prime_check(19)) + self.assertTrue(prime_check(23)) + self.assertTrue(prime_check(29)) + + def test_not_primes(self): + self.assertFalse(prime_check(-19), "Negative numbers are not prime.") + self.assertFalse( + prime_check(0), "Zero doesn't have any divider, primes must have two" + ) + self.assertFalse( + prime_check(1), "One just have 1 divider, primes must have two." + ) + self.assertFalse(prime_check(2 * 2)) + self.assertFalse(prime_check(2 * 3)) + self.assertFalse(prime_check(3 * 3)) + self.assertFalse(prime_check(3 * 5)) + self.assertFalse(prime_check(3 * 5 * 7)) + + +if __name__ == "__main__": + unittest.main() diff --git a/maths/prime_factors.py b/maths/prime_factors.py new file mode 100644 index 000000000000..eb3de00de6a7 --- /dev/null +++ b/maths/prime_factors.py @@ -0,0 +1,52 @@ +""" +python/black : True +""" +from typing import List + + +def prime_factors(n: int) -> List[int]: + """ + Returns prime factors of n as a list. + + >>> prime_factors(0) + [] + >>> prime_factors(100) + [2, 2, 5, 5] + >>> prime_factors(2560) + [2, 2, 2, 2, 2, 2, 2, 2, 2, 5] + >>> prime_factors(10**-2) + [] + >>> prime_factors(0.02) + [] + >>> x = prime_factors(10**241) # doctest: +NORMALIZE_WHITESPACE + >>> x == [2]*241 + [5]*241 + True + >>> prime_factors(10**-354) + [] + >>> prime_factors('hello') + Traceback (most recent call last): + ... + TypeError: '<=' not supported between instances of 'int' and 'str' + >>> prime_factors([1,2,'hello']) + Traceback (most recent call last): + ... + TypeError: '<=' not supported between instances of 'int' and 'list' + + """ + i = 2 + factors = [] + while i * i <= n: + if n % i: + i += 1 + else: + n //= i + factors.append(i) + if n > 1: + factors.append(n) + return factors + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/maths/prime_numbers.py b/maths/prime_numbers.py new file mode 100644 index 000000000000..a29a95ea2280 --- /dev/null +++ b/maths/prime_numbers.py @@ -0,0 +1,28 @@ +from typing import List + + +def primes(max: int) -> List[int]: + """ + Return a list of all primes numbers up to max. + >>> primes(10) + [2, 3, 5, 7] + >>> primes(11) + [2, 3, 5, 7, 11] + >>> primes(25) + [2, 3, 5, 7, 11, 13, 17, 19, 23] + >>> primes(1_000_000)[-1] + 999983 + """ + max += 1 + numbers = [False] * max + ret = [] + for i in range(2, max): + if not numbers[i]: + for j in range(i, max, i): + numbers[j] = True + ret.append(i) + return ret + + +if __name__ == "__main__": + print(primes(int(input("Calculate primes up to:\n>> ")))) diff --git a/maths/prime_sieve_eratosthenes.py b/maths/prime_sieve_eratosthenes.py new file mode 100644 index 000000000000..4fa19d6db220 --- /dev/null +++ b/maths/prime_sieve_eratosthenes.py @@ -0,0 +1,42 @@ +""" +Sieve of Eratosthenes + +Input : n =10 +Output : 2 3 5 7 + +Input : n = 20 +Output: 2 3 5 7 11 13 17 19 + +you can read in detail about this at +https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes +""" + + +def prime_sieve_eratosthenes(num): + """ + print the prime numbers upto n + + >>> prime_sieve_eratosthenes(10) + 2 3 5 7 + >>> prime_sieve_eratosthenes(20) + 2 3 5 7 11 13 17 19 + """ + + primes = [True for i in range(num + 1)] + p = 2 + + while p * p <= num: + if primes[p] == True: + for i in range(p * p, num + 1, p): + primes[i] = False + p += 1 + + for prime in range(2, num + 1): + if primes[prime]: + print(prime, end=" ") + + +if __name__ == "__main__": + num = int(input()) + + prime_sieve_eratosthenes(num) diff --git a/maths/qr_decomposition.py b/maths/qr_decomposition.py new file mode 100644 index 000000000000..5e15fede4f2a --- /dev/null +++ b/maths/qr_decomposition.py @@ -0,0 +1,71 @@ +import numpy as np + + +def qr_householder(A): + """Return a QR-decomposition of the matrix A using Householder reflection. + + The QR-decomposition decomposes the matrix A of shape (m, n) into an + orthogonal matrix Q of shape (m, m) and an upper triangular matrix R of + shape (m, n). Note that the matrix A does not have to be square. This + method of decomposing A uses the Householder reflection, which is + numerically stable and of complexity O(n^3). + + https://en.wikipedia.org/wiki/QR_decomposition#Using_Householder_reflections + + Arguments: + A -- a numpy.ndarray of shape (m, n) + + Note: several optimizations can be made for numeric efficiency, but this is + intended to demonstrate how it would be represented in a mathematics + textbook. In cases where efficiency is particularly important, an optimized + version from BLAS should be used. + + >>> A = np.array([[12, -51, 4], [6, 167, -68], [-4, 24, -41]], dtype=float) + >>> Q, R = qr_householder(A) + + >>> # check that the decomposition is correct + >>> np.allclose(Q@R, A) + True + + >>> # check that Q is orthogonal + >>> np.allclose(Q@Q.T, np.eye(A.shape[0])) + True + >>> np.allclose(Q.T@Q, np.eye(A.shape[0])) + True + + >>> # check that R is upper triangular + >>> np.allclose(np.triu(R), R) + True + """ + m, n = A.shape + t = min(m, n) + Q = np.eye(m) + R = A.copy() + + for k in range(t - 1): + # select a column of modified matrix A': + x = R[k:, [k]] + # construct first basis vector + e1 = np.zeros_like(x) + e1[0] = 1.0 + # determine scaling factor + alpha = np.linalg.norm(x) + # construct vector v for Householder reflection + v = x + np.sign(x[0]) * alpha * e1 + v /= np.linalg.norm(v) + + # construct the Householder matrix + Q_k = np.eye(m - k) - 2.0 * v @ v.T + # pad with ones and zeros as necessary + Q_k = np.block([[np.eye(k), np.zeros((k, m - k))], [np.zeros((m - k, k)), Q_k]]) + + Q = Q @ Q_k.T + R = Q_k @ R + + return Q, R + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/maths/quadratic_equations_complex_numbers.py b/maths/quadratic_equations_complex_numbers.py new file mode 100644 index 000000000000..8f97508609bf --- /dev/null +++ b/maths/quadratic_equations_complex_numbers.py @@ -0,0 +1,39 @@ +from math import sqrt +from typing import Tuple + + +def QuadraticEquation(a: int, b: int, c: int) -> Tuple[str, str]: + """ + Given the numerical coefficients a, b and c, + prints the solutions for a quadratic equation, for a*x*x + b*x + c. + + >>> QuadraticEquation(a=1, b=3, c=-4) + ('1.0', '-4.0') + >>> QuadraticEquation(5, 6, 1) + ('-0.2', '-1.0') + """ + if a == 0: + raise ValueError("Coefficient 'a' must not be zero for quadratic equations.") + delta = b * b - 4 * a * c + if delta >= 0: + return str((-b + sqrt(delta)) / (2 * a)), str((-b - sqrt(delta)) / (2 * a)) + """ + Treats cases of Complexes Solutions(i = imaginary unit) + Ex.: a = 5, b = 2, c = 1 + Solution1 = (- 2 + 4.0 *i)/2 and Solution2 = (- 2 + 4.0 *i)/ 10 + """ + snd = sqrt(-delta) + if b == 0: + return f"({snd} * i) / 2", f"({snd} * i) / {2 * a}" + b = -abs(b) + return f"({b}+{snd} * i) / 2", f"({b}+{snd} * i) / {2 * a}" + + +def main(): + solutions = QuadraticEquation(a=5, b=6, c=1) + print("The equation solutions are: {} and {}".format(*solutions)) + # The equation solutions are: -0.2 and -1.0 + + +if __name__ == "__main__": + main() diff --git a/maths/radix2_fft.py b/maths/radix2_fft.py new file mode 100644 index 000000000000..3911fea1d04d --- /dev/null +++ b/maths/radix2_fft.py @@ -0,0 +1,180 @@ +""" +Fast Polynomial Multiplication using radix-2 fast Fourier Transform. +""" + +import mpmath # for roots of unity +import numpy as np + + +class FFT: + """ + Fast Polynomial Multiplication using radix-2 fast Fourier Transform. + + Reference: + https://en.wikipedia.org/wiki/Cooley%E2%80%93Tukey_FFT_algorithm#The_radix-2_DIT_case + + For polynomials of degree m and n the algorithms has complexity + O(n*logn + m*logm) + + The main part of the algorithm is split in two parts: + 1) __DFT: We compute the discrete fourier transform (DFT) of A and B using a + bottom-up dynamic approach - + 2) __multiply: Once we obtain the DFT of A*B, we can similarly + invert it to obtain A*B + + The class FFT takes two polynomials A and B with complex coefficients as arguments; + The two polynomials should be represented as a sequence of coefficients starting + from the free term. Thus, for instance x + 2*x^3 could be represented as + [0,1,0,2] or (0,1,0,2). The constructor adds some zeros at the end so that the + polynomials have the same length which is a power of 2 at least the length of + their product. + + Example: + + Create two polynomials as sequences + >>> A = [0, 1, 0, 2] # x+2x^3 + >>> B = (2, 3, 4, 0) # 2+3x+4x^2 + + Create an FFT object with them + >>> x = FFT(A, B) + + Print product + >>> print(x.product) # 2x + 3x^2 + 8x^3 + 4x^4 + 6x^5 + [(-0+0j), (2+0j), (3+0j), (8+0j), (6+0j), (8+0j)] + + __str__ test + >>> print(x) + A = 0*x^0 + 1*x^1 + 2*x^0 + 3*x^2 + B = 0*x^2 + 1*x^3 + 2*x^4 + A*B = 0*x^(-0+0j) + 1*x^(2+0j) + 2*x^(3+0j) + 3*x^(8+0j) + 4*x^(6+0j) + 5*x^(8+0j) + """ + + def __init__(self, polyA=[0], polyB=[0]): + # Input as list + self.polyA = list(polyA)[:] + self.polyB = list(polyB)[:] + + # Remove leading zero coefficients + while self.polyA[-1] == 0: + self.polyA.pop() + self.len_A = len(self.polyA) + + while self.polyB[-1] == 0: + self.polyB.pop() + self.len_B = len(self.polyB) + + # Add 0 to make lengths equal a power of 2 + self.C_max_length = int( + 2 ** np.ceil(np.log2(len(self.polyA) + len(self.polyB) - 1)) + ) + + while len(self.polyA) < self.C_max_length: + self.polyA.append(0) + while len(self.polyB) < self.C_max_length: + self.polyB.append(0) + # A complex root used for the fourier transform + self.root = complex(mpmath.root(x=1, n=self.C_max_length, k=1)) + + # The product + self.product = self.__multiply() + + # Discrete fourier transform of A and B + def __DFT(self, which): + if which == "A": + dft = [[x] for x in self.polyA] + else: + dft = [[x] for x in self.polyB] + # Corner case + if len(dft) <= 1: + return dft[0] + # + next_ncol = self.C_max_length // 2 + while next_ncol > 0: + new_dft = [[] for i in range(next_ncol)] + root = self.root ** next_ncol + + # First half of next step + current_root = 1 + for j in range(self.C_max_length // (next_ncol * 2)): + for i in range(next_ncol): + new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]) + current_root *= root + # Second half of next step + current_root = 1 + for j in range(self.C_max_length // (next_ncol * 2)): + for i in range(next_ncol): + new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]) + current_root *= root + # Update + dft = new_dft + next_ncol = next_ncol // 2 + return dft[0] + + # multiply the DFTs of A and B and find A*B + def __multiply(self): + dftA = self.__DFT("A") + dftB = self.__DFT("B") + inverseC = [[dftA[i] * dftB[i] for i in range(self.C_max_length)]] + del dftA + del dftB + + # Corner Case + if len(inverseC[0]) <= 1: + return inverseC[0] + # Inverse DFT + next_ncol = 2 + while next_ncol <= self.C_max_length: + new_inverseC = [[] for i in range(next_ncol)] + root = self.root ** (next_ncol // 2) + current_root = 1 + # First half of next step + for j in range(self.C_max_length // next_ncol): + for i in range(next_ncol // 2): + # Even positions + new_inverseC[i].append( + ( + inverseC[i][j] + + inverseC[i][j + self.C_max_length // next_ncol] + ) + / 2 + ) + # Odd positions + new_inverseC[i + next_ncol // 2].append( + ( + inverseC[i][j] + - inverseC[i][j + self.C_max_length // next_ncol] + ) + / (2 * current_root) + ) + current_root *= root + # Update + inverseC = new_inverseC + next_ncol *= 2 + # Unpack + inverseC = [round(x[0].real, 8) + round(x[0].imag, 8) * 1j for x in inverseC] + + # Remove leading 0's + while inverseC[-1] == 0: + inverseC.pop() + return inverseC + + # Overwrite __str__ for print(); Shows A, B and A*B + def __str__(self): + A = "A = " + " + ".join( + f"{coef}*x^{i}" for coef, i in enumerate(self.polyA[: self.len_A]) + ) + B = "B = " + " + ".join( + f"{coef}*x^{i}" for coef, i in enumerate(self.polyB[: self.len_B]) + ) + C = "A*B = " + " + ".join( + f"{coef}*x^{i}" for coef, i in enumerate(self.product) + ) + + return "\n".join((A, B, C)) + + +# Unit tests +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/maths/runge_kutta.py b/maths/runge_kutta.py new file mode 100644 index 000000000000..383797daa5ac --- /dev/null +++ b/maths/runge_kutta.py @@ -0,0 +1,44 @@ +import numpy as np + + +def runge_kutta(f, y0, x0, h, x_end): + """ + Calculate the numeric solution at each step to the ODE f(x, y) using RK4 + + https://en.wikipedia.org/wiki/Runge-Kutta_methods + + Arguments: + f -- The ode as a function of x and y + y0 -- the initial value for y + x0 -- the initial value for x + h -- the stepsize + x_end -- the end value for x + + >>> # the exact solution is math.exp(x) + >>> def f(x, y): + ... return y + >>> y0 = 1 + >>> y = runge_kutta(f, y0, 0.0, 0.01, 5) + >>> y[-1] + 148.41315904125113 + """ + N = int(np.ceil((x_end - x0) / h)) + y = np.zeros((N + 1,)) + y[0] = y0 + x = x0 + + for k in range(N): + k1 = f(x, y[k]) + k2 = f(x + 0.5 * h, y[k] + 0.5 * h * k1) + k3 = f(x + 0.5 * h, y[k] + 0.5 * h * k2) + k4 = f(x + h, y[k] + h * k3) + y[k + 1] = y[k] + (1 / 6) * h * (k1 + 2 * k2 + 2 * k3 + k4) + x += h + + return y + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/maths/segmented_sieve.py b/maths/segmented_sieve.py index 52ca6fbe601d..c1cc497ad33e 100644 --- a/maths/segmented_sieve.py +++ b/maths/segmented_sieve.py @@ -1,46 +1,51 @@ +"""Segmented Sieve.""" + import math + def sieve(n): + """Segmented Sieve.""" in_prime = [] start = 2 - end = int(math.sqrt(n)) # Size of every segment + end = int(math.sqrt(n)) # Size of every segment temp = [True] * (end + 1) prime = [] - - while(start <= end): - if temp[start] == True: + + while start <= end: + if temp[start] is True: in_prime.append(start) - for i in range(start*start, end+1, start): - if temp[i] == True: + for i in range(start * start, end + 1, start): + if temp[i] is True: temp[i] = False start += 1 prime += in_prime - + low = end + 1 high = low + end - 1 if high > n: high = n - - while(low <= n): - temp = [True] * (high-low+1) + + while low <= n: + temp = [True] * (high - low + 1) for each in in_prime: - + t = math.floor(low / each) * each if t < low: t += each - - for j in range(t, high+1, each): + + for j in range(t, high + 1, each): temp[j - low] = False - + for j in range(len(temp)): - if temp[j] == True: - prime.append(j+low) - + if temp[j] is True: + prime.append(j + low) + low = high + 1 high = low + end - 1 if high > n: high = n - + return prime -print(sieve(10**6)) \ No newline at end of file + +print(sieve(10 ** 6)) diff --git a/maths/sieve_of_eratosthenes.py b/maths/sieve_of_eratosthenes.py index 26c17fa6ffec..44c7f8a02682 100644 --- a/maths/sieve_of_eratosthenes.py +++ b/maths/sieve_of_eratosthenes.py @@ -1,24 +1,61 @@ +# -*- coding: utf-8 -*- + +""" +Sieve of Eratosthones + +The sieve of Eratosthenes is an algorithm used to find prime numbers, less than or equal to a given value. +Illustration: https://upload.wikimedia.org/wikipedia/commons/b/b9/Sieve_of_Eratosthenes_animation.gif +Reference: https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes + +doctest provider: Bruno Simas Hadlich (https://github.com/brunohadlich) +Also thanks Dmitry (https://github.com/LizardWizzard) for finding the problem +""" + + import math -n = int(input("Enter n: ")) + def sieve(n): - l = [True] * (n+1) + """ + Returns a list with all prime numbers up to n. + + >>> sieve(50) + [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47] + >>> sieve(25) + [2, 3, 5, 7, 11, 13, 17, 19, 23] + >>> sieve(10) + [2, 3, 5, 7] + >>> sieve(9) + [2, 3, 5, 7] + >>> sieve(2) + [2] + >>> sieve(1) + [] + """ + + l = [True] * (n + 1) prime = [] start = 2 - end = int(math.sqrt(n)) - while(start <= end): - if l[start] == True: + end = int(math.sqrt(n)) + + while start <= end: + # If start is a prime + if l[start] is True: prime.append(start) - for i in range(start*start, n+1, start): - if l[i] == True: + + # Set multiples of start be False + for i in range(start * start, n + 1, start): + if l[i] is True: l[i] = False + start += 1 - - for j in range(end+1,n+1): - if l[j] == True: + + for j in range(end + 1, n + 1): + if l[j] is True: prime.append(j) - + return prime -print(sieve(n)) - + +if __name__ == "__main__": + print(sieve(int(input("Enter n: ").strip()))) diff --git a/maths/simpson_rule.py b/maths/simpson_rule.py index 091c86c17f1b..f4620be8e70f 100644 --- a/maths/simpson_rule.py +++ b/maths/simpson_rule.py @@ -1,49 +1,51 @@ - -''' +""" Numerical integration or quadrature for a smooth function f with known values at x_i -This method is the classical approch of suming 'Equally Spaced Abscissas' +This method is the classical approch of suming 'Equally Spaced Abscissas' -method 2: +method 2: "Simpson Rule" -''' -from __future__ import print_function +""" def method_2(boundary, steps): -# "Simpson Rule" -# int(f) = delta_x/2 * (b-a)/3*(f1 + 4f2 + 2f_3 + ... + fn) - h = (boundary[1] - boundary[0]) / steps - a = boundary[0] - b = boundary[1] - x_i = makePoints(a,b,h) - y = 0.0 - y += (h/3.0)*f(a) - cnt = 2 - for i in x_i: - y += (h/3)*(4-2*(cnt%2))*f(i) - cnt += 1 - y += (h/3.0)*f(b) - return y - -def makePoints(a,b,h): - x = a + h - while x < (b-h): - yield x - x = x + h - -def f(x): #enter your function here - y = (x-0)*(x-0) - return y + # "Simpson Rule" + # int(f) = delta_x/2 * (b-a)/3*(f1 + 4f2 + 2f_3 + ... + fn) + h = (boundary[1] - boundary[0]) / steps + a = boundary[0] + b = boundary[1] + x_i = make_points(a, b, h) + y = 0.0 + y += (h / 3.0) * f(a) + cnt = 2 + for i in x_i: + y += (h / 3) * (4 - 2 * (cnt % 2)) * f(i) + cnt += 1 + y += (h / 3.0) * f(b) + return y + + +def make_points(a, b, h): + x = a + h + while x < (b - h): + yield x + x = x + h + + +def f(x): # enter your function here + y = (x - 0) * (x - 0) + return y + def main(): - a = 0.0 #Lower bound of integration - b = 1.0 #Upper bound of integration - steps = 10.0 #define number of steps or resolution - boundary = [a, b] #define boundary of integration - y = method_2(boundary, steps) - print('y = {0}'.format(y)) - -if __name__ == '__main__': - main() + a = 0.0 # Lower bound of integration + b = 1.0 # Upper bound of integration + steps = 10.0 # define number of steps or resolution + boundary = [a, b] # define boundary of integration + y = method_2(boundary, steps) + print("y = {0}".format(y)) + + +if __name__ == "__main__": + main() diff --git a/maths/softmax.py b/maths/softmax.py new file mode 100644 index 000000000000..92ff4ca27b88 --- /dev/null +++ b/maths/softmax.py @@ -0,0 +1,56 @@ +""" +This script demonstrates the implementation of the Softmax function. + +Its a function that takes as input a vector of K real numbers, and normalizes +it into a probability distribution consisting of K probabilities proportional +to the exponentials of the input numbers. After softmax, the elements of the +vector always sum up to 1. + +Script inspired from its corresponding Wikipedia article +https://en.wikipedia.org/wiki/Softmax_function +""" + +import numpy as np + + +def softmax(vector): + """ + Implements the softmax function + + Parameters: + vector (np.array,list,tuple): A numpy array of shape (1,n) + consisting of real values or a similar list,tuple + + + Returns: + softmax_vec (np.array): The input numpy array after applying + softmax. + + The softmax vector adds up to one. We need to ceil to mitigate for + precision + >>> np.ceil(np.sum(softmax([1,2,3,4]))) + 1.0 + + >>> vec = np.array([5,5]) + >>> softmax(vec) + array([0.5, 0.5]) + + >>> softmax([0]) + array([1.]) + """ + + # Calculate e^x for each x in your vector where e is Euler's + # number (approximately 2.718) + exponentVector = np.exp(vector) + + # Add up the all the exponentials + sumOfExponents = np.sum(exponentVector) + + # Divide every exponent by the sum of all exponents + softmax_vector = exponentVector / sumOfExponents + + return softmax_vector + + +if __name__ == "__main__": + print(softmax((0,))) diff --git a/maths/sum_of_arithmetic_series.py b/maths/sum_of_arithmetic_series.py new file mode 100755 index 000000000000..74eef0f18a12 --- /dev/null +++ b/maths/sum_of_arithmetic_series.py @@ -0,0 +1,23 @@ +# DarkCoder +def sum_of_series(first_term, common_diff, num_of_terms): + """ + Find the sum of n terms in an arithmetic progression. + + >>> sum_of_series(1, 1, 10) + 55.0 + >>> sum_of_series(1, 10, 100) + 49600.0 + """ + sum = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff) + # formula for sum of series + return sum + + +def main(): + print(sum_of_series(1, 1, 10)) + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/maths/test_prime_check.py b/maths/test_prime_check.py new file mode 100644 index 000000000000..b6389684af9e --- /dev/null +++ b/maths/test_prime_check.py @@ -0,0 +1,8 @@ +""" +Minimalist file that allows pytest to find and run the Test unittest. For details, see: +http://doc.pytest.org/en/latest/goodpractices.html#conventions-for-python-test-discovery +""" + +from .prime_check import Test + +Test() diff --git a/maths/trapezoidal_rule.py b/maths/trapezoidal_rule.py index 52310c1ed3b0..0f321317614d 100644 --- a/maths/trapezoidal_rule.py +++ b/maths/trapezoidal_rule.py @@ -1,46 +1,50 @@ -''' +""" Numerical integration or quadrature for a smooth function f with known values at x_i -This method is the classical approch of suming 'Equally Spaced Abscissas' +This method is the classical approch of suming 'Equally Spaced Abscissas' -method 1: +method 1: "extended trapezoidal rule" -''' -from __future__ import print_function +""" + def method_1(boundary, steps): -# "extended trapezoidal rule" -# int(f) = dx/2 * (f1 + 2f2 + ... + fn) - h = (boundary[1] - boundary[0]) / steps - a = boundary[0] - b = boundary[1] - x_i = makePoints(a,b,h) - y = 0.0 - y += (h/2.0)*f(a) - for i in x_i: - #print(i) - y += h*f(i) - y += (h/2.0)*f(b) - return y - -def makePoints(a,b,h): - x = a + h - while x < (b-h): - yield x - x = x + h - -def f(x): #enter your function here - y = (x-0)*(x-0) - return y + # "extended trapezoidal rule" + # int(f) = dx/2 * (f1 + 2f2 + ... + fn) + h = (boundary[1] - boundary[0]) / steps + a = boundary[0] + b = boundary[1] + x_i = make_points(a, b, h) + y = 0.0 + y += (h / 2.0) * f(a) + for i in x_i: + # print(i) + y += h * f(i) + y += (h / 2.0) * f(b) + return y + + +def make_points(a, b, h): + x = a + h + while x < (b - h): + yield x + x = x + h + + +def f(x): # enter your function here + y = (x - 0) * (x - 0) + return y + def main(): - a = 0.0 #Lower bound of integration - b = 1.0 #Upper bound of integration - steps = 10.0 #define number of steps or resolution - boundary = [a, b] #define boundary of integration - y = method_1(boundary, steps) - print('y = {0}'.format(y)) - -if __name__ == '__main__': - main() + a = 0.0 # Lower bound of integration + b = 1.0 # Upper bound of integration + steps = 10.0 # define number of steps or resolution + boundary = [a, b] # define boundary of integration + y = method_1(boundary, steps) + print("y = {0}".format(y)) + + +if __name__ == "__main__": + main() diff --git a/maths/volume.py b/maths/volume.py new file mode 100644 index 000000000000..38de7516d9b2 --- /dev/null +++ b/maths/volume.py @@ -0,0 +1,96 @@ +""" +Find Volumes of Various Shapes. + +Wikipedia reference: https://en.wikipedia.org/wiki/Volume +""" + +from math import pi + + +def vol_cube(side_length): + """Calculate the Volume of a Cube.""" + # Cube side_length. + return float(side_length ** 3) + + +def vol_cuboid(width, height, length): + """Calculate the Volume of a Cuboid.""" + # Multiply lengths together. + return float(width * height * length) + + +def vol_cone(area_of_base, height): + """ + Calculate the Volume of a Cone. + + Wikipedia reference: https://en.wikipedia.org/wiki/Cone + volume = (1/3) * area_of_base * height + """ + return (float(1) / 3) * area_of_base * height + + +def vol_right_circ_cone(radius, height): + """ + Calculate the Volume of a Right Circular Cone. + + Wikipedia reference: https://en.wikipedia.org/wiki/Cone + volume = (1/3) * pi * radius^2 * height + """ + + return (float(1) / 3) * pi * (radius ** 2) * height + + +def vol_prism(area_of_base, height): + """ + Calculate the Volume of a Prism. + + V = Bh + Wikipedia reference: https://en.wikipedia.org/wiki/Prism_(geometry) + """ + return float(area_of_base * height) + + +def vol_pyramid(area_of_base, height): + """ + Calculate the Volume of a Prism. + + V = (1/3) * Bh + Wikipedia reference: https://en.wikipedia.org/wiki/Pyramid_(geometry) + """ + return (float(1) / 3) * area_of_base * height + + +def vol_sphere(radius): + """ + Calculate the Volume of a Sphere. + + V = (4/3) * pi * r^3 + Wikipedia reference: https://en.wikipedia.org/wiki/Sphere + """ + return (float(4) / 3) * pi * radius ** 3 + + +def vol_circular_cylinder(radius, height): + """Calculate the Volume of a Circular Cylinder. + + Wikipedia reference: https://en.wikipedia.org/wiki/Cylinder + volume = pi * radius^2 * height + """ + return pi * radius ** 2 * height + + +def main(): + """Print the Results of Various Volume Calculations.""" + print("Volumes:") + print("Cube: " + str(vol_cube(2))) # = 8 + print("Cuboid: " + str(vol_cuboid(2, 2, 2))) # = 8 + print("Cone: " + str(vol_cone(2, 2))) # ~= 1.33 + print("Right Circular Cone: " + str(vol_right_circ_cone(2, 2))) # ~= 8.38 + print("Prism: " + str(vol_prism(2, 2))) # = 4 + print("Pyramid: " + str(vol_pyramid(2, 2))) # ~= 1.33 + print("Sphere: " + str(vol_sphere(2))) # ~= 33.5 + print("Circular Cylinder: " + str(vol_circular_cylinder(2, 2))) # ~= 25.1 + + +if __name__ == "__main__": + main() diff --git a/maths/zellers_congruence.py b/maths/zellers_congruence.py new file mode 100644 index 000000000000..277ecfaf0da9 --- /dev/null +++ b/maths/zellers_congruence.py @@ -0,0 +1,156 @@ +import datetime +import argparse + + +def zeller(date_input: str) -> str: + + """ + Zellers Congruence Algorithm + Find the day of the week for nearly any Gregorian or Julian calendar date + + >>> zeller('01-31-2010') + 'Your date 01-31-2010, is a Sunday!' + + Validate out of range month + >>> zeller('13-31-2010') + Traceback (most recent call last): + ... + ValueError: Month must be between 1 - 12 + >>> zeller('.2-31-2010') + Traceback (most recent call last): + ... + ValueError: invalid literal for int() with base 10: '.2' + + Validate out of range date: + >>> zeller('01-33-2010') + Traceback (most recent call last): + ... + ValueError: Date must be between 1 - 31 + >>> zeller('01-.4-2010') + Traceback (most recent call last): + ... + ValueError: invalid literal for int() with base 10: '.4' + + Validate second seperator: + >>> zeller('01-31*2010') + Traceback (most recent call last): + ... + ValueError: Date seperator must be '-' or '/' + + Validate first seperator: + >>> zeller('01^31-2010') + Traceback (most recent call last): + ... + ValueError: Date seperator must be '-' or '/' + + Validate out of range year: + >>> zeller('01-31-8999') + Traceback (most recent call last): + ... + ValueError: Year out of range. There has to be some sort of limit...right? + + Test null input: + >>> zeller() + Traceback (most recent call last): + ... + TypeError: zeller() missing 1 required positional argument: 'date_input' + + Test length fo date_input: + >>> zeller('') + Traceback (most recent call last): + ... + ValueError: Must be 10 characters long + >>> zeller('01-31-19082939') + Traceback (most recent call last): + ... + ValueError: Must be 10 characters long +""" + + # Days of the week for response + days = { + "0": "Sunday", + "1": "Monday", + "2": "Tuesday", + "3": "Wednesday", + "4": "Thursday", + "5": "Friday", + "6": "Saturday", + } + + convert_datetime_days = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0} + + # Validate + if not 0 < len(date_input) < 11: + raise ValueError("Must be 10 characters long") + + # Get month + m: int = int(date_input[0] + date_input[1]) + # Validate + if not 0 < m < 13: + raise ValueError("Month must be between 1 - 12") + + sep_1: str = date_input[2] + # Validate + if sep_1 not in ["-", "/"]: + raise ValueError("Date seperator must be '-' or '/'") + + # Get day + d: int = int(date_input[3] + date_input[4]) + # Validate + if not 0 < d < 32: + raise ValueError("Date must be between 1 - 31") + + # Get second seperator + sep_2: str = date_input[5] + # Validate + if sep_2 not in ["-", "/"]: + raise ValueError("Date seperator must be '-' or '/'") + + # Get year + y: int = int(date_input[6] + date_input[7] + date_input[8] + date_input[9]) + # Arbitrary year range + if not 45 < y < 8500: + raise ValueError( + "Year out of range. There has to be some sort of limit...right?" + ) + + # Get datetime obj for validation + dt_ck = datetime.date(int(y), int(m), int(d)) + + # Start math + if m <= 2: + y = y - 1 + m = m + 12 + # maths var + c: int = int(str(y)[:2]) + k: int = int(str(y)[2:]) + t: int = int(2.6 * m - 5.39) + u: int = int(c / 4) + v: int = int(k / 4) + x: int = int(d + k) + z: int = int(t + u + v + x) + w: int = int(z - (2 * c)) + f: int = round(w % 7) + # End math + + # Validate math + if f != convert_datetime_days[dt_ck.weekday()]: + raise AssertionError("The date was evaluated incorrectly. Contact developer.") + + # Response + response: str = f"Your date {date_input}, is a {days[str(f)]}!" + return response + + +if __name__ == "__main__": + import doctest + + doctest.testmod() + parser = argparse.ArgumentParser( + description="Find out what day of the week nearly any date is or was. Enter date as a string in the mm-dd-yyyy or mm/dd/yyyy format" + ) + parser.add_argument( + "date_input", type=str, help="Date as a string (mm-dd-yyyy or mm/dd/yyyy)" + ) + args = parser.parse_args() + zeller(args.date_input) diff --git a/matrix/matrix_class.py b/matrix/matrix_class.py new file mode 100644 index 000000000000..a8066e319559 --- /dev/null +++ b/matrix/matrix_class.py @@ -0,0 +1,355 @@ +# An OOP aproach to representing and manipulating matrices + + +class Matrix: + """ + Matrix object generated from a 2D array where each element is an array representing a row. + Rows can contain type int or float. + Common operations and information available. + >>> rows = [ + ... [1, 2, 3], + ... [4, 5, 6], + ... [7, 8, 9] + ... ] + >>> matrix = Matrix(rows) + >>> print(matrix) + [[1. 2. 3.] + [4. 5. 6.] + [7. 8. 9.]] + + Matrix rows and columns are available as 2D arrays + >>> print(matrix.rows) + [[1, 2, 3], [4, 5, 6], [7, 8, 9]] + >>> print(matrix.columns()) + [[1, 4, 7], [2, 5, 8], [3, 6, 9]] + + Order is returned as a tuple + >>> matrix.order + (3, 3) + + Squareness and invertability are represented as bool + >>> matrix.is_square + True + >>> matrix.is_invertable() + False + + Identity, Minors, Cofactors and Adjugate are returned as Matrices. Inverse can be a Matrix or Nonetype + >>> print(matrix.identity()) + [[1. 0. 0.] + [0. 1. 0.] + [0. 0. 1.]] + >>> print(matrix.minors()) + [[-3. -6. -3.] + [-6. -12. -6.] + [-3. -6. -3.]] + >>> print(matrix.cofactors()) + [[-3. 6. -3.] + [6. -12. 6.] + [-3. 6. -3.]] + >>> print(matrix.adjugate()) # won't be apparent due to the nature of the cofactor matrix + [[-3. 6. -3.] + [6. -12. 6.] + [-3. 6. -3.]] + >>> print(matrix.inverse()) + None + + Determinant is an int, float, or Nonetype + >>> matrix.determinant() + 0 + + Negation, scalar multiplication, addition, subtraction, multiplication and exponentiation are available and all return a Matrix + >>> print(-matrix) + [[-1. -2. -3.] + [-4. -5. -6.] + [-7. -8. -9.]] + >>> matrix2 = matrix * 3 + >>> print(matrix2) + [[3. 6. 9.] + [12. 15. 18.] + [21. 24. 27.]] + >>> print(matrix + matrix2) + [[4. 8. 12.] + [16. 20. 24.] + [28. 32. 36.]] + >>> print(matrix - matrix2) + [[-2. -4. -6.] + [-8. -10. -12.] + [-14. -16. -18.]] + >>> print(matrix ** 3) + [[468. 576. 684.] + [1062. 1305. 1548.] + [1656. 2034. 2412.]] + + Matrices can also be modified + >>> matrix.add_row([10, 11, 12]) + >>> print(matrix) + [[1. 2. 3.] + [4. 5. 6.] + [7. 8. 9.] + [10. 11. 12.]] + >>> matrix2.add_column([8, 16, 32]) + >>> print(matrix2) + [[3. 6. 9. 8.] + [12. 15. 18. 16.] + [21. 24. 27. 32.]] + >>> print(matrix * matrix2) + [[90. 108. 126. 136.] + [198. 243. 288. 304.] + [306. 378. 450. 472.] + [414. 513. 612. 640.]] + + """ + + def __init__(self, rows): + error = TypeError( + "Matrices must be formed from a list of zero or more lists containing at least " + "one and the same number of values, each of which must be of type int or float." + ) + if len(rows) != 0: + cols = len(rows[0]) + if cols == 0: + raise error + for row in rows: + if len(row) != cols: + raise error + for value in row: + if not isinstance(value, (int, float)): + raise error + self.rows = rows + else: + self.rows = [] + + # MATRIX INFORMATION + def columns(self): + return [[row[i] for row in self.rows] for i in range(len(self.rows[0]))] + + @property + def num_rows(self): + return len(self.rows) + + @property + def num_columns(self): + return len(self.rows[0]) + + @property + def order(self): + return (self.num_rows, self.num_columns) + + @property + def is_square(self): + return self.order[0] == self.order[1] + + def identity(self): + values = [ + [0 if column_num != row_num else 1 for column_num in range(self.num_rows)] + for row_num in range(self.num_rows) + ] + return Matrix(values) + + def determinant(self): + if not self.is_square: + return None + if self.order == (0, 0): + return 1 + if self.order == (1, 1): + return self.rows[0][0] + if self.order == (2, 2): + return (self.rows[0][0] * self.rows[1][1]) - ( + self.rows[0][1] * self.rows[1][0] + ) + else: + return sum( + [ + self.rows[0][column] * self.cofactors().rows[0][column] + for column in range(self.num_columns) + ] + ) + + def is_invertable(self): + return bool(self.determinant()) + + def get_minor(self, row, column): + values = [ + [ + self.rows[other_row][other_column] + for other_column in range(self.num_columns) + if other_column != column + ] + for other_row in range(self.num_rows) + if other_row != row + ] + return Matrix(values).determinant() + + def get_cofactor(self, row, column): + if (row + column) % 2 == 0: + return self.get_minor(row, column) + return -1 * self.get_minor(row, column) + + def minors(self): + return Matrix( + [ + [self.get_minor(row, column) for column in range(self.num_columns)] + for row in range(self.num_rows) + ] + ) + + def cofactors(self): + return Matrix( + [ + [ + self.minors().rows[row][column] + if (row + column) % 2 == 0 + else self.minors().rows[row][column] * -1 + for column in range(self.minors().num_columns) + ] + for row in range(self.minors().num_rows) + ] + ) + + def adjugate(self): + values = [ + [self.cofactors().rows[column][row] for column in range(self.num_columns)] + for row in range(self.num_rows) + ] + return Matrix(values) + + def inverse(self): + determinant = self.determinant() + return None if not determinant else self.adjugate() * (1 / determinant) + + def __repr__(self): + return str(self.rows) + + def __str__(self): + if self.num_rows == 0: + return "[]" + if self.num_rows == 1: + return "[[" + ". ".join(self.rows[0]) + "]]" + return ( + "[" + + "\n ".join( + [ + "[" + ". ".join([str(value) for value in row]) + ".]" + for row in self.rows + ] + ) + + "]" + ) + + # MATRIX MANIPULATION + def add_row(self, row, position=None): + type_error = TypeError("Row must be a list containing all ints and/or floats") + if not isinstance(row, list): + raise type_error + for value in row: + if not isinstance(value, (int, float)): + raise type_error + if len(row) != self.num_columns: + raise ValueError( + "Row must be equal in length to the other rows in the matrix" + ) + if position is None: + self.rows.append(row) + else: + self.rows = self.rows[0:position] + [row] + self.rows[position:] + + def add_column(self, column, position=None): + type_error = TypeError( + "Column must be a list containing all ints and/or floats" + ) + if not isinstance(column, list): + raise type_error + for value in column: + if not isinstance(value, (int, float)): + raise type_error + if len(column) != self.num_rows: + raise ValueError( + "Column must be equal in length to the other columns in the matrix" + ) + if position is None: + self.rows = [self.rows[i] + [column[i]] for i in range(self.num_rows)] + else: + self.rows = [ + self.rows[i][0:position] + [column[i]] + self.rows[i][position:] + for i in range(self.num_rows) + ] + + # MATRIX OPERATIONS + def __eq__(self, other): + if not isinstance(other, Matrix): + raise TypeError("A Matrix can only be compared with another Matrix") + return self.rows == other.rows + + def __ne__(self, other): + return not self == other + + def __neg__(self): + return self * -1 + + def __add__(self, other): + if self.order != other.order: + raise ValueError("Addition requires matrices of the same order") + return Matrix( + [ + [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns)] + for i in range(self.num_rows) + ] + ) + + def __sub__(self, other): + if self.order != other.order: + raise ValueError("Subtraction requires matrices of the same order") + return Matrix( + [ + [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns)] + for i in range(self.num_rows) + ] + ) + + def __mul__(self, other): + if isinstance(other, (int, float)): + return Matrix([[element * other for element in row] for row in self.rows]) + elif isinstance(other, Matrix): + if self.num_columns != other.num_rows: + raise ValueError( + "The number of columns in the first matrix must " + "be equal to the number of rows in the second" + ) + return Matrix( + [ + [Matrix.dot_product(row, column) for column in other.columns()] + for row in self.rows + ] + ) + else: + raise TypeError( + "A Matrix can only be multiplied by an int, float, or another matrix" + ) + + def __pow__(self, other): + if not isinstance(other, int): + raise TypeError("A Matrix can only be raised to the power of an int") + if not self.is_square: + raise ValueError("Only square matrices can be raised to a power") + if other == 0: + return self.identity() + if other < 0: + if self.is_invertable: + return self.inverse() ** (-other) + raise ValueError( + "Only invertable matrices can be raised to a negative power" + ) + result = self + for i in range(other - 1): + result *= self + return result + + @classmethod + def dot_product(cls, row, column): + return sum([row[i] * column[i] for i in range(len(row))]) + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/matrix/matrix_multiplication_addition.py b/matrix/matrix_multiplication_addition.py deleted file mode 100644 index dd50db729e43..000000000000 --- a/matrix/matrix_multiplication_addition.py +++ /dev/null @@ -1,75 +0,0 @@ -def add(matrix_a, matrix_b): - rows = len(matrix_a) - columns = len(matrix_a[0]) - matrix_c = [] - for i in range(rows): - list_1 = [] - for j in range(columns): - val = matrix_a[i][j] + matrix_b[i][j] - list_1.append(val) - matrix_c.append(list_1) - return matrix_c - -def scalarMultiply(matrix , n): - return [[x * n for x in row] for row in matrix] - -def multiply(matrix_a, matrix_b): - matrix_c = [] - n = len(matrix_a) - for i in range(n): - list_1 = [] - for j in range(n): - val = 0 - for k in range(n): - val = val + matrix_a[i][k] * matrix_b[k][j] - list_1.append(val) - matrix_c.append(list_1) - return matrix_c - -def identity(n): - return [[int(row == column) for column in range(n)] for row in range(n)] - -def transpose(matrix): - return map(list , zip(*matrix)) - -def minor(matrix, row, column): - minor = matrix[:row] + matrix[row + 1:] - minor = [row[:column] + row[column + 1:] for row in minor] - return minor - -def determinant(matrix): - if len(matrix) == 1: return matrix[0][0] - - res = 0 - for x in range(len(matrix)): - res += matrix[0][x] * determinant(minor(matrix , 0 , x)) * (-1) ** x - return res - -def inverse(matrix): - det = determinant(matrix) - if det == 0: return None - - matrixMinor = [[] for _ in range(len(matrix))] - for i in range(len(matrix)): - for j in range(len(matrix)): - matrixMinor[i].append(determinant(minor(matrix , i , j))) - - cofactors = [[x * (-1) ** (row + col) for col, x in enumerate(matrixMinor[row])] for row in range(len(matrix))] - adjugate = transpose(cofactors) - return scalarMultiply(adjugate , 1/det) - -def main(): - matrix_a = [[12, 10], [3, 9]] - matrix_b = [[3, 4], [7, 4]] - matrix_c = [[11, 12, 13, 14], [21, 22, 23, 24], [31, 32, 33, 34], [41, 42, 43, 44]] - matrix_d = [[3, 0, 2], [2, 0, -2], [0, 1, 1]] - - print(add(matrix_a, matrix_b)) - print(multiply(matrix_a, matrix_b)) - print(identity(5)) - print(minor(matrix_c , 1 , 2)) - print(determinant(matrix_b)) - print(inverse(matrix_d)) - -if __name__ == '__main__': - main() diff --git a/matrix/matrix_operation.py b/matrix/matrix_operation.py new file mode 100644 index 000000000000..5ca61b4ed023 --- /dev/null +++ b/matrix/matrix_operation.py @@ -0,0 +1,157 @@ +""" +function based version of matrix operations, which are just 2D arrays +""" + + +def add(matrix_a, matrix_b): + if _check_not_integer(matrix_a) and _check_not_integer(matrix_b): + rows, cols = _verify_matrix_sizes(matrix_a, matrix_b) + matrix_c = [] + for i in range(rows[0]): + list_1 = [] + for j in range(cols[0]): + val = matrix_a[i][j] + matrix_b[i][j] + list_1.append(val) + matrix_c.append(list_1) + return matrix_c + + +def subtract(matrix_a, matrix_b): + if _check_not_integer(matrix_a) and _check_not_integer(matrix_b): + rows, cols = _verify_matrix_sizes(matrix_a, matrix_b) + matrix_c = [] + for i in range(rows[0]): + list_1 = [] + for j in range(cols[0]): + val = matrix_a[i][j] - matrix_b[i][j] + list_1.append(val) + matrix_c.append(list_1) + return matrix_c + + +def scalar_multiply(matrix, n): + return [[x * n for x in row] for row in matrix] + + +def multiply(matrix_a, matrix_b): + if _check_not_integer(matrix_a) and _check_not_integer(matrix_b): + matrix_c = [] + rows, cols = _verify_matrix_sizes(matrix_a, matrix_b) + + if cols[0] != rows[1]: + raise ValueError( + f"Cannot multiply matrix of dimensions ({rows[0]},{cols[0]}) " + f"and ({rows[1]},{cols[1]})" + ) + for i in range(rows[0]): + list_1 = [] + for j in range(cols[1]): + val = 0 + for k in range(cols[1]): + val = val + matrix_a[i][k] * matrix_b[k][j] + list_1.append(val) + matrix_c.append(list_1) + return matrix_c + + +def identity(n): + """ + :param n: dimension for nxn matrix + :type n: int + :return: Identity matrix of shape [n, n] + """ + n = int(n) + return [[int(row == column) for column in range(n)] for row in range(n)] + + +def transpose(matrix, return_map=True): + if _check_not_integer(matrix): + if return_map: + return map(list, zip(*matrix)) + else: + # mt = [] + # for i in range(len(matrix[0])): + # mt.append([row[i] for row in matrix]) + # return mt + return [[row[i] for row in matrix] for i in range(len(matrix[0]))] + + +def minor(matrix, row, column): + minor = matrix[:row] + matrix[row + 1 :] + minor = [row[:column] + row[column + 1 :] for row in minor] + return minor + + +def determinant(matrix): + if len(matrix) == 1: + return matrix[0][0] + + res = 0 + for x in range(len(matrix)): + res += matrix[0][x] * determinant(minor(matrix, 0, x)) * (-1) ** x + return res + + +def inverse(matrix): + det = determinant(matrix) + if det == 0: + return None + + matrix_minor = [[] for _ in range(len(matrix))] + for i in range(len(matrix)): + for j in range(len(matrix)): + matrix_minor[i].append(determinant(minor(matrix, i, j))) + + cofactors = [ + [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row])] + for row in range(len(matrix)) + ] + adjugate = transpose(cofactors) + return scalar_multiply(adjugate, 1 / det) + + +def _check_not_integer(matrix): + try: + rows = len(matrix) + cols = len(matrix[0]) + return True + except TypeError: + raise TypeError("Cannot input an integer value, it must be a matrix") + + +def _shape(matrix): + return list((len(matrix), len(matrix[0]))) + + +def _verify_matrix_sizes(matrix_a, matrix_b): + shape = _shape(matrix_a) + shape += _shape(matrix_b) + if shape[0] != shape[2] or shape[1] != shape[3]: + raise ValueError( + f"operands could not be broadcast together with shape " + f"({shape[0], shape[1]}), ({shape[2], shape[3]})" + ) + return [shape[0], shape[2]], [shape[1], shape[3]] + + +def main(): + matrix_a = [[12, 10], [3, 9]] + matrix_b = [[3, 4], [7, 4]] + matrix_c = [[11, 12, 13, 14], [21, 22, 23, 24], [31, 32, 33, 34], [41, 42, 43, 44]] + matrix_d = [[3, 0, 2], [2, 0, -2], [0, 1, 1]] + print( + "Add Operation, %s + %s = %s \n" + % (matrix_a, matrix_b, (add(matrix_a, matrix_b))) + ) + print( + "Multiply Operation, %s * %s = %s \n" + % (matrix_a, matrix_b, multiply(matrix_a, matrix_b)) + ) + print("Identity: %s \n" % identity(5)) + print("Minor of %s = %s \n" % (matrix_c, minor(matrix_c, 1, 2))) + print("Determinant of %s = %s \n" % (matrix_b, determinant(matrix_b))) + print("Inverse of %s = %s\n" % (matrix_d, inverse(matrix_d))) + + +if __name__ == "__main__": + main() diff --git a/matrix/nth_fibonacci_using_matrix_exponentiation.py b/matrix/nth_fibonacci_using_matrix_exponentiation.py new file mode 100644 index 000000000000..222779f454f9 --- /dev/null +++ b/matrix/nth_fibonacci_using_matrix_exponentiation.py @@ -0,0 +1,87 @@ +""" +Implementation of finding nth fibonacci number using matrix exponentiation. +Time Complexity is about O(log(n)*8), where 8 is the complexity of matrix multiplication of size 2 by 2. +And on the other hand complexity of bruteforce solution is O(n). +As we know + f[n] = f[n-1] + f[n-1] +Converting to matrix, + [f(n),f(n-1)] = [[1,1],[1,0]] * [f(n-1),f(n-2)] +-> [f(n),f(n-1)] = [[1,1],[1,0]]^2 * [f(n-2),f(n-3)] + ... + ... +-> [f(n),f(n-1)] = [[1,1],[1,0]]^(n-1) * [f(1),f(0)] +So we just need the n times multiplication of the matrix [1,1],[1,0]]. +We can decrease the n times multiplication by following the divide and conquer approach. +""" + + +def multiply(matrix_a, matrix_b): + matrix_c = [] + n = len(matrix_a) + for i in range(n): + list_1 = [] + for j in range(n): + val = 0 + for k in range(n): + val = val + matrix_a[i][k] * matrix_b[k][j] + list_1.append(val) + matrix_c.append(list_1) + return matrix_c + + +def identity(n): + return [[int(row == column) for column in range(n)] for row in range(n)] + + +def nth_fibonacci_matrix(n): + """ + >>> nth_fibonacci_matrix(100) + 354224848179261915075 + >>> nth_fibonacci_matrix(-100) + -100 + """ + if n <= 1: + return n + res_matrix = identity(2) + fibonacci_matrix = [[1, 1], [1, 0]] + n = n - 1 + while n > 0: + if n % 2 == 1: + res_matrix = multiply(res_matrix, fibonacci_matrix) + fibonacci_matrix = multiply(fibonacci_matrix, fibonacci_matrix) + n = int(n / 2) + return res_matrix[0][0] + + +def nth_fibonacci_bruteforce(n): + """ + >>> nth_fibonacci_bruteforce(100) + 354224848179261915075 + >>> nth_fibonacci_bruteforce(-100) + -100 + """ + if n <= 1: + return n + fib0 = 0 + fib1 = 1 + for i in range(2, n + 1): + fib0, fib1 = fib1, fib0 + fib1 + return fib1 + + +def main(): + fmt = "{} fibonacci number using matrix exponentiation is {} and using bruteforce is {}\n" + for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split(): + n = int("".join(c for c in ordinal if c in "0123456789")) # 1000th --> 1000 + print(fmt.format(ordinal, nth_fibonacci_matrix(n), nth_fibonacci_bruteforce(n))) + # from timeit import timeit + # print(timeit("nth_fibonacci_matrix(1000000)", + # "from main import nth_fibonacci_matrix", number=5)) + # print(timeit("nth_fibonacci_bruteforce(1000000)", + # "from main import nth_fibonacci_bruteforce", number=5)) + # 2.3342058970001744 + # 57.256506615000035 + + +if __name__ == "__main__": + main() diff --git a/matrix/rotate_matrix.py b/matrix/rotate_matrix.py new file mode 100644 index 000000000000..822851826121 --- /dev/null +++ b/matrix/rotate_matrix.py @@ -0,0 +1,100 @@ +# -*- coding: utf-8 -*- + +""" +In this problem, we want to rotate the matrix elements by 90, 180, 270 (counterclockwise) +Discussion in stackoverflow: +https://stackoverflow.com/questions/42519/how-do-you-rotate-a-two-dimensional-array +""" + + +def make_matrix(row_size: int = 4) -> [[int]]: + """ + >>> make_matrix() + [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]] + >>> make_matrix(1) + [[1]] + >>> make_matrix(-2) + [[1, 2], [3, 4]] + >>> make_matrix(3) + [[1, 2, 3], [4, 5, 6], [7, 8, 9]] + >>> make_matrix() == make_matrix(4) + True + """ + row_size = abs(row_size) or 4 + return [[1 + x + y * row_size for x in range(row_size)] for y in range(row_size)] + + +def rotate_90(matrix: [[]]) -> [[]]: + """ + >>> rotate_90(make_matrix()) + [[4, 8, 12, 16], [3, 7, 11, 15], [2, 6, 10, 14], [1, 5, 9, 13]] + >>> rotate_90(make_matrix()) == transpose(reverse_column(make_matrix())) + True + """ + + return reverse_row(transpose(matrix)) + # OR.. transpose(reverse_column(matrix)) + + +def rotate_180(matrix: [[]]) -> [[]]: + """ + >>> rotate_180(make_matrix()) + [[16, 15, 14, 13], [12, 11, 10, 9], [8, 7, 6, 5], [4, 3, 2, 1]] + >>> rotate_180(make_matrix()) == reverse_column(reverse_row(make_matrix())) + True + """ + + return reverse_row(reverse_column(matrix)) + # OR.. reverse_column(reverse_row(matrix)) + + +def rotate_270(matrix: [[]]) -> [[]]: + """ + >>> rotate_270(make_matrix()) + [[13, 9, 5, 1], [14, 10, 6, 2], [15, 11, 7, 3], [16, 12, 8, 4]] + >>> rotate_270(make_matrix()) == transpose(reverse_row(make_matrix())) + True + """ + + return reverse_column(transpose(matrix)) + # OR.. transpose(reverse_row(matrix)) + + +def transpose(matrix: [[]]) -> [[]]: + matrix[:] = [list(x) for x in zip(*matrix)] + return matrix + + +def reverse_row(matrix: [[]]) -> [[]]: + matrix[:] = matrix[::-1] + return matrix + + +def reverse_column(matrix: [[]]) -> [[]]: + matrix[:] = [x[::-1] for x in matrix] + return matrix + + +def print_matrix(matrix: [[]]) -> [[]]: + for i in matrix: + print(*i) + + +if __name__ == "__main__": + matrix = make_matrix() + print("\norigin:\n") + print_matrix(matrix) + print("\nrotate 90 counterclockwise:\n") + print_matrix(rotate_90(matrix)) + + matrix = make_matrix() + print("\norigin:\n") + print_matrix(matrix) + print("\nrotate 180:\n") + print_matrix(rotate_180(matrix)) + + matrix = make_matrix() + print("\norigin:\n") + print_matrix(matrix) + print("\nrotate 270 counterclockwise:\n") + print_matrix(rotate_270(matrix)) diff --git a/matrix/searching_in_sorted_matrix.py b/matrix/searching_in_sorted_matrix.py index 54913b350803..1b3eeedf3110 100644 --- a/matrix/searching_in_sorted_matrix.py +++ b/matrix/searching_in_sorted_matrix.py @@ -2,26 +2,21 @@ def search_in_a_sorted_matrix(mat, m, n, key): i, j = m - 1, 0 while i >= 0 and j < n: if key == mat[i][j]: - print('Key %s found at row- %s column- %s' % (key, i + 1, j + 1)) + print("Key %s found at row- %s column- %s" % (key, i + 1, j + 1)) return if key < mat[i][j]: i -= 1 else: j += 1 - print('Key %s not found' % (key)) + print("Key %s not found" % (key)) def main(): - mat = [ - [2, 5, 7], - [4, 8, 13], - [9, 11, 15], - [12, 17, 20] - ] + mat = [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]] x = int(input("Enter the element to be searched:")) print(mat) search_in_a_sorted_matrix(mat, len(mat), len(mat[0]), x) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/matrix/sherman_morrison.py b/matrix/sherman_morrison.py new file mode 100644 index 000000000000..531b76cdeb94 --- /dev/null +++ b/matrix/sherman_morrison.py @@ -0,0 +1,268 @@ +class Matrix: + """ + + Matrix structure. + """ + + def __init__(self, row: int, column: int, default_value: float = 0): + """ + + Initialize matrix with given size and default value. + + Example: + >>> a = Matrix(2, 3, 1) + >>> a + Matrix consist of 2 rows and 3 columns + [1, 1, 1] + [1, 1, 1] + """ + + self.row, self.column = row, column + self.array = [[default_value for c in range(column)] for r in range(row)] + + def __str__(self): + """ + + Return string representation of this matrix. + """ + + # Prefix + s = "Matrix consist of %d rows and %d columns\n" % (self.row, self.column) + + # Make string identifier + max_element_length = 0 + for row_vector in self.array: + for obj in row_vector: + max_element_length = max(max_element_length, len(str(obj))) + string_format_identifier = "%%%ds" % (max_element_length,) + + # Make string and return + def single_line(row_vector): + nonlocal string_format_identifier + line = "[" + line += ", ".join(string_format_identifier % (obj,) for obj in row_vector) + line += "]" + return line + + s += "\n".join(single_line(row_vector) for row_vector in self.array) + return s + + def __repr__(self): + return str(self) + + def validateIndices(self, loc: tuple): + """ + + Check if given indices are valid to pick element from matrix. + + Example: + >>> a = Matrix(2, 6, 0) + >>> a.validateIndices((2, 7)) + False + >>> a.validateIndices((0, 0)) + True + """ + if not (isinstance(loc, (list, tuple)) and len(loc) == 2): + return False + elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column): + return False + else: + return True + + def __getitem__(self, loc: tuple): + """ + + Return array[row][column] where loc = (row, column). + + Example: + >>> a = Matrix(3, 2, 7) + >>> a[1, 0] + 7 + """ + assert self.validateIndices(loc) + return self.array[loc[0]][loc[1]] + + def __setitem__(self, loc: tuple, value: float): + """ + + Set array[row][column] = value where loc = (row, column). + + Example: + >>> a = Matrix(2, 3, 1) + >>> a[1, 2] = 51 + >>> a + Matrix consist of 2 rows and 3 columns + [ 1, 1, 1] + [ 1, 1, 51] + """ + assert self.validateIndices(loc) + self.array[loc[0]][loc[1]] = value + + def __add__(self, another): + """ + + Return self + another. + + Example: + >>> a = Matrix(2, 1, -4) + >>> b = Matrix(2, 1, 3) + >>> a+b + Matrix consist of 2 rows and 1 columns + [-1] + [-1] + """ + + # Validation + assert isinstance(another, Matrix) + assert self.row == another.row and self.column == another.column + + # Add + result = Matrix(self.row, self.column) + for r in range(self.row): + for c in range(self.column): + result[r, c] = self[r, c] + another[r, c] + return result + + def __neg__(self): + """ + + Return -self. + + Example: + >>> a = Matrix(2, 2, 3) + >>> a[0, 1] = a[1, 0] = -2 + >>> -a + Matrix consist of 2 rows and 2 columns + [-3, 2] + [ 2, -3] + """ + + result = Matrix(self.row, self.column) + for r in range(self.row): + for c in range(self.column): + result[r, c] = -self[r, c] + return result + + def __sub__(self, another): + return self + (-another) + + def __mul__(self, another): + """ + + Return self * another. + + Example: + >>> a = Matrix(2, 3, 1) + >>> a[0,2] = a[1,2] = 3 + >>> a * -2 + Matrix consist of 2 rows and 3 columns + [-2, -2, -6] + [-2, -2, -6] + """ + + if isinstance(another, (int, float)): # Scalar multiplication + result = Matrix(self.row, self.column) + for r in range(self.row): + for c in range(self.column): + result[r, c] = self[r, c] * another + return result + elif isinstance(another, Matrix): # Matrix multiplication + assert self.column == another.row + result = Matrix(self.row, another.column) + for r in range(self.row): + for c in range(another.column): + for i in range(self.column): + result[r, c] += self[r, i] * another[i, c] + return result + else: + raise TypeError( + "Unsupported type given for another (%s)" % (type(another),) + ) + + def transpose(self): + """ + + Return self^T. + + Example: + >>> a = Matrix(2, 3) + >>> for r in range(2): + ... for c in range(3): + ... a[r,c] = r*c + ... + >>> a.transpose() + Matrix consist of 3 rows and 2 columns + [0, 0] + [0, 1] + [0, 2] + """ + + result = Matrix(self.column, self.row) + for r in range(self.row): + for c in range(self.column): + result[c, r] = self[r, c] + return result + + def ShermanMorrison(self, u, v): + """ + + Apply Sherman-Morrison formula in O(n^2). + To learn this formula, please look this: https://en.wikipedia.org/wiki/Sherman%E2%80%93Morrison_formula + This method returns (A + uv^T)^(-1) where A^(-1) is self. Returns None if it's impossible to calculate. + Warning: This method doesn't check if self is invertible. + Make sure self is invertible before execute this method. + + Example: + >>> ainv = Matrix(3, 3, 0) + >>> for i in range(3): ainv[i,i] = 1 + ... + >>> u = Matrix(3, 1, 0) + >>> u[0,0], u[1,0], u[2,0] = 1, 2, -3 + >>> v = Matrix(3, 1, 0) + >>> v[0,0], v[1,0], v[2,0] = 4, -2, 5 + >>> ainv.ShermanMorrison(u, v) + Matrix consist of 3 rows and 3 columns + [ 1.2857142857142856, -0.14285714285714285, 0.3571428571428571] + [ 0.5714285714285714, 0.7142857142857143, 0.7142857142857142] + [ -0.8571428571428571, 0.42857142857142855, -0.0714285714285714] + """ + + # Size validation + assert isinstance(u, Matrix) and isinstance(v, Matrix) + assert self.row == self.column == u.row == v.row # u, v should be column vector + assert u.column == v.column == 1 # u, v should be column vector + + # Calculate + vT = v.transpose() + numerator_factor = (vT * self * u)[0, 0] + 1 + if numerator_factor == 0: + return None # It's not invertable + return self - ((self * u) * (vT * self) * (1.0 / numerator_factor)) + + +# Testing +if __name__ == "__main__": + + def test1(): + # a^(-1) + ainv = Matrix(3, 3, 0) + for i in range(3): + ainv[i, i] = 1 + print("a^(-1) is %s" % (ainv,)) + # u, v + u = Matrix(3, 1, 0) + u[0, 0], u[1, 0], u[2, 0] = 1, 2, -3 + v = Matrix(3, 1, 0) + v[0, 0], v[1, 0], v[2, 0] = 4, -2, 5 + print("u is %s" % (u,)) + print("v is %s" % (v,)) + print("uv^T is %s" % (u * v.transpose())) + # Sherman Morrison + print("(a + uv^T)^(-1) is %s" % (ainv.ShermanMorrison(u, v),)) + + def test2(): + import doctest + + doctest.testmod() + + test2() diff --git a/matrix/spiral_print.py b/matrix/spiral_print.py new file mode 100644 index 000000000000..31d9fff84bfd --- /dev/null +++ b/matrix/spiral_print.py @@ -0,0 +1,68 @@ +""" +This program print the matix in spiral form. +This problem has been solved through recursive way. + + Matrix must satisfy below conditions + i) matrix should be only one or two dimensional + ii)column of all the row should be equal +""" + + +def checkMatrix(a): + # must be + if type(a) == list and len(a) > 0: + if type(a[0]) == list: + prevLen = 0 + for i in a: + if prevLen == 0: + prevLen = len(i) + result = True + elif prevLen == len(i): + result = True + else: + result = False + else: + result = True + else: + result = False + return result + + +def spiralPrint(a): + + if checkMatrix(a) and len(a) > 0: + + matRow = len(a) + if type(a[0]) == list: + matCol = len(a[0]) + else: + for dat in a: + print(dat), + return + + # horizotal printing increasing + for i in range(0, matCol): + print(a[0][i]), + # vertical printing down + for i in range(1, matRow): + print(a[i][matCol - 1]), + # horizotal printing decreasing + if matRow > 1: + for i in range(matCol - 2, -1, -1): + print(a[matRow - 1][i]), + # vertical printing up + for i in range(matRow - 2, 0, -1): + print(a[i][0]), + remainMat = [row[1 : matCol - 1] for row in a[1 : matRow - 1]] + if len(remainMat) > 0: + spiralPrint(remainMat) + else: + return + else: + print("Not a valid matrix") + return + + +# driver code +a = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] +spiralPrint(a) diff --git a/matrix/tests/pytest.ini b/matrix/tests/pytest.ini new file mode 100644 index 000000000000..8a978b56ef8b --- /dev/null +++ b/matrix/tests/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +markers = + mat_ops: tests for matrix operations diff --git a/matrix/tests/test_matrix_operation.py b/matrix/tests/test_matrix_operation.py new file mode 100644 index 000000000000..f9f72cf59af8 --- /dev/null +++ b/matrix/tests/test_matrix_operation.py @@ -0,0 +1,117 @@ +""" +Testing here assumes that numpy and linalg is ALWAYS correct!!!! + +If running from PyCharm you can place the following line in "Additional Arguments" for the pytest run configuration +-vv -m mat_ops -p no:cacheprovider +""" + +# standard libraries +import sys +import numpy as np +import pytest +import logging + +# Custom/local libraries +from matrix import matrix_operation as matop + +mat_a = [[12, 10], [3, 9]] +mat_b = [[3, 4], [7, 4]] +mat_c = [[3, 0, 2], [2, 0, -2], [0, 1, 1]] +mat_d = [[3, 0, -2], [2, 0, 2], [0, 1, 1]] +mat_e = [[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]] +mat_f = [1] +mat_h = [2] + +logger = logging.getLogger() +logger.level = logging.DEBUG +stream_handler = logging.StreamHandler(sys.stdout) +logger.addHandler(stream_handler) + + +@pytest.mark.mat_ops +@pytest.mark.parametrize( + ("mat1", "mat2"), [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)] +) +def test_addition(mat1, mat2): + if (np.array(mat1)).shape < (2, 2) or (np.array(mat2)).shape < (2, 2): + with pytest.raises(TypeError): + logger.info(f"\n\t{test_addition.__name__} returned integer") + matop.add(mat1, mat2) + elif (np.array(mat1)).shape == (np.array(mat2)).shape: + logger.info(f"\n\t{test_addition.__name__} with same matrix dims") + act = (np.array(mat1) + np.array(mat2)).tolist() + theo = matop.add(mat1, mat2) + assert theo == act + else: + with pytest.raises(ValueError): + logger.info(f"\n\t{test_addition.__name__} with different matrix dims") + matop.add(mat1, mat2) + + +@pytest.mark.mat_ops +@pytest.mark.parametrize( + ("mat1", "mat2"), [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)] +) +def test_subtraction(mat1, mat2): + if (np.array(mat1)).shape < (2, 2) or (np.array(mat2)).shape < (2, 2): + with pytest.raises(TypeError): + logger.info(f"\n\t{test_subtraction.__name__} returned integer") + matop.subtract(mat1, mat2) + elif (np.array(mat1)).shape == (np.array(mat2)).shape: + logger.info(f"\n\t{test_subtraction.__name__} with same matrix dims") + act = (np.array(mat1) - np.array(mat2)).tolist() + theo = matop.subtract(mat1, mat2) + assert theo == act + else: + with pytest.raises(ValueError): + logger.info(f"\n\t{test_subtraction.__name__} with different matrix dims") + assert matop.subtract(mat1, mat2) + + +@pytest.mark.mat_ops +@pytest.mark.parametrize( + ("mat1", "mat2"), [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)] +) +def test_multiplication(mat1, mat2): + if (np.array(mat1)).shape < (2, 2) or (np.array(mat2)).shape < (2, 2): + logger.info(f"\n\t{test_multiplication.__name__} returned integer") + with pytest.raises(TypeError): + matop.add(mat1, mat2) + elif (np.array(mat1)).shape == (np.array(mat2)).shape: + logger.info(f"\n\t{test_multiplication.__name__} meets dim requirements") + act = (np.matmul(mat1, mat2)).tolist() + theo = matop.multiply(mat1, mat2) + assert theo == act + else: + with pytest.raises(ValueError): + logger.info( + f"\n\t{test_multiplication.__name__} does not meet dim requirements" + ) + assert matop.subtract(mat1, mat2) + + +@pytest.mark.mat_ops +def test_scalar_multiply(): + act = (3.5 * np.array(mat_a)).tolist() + theo = matop.scalar_multiply(mat_a, 3.5) + assert theo == act + + +@pytest.mark.mat_ops +def test_identity(): + act = (np.identity(5)).tolist() + theo = matop.identity(5) + assert theo == act + + +@pytest.mark.mat_ops +@pytest.mark.parametrize("mat", [mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]) +def test_transpose(mat): + if (np.array(mat)).shape < (2, 2): + with pytest.raises(TypeError): + logger.info(f"\n\t{test_transpose.__name__} returned integer") + matop.transpose(mat) + else: + act = (np.transpose(mat)).tolist() + theo = matop.transpose(mat, return_map=False) + assert theo == act diff --git a/networking_flow/ford_fulkerson.py b/networking_flow/ford_fulkerson.py index d51f1f0661b3..0028c7cc577f 100644 --- a/networking_flow/ford_fulkerson.py +++ b/networking_flow/ford_fulkerson.py @@ -4,14 +4,15 @@ (1) Start with initial flow as 0; (2) Choose augmenting path from source to sink and add path to flow; """ - + + def BFS(graph, s, t, parent): # Return True if there is node that has not iterated. - visited = [False]*len(graph) - queue=[] + visited = [False] * len(graph) + queue = [] queue.append(s) visited[s] = True - + while queue: u = queue.pop(0) for ind in range(len(graph[u])): @@ -21,36 +22,40 @@ def BFS(graph, s, t, parent): parent[ind] = u return True if visited[t] else False - + + def FordFulkerson(graph, source, sink): # This array is filled by BFS and to store path - parent = [-1]*(len(graph)) - max_flow = 0 - while BFS(graph, source, sink, parent) : + parent = [-1] * (len(graph)) + max_flow = 0 + while BFS(graph, source, sink, parent): path_flow = float("Inf") s = sink - while(s != source): + while s != source: # Find the minimum value in select path - path_flow = min (path_flow, graph[parent[s]][s]) + path_flow = min(path_flow, graph[parent[s]][s]) s = parent[s] - max_flow += path_flow + max_flow += path_flow v = sink - while(v != source): + while v != source: u = parent[v] graph[u][v] -= path_flow graph[v][u] += path_flow v = parent[v] return max_flow -graph = [[0, 16, 13, 0, 0, 0], - [0, 0, 10 ,12, 0, 0], - [0, 4, 0, 0, 14, 0], - [0, 0, 9, 0, 0, 20], - [0, 0, 0, 7, 0, 4], - [0, 0, 0, 0, 0, 0]] + +graph = [ + [0, 16, 13, 0, 0, 0], + [0, 0, 10, 12, 0, 0], + [0, 4, 0, 0, 14, 0], + [0, 0, 9, 0, 0, 20], + [0, 0, 0, 7, 0, 4], + [0, 0, 0, 0, 0, 0], +] source, sink = 0, 5 -print(FordFulkerson(graph, source, sink)) \ No newline at end of file +print(FordFulkerson(graph, source, sink)) diff --git a/networking_flow/minimum_cut.py b/networking_flow/minimum_cut.py index 8ad6e03b00c6..7773df72f8f0 100644 --- a/networking_flow/minimum_cut.py +++ b/networking_flow/minimum_cut.py @@ -1,12 +1,21 @@ # Minimum cut on Ford_Fulkerson algorithm. - + +test_graph = [ + [0, 16, 13, 0, 0, 0], + [0, 0, 10, 12, 0, 0], + [0, 4, 0, 0, 14, 0], + [0, 0, 9, 0, 0, 20], + [0, 0, 0, 7, 0, 4], + [0, 0, 0, 0, 0, 0], +] + + def BFS(graph, s, t, parent): # Return True if there is node that has not iterated. - visited = [False]*len(graph) - queue=[] - queue.append(s) + visited = [False] * len(graph) + queue = [s] visited[s] = True - + while queue: u = queue.pop(0) for ind in range(len(graph[u])): @@ -16,26 +25,30 @@ def BFS(graph, s, t, parent): parent[ind] = u return True if visited[t] else False - + + def mincut(graph, source, sink): - # This array is filled by BFS and to store path - parent = [-1]*(len(graph)) - max_flow = 0 + """This array is filled by BFS and to store path + >>> mincut(test_graph, source=0, sink=5) + [(1, 3), (4, 3), (4, 5)] + """ + parent = [-1] * (len(graph)) + max_flow = 0 res = [] - temp = [i[:] for i in graph] # Record orignial cut, copy. - while BFS(graph, source, sink, parent) : + temp = [i[:] for i in graph] # Record orignial cut, copy. + while BFS(graph, source, sink, parent): path_flow = float("Inf") s = sink - while(s != source): + while s != source: # Find the minimum value in select path - path_flow = min (path_flow, graph[parent[s]][s]) + path_flow = min(path_flow, graph[parent[s]][s]) s = parent[s] - max_flow += path_flow + max_flow += path_flow v = sink - - while(v != source): + + while v != source: u = parent[v] graph[u][v] -= path_flow graph[v][u] += path_flow @@ -44,16 +57,10 @@ def mincut(graph, source, sink): for i in range(len(graph)): for j in range(len(graph[0])): if graph[i][j] == 0 and temp[i][j] > 0: - res.append((i,j)) + res.append((i, j)) return res -graph = [[0, 16, 13, 0, 0, 0], - [0, 0, 10 ,12, 0, 0], - [0, 4, 0, 0, 14, 0], - [0, 0, 9, 0, 0, 20], - [0, 0, 0, 7, 0, 4], - [0, 0, 0, 0, 0, 0]] -source, sink = 0, 5 -print(mincut(graph, source, sink)) \ No newline at end of file +if __name__ == "__main__": + print(mincut(test_graph, source=0, sink=5)) diff --git a/neural_network/bpnn.py b/neural_network/back_propagation_neural_network.py similarity index 63% rename from neural_network/bpnn.py rename to neural_network/back_propagation_neural_network.py index 92deaee19c6e..86797694bb0a 100644 --- a/neural_network/bpnn.py +++ b/neural_network/back_propagation_neural_network.py @@ -1,7 +1,7 @@ #!/usr/bin/python # encoding=utf8 -''' +""" A Framework of Back Propagation Neural Network（BP） model @@ -17,7 +17,7 @@ Github : https://github.com/RiptideBo Date: 2017.11.23 -''' +""" import numpy as np import matplotlib.pyplot as plt @@ -26,18 +26,22 @@ def sigmoid(x): return 1 / (1 + np.exp(-1 * x)) -class DenseLayer(): - ''' + +class DenseLayer: + """ Layers of BP neural network - ''' - def __init__(self,units,activation=None,learning_rate=None,is_input_layer=False): - ''' + """ + + def __init__( + self, units, activation=None, learning_rate=None, is_input_layer=False + ): + """ common connected layer of bp network :param units: numbers of neural units :param activation: activation function :param learning_rate: learning rate for paras :param is_input_layer: whether it is input layer or not - ''' + """ self.units = units self.weight = None self.bias = None @@ -47,21 +51,21 @@ def __init__(self,units,activation=None,learning_rate=None,is_input_layer=False) self.learn_rate = learning_rate self.is_input_layer = is_input_layer - def initializer(self,back_units): - self.weight = np.asmatrix(np.random.normal(0,0.5,(self.units,back_units))) - self.bias = np.asmatrix(np.random.normal(0,0.5,self.units)).T + def initializer(self, back_units): + self.weight = np.asmatrix(np.random.normal(0, 0.5, (self.units, back_units))) + self.bias = np.asmatrix(np.random.normal(0, 0.5, self.units)).T if self.activation is None: self.activation = sigmoid def cal_gradient(self): if self.activation == sigmoid: - gradient_mat = np.dot(self.output ,(1- self.output).T) + gradient_mat = np.dot(self.output, (1 - self.output).T) gradient_activation = np.diag(np.diag(gradient_mat)) else: gradient_activation = 1 return gradient_activation - def forward_propagation(self,xdata): + def forward_propagation(self, xdata): self.xdata = xdata if self.is_input_layer: # input layer @@ -69,22 +73,22 @@ def forward_propagation(self,xdata): self.output = xdata return xdata else: - self.wx_plus_b = np.dot(self.weight,self.xdata) - self.bias + self.wx_plus_b = np.dot(self.weight, self.xdata) - self.bias self.output = self.activation(self.wx_plus_b) return self.output - def back_propagation(self,gradient): + def back_propagation(self, gradient): - gradient_activation = self.cal_gradient() # i * i 维 - gradient = np.asmatrix(np.dot(gradient.T,gradient_activation)) + gradient_activation = self.cal_gradient() # i * i 维 + gradient = np.asmatrix(np.dot(gradient.T, gradient_activation)) self._gradient_weight = np.asmatrix(self.xdata) self._gradient_bias = -1 self._gradient_x = self.weight - self.gradient_weight = np.dot(gradient.T,self._gradient_weight.T) + self.gradient_weight = np.dot(gradient.T, self._gradient_weight.T) self.gradient_bias = gradient * self._gradient_bias - self.gradient = np.dot(gradient,self._gradient_x).T + self.gradient = np.dot(gradient, self._gradient_x).T # ----------------------upgrade # -----------the Negative gradient direction -------- self.weight = self.weight - self.learn_rate * self.gradient_weight @@ -93,33 +97,34 @@ def back_propagation(self,gradient): return self.gradient -class BPNN(): - ''' +class BPNN: + """ Back Propagation Neural Network model - ''' + """ + def __init__(self): self.layers = [] self.train_mse = [] self.fig_loss = plt.figure() - self.ax_loss = self.fig_loss.add_subplot(1,1,1) + self.ax_loss = self.fig_loss.add_subplot(1, 1, 1) - def add_layer(self,layer): + def add_layer(self, layer): self.layers.append(layer) def build(self): - for i,layer in enumerate(self.layers[:]): + for i, layer in enumerate(self.layers[:]): if i < 1: layer.is_input_layer = True else: - layer.initializer(self.layers[i-1].units) + layer.initializer(self.layers[i - 1].units) def summary(self): - for i,layer in enumerate(self.layers[:]): - print('------- layer %d -------'%i) - print('weight.shape ',np.shape(layer.weight)) - print('bias.shape ',np.shape(layer.bias)) + for i, layer in enumerate(self.layers[:]): + print("------- layer %d -------" % i) + print("weight.shape ", np.shape(layer.weight)) + print("bias.shape ", np.shape(layer.bias)) - def train(self,xdata,ydata,train_round,accuracy): + def train(self, xdata, ydata, train_round, accuracy): self.train_round = train_round self.accuracy = accuracy @@ -129,8 +134,8 @@ def train(self,xdata,ydata,train_round,accuracy): for round_i in range(train_round): all_loss = 0 for row in range(x_shape[0]): - _xdata = np.asmatrix(xdata[row,:]).T - _ydata = np.asmatrix(ydata[row,:]).T + _xdata = np.asmatrix(xdata[row, :]).T + _ydata = np.asmatrix(ydata[row, :]).T # forward propagation for layer in self.layers: @@ -144,40 +149,49 @@ def train(self,xdata,ydata,train_round,accuracy): for layer in self.layers[:0:-1]: gradient = layer.back_propagation(gradient) - mse = all_loss/x_shape[0] + mse = all_loss / x_shape[0] self.train_mse.append(mse) self.plot_loss() if mse < self.accuracy: - print('----达到精度----') + print("----达到精度----") return mse - def cal_loss(self,ydata,ydata_): - self.loss = np.sum(np.power((ydata - ydata_),2)) + def cal_loss(self, ydata, ydata_): + self.loss = np.sum(np.power((ydata - ydata_), 2)) self.loss_gradient = 2 * (ydata_ - ydata) # vector (shape is the same as _ydata.shape) - return self.loss,self.loss_gradient + return self.loss, self.loss_gradient def plot_loss(self): if self.ax_loss.lines: self.ax_loss.lines.remove(self.ax_loss.lines[0]) - self.ax_loss.plot(self.train_mse, 'r-') + self.ax_loss.plot(self.train_mse, "r-") plt.ion() - plt.xlabel('step') - plt.ylabel('loss') + plt.xlabel("step") + plt.ylabel("loss") plt.show() plt.pause(0.1) - - def example(): - x = np.random.randn(10,10) - y = np.asarray([[0.8,0.4],[0.4,0.3],[0.34,0.45],[0.67,0.32], - [0.88,0.67],[0.78,0.77],[0.55,0.66],[0.55,0.43],[0.54,0.1], - [0.1,0.5]]) + x = np.random.randn(10, 10) + y = np.asarray( + [ + [0.8, 0.4], + [0.4, 0.3], + [0.34, 0.45], + [0.67, 0.32], + [0.88, 0.67], + [0.78, 0.77], + [0.55, 0.66], + [0.55, 0.43], + [0.54, 0.1], + [0.1, 0.5], + ] + ) model = BPNN() model.add_layer(DenseLayer(10)) @@ -189,7 +203,8 @@ def example(): model.summary() - model.train(xdata=x,ydata=y,train_round=100,accuracy=0.01) + model.train(xdata=x, ydata=y, train_round=100, accuracy=0.01) + -if __name__ == '__main__': +if __name__ == "__main__": example() diff --git a/neural_network/convolution_neural_network.py b/neural_network/convolution_neural_network.py index 0dca2bc485d1..9448671abace 100644 --- a/neural_network/convolution_neural_network.py +++ b/neural_network/convolution_neural_network.py @@ -1,6 +1,6 @@ -#-*- coding: utf-8 -*- +# -*- coding: utf-8 -*- -''' +""" - - - - - -- - - - - - - - - - - - - - - - - - - - - - - Name - - CNN - Convolution Neural Network For Photo Recognizing Goal - - Recognize Handing Writting Word Photo @@ -14,16 +14,17 @@ Github: 245885195@qq.com Date: 2017.9.20 - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - ''' -from __future__ import print_function - +""" +import pickle import numpy as np import matplotlib.pyplot as plt -class CNN(): - def __init__(self,conv1_get,size_p1,bp_num1,bp_num2,bp_num3,rate_w=0.2,rate_t=0.2): - ''' +class CNN: + def __init__( + self, conv1_get, size_p1, bp_num1, bp_num2, bp_num3, rate_w=0.2, rate_t=0.2 + ): + """ :param conv1_get: [a,c,d]，size, number, step of convolution kernel :param size_p1: pooling size :param bp_num1: units number of flatten layer @@ -31,7 +32,7 @@ def __init__(self,conv1_get,size_p1,bp_num1,bp_num2,bp_num3,rate_w=0.2,rate_t=0. :param bp_num3: units number of output layer :param rate_w: rate of weight learning :param rate_t: rate of threshold learning - ''' + """ self.num_bp1 = bp_num1 self.num_bp2 = bp_num2 self.num_bp3 = bp_num3 @@ -40,208 +41,246 @@ def __init__(self,conv1_get,size_p1,bp_num1,bp_num2,bp_num3,rate_w=0.2,rate_t=0. self.size_pooling1 = size_p1 self.rate_weight = rate_w self.rate_thre = rate_t - self.w_conv1 = [np.mat(-1*np.random.rand(self.conv1[0],self.conv1[0])+0.5) for i in range(self.conv1[1])] + self.w_conv1 = [ + np.mat(-1 * np.random.rand(self.conv1[0], self.conv1[0]) + 0.5) + for i in range(self.conv1[1]) + ] self.wkj = np.mat(-1 * np.random.rand(self.num_bp3, self.num_bp2) + 0.5) - self.vji = np.mat(-1*np.random.rand(self.num_bp2, self.num_bp1)+0.5) - self.thre_conv1 = -2*np.random.rand(self.conv1[1])+1 - self.thre_bp2 = -2*np.random.rand(self.num_bp2)+1 - self.thre_bp3 = -2*np.random.rand(self.num_bp3)+1 - + self.vji = np.mat(-1 * np.random.rand(self.num_bp2, self.num_bp1) + 0.5) + self.thre_conv1 = -2 * np.random.rand(self.conv1[1]) + 1 + self.thre_bp2 = -2 * np.random.rand(self.num_bp2) + 1 + self.thre_bp3 = -2 * np.random.rand(self.num_bp3) + 1 - def save_model(self,save_path): - #save model dict with pickle - import pickle - model_dic = {'num_bp1':self.num_bp1, - 'num_bp2':self.num_bp2, - 'num_bp3':self.num_bp3, - 'conv1':self.conv1, - 'step_conv1':self.step_conv1, - 'size_pooling1':self.size_pooling1, - 'rate_weight':self.rate_weight, - 'rate_thre':self.rate_thre, - 'w_conv1':self.w_conv1, - 'wkj':self.wkj, - 'vji':self.vji, - 'thre_conv1':self.thre_conv1, - 'thre_bp2':self.thre_bp2, - 'thre_bp3':self.thre_bp3} - with open(save_path, 'wb') as f: + def save_model(self, save_path): + # save model dict with pickle + model_dic = { + "num_bp1": self.num_bp1, + "num_bp2": self.num_bp2, + "num_bp3": self.num_bp3, + "conv1": self.conv1, + "step_conv1": self.step_conv1, + "size_pooling1": self.size_pooling1, + "rate_weight": self.rate_weight, + "rate_thre": self.rate_thre, + "w_conv1": self.w_conv1, + "wkj": self.wkj, + "vji": self.vji, + "thre_conv1": self.thre_conv1, + "thre_bp2": self.thre_bp2, + "thre_bp3": self.thre_bp3, + } + with open(save_path, "wb") as f: pickle.dump(model_dic, f) - print('Model saved： %s'% save_path) + print("Model saved： %s" % save_path) @classmethod - def ReadModel(cls,model_path): - #read saved model - import pickle - with open(model_path, 'rb') as f: + def ReadModel(cls, model_path): + # read saved model + with open(model_path, "rb") as f: model_dic = pickle.load(f) - conv_get= model_dic.get('conv1') - conv_get.append(model_dic.get('step_conv1')) - size_p1 = model_dic.get('size_pooling1') - bp1 = model_dic.get('num_bp1') - bp2 = model_dic.get('num_bp2') - bp3 = model_dic.get('num_bp3') - r_w = model_dic.get('rate_weight') - r_t = model_dic.get('rate_thre') - #create model instance - conv_ins = CNN(conv_get,size_p1,bp1,bp2,bp3,r_w,r_t) - #modify model parameter - conv_ins.w_conv1 = model_dic.get('w_conv1') - conv_ins.wkj = model_dic.get('wkj') - conv_ins.vji = model_dic.get('vji') - conv_ins.thre_conv1 = model_dic.get('thre_conv1') - conv_ins.thre_bp2 = model_dic.get('thre_bp2') - conv_ins.thre_bp3 = model_dic.get('thre_bp3') + conv_get = model_dic.get("conv1") + conv_get.append(model_dic.get("step_conv1")) + size_p1 = model_dic.get("size_pooling1") + bp1 = model_dic.get("num_bp1") + bp2 = model_dic.get("num_bp2") + bp3 = model_dic.get("num_bp3") + r_w = model_dic.get("rate_weight") + r_t = model_dic.get("rate_thre") + # create model instance + conv_ins = CNN(conv_get, size_p1, bp1, bp2, bp3, r_w, r_t) + # modify model parameter + conv_ins.w_conv1 = model_dic.get("w_conv1") + conv_ins.wkj = model_dic.get("wkj") + conv_ins.vji = model_dic.get("vji") + conv_ins.thre_conv1 = model_dic.get("thre_conv1") + conv_ins.thre_bp2 = model_dic.get("thre_bp2") + conv_ins.thre_bp3 = model_dic.get("thre_bp3") return conv_ins + def sig(self, x): + return 1 / (1 + np.exp(-1 * x)) - def sig(self,x): - return 1 / (1 + np.exp(-1*x)) - - def do_round(self,x): + def do_round(self, x): return round(x, 3) - def convolute(self,data,convs,w_convs,thre_convs,conv_step): - #convolution process + def convolute(self, data, convs, w_convs, thre_convs, conv_step): + # convolution process size_conv = convs[0] - num_conv =convs[1] + num_conv = convs[1] size_data = np.shape(data)[0] - #get the data slice of original image data, data_focus + # get the data slice of original image data, data_focus data_focus = [] for i_focus in range(0, size_data - size_conv + 1, conv_step): for j_focus in range(0, size_data - size_conv + 1, conv_step): - focus = data[i_focus:i_focus + size_conv, j_focus:j_focus + size_conv] + focus = data[ + i_focus : i_focus + size_conv, j_focus : j_focus + size_conv + ] data_focus.append(focus) - #caculate the feature map of every single kernel, and saved as list of matrix + # caculate the feature map of every single kernel, and saved as list of matrix data_featuremap = [] Size_FeatureMap = int((size_data - size_conv) / conv_step + 1) for i_map in range(num_conv): featuremap = [] for i_focus in range(len(data_focus)): - net_focus = np.sum(np.multiply(data_focus[i_focus], w_convs[i_map])) - thre_convs[i_map] + net_focus = ( + np.sum(np.multiply(data_focus[i_focus], w_convs[i_map])) + - thre_convs[i_map] + ) featuremap.append(self.sig(net_focus)) - featuremap = np.asmatrix(featuremap).reshape(Size_FeatureMap, Size_FeatureMap) + featuremap = np.asmatrix(featuremap).reshape( + Size_FeatureMap, Size_FeatureMap + ) data_featuremap.append(featuremap) - #expanding the data slice to One dimenssion + # expanding the data slice to One dimenssion focus1_list = [] for each_focus in data_focus: focus1_list.extend(self.Expand_Mat(each_focus)) focus_list = np.asarray(focus1_list) - return focus_list,data_featuremap + return focus_list, data_featuremap - def pooling(self,featuremaps,size_pooling,type='average_pool'): - #pooling process + def pooling(self, featuremaps, size_pooling, type="average_pool"): + # pooling process size_map = len(featuremaps[0]) - size_pooled = int(size_map/size_pooling) + size_pooled = int(size_map / size_pooling) featuremap_pooled = [] for i_map in range(len(featuremaps)): map = featuremaps[i_map] map_pooled = [] - for i_focus in range(0,size_map,size_pooling): + for i_focus in range(0, size_map, size_pooling): for j_focus in range(0, size_map, size_pooling): - focus = map[i_focus:i_focus + size_pooling, j_focus:j_focus + size_pooling] - if type == 'average_pool': - #average pooling + focus = map[ + i_focus : i_focus + size_pooling, + j_focus : j_focus + size_pooling, + ] + if type == "average_pool": + # average pooling map_pooled.append(np.average(focus)) - elif type == 'max_pooling': - #max pooling + elif type == "max_pooling": + # max pooling map_pooled.append(np.max(focus)) - map_pooled = np.asmatrix(map_pooled).reshape(size_pooled,size_pooled) + map_pooled = np.asmatrix(map_pooled).reshape(size_pooled, size_pooled) featuremap_pooled.append(map_pooled) return featuremap_pooled - def _expand(self,datas): - #expanding three dimension data to one dimension list + def _expand(self, datas): + # expanding three dimension data to one dimension list data_expanded = [] for i in range(len(datas)): shapes = np.shape(datas[i]) - data_listed = datas[i].reshape(1,shapes[0]*shapes[1]) + data_listed = datas[i].reshape(1, shapes[0] * shapes[1]) data_listed = data_listed.getA().tolist()[0] data_expanded.extend(data_listed) data_expanded = np.asarray(data_expanded) return data_expanded - def _expand_mat(self,data_mat): - #expanding matrix to one dimension list + def _expand_mat(self, data_mat): + # expanding matrix to one dimension list data_mat = np.asarray(data_mat) shapes = np.shape(data_mat) - data_expanded = data_mat.reshape(1,shapes[0]*shapes[1]) + data_expanded = data_mat.reshape(1, shapes[0] * shapes[1]) return data_expanded - def _calculate_gradient_from_pool(self,out_map,pd_pool,num_map,size_map,size_pooling): - ''' + def _calculate_gradient_from_pool( + self, out_map, pd_pool, num_map, size_map, size_pooling + ): + """ calcluate the gradient from the data slice of pool layer pd_pool: list of matrix out_map: the shape of data slice(size_map*size_map) return: pd_all: list of matrix, [num, size_map, size_map] - ''' + """ pd_all = [] i_pool = 0 for i_map in range(num_map): pd_conv1 = np.ones((size_map, size_map)) for i in range(0, size_map, size_pooling): for j in range(0, size_map, size_pooling): - pd_conv1[i:i + size_pooling, j:j + size_pooling] = pd_pool[i_pool] + pd_conv1[i : i + size_pooling, j : j + size_pooling] = pd_pool[ + i_pool + ] i_pool = i_pool + 1 - pd_conv2 = np.multiply(pd_conv1,np.multiply(out_map[i_map],(1-out_map[i_map]))) + pd_conv2 = np.multiply( + pd_conv1, np.multiply(out_map[i_map], (1 - out_map[i_map])) + ) pd_all.append(pd_conv2) return pd_all - def trian(self,patterns,datas_train, datas_teach, n_repeat, error_accuracy,draw_e = bool): - #model traning - print('----------------------Start Training-------------------------') - print((' - - Shape: Train_Data ',np.shape(datas_train))) - print((' - - Shape: Teach_Data ',np.shape(datas_teach))) + def train( + self, patterns, datas_train, datas_teach, n_repeat, error_accuracy, draw_e=bool + ): + # model traning + print("----------------------Start Training-------------------------") + print((" - - Shape: Train_Data ", np.shape(datas_train))) + print((" - - Shape: Teach_Data ", np.shape(datas_teach))) rp = 0 all_mse = [] - mse = 10000 + mse = 10000 while rp < n_repeat and mse >= error_accuracy: alle = 0 - print('-------------Learning Time %d--------------'%rp) + print("-------------Learning Time %d--------------" % rp) for p in range(len(datas_train)): - #print('------------Learning Image: %d--------------'%p) + # print('------------Learning Image: %d--------------'%p) data_train = np.asmatrix(datas_train[p]) data_teach = np.asarray(datas_teach[p]) - data_focus1,data_conved1 = self.convolute(data_train,self.conv1,self.w_conv1, - self.thre_conv1,conv_step=self.step_conv1) - data_pooled1 = self.pooling(data_conved1,self.size_pooling1) + data_focus1, data_conved1 = self.convolute( + data_train, + self.conv1, + self.w_conv1, + self.thre_conv1, + conv_step=self.step_conv1, + ) + data_pooled1 = self.pooling(data_conved1, self.size_pooling1) shape_featuremap1 = np.shape(data_conved1) - ''' + """ print(' -----original shape ', np.shape(data_train)) print(' ---- after convolution ',np.shape(data_conv1)) print(' -----after pooling ',np.shape(data_pooled1)) - ''' + """ data_bp_input = self._expand(data_pooled1) bp_out1 = data_bp_input - bp_net_j = np.dot(bp_out1,self.vji.T) - self.thre_bp2 + bp_net_j = np.dot(bp_out1, self.vji.T) - self.thre_bp2 bp_out2 = self.sig(bp_net_j) - bp_net_k = np.dot(bp_out2 ,self.wkj.T) - self.thre_bp3 + bp_net_k = np.dot(bp_out2, self.wkj.T) - self.thre_bp3 bp_out3 = self.sig(bp_net_k) - #--------------Model Leaning ------------------------ + # --------------Model Leaning ------------------------ # calcluate error and gradient--------------- - pd_k_all = np.multiply((data_teach - bp_out3), np.multiply(bp_out3, (1 - bp_out3))) - pd_j_all = np.multiply(np.dot(pd_k_all,self.wkj), np.multiply(bp_out2, (1 - bp_out2))) - pd_i_all = np.dot(pd_j_all,self.vji) + pd_k_all = np.multiply( + (data_teach - bp_out3), np.multiply(bp_out3, (1 - bp_out3)) + ) + pd_j_all = np.multiply( + np.dot(pd_k_all, self.wkj), np.multiply(bp_out2, (1 - bp_out2)) + ) + pd_i_all = np.dot(pd_j_all, self.vji) - pd_conv1_pooled = pd_i_all / (self.size_pooling1*self.size_pooling1) + pd_conv1_pooled = pd_i_all / (self.size_pooling1 * self.size_pooling1) pd_conv1_pooled = pd_conv1_pooled.T.getA().tolist() - pd_conv1_all = self._calculate_gradient_from_pool(data_conved1,pd_conv1_pooled,shape_featuremap1[0], - shape_featuremap1[1],self.size_pooling1) - #weight and threshold learning process--------- - #convolution layer + pd_conv1_all = self._calculate_gradient_from_pool( + data_conved1, + pd_conv1_pooled, + shape_featuremap1[0], + shape_featuremap1[1], + self.size_pooling1, + ) + # weight and threshold learning process--------- + # convolution layer for k_conv in range(self.conv1[1]): pd_conv_list = self._expand_mat(pd_conv1_all[k_conv]) - delta_w = self.rate_weight * np.dot(pd_conv_list,data_focus1) + delta_w = self.rate_weight * np.dot(pd_conv_list, data_focus1) - self.w_conv1[k_conv] = self.w_conv1[k_conv] + delta_w.reshape((self.conv1[0],self.conv1[0])) + self.w_conv1[k_conv] = self.w_conv1[k_conv] + delta_w.reshape( + (self.conv1[0], self.conv1[0]) + ) - self.thre_conv1[k_conv] = self.thre_conv1[k_conv] - np.sum(pd_conv1_all[k_conv]) * self.rate_thre - #all connected layer + self.thre_conv1[k_conv] = ( + self.thre_conv1[k_conv] + - np.sum(pd_conv1_all[k_conv]) * self.rate_thre + ) + # all connected layer self.wkj = self.wkj + pd_k_all.T * bp_out2 * self.rate_weight self.vji = self.vji + pd_j_all.T * bp_out1 * self.rate_weight self.thre_bp3 = self.thre_bp3 - pd_k_all * self.rate_thre @@ -249,34 +288,41 @@ def trian(self,patterns,datas_train, datas_teach, n_repeat, error_accuracy,draw_ # calculate the sum error of all single image errors = np.sum(abs((data_teach - bp_out3))) alle = alle + errors - #print(' ----Teach ',data_teach) - #print(' ----BP_output ',bp_out3) + # print(' ----Teach ',data_teach) + # print(' ----BP_output ',bp_out3) rp = rp + 1 - mse = alle/patterns + mse = alle / patterns all_mse.append(mse) + def draw_error(): yplot = [error_accuracy for i in range(int(n_repeat * 1.2))] - plt.plot(all_mse, '+-') - plt.plot(yplot, 'r--') - plt.xlabel('Learning Times') - plt.ylabel('All_mse') + plt.plot(all_mse, "+-") + plt.plot(yplot, "r--") + plt.xlabel("Learning Times") + plt.ylabel("All_mse") plt.grid(True, alpha=0.5) plt.show() - print('------------------Training Complished---------------------') - print((' - - Training epoch: ', rp, ' - - Mse: %.6f' % mse)) + + print("------------------Training Complished---------------------") + print((" - - Training epoch: ", rp, " - - Mse: %.6f" % mse)) if draw_e: draw_error() return mse - def predict(self,datas_test): - #model predict + def predict(self, datas_test): + # model predict produce_out = [] - print('-------------------Start Testing-------------------------') - print((' - - Shape: Test_Data ',np.shape(datas_test))) + print("-------------------Start Testing-------------------------") + print((" - - Shape: Test_Data ", np.shape(datas_test))) for p in range(len(datas_test)): data_test = np.asmatrix(datas_test[p]) - data_focus1, data_conved1 = self.convolute(data_test, self.conv1, self.w_conv1, - self.thre_conv1, conv_step=self.step_conv1) + data_focus1, data_conved1 = self.convolute( + data_test, + self.conv1, + self.w_conv1, + self.thre_conv1, + conv_step=self.step_conv1, + ) data_pooled1 = self.pooling(data_conved1, self.size_pooling1) data_bp_input = self._expand(data_pooled1) @@ -286,21 +332,25 @@ def predict(self,datas_test): bp_net_k = bp_out2 * self.wkj.T - self.thre_bp3 bp_out3 = self.sig(bp_net_k) produce_out.extend(bp_out3.getA().tolist()) - res = [list(map(self.do_round,each)) for each in produce_out] + res = [list(map(self.do_round, each)) for each in produce_out] return np.asarray(res) - def convolution(self,data): - #return the data of image after convoluting process so we can check it out + def convolution(self, data): + # return the data of image after convoluting process so we can check it out data_test = np.asmatrix(data) - data_focus1, data_conved1 = self.convolute(data_test, self.conv1, self.w_conv1, - self.thre_conv1, conv_step=self.step_conv1) + data_focus1, data_conved1 = self.convolute( + data_test, + self.conv1, + self.w_conv1, + self.thre_conv1, + conv_step=self.step_conv1, + ) data_pooled1 = self.pooling(data_conved1, self.size_pooling1) - return data_conved1,data_pooled1 + return data_conved1, data_pooled1 -if __name__ == '__main__': - pass - ''' +if __name__ == "__main__": + """ I will put the example on other file - ''' \ No newline at end of file + """ diff --git a/neural_network/fcn.ipynb b/neural_network/fcn.ipynb deleted file mode 100644 index a8bcf4beeea1..000000000000 --- a/neural_network/fcn.ipynb +++ /dev/null @@ -1,327 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Standard (Fully Connected) Neural Network" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "#Use in Markup cell type\n", - "#![alt text](imagename.png \"Title\") " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Implementing Fully connected Neural Net" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Loading Required packages and Data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - } - ], - "source": [ - "###1. Load Data and Splot Data\n", - "from keras.datasets import mnist\n", - "from keras.models import Sequential \n", - "from keras.layers.core import Dense, Activation\n", - "from keras.utils import np_utils\n", - "(X_train, Y_train), (X_test, Y_test) = mnist.load_data()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Preprocessing" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "n = 10 # how many digits we will display\n", - "plt.figure(figsize=(20, 4))\n", - "for i in range(n):\n", - " # display original\n", - " ax = plt.subplot(2, n, i + 1)\n", - " plt.imshow(X_test[i].reshape(28, 28))\n", - " plt.gray()\n", - " ax.get_xaxis().set_visible(False)\n", - " ax.get_yaxis().set_visible(False)\n", - "plt.show()\n", - "plt.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Previous X_train shape: (60000, 28, 28) \n", - "Previous Y_train shape:(60000,)\n", - "New X_train shape: (60000, 784) \n", - "New Y_train shape:(60000, 10)\n" - ] - } - ], - "source": [ - "print(\"Previous X_train shape: {} \\nPrevious Y_train shape:{}\".format(X_train.shape, Y_train.shape))\n", - "X_train = X_train.reshape(60000, 784) \n", - "X_test = X_test.reshape(10000, 784)\n", - "X_train = X_train.astype('float32') \n", - "X_test = X_test.astype('float32') \n", - "X_train /= 255 \n", - "X_test /= 255\n", - "classes = 10\n", - "Y_train = np_utils.to_categorical(Y_train, classes) \n", - "Y_test = np_utils.to_categorical(Y_test, classes)\n", - "print(\"New X_train shape: {} \\nNew Y_train shape:{}\".format(X_train.shape, Y_train.shape))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Setting up parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "input_size = 784\n", - "batch_size = 200 \n", - "hidden1 = 400\n", - "hidden2 = 20\n", - "epochs = 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Building the FCN Model" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "dense_1 (Dense) (None, 400) 314000 \n", - "_________________________________________________________________\n", - "dense_2 (Dense) (None, 20) 8020 \n", - "_________________________________________________________________\n", - "dense_3 (Dense) (None, 10) 210 \n", - "=================================================================\n", - "Total params: 322,230\n", - "Trainable params: 322,230\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "###4.Build the model\n", - "model = Sequential() \n", - "model.add(Dense(hidden1, input_dim=input_size, activation='relu'))\n", - "# output = relu (dot (W, input) + bias)\n", - "model.add(Dense(hidden2, activation='relu'))\n", - "model.add(Dense(classes, activation='softmax')) \n", - "\n", - "# Compilation\n", - "model.compile(loss='categorical_crossentropy', \n", - " metrics=['accuracy'], optimizer='sgd')\n", - "model.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Training The Model" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/10\n", - " - 12s - loss: 1.4482 - acc: 0.6251\n", - "Epoch 2/10\n", - " - 3s - loss: 0.6239 - acc: 0.8482\n", - "Epoch 3/10\n", - " - 3s - loss: 0.4582 - acc: 0.8798\n", - "Epoch 4/10\n", - " - 3s - loss: 0.3941 - acc: 0.8936\n", - "Epoch 5/10\n", - " - 3s - loss: 0.3579 - acc: 0.9011\n", - "Epoch 6/10\n", - " - 4s - loss: 0.3328 - acc: 0.9070\n", - "Epoch 7/10\n", - " - 3s - loss: 0.3138 - acc: 0.9118\n", - "Epoch 8/10\n", - " - 3s - loss: 0.2980 - acc: 0.9157\n", - "Epoch 9/10\n", - " - 3s - loss: 0.2849 - acc: 0.9191\n", - "Epoch 10/10\n", - " - 3s - loss: 0.2733 - acc: 0.9223\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Fitting on Data\n", - "model.fit(X_train, Y_train, batch_size=batch_size, epochs=10, verbose=2)\n", - "###5.Test " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "#### Testing The Model" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10000/10000 [==============================] - 1s 121us/step\n", - "\n", - "Test accuracy: 0.9257\n", - "[0 6 9 0 1 5 9 7 3 4]\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "score = model.evaluate(X_test, Y_test, verbose=1)\n", - "print('\\n''Test accuracy:', score[1])\n", - "mask = range(10,20)\n", - "X_valid = X_test[mask]\n", - "y_pred = model.predict_classes(X_valid)\n", - "print(y_pred)\n", - "plt.figure(figsize=(20, 4))\n", - "for i in range(n):\n", - " # display original\n", - " ax = plt.subplot(2, n, i + 1)\n", - " plt.imshow(X_valid[i].reshape(28, 28))\n", - " plt.gray()\n", - " ax.get_xaxis().set_visible(False)\n", - " ax.get_yaxis().set_visible(False)\n", - "plt.show()\n", - "plt.close()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/neural_network/gan.py b/neural_network/gan.py new file mode 100644 index 000000000000..edfff420547b --- /dev/null +++ b/neural_network/gan.py @@ -0,0 +1,391 @@ +import matplotlib.gridspec as gridspec +import matplotlib.pyplot as plt +import numpy as np +from sklearn.utils import shuffle +import input_data + +random_numer = 42 + +np.random.seed(random_numer) +def ReLu(x): + mask = (x>0) * 1.0 + return mask *x +def d_ReLu(x): + mask = (x>0) * 1.0 + return mask + +def arctan(x): + return np.arctan(x) +def d_arctan(x): + return 1 / (1 + x ** 2) + +def log(x): + return 1 / ( 1+ np.exp(-1*x)) +def d_log(x): + return log(x) * (1 - log(x)) + +def tanh(x): + return np.tanh(x) +def d_tanh(x): + return 1 - np.tanh(x) ** 2 + +def plot(samples): + fig = plt.figure(figsize=(4, 4)) + gs = gridspec.GridSpec(4, 4) + gs.update(wspace=0.05, hspace=0.05) + + for i, sample in enumerate(samples): + ax = plt.subplot(gs[i]) + plt.axis('off') + ax.set_xticklabels([]) + ax.set_yticklabels([]) + ax.set_aspect('equal') + plt.imshow(sample.reshape(28, 28), cmap='Greys_r') + + return fig + + + +# 1. Load Data and declare hyper +print('--------- Load Data ----------') +mnist = input_data.read_data_sets('MNIST_data', one_hot=False) +temp = mnist.test +images, labels = temp.images, temp.labels +images, labels = shuffle(np.asarray(images),np.asarray(labels)) +num_epoch = 10 +learing_rate = 0.00009 +G_input = 100 +hidden_input,hidden_input2,hidden_input3 = 128,256,346 +hidden_input4,hidden_input5,hidden_input6 = 480,560,686 + + + +print('--------- Declare Hyper Parameters ----------') +# 2. Declare Weights +D_W1 = np.random.normal(size=(784,hidden_input),scale=(1. / np.sqrt(784 / 2.))) *0.002 +# D_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002 +D_b1 = np.zeros(hidden_input) + +D_W2 = np.random.normal(size=(hidden_input,1),scale=(1. / np.sqrt(hidden_input / 2.))) *0.002 +# D_b2 = np.random.normal(size=(1),scale=(1. / np.sqrt(1 / 2.))) *0.002 +D_b2 = np.zeros(1) + + +G_W1 = np.random.normal(size=(G_input,hidden_input),scale=(1. / np.sqrt(G_input / 2.))) *0.002 +# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002 +G_b1 = np.zeros(hidden_input) + +G_W2 = np.random.normal(size=(hidden_input,hidden_input2),scale=(1. / np.sqrt(hidden_input / 2.))) *0.002 +# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002 +G_b2 = np.zeros(hidden_input2) + +G_W3 = np.random.normal(size=(hidden_input2,hidden_input3),scale=(1. / np.sqrt(hidden_input2 / 2.))) *0.002 +# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002 +G_b3 = np.zeros(hidden_input3) + +G_W4 = np.random.normal(size=(hidden_input3,hidden_input4),scale=(1. / np.sqrt(hidden_input3 / 2.))) *0.002 +# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002 +G_b4 = np.zeros(hidden_input4) + +G_W5 = np.random.normal(size=(hidden_input4,hidden_input5),scale=(1. / np.sqrt(hidden_input4 / 2.))) *0.002 +# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002 +G_b5 = np.zeros(hidden_input5) + +G_W6 = np.random.normal(size=(hidden_input5,hidden_input6),scale=(1. / np.sqrt(hidden_input5 / 2.))) *0.002 +# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002 +G_b6 = np.zeros(hidden_input6) + +G_W7 = np.random.normal(size=(hidden_input6,784),scale=(1. / np.sqrt(hidden_input6 / 2.))) *0.002 +# G_b2 = np.random.normal(size=(784),scale=(1. / np.sqrt(784 / 2.))) *0.002 +G_b7 = np.zeros(784) + +# 3. For Adam Optimzier +v1,m1 = 0,0 +v2,m2 = 0,0 +v3,m3 = 0,0 +v4,m4 = 0,0 + +v5,m5 = 0,0 +v6,m6 = 0,0 +v7,m7 = 0,0 +v8,m8 = 0,0 +v9,m9 = 0,0 +v10,m10 = 0,0 +v11,m11 = 0,0 +v12,m12 = 0,0 + +v13,m13 = 0,0 +v14,m14 = 0,0 + +v15,m15 = 0,0 +v16,m16 = 0,0 + +v17,m17 = 0,0 +v18,m18 = 0,0 + + +beta_1,beta_2,eps = 0.9,0.999,0.00000001 + +print('--------- Started Training ----------') +for iter in range(num_epoch): + + random_int = np.random.randint(len(images) - 5) + current_image = np.expand_dims(images[random_int],axis=0) + + # Func: Generate The first Fake Data + Z = np.random.uniform(-1., 1., size=[1, G_input]) + Gl1 = Z.dot(G_W1) + G_b1 + Gl1A = arctan(Gl1) + Gl2 = Gl1A.dot(G_W2) + G_b2 + Gl2A = ReLu(Gl2) + Gl3 = Gl2A.dot(G_W3) + G_b3 + Gl3A = arctan(Gl3) + + Gl4 = Gl3A.dot(G_W4) + G_b4 + Gl4A = ReLu(Gl4) + Gl5 = Gl4A.dot(G_W5) + G_b5 + Gl5A = tanh(Gl5) + Gl6 = Gl5A.dot(G_W6) + G_b6 + Gl6A = ReLu(Gl6) + Gl7 = Gl6A.dot(G_W7) + G_b7 + + current_fake_data = log(Gl7) + + # Func: Forward Feed for Real data + Dl1_r = current_image.dot(D_W1) + D_b1 + Dl1_rA = ReLu(Dl1_r) + Dl2_r = Dl1_rA.dot(D_W2) + D_b2 + Dl2_rA = log(Dl2_r) + + # Func: Forward Feed for Fake Data + Dl1_f = current_fake_data.dot(D_W1) + D_b1 + Dl1_fA = ReLu(Dl1_f) + Dl2_f = Dl1_fA.dot(D_W2) + D_b2 + Dl2_fA = log(Dl2_f) + + # Func: Cost D + D_cost = -np.log(Dl2_rA) + np.log(1.0- Dl2_fA) + + # Func: Gradient + grad_f_w2_part_1 = 1/(1.0- Dl2_fA) + grad_f_w2_part_2 = d_log(Dl2_f) + grad_f_w2_part_3 = Dl1_fA + grad_f_w2 = grad_f_w2_part_3.T.dot(grad_f_w2_part_1 * grad_f_w2_part_2) + grad_f_b2 = grad_f_w2_part_1 * grad_f_w2_part_2 + + grad_f_w1_part_1 = (grad_f_w2_part_1 * grad_f_w2_part_2).dot(D_W2.T) + grad_f_w1_part_2 = d_ReLu(Dl1_f) + grad_f_w1_part_3 = current_fake_data + grad_f_w1 = grad_f_w1_part_3.T.dot(grad_f_w1_part_1 * grad_f_w1_part_2) + grad_f_b1 = grad_f_w1_part_1 * grad_f_w1_part_2 + + grad_r_w2_part_1 = - 1/Dl2_rA + grad_r_w2_part_2 = d_log(Dl2_r) + grad_r_w2_part_3 = Dl1_rA + grad_r_w2 = grad_r_w2_part_3.T.dot(grad_r_w2_part_1 * grad_r_w2_part_2) + grad_r_b2 = grad_r_w2_part_1 * grad_r_w2_part_2 + + grad_r_w1_part_1 = (grad_r_w2_part_1 * grad_r_w2_part_2).dot(D_W2.T) + grad_r_w1_part_2 = d_ReLu(Dl1_r) + grad_r_w1_part_3 = current_image + grad_r_w1 = grad_r_w1_part_3.T.dot(grad_r_w1_part_1 * grad_r_w1_part_2) + grad_r_b1 = grad_r_w1_part_1 * grad_r_w1_part_2 + + grad_w1 =grad_f_w1 + grad_r_w1 + grad_b1 =grad_f_b1 + grad_r_b1 + + grad_w2 =grad_f_w2 + grad_r_w2 + grad_b2 =grad_f_b2 + grad_r_b2 + + # ---- Update Gradient ---- + m1 = beta_1 * m1 + (1 - beta_1) * grad_w1 + v1 = beta_2 * v1 + (1 - beta_2) * grad_w1 ** 2 + + m2 = beta_1 * m2 + (1 - beta_1) * grad_b1 + v2 = beta_2 * v2 + (1 - beta_2) * grad_b1 ** 2 + + m3 = beta_1 * m3 + (1 - beta_1) * grad_w2 + v3 = beta_2 * v3 + (1 - beta_2) * grad_w2 ** 2 + + m4 = beta_1 * m4 + (1 - beta_1) * grad_b2 + v4 = beta_2 * v4 + (1 - beta_2) * grad_b2 ** 2 + + D_W1 = D_W1 - (learing_rate / (np.sqrt(v1 /(1-beta_2) ) + eps)) * (m1/(1-beta_1)) + D_b1 = D_b1 - (learing_rate / (np.sqrt(v2 /(1-beta_2) ) + eps)) * (m2/(1-beta_1)) + + D_W2 = D_W2 - (learing_rate / (np.sqrt(v3 /(1-beta_2) ) + eps)) * (m3/(1-beta_1)) + D_b2 = D_b2 - (learing_rate / (np.sqrt(v4 /(1-beta_2) ) + eps)) * (m4/(1-beta_1)) + + # Func: Forward Feed for G + Z = np.random.uniform(-1., 1., size=[1, G_input]) + Gl1 = Z.dot(G_W1) + G_b1 + Gl1A = arctan(Gl1) + Gl2 = Gl1A.dot(G_W2) + G_b2 + Gl2A = ReLu(Gl2) + Gl3 = Gl2A.dot(G_W3) + G_b3 + Gl3A = arctan(Gl3) + + Gl4 = Gl3A.dot(G_W4) + G_b4 + Gl4A = ReLu(Gl4) + Gl5 = Gl4A.dot(G_W5) + G_b5 + Gl5A = tanh(Gl5) + Gl6 = Gl5A.dot(G_W6) + G_b6 + Gl6A = ReLu(Gl6) + Gl7 = Gl6A.dot(G_W7) + G_b7 + + current_fake_data = log(Gl7) + + Dl1 = current_fake_data.dot(D_W1) + D_b1 + Dl1_A = ReLu(Dl1) + Dl2 = Dl1_A.dot(D_W2) + D_b2 + Dl2_A = log(Dl2) + + # Func: Cost G + G_cost = -np.log(Dl2_A) + + # Func: Gradient + grad_G_w7_part_1 = ((-1/Dl2_A) * d_log(Dl2).dot(D_W2.T) * (d_ReLu(Dl1))).dot(D_W1.T) + grad_G_w7_part_2 = d_log(Gl7) + grad_G_w7_part_3 = Gl6A + grad_G_w7 = grad_G_w7_part_3.T.dot(grad_G_w7_part_1 * grad_G_w7_part_1) + grad_G_b7 = grad_G_w7_part_1 * grad_G_w7_part_2 + + grad_G_w6_part_1 = (grad_G_w7_part_1 * grad_G_w7_part_2).dot(G_W7.T) + grad_G_w6_part_2 = d_ReLu(Gl6) + grad_G_w6_part_3 = Gl5A + grad_G_w6 = grad_G_w6_part_3.T.dot(grad_G_w6_part_1 * grad_G_w6_part_2) + grad_G_b6 = (grad_G_w6_part_1 * grad_G_w6_part_2) + + grad_G_w5_part_1 = (grad_G_w6_part_1 * grad_G_w6_part_2).dot(G_W6.T) + grad_G_w5_part_2 = d_tanh(Gl5) + grad_G_w5_part_3 = Gl4A + grad_G_w5 = grad_G_w5_part_3.T.dot(grad_G_w5_part_1 * grad_G_w5_part_2) + grad_G_b5 = (grad_G_w5_part_1 * grad_G_w5_part_2) + + grad_G_w4_part_1 = (grad_G_w5_part_1 * grad_G_w5_part_2).dot(G_W5.T) + grad_G_w4_part_2 = d_ReLu(Gl4) + grad_G_w4_part_3 = Gl3A + grad_G_w4 = grad_G_w4_part_3.T.dot(grad_G_w4_part_1 * grad_G_w4_part_2) + grad_G_b4 = (grad_G_w4_part_1 * grad_G_w4_part_2) + + grad_G_w3_part_1 = (grad_G_w4_part_1 * grad_G_w4_part_2).dot(G_W4.T) + grad_G_w3_part_2 = d_arctan(Gl3) + grad_G_w3_part_3 = Gl2A + grad_G_w3 = grad_G_w3_part_3.T.dot(grad_G_w3_part_1 * grad_G_w3_part_2) + grad_G_b3 = (grad_G_w3_part_1 * grad_G_w3_part_2) + + grad_G_w2_part_1 = (grad_G_w3_part_1 * grad_G_w3_part_2).dot(G_W3.T) + grad_G_w2_part_2 = d_ReLu(Gl2) + grad_G_w2_part_3 = Gl1A + grad_G_w2 = grad_G_w2_part_3.T.dot(grad_G_w2_part_1 * grad_G_w2_part_2) + grad_G_b2 = (grad_G_w2_part_1 * grad_G_w2_part_2) + + grad_G_w1_part_1 = (grad_G_w2_part_1 * grad_G_w2_part_2).dot(G_W2.T) + grad_G_w1_part_2 = d_arctan(Gl1) + grad_G_w1_part_3 = Z + grad_G_w1 = grad_G_w1_part_3.T.dot(grad_G_w1_part_1 * grad_G_w1_part_2) + grad_G_b1 = grad_G_w1_part_1 * grad_G_w1_part_2 + + # ---- Update Gradient ---- + m5 = beta_1 * m5 + (1 - beta_1) * grad_G_w1 + v5 = beta_2 * v5 + (1 - beta_2) * grad_G_w1 ** 2 + + m6 = beta_1 * m6 + (1 - beta_1) * grad_G_b1 + v6 = beta_2 * v6 + (1 - beta_2) * grad_G_b1 ** 2 + + m7 = beta_1 * m7 + (1 - beta_1) * grad_G_w2 + v7 = beta_2 * v7 + (1 - beta_2) * grad_G_w2 ** 2 + + m8 = beta_1 * m8 + (1 - beta_1) * grad_G_b2 + v8 = beta_2 * v8 + (1 - beta_2) * grad_G_b2 ** 2 + + m9 = beta_1 * m9 + (1 - beta_1) * grad_G_w3 + v9 = beta_2 * v9 + (1 - beta_2) * grad_G_w3 ** 2 + + m10 = beta_1 * m10 + (1 - beta_1) * grad_G_b3 + v10 = beta_2 * v10 + (1 - beta_2) * grad_G_b3 ** 2 + + m11 = beta_1 * m11 + (1 - beta_1) * grad_G_w4 + v11 = beta_2 * v11 + (1 - beta_2) * grad_G_w4 ** 2 + + m12 = beta_1 * m12 + (1 - beta_1) * grad_G_b4 + v12 = beta_2 * v12 + (1 - beta_2) * grad_G_b4 ** 2 + + m13 = beta_1 * m13 + (1 - beta_1) * grad_G_w5 + v13 = beta_2 * v13 + (1 - beta_2) * grad_G_w5 ** 2 + + m14 = beta_1 * m14 + (1 - beta_1) * grad_G_b5 + v14 = beta_2 * v14 + (1 - beta_2) * grad_G_b5 ** 2 + + m15 = beta_1 * m15 + (1 - beta_1) * grad_G_w6 + v15 = beta_2 * v15 + (1 - beta_2) * grad_G_w6 ** 2 + + m16 = beta_1 * m16 + (1 - beta_1) * grad_G_b6 + v16 = beta_2 * v16 + (1 - beta_2) * grad_G_b6 ** 2 + + m17 = beta_1 * m17 + (1 - beta_1) * grad_G_w7 + v17 = beta_2 * v17 + (1 - beta_2) * grad_G_w7 ** 2 + + m18 = beta_1 * m18 + (1 - beta_1) * grad_G_b7 + v18 = beta_2 * v18 + (1 - beta_2) * grad_G_b7 ** 2 + + G_W1 = G_W1 - (learing_rate / (np.sqrt(v5 /(1-beta_2) ) + eps)) * (m5/(1-beta_1)) + G_b1 = G_b1 - (learing_rate / (np.sqrt(v6 /(1-beta_2) ) + eps)) * (m6/(1-beta_1)) + + G_W2 = G_W2 - (learing_rate / (np.sqrt(v7 /(1-beta_2) ) + eps)) * (m7/(1-beta_1)) + G_b2 = G_b2 - (learing_rate / (np.sqrt(v8 /(1-beta_2) ) + eps)) * (m8/(1-beta_1)) + + G_W3 = G_W3 - (learing_rate / (np.sqrt(v9 /(1-beta_2) ) + eps)) * (m9/(1-beta_1)) + G_b3 = G_b3 - (learing_rate / (np.sqrt(v10 /(1-beta_2) ) + eps)) * (m10/(1-beta_1)) + + G_W4 = G_W4 - (learing_rate / (np.sqrt(v11 /(1-beta_2) ) + eps)) * (m11/(1-beta_1)) + G_b4 = G_b4 - (learing_rate / (np.sqrt(v12 /(1-beta_2) ) + eps)) * (m12/(1-beta_1)) + + G_W5 = G_W5 - (learing_rate / (np.sqrt(v13 /(1-beta_2) ) + eps)) * (m13/(1-beta_1)) + G_b5 = G_b5 - (learing_rate / (np.sqrt(v14 /(1-beta_2) ) + eps)) * (m14/(1-beta_1)) + + G_W6 = G_W6 - (learing_rate / (np.sqrt(v15 /(1-beta_2) ) + eps)) * (m15/(1-beta_1)) + G_b6 = G_b6 - (learing_rate / (np.sqrt(v16 /(1-beta_2) ) + eps)) * (m16/(1-beta_1)) + + G_W7 = G_W7 - (learing_rate / (np.sqrt(v17 /(1-beta_2) ) + eps)) * (m17/(1-beta_1)) + G_b7 = G_b7 - (learing_rate / (np.sqrt(v18 /(1-beta_2) ) + eps)) * (m18/(1-beta_1)) + + # --- Print Error ---- + #print("Current Iter: ",iter, " Current D cost:",D_cost, " Current G cost: ", G_cost,end='\r') + + if iter == 0: + learing_rate = learing_rate * 0.01 + if iter == 40: + learing_rate = learing_rate * 0.01 + + # ---- Print to Out put ---- + if iter%10 == 0: + + print("Current Iter: ",iter, " Current D cost:",D_cost, " Current G cost: ", G_cost,end='\r') + print('--------- Show Example Result See Tab Above ----------') + print('--------- Wait for the image to load ---------') + Z = np.random.uniform(-1., 1., size=[16, G_input]) + + Gl1 = Z.dot(G_W1) + G_b1 + Gl1A = arctan(Gl1) + Gl2 = Gl1A.dot(G_W2) + G_b2 + Gl2A = ReLu(Gl2) + Gl3 = Gl2A.dot(G_W3) + G_b3 + Gl3A = arctan(Gl3) + + Gl4 = Gl3A.dot(G_W4) + G_b4 + Gl4A = ReLu(Gl4) + Gl5 = Gl4A.dot(G_W5) + G_b5 + Gl5A = tanh(Gl5) + Gl6 = Gl5A.dot(G_W6) + G_b6 + Gl6A = ReLu(Gl6) + Gl7 = Gl6A.dot(G_W7) + G_b7 + + current_fake_data = log(Gl7) + + fig = plot(current_fake_data) + fig.savefig('Click_Me_{}.png'.format(str(iter).zfill(3)+"_Ginput_"+str(G_input)+ \ + "_hiddenone"+str(hidden_input) + "_hiddentwo"+str(hidden_input2) + "_LR_" + str(learing_rate) + ), bbox_inches='tight') +#for complete explanation visit https://towardsdatascience.com/only-numpy-implementing-gan-general-adversarial-networks-and-adam-optimizer-using-numpy-with-2a7e4e032021 +# -- end code -- diff --git a/neural_network/input_data.py b/neural_network/input_data.py new file mode 100644 index 000000000000..983063f0b72d --- /dev/null +++ b/neural_network/input_data.py @@ -0,0 +1,332 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Functions for downloading and reading MNIST data (deprecated). + +This module and all its submodules are deprecated. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections +import gzip +import os + +import numpy +from six.moves import urllib +from six.moves import xrange # pylint: disable=redefined-builtin + +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import random_seed +from tensorflow.python.platform import gfile +from tensorflow.python.util.deprecation import deprecated + +_Datasets = collections.namedtuple('_Datasets', ['train', 'validation', 'test']) + +# CVDF mirror of http://yann.lecun.com/exdb/mnist/ +DEFAULT_SOURCE_URL = 'https://storage.googleapis.com/cvdf-datasets/mnist/' + + +def _read32(bytestream): + dt = numpy.dtype(numpy.uint32).newbyteorder('>') + return numpy.frombuffer(bytestream.read(4), dtype=dt)[0] + + +@deprecated(None, 'Please use tf.data to implement this functionality.') +def _extract_images(f): + """Extract the images into a 4D uint8 numpy array [index, y, x, depth]. + + Args: + f: A file object that can be passed into a gzip reader. + + Returns: + data: A 4D uint8 numpy array [index, y, x, depth]. + + Raises: + ValueError: If the bytestream does not start with 2051. + + """ + print('Extracting', f.name) + with gzip.GzipFile(fileobj=f) as bytestream: + magic = _read32(bytestream) + if magic != 2051: + raise ValueError('Invalid magic number %d in MNIST image file: %s' % + (magic, f.name)) + num_images = _read32(bytestream) + rows = _read32(bytestream) + cols = _read32(bytestream) + buf = bytestream.read(rows * cols * num_images) + data = numpy.frombuffer(buf, dtype=numpy.uint8) + data = data.reshape(num_images, rows, cols, 1) + return data + + +@deprecated(None, 'Please use tf.one_hot on tensors.') +def _dense_to_one_hot(labels_dense, num_classes): + """Convert class labels from scalars to one-hot vectors.""" + num_labels = labels_dense.shape[0] + index_offset = numpy.arange(num_labels) * num_classes + labels_one_hot = numpy.zeros((num_labels, num_classes)) + labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1 + return labels_one_hot + + +@deprecated(None, 'Please use tf.data to implement this functionality.') +def _extract_labels(f, one_hot=False, num_classes=10): + """Extract the labels into a 1D uint8 numpy array [index]. + + Args: + f: A file object that can be passed into a gzip reader. + one_hot: Does one hot encoding for the result. + num_classes: Number of classes for the one hot encoding. + + Returns: + labels: a 1D uint8 numpy array. + + Raises: + ValueError: If the bystream doesn't start with 2049. + """ + print('Extracting', f.name) + with gzip.GzipFile(fileobj=f) as bytestream: + magic = _read32(bytestream) + if magic != 2049: + raise ValueError('Invalid magic number %d in MNIST label file: %s' % + (magic, f.name)) + num_items = _read32(bytestream) + buf = bytestream.read(num_items) + labels = numpy.frombuffer(buf, dtype=numpy.uint8) + if one_hot: + return _dense_to_one_hot(labels, num_classes) + return labels + + +class _DataSet(object): + """Container class for a _DataSet (deprecated). + + THIS CLASS IS DEPRECATED. + """ + + @deprecated(None, 'Please use alternatives such as official/mnist/_DataSet.py' + ' from tensorflow/models.') + def __init__(self, + images, + labels, + fake_data=False, + one_hot=False, + dtype=dtypes.float32, + reshape=True, + seed=None): + """Construct a _DataSet. + + one_hot arg is used only if fake_data is true. `dtype` can be either + `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into + `[0, 1]`. Seed arg provides for convenient deterministic testing. + + Args: + images: The images + labels: The labels + fake_data: Ignore inages and labels, use fake data. + one_hot: Bool, return the labels as one hot vectors (if True) or ints (if + False). + dtype: Output image dtype. One of [uint8, float32]. `uint8` output has + range [0,255]. float32 output has range [0,1]. + reshape: Bool. If True returned images are returned flattened to vectors. + seed: The random seed to use. + """ + seed1, seed2 = random_seed.get_seed(seed) + # If op level seed is not set, use whatever graph level seed is returned + numpy.random.seed(seed1 if seed is None else seed2) + dtype = dtypes.as_dtype(dtype).base_dtype + if dtype not in (dtypes.uint8, dtypes.float32): + raise TypeError('Invalid image dtype %r, expected uint8 or float32' % + dtype) + if fake_data: + self._num_examples = 10000 + self.one_hot = one_hot + else: + assert images.shape[0] == labels.shape[0], ( + 'images.shape: %s labels.shape: %s' % (images.shape, labels.shape)) + self._num_examples = images.shape[0] + + # Convert shape from [num examples, rows, columns, depth] + # to [num examples, rows*columns] (assuming depth == 1) + if reshape: + assert images.shape[3] == 1 + images = images.reshape(images.shape[0], + images.shape[1] * images.shape[2]) + if dtype == dtypes.float32: + # Convert from [0, 255] -> [0.0, 1.0]. + images = images.astype(numpy.float32) + images = numpy.multiply(images, 1.0 / 255.0) + self._images = images + self._labels = labels + self._epochs_completed = 0 + self._index_in_epoch = 0 + + @property + def images(self): + return self._images + + @property + def labels(self): + return self._labels + + @property + def num_examples(self): + return self._num_examples + + @property + def epochs_completed(self): + return self._epochs_completed + + def next_batch(self, batch_size, fake_data=False, shuffle=True): + """Return the next `batch_size` examples from this data set.""" + if fake_data: + fake_image = [1] * 784 + if self.one_hot: + fake_label = [1] + [0] * 9 + else: + fake_label = 0 + return [fake_image for _ in xrange(batch_size) + ], [fake_label for _ in xrange(batch_size)] + start = self._index_in_epoch + # Shuffle for the first epoch + if self._epochs_completed == 0 and start == 0 and shuffle: + perm0 = numpy.arange(self._num_examples) + numpy.random.shuffle(perm0) + self._images = self.images[perm0] + self._labels = self.labels[perm0] + # Go to the next epoch + if start + batch_size > self._num_examples: + # Finished epoch + self._epochs_completed += 1 + # Get the rest examples in this epoch + rest_num_examples = self._num_examples - start + images_rest_part = self._images[start:self._num_examples] + labels_rest_part = self._labels[start:self._num_examples] + # Shuffle the data + if shuffle: + perm = numpy.arange(self._num_examples) + numpy.random.shuffle(perm) + self._images = self.images[perm] + self._labels = self.labels[perm] + # Start next epoch + start = 0 + self._index_in_epoch = batch_size - rest_num_examples + end = self._index_in_epoch + images_new_part = self._images[start:end] + labels_new_part = self._labels[start:end] + return numpy.concatenate((images_rest_part, images_new_part), + axis=0), numpy.concatenate( + (labels_rest_part, labels_new_part), axis=0) + else: + self._index_in_epoch += batch_size + end = self._index_in_epoch + return self._images[start:end], self._labels[start:end] + + +@deprecated(None, 'Please write your own downloading logic.') +def _maybe_download(filename, work_directory, source_url): + """Download the data from source url, unless it's already here. + + Args: + filename: string, name of the file in the directory. + work_directory: string, path to working directory. + source_url: url to download from if file doesn't exist. + + Returns: + Path to resulting file. + """ + if not gfile.Exists(work_directory): + gfile.MakeDirs(work_directory) + filepath = os.path.join(work_directory, filename) + if not gfile.Exists(filepath): + urllib.request.urlretrieve(source_url, filepath) + with gfile.GFile(filepath) as f: + size = f.size() + print('Successfully downloaded', filename, size, 'bytes.') + return filepath + + +@deprecated(None, 'Please use alternatives such as:' + ' tensorflow_datasets.load(\'mnist\')') +def read_data_sets(train_dir, + fake_data=False, + one_hot=False, + dtype=dtypes.float32, + reshape=True, + validation_size=5000, + seed=None, + source_url=DEFAULT_SOURCE_URL): + if fake_data: + + def fake(): + return _DataSet([], [], + fake_data=True, + one_hot=one_hot, + dtype=dtype, + seed=seed) + + train = fake() + validation = fake() + test = fake() + return _Datasets(train=train, validation=validation, test=test) + + if not source_url: # empty string check + source_url = DEFAULT_SOURCE_URL + + train_images_file = 'train-images-idx3-ubyte.gz' + train_labels_file = 'train-labels-idx1-ubyte.gz' + test_images_file = 't10k-images-idx3-ubyte.gz' + test_labels_file = 't10k-labels-idx1-ubyte.gz' + + local_file = _maybe_download(train_images_file, train_dir, + source_url + train_images_file) + with gfile.Open(local_file, 'rb') as f: + train_images = _extract_images(f) + + local_file = _maybe_download(train_labels_file, train_dir, + source_url + train_labels_file) + with gfile.Open(local_file, 'rb') as f: + train_labels = _extract_labels(f, one_hot=one_hot) + + local_file = _maybe_download(test_images_file, train_dir, + source_url + test_images_file) + with gfile.Open(local_file, 'rb') as f: + test_images = _extract_images(f) + + local_file = _maybe_download(test_labels_file, train_dir, + source_url + test_labels_file) + with gfile.Open(local_file, 'rb') as f: + test_labels = _extract_labels(f, one_hot=one_hot) + + if not 0 <= validation_size <= len(train_images): + raise ValueError( + 'Validation size should be between 0 and {}. Received: {}.'.format( + len(train_images), validation_size)) + + validation_images = train_images[:validation_size] + validation_labels = train_labels[:validation_size] + train_images = train_images[validation_size:] + train_labels = train_labels[validation_size:] + + options = dict(dtype=dtype, reshape=reshape, seed=seed) + + train = _DataSet(train_images, train_labels, **options) + validation = _DataSet(validation_images, validation_labels, **options) + test = _DataSet(test_images, test_labels, **options) + + return _Datasets(train=train, validation=validation, test=test) diff --git a/neural_network/perceptron.py b/neural_network/perceptron.py index eb8b04e855d3..3610dd2ab227 100644 --- a/neural_network/perceptron.py +++ b/neural_network/perceptron.py @@ -1,64 +1,104 @@ -''' - - Perceptron - w = w + N * (d(k) - y) * x(k) - - Using perceptron network for oil analysis, - with Measuring of 3 parameters that represent chemical characteristics we can classify the oil, in p1 or p2 - p1 = -1 - p2 = 1 - -''' -from __future__ import print_function - +""" + Perceptron + w = w + N * (d(k) - y) * x(k) + + Using perceptron network for oil analysis, with Measuring of 3 parameters + that represent chemical characteristics we can classify the oil, in p1 or p2 + p1 = -1 + p2 = 1 +""" import random class Perceptron: - def __init__(self, sample, exit, learn_rate=0.01, epoch_number=1000, bias=-1): + def __init__(self, sample, target, learning_rate=0.01, epoch_number=1000, bias=-1): + """ + Initializes a Perceptron network for oil analysis + :param sample: sample dataset of 3 parameters with shape [30,3] + :param target: variable for classification with two possible states -1 or 1 + :param learning_rate: learning rate used in optimizing. + :param epoch_number: number of epochs to train network on. + :param bias: bias value for the network. + """ self.sample = sample - self.exit = exit - self.learn_rate = learn_rate + if len(self.sample) == 0: + raise AttributeError("Sample data can not be empty") + self.target = target + if len(self.target) == 0: + raise AttributeError("Target data can not be empty") + if len(self.sample) != len(self.target): + raise AttributeError( + "Sample data and Target data do not have matching lengths" + ) + self.learning_rate = learning_rate self.epoch_number = epoch_number self.bias = bias self.number_sample = len(sample) - self.col_sample = len(sample[0]) + self.col_sample = len(sample[0]) # number of columns in dataset self.weight = [] - def training(self): + def training(self) -> None: + """ + Trains perceptron for epochs <= given number of epochs + :return: None + >>> data = [[2.0149, 0.6192, 10.9263]] + >>> targets = [-1] + >>> perceptron = Perceptron(data,targets) + >>> perceptron.training() # doctest: +ELLIPSIS + ('\\nEpoch:\\n', ...) + ... + """ for sample in self.sample: sample.insert(0, self.bias) for i in range(self.col_sample): - self.weight.append(random.random()) + self.weight.append(random.random()) self.weight.insert(0, self.bias) epoch_count = 0 while True: - erro = False + has_misclassified = False for i in range(self.number_sample): u = 0 for j in range(self.col_sample + 1): u = u + self.weight[j] * self.sample[i][j] y = self.sign(u) - if y != self.exit[i]: - + if y != self.target[i]: for j in range(self.col_sample + 1): - - self.weight[j] = self.weight[j] + self.learn_rate * (self.exit[i] - y) * self.sample[i][j] - erro = True - #print('Epoch: \n',epoch_count) + self.weight[j] = ( + self.weight[j] + + self.learning_rate + * (self.target[i] - y) + * self.sample[i][j] + ) + has_misclassified = True + # print('Epoch: \n',epoch_count) epoch_count = epoch_count + 1 # if you want controle the epoch or just by erro - if erro == False: - print(('\nEpoch:\n',epoch_count)) - print('------------------------\n') - #if epoch_count > self.epoch_number or not erro: + if not has_misclassified: + print(("\nEpoch:\n", epoch_count)) + print("------------------------\n") + # if epoch_count > self.epoch_number or not erro: break - def sort(self, sample): + def sort(self, sample) -> None: + """ + :param sample: example row to classify as P1 or P2 + :return: None + >>> data = [[2.0149, 0.6192, 10.9263]] + >>> targets = [-1] + >>> perceptron = Perceptron(data,targets) + >>> perceptron.training() # doctest: +ELLIPSIS + ('\\nEpoch:\\n', ...) + ... + >>> perceptron.sort([-0.6508, 0.1097, 4.0009]) # doctest: +ELLIPSIS + ('Sample: ', ...) + classification: P... + """ + if len(self.sample) == 0: + raise AttributeError("Sample data can not be empty") sample.insert(0, self.bias) u = 0 for i in range(self.col_sample + 1): @@ -66,14 +106,28 @@ def sort(self, sample): y = self.sign(u) - if y == -1: - print(('Sample: ', sample)) - print('classification: P1') + if y == -1: + print(("Sample: ", sample)) + print("classification: P1") else: - print(('Sample: ', sample)) - print('classification: P2') - - def sign(self, u): + print(("Sample: ", sample)) + print("classification: P2") + + def sign(self, u: float) -> int: + """ + threshold function for classification + :param u: input number + :return: 1 if the input is greater than 0, otherwise -1 + >>> data = [[0],[-0.5],[0.5]] + >>> targets = [1,-1,1] + >>> perceptron = Perceptron(data,targets) + >>> perceptron.sign(0) + 1 + >>> perceptron.sign(-0.5) + -1 + >>> perceptron.sign(0.5) + 1 + """ return 1 if u >= 0 else -1 @@ -107,18 +161,60 @@ def sign(self, u): [-0.1013, 0.5989, 7.1812], [2.4482, 0.9455, 11.2095], [2.0149, 0.6192, 10.9263], - [0.2012, 0.2611, 5.4631] + [0.2012, 0.2611, 5.4631], +] +exit = [ + -1, + -1, + -1, + 1, + 1, + -1, + 1, + -1, + 1, + 1, + -1, + 1, + -1, + -1, + -1, + -1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + 1, + -1, + -1, + 1, + -1, + 1, ] -exit = [-1, -1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1] -network = Perceptron(sample=samples, exit = exit, learn_rate=0.01, epoch_number=1000, bias=-1) +if __name__ == "__main__": + import doctest -network.training() + doctest.testmod() -while True: - sample = [] - for i in range(3): - sample.insert(i, float(input('value: '))) - network.sort(sample) + network = Perceptron( + sample=samples, target=exit, learning_rate=0.01, epoch_number=1000, bias=-1 + ) + network.training() + print("Finished training perceptron") + print("Enter values to predict or q to exit") + while True: + sample = [] + for i in range(len(samples[0])): + observation = input("value: ").strip() + if observation == "q": + break + observation = float(observation) + sample.insert(i, observation) + network.sort(sample) diff --git a/other/activity_selection.py b/other/activity_selection.py new file mode 100644 index 000000000000..4e8e6c78e3f5 --- /dev/null +++ b/other/activity_selection.py @@ -0,0 +1,45 @@ +"""The following implementation assumes that the activities +are already sorted according to their finish time""" + +"""Prints a maximum set of activities that can be done by a +single person, one at a time""" +# n --> Total number of activities +# start[]--> An array that contains start time of all activities +# finish[] --> An array that contains finish time of all activities + + +def printMaxActivities(start, finish): + """ + >>> start = [1, 3, 0, 5, 8, 5] + >>> finish = [2, 4, 6, 7, 9, 9] + >>> printMaxActivities(start, finish) + The following activities are selected: + 0 1 3 4 + """ + n = len(finish) + print("The following activities are selected:") + + # The first activity is always selected + i = 0 + print(i, end=" ") + + # Consider rest of the activities + for j in range(n): + + # If this activity has start time greater than + # or equal to the finish time of previously + # selected activity, then select it + if start[j] >= finish[i]: + print(j, end=" ") + i = j + + +# Driver program to test above function +start = [1, 3, 0, 5, 8, 5] +finish = [2, 4, 6, 7, 9, 9] +printMaxActivities(start, finish) + +""" +The following activities are selected: +0 1 3 4 +""" diff --git a/other/anagrams.py b/other/anagrams.py index 29b34fbdc5d3..9e103296b382 100644 --- a/other/anagrams.py +++ b/other/anagrams.py @@ -1,30 +1,32 @@ -from __future__ import print_function import collections, pprint, time, os start_time = time.time() -print('creating word list...') +print("creating word list...") path = os.path.split(os.path.realpath(__file__)) -with open(path[0] + '/words') as f: +with open(path[0] + "/words") as f: word_list = sorted(list(set([word.strip().lower() for word in f]))) + def signature(word): - return ''.join(sorted(word)) + return "".join(sorted(word)) + word_bysig = collections.defaultdict(list) for word in word_list: word_bysig[signature(word)].append(word) + def anagram(myword): return word_bysig[signature(myword)] -print('finding anagrams...') -all_anagrams = {word: anagram(word) - for word in word_list if len(anagram(word)) > 1} -print('writing anagrams to file...') -with open('anagrams.txt', 'w') as file: - file.write('all_anagrams = ') +print("finding anagrams...") +all_anagrams = {word: anagram(word) for word in word_list if len(anagram(word)) > 1} + +print("writing anagrams to file...") +with open("anagrams.txt", "w") as file: + file.write("all_anagrams = ") file.write(pprint.pformat(all_anagrams)) total_time = round(time.time() - start_time, 2) -print(('Done [', total_time, 'seconds ]')) +print(("Done [", total_time, "seconds ]")) diff --git a/other/autocomplete_using_trie.py b/other/autocomplete_using_trie.py new file mode 100644 index 000000000000..eb906f8efa9a --- /dev/null +++ b/other/autocomplete_using_trie.py @@ -0,0 +1,64 @@ +END = "#" + + +class Trie: + def __init__(self): + self._trie = {} + + def insert_word(self, text): + trie = self._trie + for char in text: + if char not in trie: + trie[char] = {} + trie = trie[char] + trie[END] = True + + def find_word(self, prefix): + trie = self._trie + for char in prefix: + if char in trie: + trie = trie[char] + else: + return [] + return self._elements(trie) + + def _elements(self, d): + result = [] + for c, v in d.items(): + if c == END: + subresult = [" "] + else: + subresult = [c + s for s in self._elements(v)] + result.extend(subresult) + return tuple(result) + + +trie = Trie() +words = ("depart", "detergent", "daring", "dog", "deer", "deal") +for word in words: + trie.insert_word(word) + + +def autocomplete_using_trie(s): + """ + >>> trie = Trie() + >>> for word in words: + ... trie.insert_word(word) + ... + >>> matches = autocomplete_using_trie("de") + + "detergent " in matches + True + "dog " in matches + False + """ + suffixes = trie.find_word(s) + return tuple(s + w for w in suffixes) + + +def main(): + print(autocomplete_using_trie("de")) + + +if __name__ == "__main__": + main() diff --git a/other/binary_exponentiation.py b/other/binary_exponentiation.py index 1a30fb8fd266..dd4e70e74129 100644 --- a/other/binary_exponentiation.py +++ b/other/binary_exponentiation.py @@ -14,7 +14,7 @@ def b_expo(a, b): res = 1 while b > 0: - if b&1: + if b & 1: res *= a a *= a @@ -26,14 +26,15 @@ def b_expo(a, b): def b_expo_mod(a, b, c): res = 1 while b > 0: - if b&1: - res = ((res%c) * (a%c)) % c + if b & 1: + res = ((res % c) * (a % c)) % c a *= a b >>= 1 return res + """ * Wondering how this method works ! * It's pretty simple. diff --git a/other/binary_exponentiation_2.py b/other/binary_exponentiation_2.py index 217a616c99fb..51ec4baf2598 100644 --- a/other/binary_exponentiation_2.py +++ b/other/binary_exponentiation_2.py @@ -14,7 +14,7 @@ def b_expo(a, b): res = 0 while b > 0: - if b&1: + if b & 1: res += a a += a @@ -26,8 +26,8 @@ def b_expo(a, b): def b_expo_mod(a, b, c): res = 0 while b > 0: - if b&1: - res = ((res%c) + (a%c)) % c + if b & 1: + res = ((res % c) + (a % c)) % c a += a b >>= 1 diff --git a/other/detecting_english_programmatically.py b/other/detecting_english_programmatically.py index 005fd3c10ca3..4b0bb37ce520 100644 --- a/other/detecting_english_programmatically.py +++ b/other/detecting_english_programmatically.py @@ -1,18 +1,21 @@ import os -UPPERLETTERS = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' -LETTERS_AND_SPACE = UPPERLETTERS + UPPERLETTERS.lower() + ' \t\n' +UPPERLETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" +LETTERS_AND_SPACE = UPPERLETTERS + UPPERLETTERS.lower() + " \t\n" + def loadDictionary(): path = os.path.split(os.path.realpath(__file__)) englishWords = {} - with open(path[0] + '/Dictionary.txt') as dictionaryFile: - for word in dictionaryFile.read().split('\n'): + with open(path[0] + "/dictionary.txt") as dictionaryFile: + for word in dictionaryFile.read().split("\n"): englishWords[word] = None return englishWords + ENGLISH_WORDS = loadDictionary() + def getEnglishCount(message): message = message.upper() message = removeNonLetters(message) @@ -28,14 +31,16 @@ def getEnglishCount(message): return float(matches) / len(possibleWords) + def removeNonLetters(message): lettersOnly = [] for symbol in message: if symbol in LETTERS_AND_SPACE: lettersOnly.append(symbol) - return ''.join(lettersOnly) + return "".join(lettersOnly) + -def isEnglish(message, wordPercentage = 20, letterPercentage = 85): +def isEnglish(message, wordPercentage=20, letterPercentage=85): """ >>> isEnglish('Hello World') True @@ -51,4 +56,5 @@ def isEnglish(message, wordPercentage = 20, letterPercentage = 85): import doctest + doctest.testmod() diff --git a/other/dictionary.txt b/other/dictionary.txt index 14528efe844f..75838996ba08 100644 --- a/other/dictionary.txt +++ b/other/dictionary.txt @@ -26131,6 +26131,7 @@ MICROECONOMICS MICROELECTRONICS MICROFILM MICROFILMS +MICROFINANCE MICROGRAMMING MICROINSTRUCTION MICROINSTRUCTIONS @@ -45330,4 +45331,4 @@ ZOROASTER ZOROASTRIAN ZULU ZULUS -ZURICH \ No newline at end of file +ZURICH diff --git a/other/euclidean_gcd.py b/other/euclidean_gcd.py index 30853e172076..c6c11f947a08 100644 --- a/other/euclidean_gcd.py +++ b/other/euclidean_gcd.py @@ -1,6 +1,6 @@ -from __future__ import print_function # https://en.wikipedia.org/wiki/Euclidean_algorithm + def euclidean_gcd(a, b): while b: t = b @@ -8,6 +8,7 @@ def euclidean_gcd(a, b): a = t return a + def main(): print("GCD(3, 5) = " + str(euclidean_gcd(3, 5))) print("GCD(5, 3) = " + str(euclidean_gcd(5, 3))) @@ -15,5 +16,6 @@ def main(): print("GCD(3, 6) = " + str(euclidean_gcd(3, 6))) print("GCD(6, 3) = " + str(euclidean_gcd(6, 3))) -if __name__ == '__main__': + +if __name__ == "__main__": main() diff --git a/other/findingPrimes.py b/other/findingPrimes.py deleted file mode 100644 index 035a14f4a335..000000000000 --- a/other/findingPrimes.py +++ /dev/null @@ -1,21 +0,0 @@ -''' --The sieve of Eratosthenes is an algorithm used to find prime numbers, less than or equal to a given value. --Illustration: https://upload.wikimedia.org/wikipedia/commons/b/b9/Sieve_of_Eratosthenes_animation.gif -''' -from __future__ import print_function - - -from math import sqrt -def SOE(n): - check = round(sqrt(n)) #Need not check for multiples past the square root of n - - sieve = [False if i <2 else True for i in range(n+1)] #Set every index to False except for index 0 and 1 - - for i in range(2, check): - if(sieve[i] == True): #If i is a prime - for j in range(i+i, n+1, i): #Step through the list in increments of i(the multiples of the prime) - sieve[j] = False #Sets every multiple of i to False - - for i in range(n+1): - if(sieve[i] == True): - print(i, end=" ") diff --git a/other/fischer_yates_shuffle.py b/other/fischer_yates_shuffle.py index d87792f45558..977e5f131e4f 100644 --- a/other/fischer_yates_shuffle.py +++ b/other/fischer_yates_shuffle.py @@ -7,16 +7,18 @@ """ import random + def FYshuffle(LIST): for i in range(len(LIST)): - a = random.randint(0, len(LIST)-1) - b = random.randint(0, len(LIST)-1) + a = random.randint(0, len(LIST) - 1) + b = random.randint(0, len(LIST) - 1) LIST[a], LIST[b] = LIST[b], LIST[a] return LIST -if __name__ == '__main__': - integers = [0,1,2,3,4,5,6,7] - strings = ['python', 'says', 'hello', '!'] - print ('Fisher-Yates Shuffle:') - print ('List',integers, strings) - print ('FY Shuffle',FYshuffle(integers), FYshuffle(strings)) + +if __name__ == "__main__": + integers = [0, 1, 2, 3, 4, 5, 6, 7] + strings = ["python", "says", "hello", "!"] + print("Fisher-Yates Shuffle:") + print("List", integers, strings) + print("FY Shuffle", FYshuffle(integers), FYshuffle(strings)) diff --git a/other/food_wastage_analysis_from_1961-2013_fao.ipynb b/other/food_wastage_analysis_from_1961-2013_fao.ipynb new file mode 100644 index 000000000000..384314c7e8f1 --- /dev/null +++ b/other/food_wastage_analysis_from_1961-2013_fao.ipynb @@ -0,0 +1,5916 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "1eecdb4a-89ca-4a1e-9c4c-7c44b2e628a1", + "_uuid": "110a8132a8179a9bed2fc8f1096592dc791f1661" + }, + "source": [ + "# About the dataset\n", + "\n", + "Context\n", + "Our world population is expected to grow from 7.3 billion today to 9.7 billion in the year 2050. Finding solutions for feeding the growing world population has become a hot topic for food and agriculture organizations, entrepreneurs and philanthropists. These solutions range from changing the way we grow our food to changing the way we eat. To make things harder, the world's climate is changing and it is both affecting and affected by the way we grow our food – agriculture. This dataset provides an insight on our worldwide food production - focusing on a comparison between food produced for human consumption and feed produced for animals.\n", + "\n", + "Content\n", + "The Food and Agriculture Organization of the United Nations provides free access to food and agriculture data for over 245 countries and territories, from the year 1961 to the most recent update (depends on the dataset). One dataset from the FAO's database is the Food Balance Sheets. It presents a comprehensive picture of the pattern of a country's food supply during a specified reference period, the last time an update was loaded to the FAO database was in 2013. The food balance sheet shows for each food item the sources of supply and its utilization. This chunk of the dataset is focused on two utilizations of each food item available:\n", + "\n", + "Food - refers to the total amount of the food item available as human food during the reference period.\n", + "Feed - refers to the quantity of the food item available for feeding to the livestock and poultry during the reference period.\n", + "Dataset's attributes:\n", + "\n", + "Area code - Country name abbreviation\n", + "Area - County name\n", + "Item - Food item\n", + "Element - Food or Feed\n", + "Latitude - geographic coordinate that specifies the north–south position of a point on the Earth's surface\n", + "Longitude - geographic coordinate that specifies the east-west position of a point on the Earth's surface\n", + "Production per year - Amount of food item produced in 1000 tonnes\n", + "\n", + "This is a simple exploratory notebook that heavily expolits pandas and seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", + "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5" + }, + "outputs": [], + "source": [ + "# Importing libraries\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "%matplotlib inline\n", + "# importing data\n", + "df = pd.read_csv(\"FAO.csv\", encoding = \"ISO-8859-1\")\n", + "pd.options.mode.chained_assignment = None\n", + "from sklearn.linear_model import LinearRegression" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Area AbbreviationArea CodeAreaItem CodeItemElement CodeElementUnitlatitudelongitude...Y2004Y2005Y2006Y2007Y2008Y2009Y2010Y2011Y2012Y2013
0AFG2Afghanistan2511Wheat and products5142Food1000 tonnes33.9467.71...3249.03486.03704.04164.04252.04538.04605.04711.048104895
1AFG2Afghanistan2805Rice (Milled Equivalent)5142Food1000 tonnes33.9467.71...419.0445.0546.0455.0490.0415.0442.0476.0425422
2AFG2Afghanistan2513Barley and products5521Feed1000 tonnes33.9467.71...58.0236.0262.0263.0230.0379.0315.0203.0367360
3AFG2Afghanistan2513Barley and products5142Food1000 tonnes33.9467.71...185.043.044.048.062.055.060.072.07889
4AFG2Afghanistan2514Maize and products5521Feed1000 tonnes33.9467.71...120.0208.0233.0249.0247.0195.0178.0191.0200200
5AFG2Afghanistan2514Maize and products5142Food1000 tonnes33.9467.71...231.067.082.067.069.071.082.073.07776
6AFG2Afghanistan2517Millet and products5142Food1000 tonnes33.9467.71...15.021.011.019.021.018.014.014.01412
7AFG2Afghanistan2520Cereals, Other5142Food1000 tonnes33.9467.71...2.01.01.00.00.00.00.00.000
8AFG2Afghanistan2531Potatoes and products5142Food1000 tonnes33.9467.71...276.0294.0294.0260.0242.0250.0192.0169.0196230
9AFG2Afghanistan2536Sugar cane5521Feed1000 tonnes33.9467.71...50.029.061.065.054.0114.083.083.06981
10AFG2Afghanistan2537Sugar beet5521Feed1000 tonnes33.9467.71...0.00.00.00.00.00.00.00.000
11AFG2Afghanistan2542Sugar (Raw Equivalent)5142Food1000 tonnes33.9467.71...124.0152.0169.0192.0217.0231.0240.0240.0250255
12AFG2Afghanistan2543Sweeteners, Other5142Food1000 tonnes33.9467.71...9.015.012.06.011.02.09.021.02416
13AFG2Afghanistan2745Honey5142Food1000 tonnes33.9467.71...3.03.03.03.03.03.03.02.022
14AFG2Afghanistan2549Pulses, Other and products5521Feed1000 tonnes33.9467.71...3.02.03.03.03.05.04.05.044
15AFG2Afghanistan2549Pulses, Other and products5142Food1000 tonnes33.9467.71...17.035.037.040.054.080.066.081.06374
16AFG2Afghanistan2551Nuts and products5142Food1000 tonnes33.9467.71...11.013.024.034.042.028.066.071.07044
17AFG2Afghanistan2560Coconuts - Incl Copra5142Food1000 tonnes33.9467.71...0.00.00.00.00.00.00.00.000
18AFG2Afghanistan2561Sesame seed5142Food1000 tonnes33.9467.71...16.016.013.016.016.016.019.017.01616
19AFG2Afghanistan2563Olives (including preserved)5142Food1000 tonnes33.9467.71...1.01.00.00.02.03.02.02.022
20AFG2Afghanistan2571Soyabean Oil5142Food1000 tonnes33.9467.71...6.035.018.021.011.06.015.016.01616
21AFG2Afghanistan2572Groundnut Oil5142Food1000 tonnes33.9467.71...0.00.00.00.00.00.00.00.000
22AFG2Afghanistan2573Sunflowerseed Oil5142Food1000 tonnes33.9467.71...4.06.05.09.03.08.015.016.01723
23AFG2Afghanistan2574Rape and Mustard Oil5142Food1000 tonnes33.9467.71...0.01.03.05.06.06.01.02.022
24AFG2Afghanistan2575Cottonseed Oil5142Food1000 tonnes33.9467.71...2.03.03.03.03.04.03.03.034
25AFG2Afghanistan2577Palm Oil5142Food1000 tonnes33.9467.71...71.069.056.051.036.053.059.051.06164
26AFG2Afghanistan2579Sesameseed Oil5142Food1000 tonnes33.9467.71...1.01.01.02.02.01.01.02.011
27AFG2Afghanistan2580Olive Oil5142Food1000 tonnes33.9467.71...0.00.00.00.00.01.01.01.011
28AFG2Afghanistan2586Oilcrops Oil, Other5142Food1000 tonnes33.9467.71...0.01.00.00.03.01.02.02.022
29AFG2Afghanistan2601Tomatoes and products5142Food1000 tonnes33.9467.71...2.02.08.01.00.00.00.00.000
..................................................................
21447ZWE181Zimbabwe2765Crustaceans5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
21448ZWE181Zimbabwe2766Cephalopods5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
21449ZWE181Zimbabwe2767Molluscs, Other5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.01.00.000
21450ZWE181Zimbabwe2775Aquatic Plants5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
21451ZWE181Zimbabwe2680Infant food5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
21452ZWE181Zimbabwe2905Cereals - Excluding Beer5521Feed1000 tonnes-19.0229.15...75.054.075.055.063.062.055.055.05555
21453ZWE181Zimbabwe2905Cereals - Excluding Beer5142Food1000 tonnes-19.0229.15...1844.01842.01944.01962.01918.01980.02011.02094.020712016
21454ZWE181Zimbabwe2907Starchy Roots5142Food1000 tonnes-19.0229.15...223.0236.0238.0228.0245.0258.0258.0269.0272276
21455ZWE181Zimbabwe2908Sugar Crops5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
21456ZWE181Zimbabwe2909Sugar & Sweeteners5142Food1000 tonnes-19.0229.15...335.0313.0339.0302.0285.0287.0314.0336.0396416
21457ZWE181Zimbabwe2911Pulses5142Food1000 tonnes-19.0229.15...63.059.061.057.069.078.068.056.05255
21458ZWE181Zimbabwe2912Treenuts5142Food1000 tonnes-19.0229.15...1.02.01.02.02.03.04.02.043
21459ZWE181Zimbabwe2913Oilcrops5521Feed1000 tonnes-19.0229.15...36.046.041.033.031.019.024.017.02730
21460ZWE181Zimbabwe2913Oilcrops5142Food1000 tonnes-19.0229.15...60.059.061.062.048.044.041.040.03838
21461ZWE181Zimbabwe2914Vegetable Oils5142Food1000 tonnes-19.0229.15...111.0114.0112.0114.0134.0135.0137.0147.0159160
21462ZWE181Zimbabwe2918Vegetables5142Food1000 tonnes-19.0229.15...161.0166.0208.0185.0137.0179.0215.0217.0227227
21463ZWE181Zimbabwe2919Fruits - Excluding Wine5142Food1000 tonnes-19.0229.15...191.0134.0167.0177.0185.0184.0211.0230.0246217
21464ZWE181Zimbabwe2922Stimulants5142Food1000 tonnes-19.0229.15...7.021.014.024.016.011.023.011.01010
21465ZWE181Zimbabwe2923Spices5142Food1000 tonnes-19.0229.15...7.011.07.012.016.016.014.011.01212
21466ZWE181Zimbabwe2924Alcoholic Beverages5142Food1000 tonnes-19.0229.15...294.0290.0316.0355.0398.0437.0448.0476.0525516
21467ZWE181Zimbabwe2943Meat5142Food1000 tonnes-19.0229.15...222.0228.0233.0238.0242.0265.0262.0277.0280258
21468ZWE181Zimbabwe2945Offals5142Food1000 tonnes-19.0229.15...20.020.021.021.021.021.021.021.02222
21469ZWE181Zimbabwe2946Animal fats5142Food1000 tonnes-19.0229.15...26.026.029.029.027.031.030.025.02620
21470ZWE181Zimbabwe2949Eggs5142Food1000 tonnes-19.0229.15...15.018.018.021.022.027.027.024.02425
21471ZWE181Zimbabwe2948Milk - Excluding Butter5521Feed1000 tonnes-19.0229.15...21.021.021.021.021.023.025.025.03031
21472ZWE181Zimbabwe2948Milk - Excluding Butter5142Food1000 tonnes-19.0229.15...373.0357.0359.0356.0341.0385.0418.0457.0426451
21473ZWE181Zimbabwe2960Fish, Seafood5521Feed1000 tonnes-19.0229.15...5.04.09.06.09.05.015.015.01515
21474ZWE181Zimbabwe2960Fish, Seafood5142Food1000 tonnes-19.0229.15...18.014.017.014.015.018.029.040.04040
21475ZWE181Zimbabwe2961Aquatic Products, Other5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
21476ZWE181Zimbabwe2928Miscellaneous5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
\n", + "

21477 rows × 63 columns

\n", + "
" + ], + "text/plain": [ + " Area Abbreviation Area Code Area Item Code \\\n", + "0 AFG 2 Afghanistan 2511 \n", + "1 AFG 2 Afghanistan 2805 \n", + "2 AFG 2 Afghanistan 2513 \n", + "3 AFG 2 Afghanistan 2513 \n", + "4 AFG 2 Afghanistan 2514 \n", + "5 AFG 2 Afghanistan 2514 \n", + "6 AFG 2 Afghanistan 2517 \n", + "7 AFG 2 Afghanistan 2520 \n", + "8 AFG 2 Afghanistan 2531 \n", + "9 AFG 2 Afghanistan 2536 \n", + "10 AFG 2 Afghanistan 2537 \n", + "11 AFG 2 Afghanistan 2542 \n", + "12 AFG 2 Afghanistan 2543 \n", + "13 AFG 2 Afghanistan 2745 \n", + "14 AFG 2 Afghanistan 2549 \n", + "15 AFG 2 Afghanistan 2549 \n", + "16 AFG 2 Afghanistan 2551 \n", + "17 AFG 2 Afghanistan 2560 \n", + "18 AFG 2 Afghanistan 2561 \n", + "19 AFG 2 Afghanistan 2563 \n", + "20 AFG 2 Afghanistan 2571 \n", + "21 AFG 2 Afghanistan 2572 \n", + "22 AFG 2 Afghanistan 2573 \n", + "23 AFG 2 Afghanistan 2574 \n", + "24 AFG 2 Afghanistan 2575 \n", + "25 AFG 2 Afghanistan 2577 \n", + "26 AFG 2 Afghanistan 2579 \n", + "27 AFG 2 Afghanistan 2580 \n", + "28 AFG 2 Afghanistan 2586 \n", + "29 AFG 2 Afghanistan 2601 \n", + "... ... ... ... ... \n", + "21447 ZWE 181 Zimbabwe 2765 \n", + "21448 ZWE 181 Zimbabwe 2766 \n", + "21449 ZWE 181 Zimbabwe 2767 \n", + "21450 ZWE 181 Zimbabwe 2775 \n", + "21451 ZWE 181 Zimbabwe 2680 \n", + "21452 ZWE 181 Zimbabwe 2905 \n", + "21453 ZWE 181 Zimbabwe 2905 \n", + "21454 ZWE 181 Zimbabwe 2907 \n", + "21455 ZWE 181 Zimbabwe 2908 \n", + "21456 ZWE 181 Zimbabwe 2909 \n", + "21457 ZWE 181 Zimbabwe 2911 \n", + "21458 ZWE 181 Zimbabwe 2912 \n", + "21459 ZWE 181 Zimbabwe 2913 \n", + "21460 ZWE 181 Zimbabwe 2913 \n", + "21461 ZWE 181 Zimbabwe 2914 \n", + "21462 ZWE 181 Zimbabwe 2918 \n", + "21463 ZWE 181 Zimbabwe 2919 \n", + "21464 ZWE 181 Zimbabwe 2922 \n", + "21465 ZWE 181 Zimbabwe 2923 \n", + "21466 ZWE 181 Zimbabwe 2924 \n", + "21467 ZWE 181 Zimbabwe 2943 \n", + "21468 ZWE 181 Zimbabwe 2945 \n", + "21469 ZWE 181 Zimbabwe 2946 \n", + "21470 ZWE 181 Zimbabwe 2949 \n", + "21471 ZWE 181 Zimbabwe 2948 \n", + "21472 ZWE 181 Zimbabwe 2948 \n", + "21473 ZWE 181 Zimbabwe 2960 \n", + "21474 ZWE 181 Zimbabwe 2960 \n", + "21475 ZWE 181 Zimbabwe 2961 \n", + "21476 ZWE 181 Zimbabwe 2928 \n", + "\n", + " Item Element Code Element Unit \\\n", + "0 Wheat and products 5142 Food 1000 tonnes \n", + "1 Rice (Milled Equivalent) 5142 Food 1000 tonnes \n", + "2 Barley and products 5521 Feed 1000 tonnes \n", + "3 Barley and products 5142 Food 1000 tonnes \n", + "4 Maize and products 5521 Feed 1000 tonnes \n", + "5 Maize and products 5142 Food 1000 tonnes \n", + "6 Millet and products 5142 Food 1000 tonnes \n", + "7 Cereals, Other 5142 Food 1000 tonnes \n", + "8 Potatoes and products 5142 Food 1000 tonnes \n", + "9 Sugar cane 5521 Feed 1000 tonnes \n", + "10 Sugar beet 5521 Feed 1000 tonnes \n", + "11 Sugar (Raw Equivalent) 5142 Food 1000 tonnes \n", + "12 Sweeteners, Other 5142 Food 1000 tonnes \n", + "13 Honey 5142 Food 1000 tonnes \n", + "14 Pulses, Other and products 5521 Feed 1000 tonnes \n", + "15 Pulses, Other and products 5142 Food 1000 tonnes \n", + "16 Nuts and products 5142 Food 1000 tonnes \n", + "17 Coconuts - Incl Copra 5142 Food 1000 tonnes \n", + "18 Sesame seed 5142 Food 1000 tonnes \n", + "19 Olives (including preserved) 5142 Food 1000 tonnes \n", + "20 Soyabean Oil 5142 Food 1000 tonnes \n", + "21 Groundnut Oil 5142 Food 1000 tonnes \n", + "22 Sunflowerseed Oil 5142 Food 1000 tonnes \n", + "23 Rape and Mustard Oil 5142 Food 1000 tonnes \n", + "24 Cottonseed Oil 5142 Food 1000 tonnes \n", + "25 Palm Oil 5142 Food 1000 tonnes \n", + "26 Sesameseed Oil 5142 Food 1000 tonnes \n", + "27 Olive Oil 5142 Food 1000 tonnes \n", + "28 Oilcrops Oil, Other 5142 Food 1000 tonnes \n", + "29 Tomatoes and products 5142 Food 1000 tonnes \n", + "... ... ... ... ... \n", + "21447 Crustaceans 5142 Food 1000 tonnes \n", + "21448 Cephalopods 5142 Food 1000 tonnes \n", + "21449 Molluscs, Other 5142 Food 1000 tonnes \n", + "21450 Aquatic Plants 5142 Food 1000 tonnes \n", + "21451 Infant food 5142 Food 1000 tonnes \n", + "21452 Cereals - Excluding Beer 5521 Feed 1000 tonnes \n", + "21453 Cereals - Excluding Beer 5142 Food 1000 tonnes \n", + "21454 Starchy Roots 5142 Food 1000 tonnes \n", + "21455 Sugar Crops 5142 Food 1000 tonnes \n", + "21456 Sugar & Sweeteners 5142 Food 1000 tonnes \n", + "21457 Pulses 5142 Food 1000 tonnes \n", + "21458 Treenuts 5142 Food 1000 tonnes \n", + "21459 Oilcrops 5521 Feed 1000 tonnes \n", + "21460 Oilcrops 5142 Food 1000 tonnes \n", + "21461 Vegetable Oils 5142 Food 1000 tonnes \n", + "21462 Vegetables 5142 Food 1000 tonnes \n", + "21463 Fruits - Excluding Wine 5142 Food 1000 tonnes \n", + "21464 Stimulants 5142 Food 1000 tonnes \n", + "21465 Spices 5142 Food 1000 tonnes \n", + "21466 Alcoholic Beverages 5142 Food 1000 tonnes \n", + "21467 Meat 5142 Food 1000 tonnes \n", + "21468 Offals 5142 Food 1000 tonnes \n", + "21469 Animal fats 5142 Food 1000 tonnes \n", + "21470 Eggs 5142 Food 1000 tonnes \n", + "21471 Milk - Excluding Butter 5521 Feed 1000 tonnes \n", + "21472 Milk - Excluding Butter 5142 Food 1000 tonnes \n", + "21473 Fish, Seafood 5521 Feed 1000 tonnes \n", + "21474 Fish, Seafood 5142 Food 1000 tonnes \n", + "21475 Aquatic Products, Other 5142 Food 1000 tonnes \n", + "21476 Miscellaneous 5142 Food 1000 tonnes \n", + "\n", + " latitude longitude ... Y2004 Y2005 Y2006 Y2007 Y2008 \\\n", + "0 33.94 67.71 ... 3249.0 3486.0 3704.0 4164.0 4252.0 \n", + "1 33.94 67.71 ... 419.0 445.0 546.0 455.0 490.0 \n", + "2 33.94 67.71 ... 58.0 236.0 262.0 263.0 230.0 \n", + "3 33.94 67.71 ... 185.0 43.0 44.0 48.0 62.0 \n", + "4 33.94 67.71 ... 120.0 208.0 233.0 249.0 247.0 \n", + "5 33.94 67.71 ... 231.0 67.0 82.0 67.0 69.0 \n", + "6 33.94 67.71 ... 15.0 21.0 11.0 19.0 21.0 \n", + "7 33.94 67.71 ... 2.0 1.0 1.0 0.0 0.0 \n", + "8 33.94 67.71 ... 276.0 294.0 294.0 260.0 242.0 \n", + "9 33.94 67.71 ... 50.0 29.0 61.0 65.0 54.0 \n", + "10 33.94 67.71 ... 0.0 0.0 0.0 0.0 0.0 \n", + "11 33.94 67.71 ... 124.0 152.0 169.0 192.0 217.0 \n", + "12 33.94 67.71 ... 9.0 15.0 12.0 6.0 11.0 \n", + "13 33.94 67.71 ... 3.0 3.0 3.0 3.0 3.0 \n", + "14 33.94 67.71 ... 3.0 2.0 3.0 3.0 3.0 \n", + "15 33.94 67.71 ... 17.0 35.0 37.0 40.0 54.0 \n", + "16 33.94 67.71 ... 11.0 13.0 24.0 34.0 42.0 \n", + "17 33.94 67.71 ... 0.0 0.0 0.0 0.0 0.0 \n", + "18 33.94 67.71 ... 16.0 16.0 13.0 16.0 16.0 \n", + "19 33.94 67.71 ... 1.0 1.0 0.0 0.0 2.0 \n", + "20 33.94 67.71 ... 6.0 35.0 18.0 21.0 11.0 \n", + "21 33.94 67.71 ... 0.0 0.0 0.0 0.0 0.0 \n", + "22 33.94 67.71 ... 4.0 6.0 5.0 9.0 3.0 \n", + "23 33.94 67.71 ... 0.0 1.0 3.0 5.0 6.0 \n", + "24 33.94 67.71 ... 2.0 3.0 3.0 3.0 3.0 \n", + "25 33.94 67.71 ... 71.0 69.0 56.0 51.0 36.0 \n", + "26 33.94 67.71 ... 1.0 1.0 1.0 2.0 2.0 \n", + "27 33.94 67.71 ... 0.0 0.0 0.0 0.0 0.0 \n", + "28 33.94 67.71 ... 0.0 1.0 0.0 0.0 3.0 \n", + "29 33.94 67.71 ... 2.0 2.0 8.0 1.0 0.0 \n", + "... ... ... ... ... ... ... ... ... \n", + "21447 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21448 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21449 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21450 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21451 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21452 -19.02 29.15 ... 75.0 54.0 75.0 55.0 63.0 \n", + "21453 -19.02 29.15 ... 1844.0 1842.0 1944.0 1962.0 1918.0 \n", + "21454 -19.02 29.15 ... 223.0 236.0 238.0 228.0 245.0 \n", + "21455 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21456 -19.02 29.15 ... 335.0 313.0 339.0 302.0 285.0 \n", + "21457 -19.02 29.15 ... 63.0 59.0 61.0 57.0 69.0 \n", + "21458 -19.02 29.15 ... 1.0 2.0 1.0 2.0 2.0 \n", + "21459 -19.02 29.15 ... 36.0 46.0 41.0 33.0 31.0 \n", + "21460 -19.02 29.15 ... 60.0 59.0 61.0 62.0 48.0 \n", + "21461 -19.02 29.15 ... 111.0 114.0 112.0 114.0 134.0 \n", + "21462 -19.02 29.15 ... 161.0 166.0 208.0 185.0 137.0 \n", + "21463 -19.02 29.15 ... 191.0 134.0 167.0 177.0 185.0 \n", + "21464 -19.02 29.15 ... 7.0 21.0 14.0 24.0 16.0 \n", + "21465 -19.02 29.15 ... 7.0 11.0 7.0 12.0 16.0 \n", + "21466 -19.02 29.15 ... 294.0 290.0 316.0 355.0 398.0 \n", + "21467 -19.02 29.15 ... 222.0 228.0 233.0 238.0 242.0 \n", + "21468 -19.02 29.15 ... 20.0 20.0 21.0 21.0 21.0 \n", + "21469 -19.02 29.15 ... 26.0 26.0 29.0 29.0 27.0 \n", + "21470 -19.02 29.15 ... 15.0 18.0 18.0 21.0 22.0 \n", + "21471 -19.02 29.15 ... 21.0 21.0 21.0 21.0 21.0 \n", + "21472 -19.02 29.15 ... 373.0 357.0 359.0 356.0 341.0 \n", + "21473 -19.02 29.15 ... 5.0 4.0 9.0 6.0 9.0 \n", + "21474 -19.02 29.15 ... 18.0 14.0 17.0 14.0 15.0 \n", + "21475 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21476 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " Y2009 Y2010 Y2011 Y2012 Y2013 \n", + "0 4538.0 4605.0 4711.0 4810 4895 \n", + "1 415.0 442.0 476.0 425 422 \n", + "2 379.0 315.0 203.0 367 360 \n", + "3 55.0 60.0 72.0 78 89 \n", + "4 195.0 178.0 191.0 200 200 \n", + "5 71.0 82.0 73.0 77 76 \n", + "6 18.0 14.0 14.0 14 12 \n", + "7 0.0 0.0 0.0 0 0 \n", + "8 250.0 192.0 169.0 196 230 \n", + "9 114.0 83.0 83.0 69 81 \n", + "10 0.0 0.0 0.0 0 0 \n", + "11 231.0 240.0 240.0 250 255 \n", + "12 2.0 9.0 21.0 24 16 \n", + "13 3.0 3.0 2.0 2 2 \n", + "14 5.0 4.0 5.0 4 4 \n", + "15 80.0 66.0 81.0 63 74 \n", + "16 28.0 66.0 71.0 70 44 \n", + "17 0.0 0.0 0.0 0 0 \n", + "18 16.0 19.0 17.0 16 16 \n", + "19 3.0 2.0 2.0 2 2 \n", + "20 6.0 15.0 16.0 16 16 \n", + "21 0.0 0.0 0.0 0 0 \n", + "22 8.0 15.0 16.0 17 23 \n", + "23 6.0 1.0 2.0 2 2 \n", + "24 4.0 3.0 3.0 3 4 \n", + "25 53.0 59.0 51.0 61 64 \n", + "26 1.0 1.0 2.0 1 1 \n", + "27 1.0 1.0 1.0 1 1 \n", + "28 1.0 2.0 2.0 2 2 \n", + "29 0.0 0.0 0.0 0 0 \n", + "... ... ... ... ... ... \n", + "21447 0.0 0.0 0.0 0 0 \n", + "21448 0.0 0.0 0.0 0 0 \n", + "21449 0.0 1.0 0.0 0 0 \n", + "21450 0.0 0.0 0.0 0 0 \n", + "21451 0.0 0.0 0.0 0 0 \n", + "21452 62.0 55.0 55.0 55 55 \n", + "21453 1980.0 2011.0 2094.0 2071 2016 \n", + "21454 258.0 258.0 269.0 272 276 \n", + "21455 0.0 0.0 0.0 0 0 \n", + "21456 287.0 314.0 336.0 396 416 \n", + "21457 78.0 68.0 56.0 52 55 \n", + "21458 3.0 4.0 2.0 4 3 \n", + "21459 19.0 24.0 17.0 27 30 \n", + "21460 44.0 41.0 40.0 38 38 \n", + "21461 135.0 137.0 147.0 159 160 \n", + "21462 179.0 215.0 217.0 227 227 \n", + "21463 184.0 211.0 230.0 246 217 \n", + "21464 11.0 23.0 11.0 10 10 \n", + "21465 16.0 14.0 11.0 12 12 \n", + "21466 437.0 448.0 476.0 525 516 \n", + "21467 265.0 262.0 277.0 280 258 \n", + "21468 21.0 21.0 21.0 22 22 \n", + "21469 31.0 30.0 25.0 26 20 \n", + "21470 27.0 27.0 24.0 24 25 \n", + "21471 23.0 25.0 25.0 30 31 \n", + "21472 385.0 418.0 457.0 426 451 \n", + "21473 5.0 15.0 15.0 15 15 \n", + "21474 18.0 29.0 40.0 40 40 \n", + "21475 0.0 0.0 0.0 0 0 \n", + "21476 0.0 0.0 0.0 0 0 \n", + "\n", + "[21477 rows x 63 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "731a952c-b292-46e3-be7a-4afffe2b4ff1", + "_uuid": "5d165c279ce22afc0a874e32931d7b0ebb0717f9" + }, + "source": [ + "Let's see what the data looks like..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0", + "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a", + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "25c3f986-fd14-4a3f-baff-02571ad665eb", + "_uuid": "5a7da58320ab35ab1bcf83a62209afbe40b672fe" + }, + "source": [ + "# Plot for annual produce of different countries with quantity in y-axis and years in x-axis" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Area AbbreviationArea CodeAreaItem CodeItemElement CodeElementUnitlatitudelongitude...Y2004Y2005Y2006Y2007Y2008Y2009Y2010Y2011Y2012Y2013
0AFG2Afghanistan2511Wheat and products5142Food1000 tonnes33.9467.71...3249.03486.03704.04164.04252.04538.04605.04711.048104895
1AFG2Afghanistan2805Rice (Milled Equivalent)5142Food1000 tonnes33.9467.71...419.0445.0546.0455.0490.0415.0442.0476.0425422
2AFG2Afghanistan2513Barley and products5521Feed1000 tonnes33.9467.71...58.0236.0262.0263.0230.0379.0315.0203.0367360
3AFG2Afghanistan2513Barley and products5142Food1000 tonnes33.9467.71...185.043.044.048.062.055.060.072.07889
4AFG2Afghanistan2514Maize and products5521Feed1000 tonnes33.9467.71...120.0208.0233.0249.0247.0195.0178.0191.0200200
5AFG2Afghanistan2514Maize and products5142Food1000 tonnes33.9467.71...231.067.082.067.069.071.082.073.07776
6AFG2Afghanistan2517Millet and products5142Food1000 tonnes33.9467.71...15.021.011.019.021.018.014.014.01412
7AFG2Afghanistan2520Cereals, Other5142Food1000 tonnes33.9467.71...2.01.01.00.00.00.00.00.000
8AFG2Afghanistan2531Potatoes and products5142Food1000 tonnes33.9467.71...276.0294.0294.0260.0242.0250.0192.0169.0196230
9AFG2Afghanistan2536Sugar cane5521Feed1000 tonnes33.9467.71...50.029.061.065.054.0114.083.083.06981
10AFG2Afghanistan2537Sugar beet5521Feed1000 tonnes33.9467.71...0.00.00.00.00.00.00.00.000
11AFG2Afghanistan2542Sugar (Raw Equivalent)5142Food1000 tonnes33.9467.71...124.0152.0169.0192.0217.0231.0240.0240.0250255
12AFG2Afghanistan2543Sweeteners, Other5142Food1000 tonnes33.9467.71...9.015.012.06.011.02.09.021.02416
13AFG2Afghanistan2745Honey5142Food1000 tonnes33.9467.71...3.03.03.03.03.03.03.02.022
14AFG2Afghanistan2549Pulses, Other and products5521Feed1000 tonnes33.9467.71...3.02.03.03.03.05.04.05.044
15AFG2Afghanistan2549Pulses, Other and products5142Food1000 tonnes33.9467.71...17.035.037.040.054.080.066.081.06374
16AFG2Afghanistan2551Nuts and products5142Food1000 tonnes33.9467.71...11.013.024.034.042.028.066.071.07044
17AFG2Afghanistan2560Coconuts - Incl Copra5142Food1000 tonnes33.9467.71...0.00.00.00.00.00.00.00.000
18AFG2Afghanistan2561Sesame seed5142Food1000 tonnes33.9467.71...16.016.013.016.016.016.019.017.01616
19AFG2Afghanistan2563Olives (including preserved)5142Food1000 tonnes33.9467.71...1.01.00.00.02.03.02.02.022
20AFG2Afghanistan2571Soyabean Oil5142Food1000 tonnes33.9467.71...6.035.018.021.011.06.015.016.01616
21AFG2Afghanistan2572Groundnut Oil5142Food1000 tonnes33.9467.71...0.00.00.00.00.00.00.00.000
22AFG2Afghanistan2573Sunflowerseed Oil5142Food1000 tonnes33.9467.71...4.06.05.09.03.08.015.016.01723
23AFG2Afghanistan2574Rape and Mustard Oil5142Food1000 tonnes33.9467.71...0.01.03.05.06.06.01.02.022
24AFG2Afghanistan2575Cottonseed Oil5142Food1000 tonnes33.9467.71...2.03.03.03.03.04.03.03.034
25AFG2Afghanistan2577Palm Oil5142Food1000 tonnes33.9467.71...71.069.056.051.036.053.059.051.06164
26AFG2Afghanistan2579Sesameseed Oil5142Food1000 tonnes33.9467.71...1.01.01.02.02.01.01.02.011
27AFG2Afghanistan2580Olive Oil5142Food1000 tonnes33.9467.71...0.00.00.00.00.01.01.01.011
28AFG2Afghanistan2586Oilcrops Oil, Other5142Food1000 tonnes33.9467.71...0.01.00.00.03.01.02.02.022
29AFG2Afghanistan2601Tomatoes and products5142Food1000 tonnes33.9467.71...2.02.08.01.00.00.00.00.000
..................................................................
21447ZWE181Zimbabwe2765Crustaceans5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
21448ZWE181Zimbabwe2766Cephalopods5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
21449ZWE181Zimbabwe2767Molluscs, Other5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.01.00.000
21450ZWE181Zimbabwe2775Aquatic Plants5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
21451ZWE181Zimbabwe2680Infant food5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
21452ZWE181Zimbabwe2905Cereals - Excluding Beer5521Feed1000 tonnes-19.0229.15...75.054.075.055.063.062.055.055.05555
21453ZWE181Zimbabwe2905Cereals - Excluding Beer5142Food1000 tonnes-19.0229.15...1844.01842.01944.01962.01918.01980.02011.02094.020712016
21454ZWE181Zimbabwe2907Starchy Roots5142Food1000 tonnes-19.0229.15...223.0236.0238.0228.0245.0258.0258.0269.0272276
21455ZWE181Zimbabwe2908Sugar Crops5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
21456ZWE181Zimbabwe2909Sugar & Sweeteners5142Food1000 tonnes-19.0229.15...335.0313.0339.0302.0285.0287.0314.0336.0396416
21457ZWE181Zimbabwe2911Pulses5142Food1000 tonnes-19.0229.15...63.059.061.057.069.078.068.056.05255
21458ZWE181Zimbabwe2912Treenuts5142Food1000 tonnes-19.0229.15...1.02.01.02.02.03.04.02.043
21459ZWE181Zimbabwe2913Oilcrops5521Feed1000 tonnes-19.0229.15...36.046.041.033.031.019.024.017.02730
21460ZWE181Zimbabwe2913Oilcrops5142Food1000 tonnes-19.0229.15...60.059.061.062.048.044.041.040.03838
21461ZWE181Zimbabwe2914Vegetable Oils5142Food1000 tonnes-19.0229.15...111.0114.0112.0114.0134.0135.0137.0147.0159160
21462ZWE181Zimbabwe2918Vegetables5142Food1000 tonnes-19.0229.15...161.0166.0208.0185.0137.0179.0215.0217.0227227
21463ZWE181Zimbabwe2919Fruits - Excluding Wine5142Food1000 tonnes-19.0229.15...191.0134.0167.0177.0185.0184.0211.0230.0246217
21464ZWE181Zimbabwe2922Stimulants5142Food1000 tonnes-19.0229.15...7.021.014.024.016.011.023.011.01010
21465ZWE181Zimbabwe2923Spices5142Food1000 tonnes-19.0229.15...7.011.07.012.016.016.014.011.01212
21466ZWE181Zimbabwe2924Alcoholic Beverages5142Food1000 tonnes-19.0229.15...294.0290.0316.0355.0398.0437.0448.0476.0525516
21467ZWE181Zimbabwe2943Meat5142Food1000 tonnes-19.0229.15...222.0228.0233.0238.0242.0265.0262.0277.0280258
21468ZWE181Zimbabwe2945Offals5142Food1000 tonnes-19.0229.15...20.020.021.021.021.021.021.021.02222
21469ZWE181Zimbabwe2946Animal fats5142Food1000 tonnes-19.0229.15...26.026.029.029.027.031.030.025.02620
21470ZWE181Zimbabwe2949Eggs5142Food1000 tonnes-19.0229.15...15.018.018.021.022.027.027.024.02425
21471ZWE181Zimbabwe2948Milk - Excluding Butter5521Feed1000 tonnes-19.0229.15...21.021.021.021.021.023.025.025.03031
21472ZWE181Zimbabwe2948Milk - Excluding Butter5142Food1000 tonnes-19.0229.15...373.0357.0359.0356.0341.0385.0418.0457.0426451
21473ZWE181Zimbabwe2960Fish, Seafood5521Feed1000 tonnes-19.0229.15...5.04.09.06.09.05.015.015.01515
21474ZWE181Zimbabwe2960Fish, Seafood5142Food1000 tonnes-19.0229.15...18.014.017.014.015.018.029.040.04040
21475ZWE181Zimbabwe2961Aquatic Products, Other5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
21476ZWE181Zimbabwe2928Miscellaneous5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
\n", + "

21477 rows × 63 columns

\n", + "
" + ], + "text/plain": [ + " Area Abbreviation Area Code Area Item Code \\\n", + "0 AFG 2 Afghanistan 2511 \n", + "1 AFG 2 Afghanistan 2805 \n", + "2 AFG 2 Afghanistan 2513 \n", + "3 AFG 2 Afghanistan 2513 \n", + "4 AFG 2 Afghanistan 2514 \n", + "5 AFG 2 Afghanistan 2514 \n", + "6 AFG 2 Afghanistan 2517 \n", + "7 AFG 2 Afghanistan 2520 \n", + "8 AFG 2 Afghanistan 2531 \n", + "9 AFG 2 Afghanistan 2536 \n", + "10 AFG 2 Afghanistan 2537 \n", + "11 AFG 2 Afghanistan 2542 \n", + "12 AFG 2 Afghanistan 2543 \n", + "13 AFG 2 Afghanistan 2745 \n", + "14 AFG 2 Afghanistan 2549 \n", + "15 AFG 2 Afghanistan 2549 \n", + "16 AFG 2 Afghanistan 2551 \n", + "17 AFG 2 Afghanistan 2560 \n", + "18 AFG 2 Afghanistan 2561 \n", + "19 AFG 2 Afghanistan 2563 \n", + "20 AFG 2 Afghanistan 2571 \n", + "21 AFG 2 Afghanistan 2572 \n", + "22 AFG 2 Afghanistan 2573 \n", + "23 AFG 2 Afghanistan 2574 \n", + "24 AFG 2 Afghanistan 2575 \n", + "25 AFG 2 Afghanistan 2577 \n", + "26 AFG 2 Afghanistan 2579 \n", + "27 AFG 2 Afghanistan 2580 \n", + "28 AFG 2 Afghanistan 2586 \n", + "29 AFG 2 Afghanistan 2601 \n", + "... ... ... ... ... \n", + "21447 ZWE 181 Zimbabwe 2765 \n", + "21448 ZWE 181 Zimbabwe 2766 \n", + "21449 ZWE 181 Zimbabwe 2767 \n", + "21450 ZWE 181 Zimbabwe 2775 \n", + "21451 ZWE 181 Zimbabwe 2680 \n", + "21452 ZWE 181 Zimbabwe 2905 \n", + "21453 ZWE 181 Zimbabwe 2905 \n", + "21454 ZWE 181 Zimbabwe 2907 \n", + "21455 ZWE 181 Zimbabwe 2908 \n", + "21456 ZWE 181 Zimbabwe 2909 \n", + "21457 ZWE 181 Zimbabwe 2911 \n", + "21458 ZWE 181 Zimbabwe 2912 \n", + "21459 ZWE 181 Zimbabwe 2913 \n", + "21460 ZWE 181 Zimbabwe 2913 \n", + "21461 ZWE 181 Zimbabwe 2914 \n", + "21462 ZWE 181 Zimbabwe 2918 \n", + "21463 ZWE 181 Zimbabwe 2919 \n", + "21464 ZWE 181 Zimbabwe 2922 \n", + "21465 ZWE 181 Zimbabwe 2923 \n", + "21466 ZWE 181 Zimbabwe 2924 \n", + "21467 ZWE 181 Zimbabwe 2943 \n", + "21468 ZWE 181 Zimbabwe 2945 \n", + "21469 ZWE 181 Zimbabwe 2946 \n", + "21470 ZWE 181 Zimbabwe 2949 \n", + "21471 ZWE 181 Zimbabwe 2948 \n", + "21472 ZWE 181 Zimbabwe 2948 \n", + "21473 ZWE 181 Zimbabwe 2960 \n", + "21474 ZWE 181 Zimbabwe 2960 \n", + "21475 ZWE 181 Zimbabwe 2961 \n", + "21476 ZWE 181 Zimbabwe 2928 \n", + "\n", + " Item Element Code Element Unit \\\n", + "0 Wheat and products 5142 Food 1000 tonnes \n", + "1 Rice (Milled Equivalent) 5142 Food 1000 tonnes \n", + "2 Barley and products 5521 Feed 1000 tonnes \n", + "3 Barley and products 5142 Food 1000 tonnes \n", + "4 Maize and products 5521 Feed 1000 tonnes \n", + "5 Maize and products 5142 Food 1000 tonnes \n", + "6 Millet and products 5142 Food 1000 tonnes \n", + "7 Cereals, Other 5142 Food 1000 tonnes \n", + "8 Potatoes and products 5142 Food 1000 tonnes \n", + "9 Sugar cane 5521 Feed 1000 tonnes \n", + "10 Sugar beet 5521 Feed 1000 tonnes \n", + "11 Sugar (Raw Equivalent) 5142 Food 1000 tonnes \n", + "12 Sweeteners, Other 5142 Food 1000 tonnes \n", + "13 Honey 5142 Food 1000 tonnes \n", + "14 Pulses, Other and products 5521 Feed 1000 tonnes \n", + "15 Pulses, Other and products 5142 Food 1000 tonnes \n", + "16 Nuts and products 5142 Food 1000 tonnes \n", + "17 Coconuts - Incl Copra 5142 Food 1000 tonnes \n", + "18 Sesame seed 5142 Food 1000 tonnes \n", + "19 Olives (including preserved) 5142 Food 1000 tonnes \n", + "20 Soyabean Oil 5142 Food 1000 tonnes \n", + "21 Groundnut Oil 5142 Food 1000 tonnes \n", + "22 Sunflowerseed Oil 5142 Food 1000 tonnes \n", + "23 Rape and Mustard Oil 5142 Food 1000 tonnes \n", + "24 Cottonseed Oil 5142 Food 1000 tonnes \n", + "25 Palm Oil 5142 Food 1000 tonnes \n", + "26 Sesameseed Oil 5142 Food 1000 tonnes \n", + "27 Olive Oil 5142 Food 1000 tonnes \n", + "28 Oilcrops Oil, Other 5142 Food 1000 tonnes \n", + "29 Tomatoes and products 5142 Food 1000 tonnes \n", + "... ... ... ... ... \n", + "21447 Crustaceans 5142 Food 1000 tonnes \n", + "21448 Cephalopods 5142 Food 1000 tonnes \n", + "21449 Molluscs, Other 5142 Food 1000 tonnes \n", + "21450 Aquatic Plants 5142 Food 1000 tonnes \n", + "21451 Infant food 5142 Food 1000 tonnes \n", + "21452 Cereals - Excluding Beer 5521 Feed 1000 tonnes \n", + "21453 Cereals - Excluding Beer 5142 Food 1000 tonnes \n", + "21454 Starchy Roots 5142 Food 1000 tonnes \n", + "21455 Sugar Crops 5142 Food 1000 tonnes \n", + "21456 Sugar & Sweeteners 5142 Food 1000 tonnes \n", + "21457 Pulses 5142 Food 1000 tonnes \n", + "21458 Treenuts 5142 Food 1000 tonnes \n", + "21459 Oilcrops 5521 Feed 1000 tonnes \n", + "21460 Oilcrops 5142 Food 1000 tonnes \n", + "21461 Vegetable Oils 5142 Food 1000 tonnes \n", + "21462 Vegetables 5142 Food 1000 tonnes \n", + "21463 Fruits - Excluding Wine 5142 Food 1000 tonnes \n", + "21464 Stimulants 5142 Food 1000 tonnes \n", + "21465 Spices 5142 Food 1000 tonnes \n", + "21466 Alcoholic Beverages 5142 Food 1000 tonnes \n", + "21467 Meat 5142 Food 1000 tonnes \n", + "21468 Offals 5142 Food 1000 tonnes \n", + "21469 Animal fats 5142 Food 1000 tonnes \n", + "21470 Eggs 5142 Food 1000 tonnes \n", + "21471 Milk - Excluding Butter 5521 Feed 1000 tonnes \n", + "21472 Milk - Excluding Butter 5142 Food 1000 tonnes \n", + "21473 Fish, Seafood 5521 Feed 1000 tonnes \n", + "21474 Fish, Seafood 5142 Food 1000 tonnes \n", + "21475 Aquatic Products, Other 5142 Food 1000 tonnes \n", + "21476 Miscellaneous 5142 Food 1000 tonnes \n", + "\n", + " latitude longitude ... Y2004 Y2005 Y2006 Y2007 Y2008 \\\n", + "0 33.94 67.71 ... 3249.0 3486.0 3704.0 4164.0 4252.0 \n", + "1 33.94 67.71 ... 419.0 445.0 546.0 455.0 490.0 \n", + "2 33.94 67.71 ... 58.0 236.0 262.0 263.0 230.0 \n", + "3 33.94 67.71 ... 185.0 43.0 44.0 48.0 62.0 \n", + "4 33.94 67.71 ... 120.0 208.0 233.0 249.0 247.0 \n", + "5 33.94 67.71 ... 231.0 67.0 82.0 67.0 69.0 \n", + "6 33.94 67.71 ... 15.0 21.0 11.0 19.0 21.0 \n", + "7 33.94 67.71 ... 2.0 1.0 1.0 0.0 0.0 \n", + "8 33.94 67.71 ... 276.0 294.0 294.0 260.0 242.0 \n", + "9 33.94 67.71 ... 50.0 29.0 61.0 65.0 54.0 \n", + "10 33.94 67.71 ... 0.0 0.0 0.0 0.0 0.0 \n", + "11 33.94 67.71 ... 124.0 152.0 169.0 192.0 217.0 \n", + "12 33.94 67.71 ... 9.0 15.0 12.0 6.0 11.0 \n", + "13 33.94 67.71 ... 3.0 3.0 3.0 3.0 3.0 \n", + "14 33.94 67.71 ... 3.0 2.0 3.0 3.0 3.0 \n", + "15 33.94 67.71 ... 17.0 35.0 37.0 40.0 54.0 \n", + "16 33.94 67.71 ... 11.0 13.0 24.0 34.0 42.0 \n", + "17 33.94 67.71 ... 0.0 0.0 0.0 0.0 0.0 \n", + "18 33.94 67.71 ... 16.0 16.0 13.0 16.0 16.0 \n", + "19 33.94 67.71 ... 1.0 1.0 0.0 0.0 2.0 \n", + "20 33.94 67.71 ... 6.0 35.0 18.0 21.0 11.0 \n", + "21 33.94 67.71 ... 0.0 0.0 0.0 0.0 0.0 \n", + "22 33.94 67.71 ... 4.0 6.0 5.0 9.0 3.0 \n", + "23 33.94 67.71 ... 0.0 1.0 3.0 5.0 6.0 \n", + "24 33.94 67.71 ... 2.0 3.0 3.0 3.0 3.0 \n", + "25 33.94 67.71 ... 71.0 69.0 56.0 51.0 36.0 \n", + "26 33.94 67.71 ... 1.0 1.0 1.0 2.0 2.0 \n", + "27 33.94 67.71 ... 0.0 0.0 0.0 0.0 0.0 \n", + "28 33.94 67.71 ... 0.0 1.0 0.0 0.0 3.0 \n", + "29 33.94 67.71 ... 2.0 2.0 8.0 1.0 0.0 \n", + "... ... ... ... ... ... ... ... ... \n", + "21447 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21448 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21449 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21450 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21451 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21452 -19.02 29.15 ... 75.0 54.0 75.0 55.0 63.0 \n", + "21453 -19.02 29.15 ... 1844.0 1842.0 1944.0 1962.0 1918.0 \n", + "21454 -19.02 29.15 ... 223.0 236.0 238.0 228.0 245.0 \n", + "21455 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21456 -19.02 29.15 ... 335.0 313.0 339.0 302.0 285.0 \n", + "21457 -19.02 29.15 ... 63.0 59.0 61.0 57.0 69.0 \n", + "21458 -19.02 29.15 ... 1.0 2.0 1.0 2.0 2.0 \n", + "21459 -19.02 29.15 ... 36.0 46.0 41.0 33.0 31.0 \n", + "21460 -19.02 29.15 ... 60.0 59.0 61.0 62.0 48.0 \n", + "21461 -19.02 29.15 ... 111.0 114.0 112.0 114.0 134.0 \n", + "21462 -19.02 29.15 ... 161.0 166.0 208.0 185.0 137.0 \n", + "21463 -19.02 29.15 ... 191.0 134.0 167.0 177.0 185.0 \n", + "21464 -19.02 29.15 ... 7.0 21.0 14.0 24.0 16.0 \n", + "21465 -19.02 29.15 ... 7.0 11.0 7.0 12.0 16.0 \n", + "21466 -19.02 29.15 ... 294.0 290.0 316.0 355.0 398.0 \n", + "21467 -19.02 29.15 ... 222.0 228.0 233.0 238.0 242.0 \n", + "21468 -19.02 29.15 ... 20.0 20.0 21.0 21.0 21.0 \n", + "21469 -19.02 29.15 ... 26.0 26.0 29.0 29.0 27.0 \n", + "21470 -19.02 29.15 ... 15.0 18.0 18.0 21.0 22.0 \n", + "21471 -19.02 29.15 ... 21.0 21.0 21.0 21.0 21.0 \n", + "21472 -19.02 29.15 ... 373.0 357.0 359.0 356.0 341.0 \n", + "21473 -19.02 29.15 ... 5.0 4.0 9.0 6.0 9.0 \n", + "21474 -19.02 29.15 ... 18.0 14.0 17.0 14.0 15.0 \n", + "21475 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21476 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " Y2009 Y2010 Y2011 Y2012 Y2013 \n", + "0 4538.0 4605.0 4711.0 4810 4895 \n", + "1 415.0 442.0 476.0 425 422 \n", + "2 379.0 315.0 203.0 367 360 \n", + "3 55.0 60.0 72.0 78 89 \n", + "4 195.0 178.0 191.0 200 200 \n", + "5 71.0 82.0 73.0 77 76 \n", + "6 18.0 14.0 14.0 14 12 \n", + "7 0.0 0.0 0.0 0 0 \n", + "8 250.0 192.0 169.0 196 230 \n", + "9 114.0 83.0 83.0 69 81 \n", + "10 0.0 0.0 0.0 0 0 \n", + "11 231.0 240.0 240.0 250 255 \n", + "12 2.0 9.0 21.0 24 16 \n", + "13 3.0 3.0 2.0 2 2 \n", + "14 5.0 4.0 5.0 4 4 \n", + "15 80.0 66.0 81.0 63 74 \n", + "16 28.0 66.0 71.0 70 44 \n", + "17 0.0 0.0 0.0 0 0 \n", + "18 16.0 19.0 17.0 16 16 \n", + "19 3.0 2.0 2.0 2 2 \n", + "20 6.0 15.0 16.0 16 16 \n", + "21 0.0 0.0 0.0 0 0 \n", + "22 8.0 15.0 16.0 17 23 \n", + "23 6.0 1.0 2.0 2 2 \n", + "24 4.0 3.0 3.0 3 4 \n", + "25 53.0 59.0 51.0 61 64 \n", + "26 1.0 1.0 2.0 1 1 \n", + "27 1.0 1.0 1.0 1 1 \n", + "28 1.0 2.0 2.0 2 2 \n", + "29 0.0 0.0 0.0 0 0 \n", + "... ... ... ... ... ... \n", + "21447 0.0 0.0 0.0 0 0 \n", + "21448 0.0 0.0 0.0 0 0 \n", + "21449 0.0 1.0 0.0 0 0 \n", + "21450 0.0 0.0 0.0 0 0 \n", + "21451 0.0 0.0 0.0 0 0 \n", + "21452 62.0 55.0 55.0 55 55 \n", + "21453 1980.0 2011.0 2094.0 2071 2016 \n", + "21454 258.0 258.0 269.0 272 276 \n", + "21455 0.0 0.0 0.0 0 0 \n", + "21456 287.0 314.0 336.0 396 416 \n", + "21457 78.0 68.0 56.0 52 55 \n", + "21458 3.0 4.0 2.0 4 3 \n", + "21459 19.0 24.0 17.0 27 30 \n", + "21460 44.0 41.0 40.0 38 38 \n", + "21461 135.0 137.0 147.0 159 160 \n", + "21462 179.0 215.0 217.0 227 227 \n", + "21463 184.0 211.0 230.0 246 217 \n", + "21464 11.0 23.0 11.0 10 10 \n", + "21465 16.0 14.0 11.0 12 12 \n", + "21466 437.0 448.0 476.0 525 516 \n", + "21467 265.0 262.0 277.0 280 258 \n", + "21468 21.0 21.0 21.0 22 22 \n", + "21469 31.0 30.0 25.0 26 20 \n", + "21470 27.0 27.0 24.0 24 25 \n", + "21471 23.0 25.0 25.0 30 31 \n", + "21472 385.0 418.0 457.0 426 451 \n", + "21473 5.0 15.0 15.0 15 15 \n", + "21474 18.0 29.0 40.0 40 40 \n", + "21475 0.0 0.0 0.0 0 0 \n", + "21476 0.0 0.0 0.0 0 0 \n", + "\n", + "[21477 rows x 63 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "_cell_guid": "347e620f-b0e4-448e-81c7-e164f560c5a3", + "_uuid": "0acdd759950f5df3298224b0804562973663a11d", + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "area_list = list(df['Area'].unique())\n", + "year_list = list(df.iloc[:,10:].columns)\n", + "\n", + "plt.figure(figsize=(24,12))\n", + "for ar in area_list:\n", + " yearly_produce = []\n", + " for yr in year_list:\n", + " yearly_produce.append(df[yr][df['Area'] == ar].sum())\n", + " plt.plot(yearly_produce, label=ar)\n", + "plt.xticks(np.arange(53), tuple(year_list), rotation=60)\n", + "plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=8, mode=\"expand\", borderaxespad=0.)\n", + "plt.savefig('p.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(24,12))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "2ebe07e3-739b-4f39-8736-a512426c05bf", + "_uuid": "70900ec0ff5e248cd382ee53b5927cb671efa80e", + "collapsed": true + }, + "source": [ + "Clearly, China, India and US stand out here. So, these are the countries with most food and feed production.\n", + "\n", + "Now, let's have a close look at their food and feed data\n", + "\n", + "# Food and feed plot for the whole dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "_cell_guid": "ec0c911d-e154-4f8a-a79f-ced4896d5115", + "_uuid": "683dc56125b3a4c66b1e140098ec91490cbbe96f", + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n", + " warnings.warn(msg)\n" + ] + }, + { + "data": { + "image/png": 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yNcnd7fOAVk+SC5JMJ7ktyauG9rOxtb87ycZJHY8kjcukz7h/parWVdX6tnwucENVrQVuaMsAbwLWtmkTcBEMgh44D3g1cBxw3lzYS9JKNeng3tnJwKVt/lLglKH6ZTXwDWD/JIcAJwFbq+rRqvoRsBXYMO5OS9I4TTK4C7g+yS1JNrXai6tqO0D7PLjVDwUeGNp2ptUWqj9Fkk1JppJMzc7O7ubDkKTx2muC3/2aqnowycHA1iTfXaRt5qnVIvWnFqouBi4GWL9+/dPWS1JPJnbGXVUPts+HgasZjFE/1IZAaJ8Pt+YzwGFDm68BHlykLkkr1kSCO8nPJ3nR3DxwInAHsAWYuzNkI/CVNr8FOKPdXXI88OM2lHIdcGKSA9pFyRNbTZJWrEkNlbwYuDrJXB++WFXXJrkZuDLJmcD9wNtb+2uANwPTwOPAuwGq6tEkHwVubu0+UlWPju8wJGn8JhLcVXUv8I/nqf8QOGGeegFnL7CvzcDm3d1HSVqultvtgJKkXTC4JakzBrckdcbglqTOGNyS1BmDW5I6Y3BLUmcMbknqjMEtSZ0xuCWpMwa3JHXG4JakzkzyRQrSHu3+j/zSpLugEXjpv7195N/hGbckdcbglqTOGNyS1BmDW5I6Y3BLUmcMbknqjMEtSZ0xuCWpMwa3JHXG4JakzhjcktQZg1uSOmNwS1JnDG5J6ozBLUmdMbglqTMGtyR1xuCWpM4Y3JLUmbEHd5LDkvxZku8kuTPJv2j1Dyf5QZJtbXrz0DYfSjKd5K4kJw3VN7TadJJzx30skjQJk3hZ8A7gX1bVrUleBNySZGtb96mq+sRw4yRHAacBrwBeAnwtyZFt9YXAG4EZ4OYkW6rq22M5CkmakLEHd1VtB7a3+ceSfAc4dJFNTgauqKongO8lmQaOa+umq+pegCRXtLYGt6QVbaJj3EkOB14J/EUrnZPktiSbkxzQaocCDwxtNtNqC9UlaUWbWHAneSFwFfD+qvoJcBHwMmAdgzPyT841nWfzWqQ+33dtSjKVZGp2dvY5912SJmkiwZ3k+QxC+wtV9ScAVfVQVT1ZVX8HfJafDYfMAIcNbb4GeHCR+tNU1cVVtb6q1q9evXr3Howkjdkk7ioJ8DngO1X1n4fqhww1OxW4o81vAU5Lsk+SI4C1wDeBm4G1SY5IsjeDC5hbxnEMkjRJk7ir5DXAbwG3J9nWav8aOD3JOgbDHfcB7wWoqjuTXMngouMO4OyqehIgyTnAdcAqYHNV3TnOA5GkSZjEXSX/g/nHp69ZZJvzgfPnqV+z2HaStBL5y0lJ6ozBLUmdMbglqTMGtyR1xuCWpM4Y3JLUGYNbkjpjcEtSZwxuSeqMwS1JnTG4JakzBrckdcbglqTOGNyS1BmDW5I6Y3BLUmcMbknqjMEtSZ0xuCWpMwa3JHXG4JakzhjcktQZg1uSOmNwS1JnDG5J6ozBLUmdMbglqTMGtyR1xuCWpM4Y3JLUGYNbkjpjcEtSZwxuSepM98GdZEOSu5JMJzl30v2RpFHrOriTrAIuBN4EHAWcnuSoyfZKkkar6+AGjgOmq+reqvpb4Arg5An3SZJGaq9Jd+A5OhR4YGh5Bnj1zo2SbAI2tcW/TnLXGPrWu4OARybdiXHIJzZOugt7gj3m74nz8ly2vraqNuyqUe/BPd+/UD2tUHUxcPHou7NyJJmqqvWT7odWBv+edq/eh0pmgMOGltcAD06oL5I0Fr0H983A2iRHJNkbOA3YMuE+SdJIdT1UUlU7kpwDXAesAjZX1Z0T7tZK4dCSdif/nnajVD1tSFiStIz1PlQiSXscg1uSOmNw70GSPJlk29B0+G7Y558n8TavPdCI/p4+nORfPfferWxdX5zUM/bTqlo36U5oxfDvaUI8497DJfm5JJ9PcnuSbyX5lV3U901yRZLbknwJ2HeiB6BlJcmqJB9PcnP7G3nv0LrfGar/u6H677YHxX0NePlEOt4Zz7j3LPsm2dbmv1dVpwJnA1TVLyX5h8D1SY5cpH4W8HhVHZPkGODW8R+Glon5/p7OBH5cVf8kyT7A/0xyPbC2Tccx+MXzliSvA/6Gwe8vXskgj24FbhnzcXTH4N6zzPdf238GfBqgqr6b5PvAkYvUXwdc0Oq3JbltXJ3XsjPf39OJwDFJ3taW/x6DwD6xTd9q9Re2+ouAq6vqcYAk/oBuCQxuLfREnMWelOPN/1pIgN+uquueUkxOAv5jVf2Xnervx7+nZ8wxbt0I/CZAGwp5KXDXEutHA8eMv8taxq4DzkryfBj87ST5+VZ/T5IXtvqhSQ5m8Pd0art28iLg1yfV8Z54xq0/AD6T5HZgB/CuqnoiyUL1i4DPtyGSbcA3J9ZzLUd/CBwO3JokwCxwSlVdn+QfATcNyvw18M6qurVd5N4GfB/475Ppdl/8ybskdcahEknqjMEtSZ0xuCWpMwa3JHXG4Jakzhjc2qPM80S7c1t9Yk85TPKuJC+ZxHerT97HrT3Ncnyi3buAO/BF11oiz7ilnSQ5MclNSW5N8sdDv/a7L8l/aOumkrwqyXVJ7knyvqHtn/YUvCSHJ/lOks8muTPJ9e3Xgm8D1gNfaP8D8GmL2iWDW3uafXcaKvmN4ZVJDgL+DfCrVfUqYAr4wFCTB6rqlxn8wu8S4G3A8cBH2vYn8rOn4K0Djm1PwaPVL6yqVwB/Bfzzqvpy+47frKp1VfXTkRy1VhSHSrSn2dVQyfHAUQweRwqwN3DT0Pq5p9fdDrywqh4DHkvyf5Psz8JPwbufwaNP5x6DeguDn4ZLz5jBLT1VgK1VdfoC659on383ND+3vFfbfr6n4B2+U/sn8SUUepYcKpGe6hvAa5L8A4AkL2hPR1yqhZ6Ct5jHGDyXWloSz7i1pxl+awvAtVV17txCVc0meRdweXuDCwzGvP9yKTtf6Cl4DM6wF3IJgycx/hT4Zce5tSs+HVCSOuNQiSR1xuCWpM4Y3JLUGYNbkjpjcEtSZwxuSeqMwS1Jnfl/+L4Y6b2CQ0EAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.factorplot(\"Element\", data=df, kind=\"count\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "189c74af-e6e4-4ddd-a73c-3725f3aa8124", + "_uuid": "bfd404fb5dbb48c3e3bd1dcd45fb27a5fb475a00" + }, + "source": [ + "So, there is a huge difference in food and feed production. Now, we have obvious assumptions about the following plots after looking at this huge difference.\n", + "\n", + "# Food and feed plot for the largest producers(India, USA, China)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "_cell_guid": "0bf44e4e-d4c4-4f74-ae9f-82f52139d182", + "_uuid": "be1bc3d49c8cee62f48a09ada0db3170adcedc17" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n", + " warnings.warn(msg)\n", + "/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3672: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n", + " warnings.warn(msg, UserWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.factorplot(\"Area\", data=df[(df['Area'] == \"India\") | (df['Area'] == \"China, mainland\") | (df['Area'] == \"United States of America\")], kind=\"count\", hue=\"Element\", size=8, aspect=.8)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "94c19dc8-b1e7-4b61-b81f-422c27184c4e", + "_uuid": "0d1cfc7acc74847dbc5813b9b3bd0eb9db450985" + }, + "source": [ + "Though, there is a huge difference between feed and food production, these countries' total production and their ranks depend on feed production." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "9dba87b4-fa51-43ef-95ae-f31396c20146", + "_uuid": "43e0f00abf706ab1782ebb78cefc38aca17316e6" + }, + "source": [ + "Now, we create a dataframe with countries as index and their annual produce as columns from 1961 to 2013." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "_cell_guid": "c4a5f859-0384-4c8e-b894-3f747aec8cf9", + "_uuid": "84dd7a2b601479728dd172d3100951553c2daff5", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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5 rows × 53 columns

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" + ], + "text/plain": [ + " Y1961 Y1962 Y1963 Y1964 Y1965 Y1966 \\\n", + "Afghanistan 9481.0 9414.0 9194.0 10170.0 10473.0 10169.0 \n", + "Albania 1706.0 1749.0 1767.0 1889.0 1884.0 1995.0 \n", + "Algeria 7488.0 7235.0 6861.0 7255.0 7509.0 7536.0 \n", + "Angola 4834.0 4775.0 5240.0 5286.0 5527.0 5677.0 \n", + "Antigua and Barbuda 92.0 94.0 105.0 95.0 84.0 73.0 \n", + "\n", + " Y1967 Y1968 Y1969 Y1970 ... Y2004 \\\n", + "Afghanistan 11289.0 11508.0 11815.0 10454.0 ... 16542.0 \n", + "Albania 2046.0 2169.0 2230.0 2395.0 ... 6637.0 \n", + "Algeria 7986.0 8839.0 9003.0 9355.0 ... 48619.0 \n", + "Angola 5833.0 5685.0 6219.0 6460.0 ... 25541.0 \n", + "Antigua and Barbuda 64.0 59.0 68.0 77.0 ... 92.0 \n", + "\n", + " Y2005 Y2006 Y2007 Y2008 Y2009 Y2010 \\\n", + "Afghanistan 17658.0 18317.0 19248.0 19381.0 20661.0 21030.0 \n", + "Albania 6719.0 6911.0 6744.0 7168.0 7316.0 7907.0 \n", + "Algeria 49562.0 51067.0 49933.0 50916.0 57505.0 60071.0 \n", + "Angola 26696.0 28247.0 29877.0 32053.0 36985.0 38400.0 \n", + "Antigua and Barbuda 115.0 110.0 122.0 115.0 114.0 115.0 \n", + "\n", + " Y2011 Y2012 Y2013 \n", + "Afghanistan 21100.0 22706.0 23007.0 \n", + "Albania 8114.0 8221.0 8271.0 \n", + "Algeria 65852.0 69365.0 72161.0 \n", + "Angola 40573.0 38064.0 48639.0 \n", + "Antigua and Barbuda 118.0 113.0 119.0 \n", + "\n", + "[5 rows x 53 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_df = pd.DataFrame.transpose(new_df)\n", + "new_df.columns = year_list\n", + "\n", + "new_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "57929d23-e3d7-4955-92d1-6fa388eb774d", + "_uuid": "605f908af9ff88120fce2a2b59160816fcdcfa67" + }, + "source": [ + "Perfect! Now, we will do some feature engineering.\n", + "\n", + "# First, a new column which indicates mean produce of each state over the given years. Second, a ranking column which ranks countries on the basis of mean produce." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "_cell_guid": "ab91a322-0cb9-4edf-b5a2-cde82a237824", + "_uuid": "979f875019abef3ed85af75e000fe59d1de5a381" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Y1961Y1962Y1963Y1964Y1965Y1966Y1967Y1968Y1969Y1970...Y2006Y2007Y2008Y2009Y2010Y2011Y2012Y2013Mean_ProduceRank
Afghanistan9481.09414.09194.010170.010473.010169.011289.011508.011815.010454.0...18317.019248.019381.020661.021030.021100.022706.023007.013003.05660469.0
Albania1706.01749.01767.01889.01884.01995.02046.02169.02230.02395.0...6911.06744.07168.07316.07907.08114.08221.08271.04475.509434104.0
Algeria7488.07235.06861.07255.07509.07536.07986.08839.09003.09355.0...51067.049933.050916.057505.060071.065852.069365.072161.028879.49056638.0
Angola4834.04775.05240.05286.05527.05677.05833.05685.06219.06460.0...28247.029877.032053.036985.038400.040573.038064.048639.013321.05660468.0
Antigua and Barbuda92.094.0105.095.084.073.064.059.068.077.0...110.0122.0115.0114.0115.0118.0113.0119.083.886792172.0
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5 rows × 55 columns

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" + ], + "text/plain": [ + " Y1961 Y1962 Y1963 Y1964 Y1965 Y1966 \\\n", + "Afghanistan 9481.0 9414.0 9194.0 10170.0 10473.0 10169.0 \n", + "Albania 1706.0 1749.0 1767.0 1889.0 1884.0 1995.0 \n", + "Algeria 7488.0 7235.0 6861.0 7255.0 7509.0 7536.0 \n", + "Angola 4834.0 4775.0 5240.0 5286.0 5527.0 5677.0 \n", + "Antigua and Barbuda 92.0 94.0 105.0 95.0 84.0 73.0 \n", + "\n", + " Y1967 Y1968 Y1969 Y1970 ... Y2006 \\\n", + "Afghanistan 11289.0 11508.0 11815.0 10454.0 ... 18317.0 \n", + "Albania 2046.0 2169.0 2230.0 2395.0 ... 6911.0 \n", + "Algeria 7986.0 8839.0 9003.0 9355.0 ... 51067.0 \n", + "Angola 5833.0 5685.0 6219.0 6460.0 ... 28247.0 \n", + "Antigua and Barbuda 64.0 59.0 68.0 77.0 ... 110.0 \n", + "\n", + " Y2007 Y2008 Y2009 Y2010 Y2011 Y2012 \\\n", + "Afghanistan 19248.0 19381.0 20661.0 21030.0 21100.0 22706.0 \n", + "Albania 6744.0 7168.0 7316.0 7907.0 8114.0 8221.0 \n", + "Algeria 49933.0 50916.0 57505.0 60071.0 65852.0 69365.0 \n", + "Angola 29877.0 32053.0 36985.0 38400.0 40573.0 38064.0 \n", + "Antigua and Barbuda 122.0 115.0 114.0 115.0 118.0 113.0 \n", + "\n", + " Y2013 Mean_Produce Rank \n", + "Afghanistan 23007.0 13003.056604 69.0 \n", + "Albania 8271.0 4475.509434 104.0 \n", + "Algeria 72161.0 28879.490566 38.0 \n", + "Angola 48639.0 13321.056604 68.0 \n", + "Antigua and Barbuda 119.0 83.886792 172.0 \n", + "\n", + "[5 rows x 55 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_produce = []\n", + "for i in range(174):\n", + " mean_produce.append(new_df.iloc[i,:].values.mean())\n", + "new_df['Mean_Produce'] = mean_produce\n", + "\n", + "new_df['Rank'] = new_df['Mean_Produce'].rank(ascending=False)\n", + "\n", + "new_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "6f7c4fb7-1475-439f-9929-4cf4b29d8de7", + "_uuid": "da6c9c98eaff45edba1179103ae539bbfbe9753b" + }, + "source": [ + "Now, we create another dataframe with items and their total production each year from 1961 to 2013" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "_cell_guid": "bfd692bc-dce4-4870-9ab9-9775cf69a87f", + "_uuid": "9e11017d381f175eee714643bc5fa763600aaa0b" + }, + "outputs": [], + "source": [ + "item_list = list(df['Item'].unique())\n", + "\n", + "item_df = pd.DataFrame()\n", + "item_df['Item_Name'] = item_list\n", + "\n", + "for yr in year_list:\n", + " item_produce = []\n", + " for it in item_list:\n", + " item_produce.append(df[yr][df['Item']==it].sum())\n", + " item_df[yr] = item_produce\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "_cell_guid": "3b7ed0c2-6140-4285-861c-d0cd2324a1f5", + "_uuid": "cb4641df5ce90f516f88c536e8a6c6870c5b4f65" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Item_NameY1961Y1962Y1963Y1964Y1965Y1966Y1967Y1968Y1969...Y2004Y2005Y2006Y2007Y2008Y2009Y2010Y2011Y2012Y2013
0Wheat and products138829.0144643.0147325.0156273.0168822.0169832.0171469.0179530.0189658.0...527394.0532263.0537279.0529271.0562239.0557245.0549926.0578179.0576597587492
1Rice (Milled Equivalent)122700.0131842.0139507.0148304.0150056.0155583.0158587.0164614.0167922.0...361107.0366025.0372629.0378698.0389708.0394221.0398559.0404152.0406787410880
2Barley and products46180.048915.051642.054184.054945.055463.056424.060455.065501.0...102055.097185.0100981.093310.098209.099135.092563.092570.08876699452
3Maize and products168039.0168305.0172905.0175468.0190304.0200860.0213050.0215613.0221953.0...545024.0549036.0543280.0573892.0592231.0557940.0584337.0603297.0608730671300
4Millet and products19075.019019.019740.020353.018377.020860.022997.021785.023966.0...25789.025496.025997.026750.026373.024575.027039.025740.02610526346
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5 rows × 54 columns

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" + ], + "text/plain": [ + " Item_Name Y1961 Y1962 Y1963 Y1964 Y1965 \\\n", + "0 Wheat and products 138829.0 144643.0 147325.0 156273.0 168822.0 \n", + "1 Rice (Milled Equivalent) 122700.0 131842.0 139507.0 148304.0 150056.0 \n", + "2 Barley and products 46180.0 48915.0 51642.0 54184.0 54945.0 \n", + "3 Maize and products 168039.0 168305.0 172905.0 175468.0 190304.0 \n", + "4 Millet and products 19075.0 19019.0 19740.0 20353.0 18377.0 \n", + "\n", + " Y1966 Y1967 Y1968 Y1969 ... Y2004 Y2005 \\\n", + "0 169832.0 171469.0 179530.0 189658.0 ... 527394.0 532263.0 \n", + "1 155583.0 158587.0 164614.0 167922.0 ... 361107.0 366025.0 \n", + "2 55463.0 56424.0 60455.0 65501.0 ... 102055.0 97185.0 \n", + "3 200860.0 213050.0 215613.0 221953.0 ... 545024.0 549036.0 \n", + "4 20860.0 22997.0 21785.0 23966.0 ... 25789.0 25496.0 \n", + "\n", + " Y2006 Y2007 Y2008 Y2009 Y2010 Y2011 Y2012 Y2013 \n", + "0 537279.0 529271.0 562239.0 557245.0 549926.0 578179.0 576597 587492 \n", + "1 372629.0 378698.0 389708.0 394221.0 398559.0 404152.0 406787 410880 \n", + "2 100981.0 93310.0 98209.0 99135.0 92563.0 92570.0 88766 99452 \n", + "3 543280.0 573892.0 592231.0 557940.0 584337.0 603297.0 608730 671300 \n", + "4 25997.0 26750.0 26373.0 24575.0 27039.0 25740.0 26105 26346 \n", + "\n", + "[5 rows x 54 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "item_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "3fa01e1f-bedd-431b-90c3-8d7d70545f34", + "_uuid": "56a647293f1c1aba7c184f249021e008a4d5a8f2" + }, + "source": [ + "# Some more feature engineering\n", + "\n", + "This time, we will use the new features to get some good conclusions.\n", + "\n", + "# 1. Total amount of item produced from 1961 to 2013\n", + "# 2. Providing a rank to the items to know the most produced item" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "_cell_guid": "3a6bb102-6749-4818-860d-59aaad6de07f", + "_uuid": "9e816786e7a161227ae72d164b25c0029e01e5b4", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Item_NameY1961Y1962Y1963Y1964Y1965Y1966Y1967Y1968Y1969...Y2006Y2007Y2008Y2009Y2010Y2011Y2012Y2013SumProduction_Rank
0Wheat and products138829.0144643.0147325.0156273.0168822.0169832.0171469.0179530.0189658.0...537279.0529271.0562239.0557245.0549926.0578179.057659758749219194671.06.0
1Rice (Milled Equivalent)122700.0131842.0139507.0148304.0150056.0155583.0158587.0164614.0167922.0...372629.0378698.0389708.0394221.0398559.0404152.040678741088014475448.08.0
2Barley and products46180.048915.051642.054184.054945.055463.056424.060455.065501.0...100981.093310.098209.099135.092563.092570.088766994524442742.020.0
3Maize and products168039.0168305.0172905.0175468.0190304.0200860.0213050.0215613.0221953.0...543280.0573892.0592231.0557940.0584337.0603297.060873067130019960640.05.0
4Millet and products19075.019019.019740.020353.018377.020860.022997.021785.023966.0...25997.026750.026373.024575.027039.025740.026105263461225400.038.0
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5 rows × 56 columns

\n", + "
" + ], + "text/plain": [ + " Item_Name Y1961 Y1962 Y1963 Y1964 Y1965 \\\n", + "0 Wheat and products 138829.0 144643.0 147325.0 156273.0 168822.0 \n", + "1 Rice (Milled Equivalent) 122700.0 131842.0 139507.0 148304.0 150056.0 \n", + "2 Barley and products 46180.0 48915.0 51642.0 54184.0 54945.0 \n", + "3 Maize and products 168039.0 168305.0 172905.0 175468.0 190304.0 \n", + "4 Millet and products 19075.0 19019.0 19740.0 20353.0 18377.0 \n", + "\n", + " Y1966 Y1967 Y1968 Y1969 ... Y2006 \\\n", + "0 169832.0 171469.0 179530.0 189658.0 ... 537279.0 \n", + "1 155583.0 158587.0 164614.0 167922.0 ... 372629.0 \n", + "2 55463.0 56424.0 60455.0 65501.0 ... 100981.0 \n", + "3 200860.0 213050.0 215613.0 221953.0 ... 543280.0 \n", + "4 20860.0 22997.0 21785.0 23966.0 ... 25997.0 \n", + "\n", + " Y2007 Y2008 Y2009 Y2010 Y2011 Y2012 Y2013 \\\n", + "0 529271.0 562239.0 557245.0 549926.0 578179.0 576597 587492 \n", + "1 378698.0 389708.0 394221.0 398559.0 404152.0 406787 410880 \n", + "2 93310.0 98209.0 99135.0 92563.0 92570.0 88766 99452 \n", + "3 573892.0 592231.0 557940.0 584337.0 603297.0 608730 671300 \n", + "4 26750.0 26373.0 24575.0 27039.0 25740.0 26105 26346 \n", + "\n", + " Sum Production_Rank \n", + "0 19194671.0 6.0 \n", + "1 14475448.0 8.0 \n", + "2 4442742.0 20.0 \n", + "3 19960640.0 5.0 \n", + "4 1225400.0 38.0 \n", + "\n", + "[5 rows x 56 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum_col = []\n", + "for i in range(115):\n", + " sum_col.append(item_df.iloc[i,1:].values.sum())\n", + "item_df['Sum'] = sum_col\n", + "item_df['Production_Rank'] = item_df['Sum'].rank(ascending=False)\n", + "\n", + "item_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "7e20740c-565b-4969-a52e-d986e462b750", + "_uuid": "f483c9add5f6af9af9162b5425f6d65eb1c5f4aa" + }, + "source": [ + "# Now, we find the most produced food items in the last half-century" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "_cell_guid": "3130fe83-404c-4b3c-addc-560b2e2f32bf", + "_uuid": "0403e9ab2e13587588e3a30d64b8b6638571d3d5" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "56 Cereals - Excluding Beer\n", + "65 Fruits - Excluding Wine\n", + "3 Maize and products\n", + "53 Milk - Excluding Butter\n", + "6 Potatoes and products\n", + "1 Rice (Milled Equivalent)\n", + "57 Starchy Roots\n", + "64 Vegetables\n", + "27 Vegetables, Other\n", + "0 Wheat and products\n", + "Name: Item_Name, dtype: object" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "item_df['Item_Name'][item_df['Production_Rank'] < 11.0].sort_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "b6212fed-588b-426e-9271-6d857cd6aacb", + "_uuid": "e2c83f4c851b755ea6cf19f1bca168e705bd4edd" + }, + "source": [ + "So, cereals, fruits and maize are the most produced items in the last 50 years\n", + "\n", + "# Food and feed plot for most produced items " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "_cell_guid": "493f9940-1762-4718-acb4-fba5c4c73f4b", + "_uuid": "f8454c5200bdeb3995b9a0ada3deb5ca1c31f181" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n", + " warnings.warn(msg)\n", + "/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3672: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n", + " warnings.warn(msg, UserWarning)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.factorplot(\"Item\", data=df[(df['Item']=='Wheat and products') | (df['Item']=='Rice (Milled Equivalent)') | (df['Item']=='Maize and products') | (df['Item']=='Potatoes and products') | (df['Item']=='Vegetables, Other') | (df['Item']=='Milk - Excluding Butter') | (df['Item']=='Cereals - Excluding Beer') | (df['Item']=='Starchy Roots') | (df['Item']=='Vegetables') | (df['Item']=='Fruits - Excluding Wine')], kind=\"count\", hue=\"Element\", size=20, aspect=.8)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "45dda825-49a0-41ab-9ebd-eaa609aac986", + "_uuid": "ce5b2d38ff24ea08da632c4e2773dbd0bd026b9d", + "collapsed": true + }, + "source": [ + "# Now, we plot a heatmap of correlation of produce in difference years" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "_cell_guid": "b1bab0ec-6615-452c-8d06-a81d4f2ae252", + "_uuid": "a2ed2aae2364810ce640648cf50880adcf2cdcc4" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "year_df = df.iloc[:,10:]\n", + "fig, ax = plt.subplots(figsize=(16,10))\n", + "sns.heatmap(year_df.corr(), ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "43e1af94-ba07-4b95-8da3-1d774db940cd", + "_uuid": "70d2b0a7db9b8a5535b3c5b3c2eb927b904bf6d3" + }, + "source": [ + "So, we gather that a given year's production is more similar to its immediate previous and immediate following years." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "_cell_guid": "58cde27d-5ddc-4ebe-a8e1-80a8257f44c1", + "_uuid": "6f48b52c09ea6a207644044cace5a88c983bf316" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", + " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex='col', sharey='row', figsize=(10,10))\n", + "ax1.set(xlabel='Y1968', ylabel='Y1961')\n", + "ax2.set(xlabel='Y1968', ylabel='Y1963')\n", + "ax3.set(xlabel='Y1968', ylabel='Y1986')\n", + "ax4.set(xlabel='Y1968', ylabel='Y2013')\n", + "sns.jointplot(x=\"Y1968\", y=\"Y1961\", data=df, kind=\"reg\", ax=ax1)\n", + "sns.jointplot(x=\"Y1968\", y=\"Y1963\", data=df, kind=\"reg\", ax=ax2)\n", + "sns.jointplot(x=\"Y1968\", y=\"Y1986\", data=df, kind=\"reg\", ax=ax3)\n", + "sns.jointplot(x=\"Y1968\", y=\"Y2013\", data=df, kind=\"reg\", ax=ax4)\n", + "plt.close(2)\n", + "plt.close(3)\n", + "plt.close(4)\n", + "plt.close(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "8a297a06-977f-4ff7-a9ad-c7e8804930a8", + "_uuid": "6b738ce8b15a764fab90fac96f9534f94c14342e" + }, + "source": [ + "# Heatmap of production of food items over years\n", + "\n", + "This will detect the items whose production has drastically increased over the years" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "_cell_guid": "588cebd9-e97c-460d-8ed5-e663ac293711", + "_uuid": "16ce47d43a3038874a74d8bbb9a2e26f6ee54437" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "new_item_df = item_df.drop([\"Item_Name\",\"Sum\",\"Production_Rank\"], axis = 1)\n", + "fig, ax = plt.subplots(figsize=(12,24))\n", + "sns.heatmap(new_item_df,ax=ax)\n", + "ax.set_yticklabels(item_df.Item_Name.values[::-1])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "825620f9-7ab5-4fe2-9529-c4f1a300138e", + "_uuid": "5c42595537332ea71089d8c3dc041d3bf7d41b55" + }, + "source": [ + "There is considerable growth in production of Palmkernel oil, Meat/Aquatic animals, ricebran oil, cottonseed, seafood, offals, roots, poultry meat, mutton, bear, cocoa, coffee and soyabean oil.\n", + "There has been exceptional growth in production of onions, cream, sugar crops, treenuts, butter/ghee and to some extent starchy roots." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "80428f51-2fd4-468d-9530-9279215b4218", + "_uuid": "4c9bb27cd76099c5348243a99448c509ef0c5ded" + }, + "source": [ + "Now, we look at clustering." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "a3f1db3a-1b82-4e42-8e7d-f1a26915693b", + "_uuid": "da167de5a5b92e164fc6993b32ebbfab4ef9a6e3", + "collapsed": true + }, + "source": [ + "# What is clustering?\n", + "Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "136315a0-b37d-4d89-bd0d-037727062c34", + "_uuid": "04ab802ec92eaf6a27706f2008933dcf3865855a" + }, + "source": [ + "# Today, we will form clusters to classify countries based on productivity scale" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "27ba0b5d-c57e-485d-9588-017e16fe1904", + "_uuid": "659afdada04e8854765b5e7208394915b30f859a" + }, + "source": [ + "For this, we will use k-means clustering algorithm.\n", + "# K-means clustering\n", + "(Source [Wikipedia](https://en.wikipedia.org/wiki/K-means_clustering#Standard_algorithm) )\n", + "![http://gdurl.com/5BbP](http://gdurl.com/5BbP)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "7aeb3175-33bd-4f49-903a-57d43380e90e", + "_uuid": "6b0b4881e623ed3c133b68b98e6fb6755e18fd78" + }, + "source": [ + "This is the data we will use." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "_cell_guid": "a5b99ea8-975f-4467-9895-bffe1db876eb", + "_uuid": "57aba4000bfc422e848b14ad24b02a570d6c0554" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Y1961Y1962Y1963Y1964Y1965Y1966Y1967Y1968Y1969Y1970...Y2006Y2007Y2008Y2009Y2010Y2011Y2012Y2013Mean_ProduceRank
Afghanistan9481.09414.09194.010170.010473.010169.011289.011508.011815.010454.0...18317.019248.019381.020661.021030.021100.022706.023007.013003.05660469.0
Albania1706.01749.01767.01889.01884.01995.02046.02169.02230.02395.0...6911.06744.07168.07316.07907.08114.08221.08271.04475.509434104.0
Algeria7488.07235.06861.07255.07509.07536.07986.08839.09003.09355.0...51067.049933.050916.057505.060071.065852.069365.072161.028879.49056638.0
Angola4834.04775.05240.05286.05527.05677.05833.05685.06219.06460.0...28247.029877.032053.036985.038400.040573.038064.048639.013321.05660468.0
Antigua and Barbuda92.094.0105.095.084.073.064.059.068.077.0...110.0122.0115.0114.0115.0118.0113.0119.083.886792172.0
\n", + "

5 rows × 55 columns

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" + ], + "text/plain": [ + " Y1961 Y1962 Y1963 Y1964 Y1965 Y1966 \\\n", + "Afghanistan 9481.0 9414.0 9194.0 10170.0 10473.0 10169.0 \n", + "Albania 1706.0 1749.0 1767.0 1889.0 1884.0 1995.0 \n", + "Algeria 7488.0 7235.0 6861.0 7255.0 7509.0 7536.0 \n", + "Angola 4834.0 4775.0 5240.0 5286.0 5527.0 5677.0 \n", + "Antigua and Barbuda 92.0 94.0 105.0 95.0 84.0 73.0 \n", + "\n", + " Y1967 Y1968 Y1969 Y1970 ... Y2006 \\\n", + "Afghanistan 11289.0 11508.0 11815.0 10454.0 ... 18317.0 \n", + "Albania 2046.0 2169.0 2230.0 2395.0 ... 6911.0 \n", + "Algeria 7986.0 8839.0 9003.0 9355.0 ... 51067.0 \n", + "Angola 5833.0 5685.0 6219.0 6460.0 ... 28247.0 \n", + "Antigua and Barbuda 64.0 59.0 68.0 77.0 ... 110.0 \n", + "\n", + " Y2007 Y2008 Y2009 Y2010 Y2011 Y2012 \\\n", + "Afghanistan 19248.0 19381.0 20661.0 21030.0 21100.0 22706.0 \n", + "Albania 6744.0 7168.0 7316.0 7907.0 8114.0 8221.0 \n", + "Algeria 49933.0 50916.0 57505.0 60071.0 65852.0 69365.0 \n", + "Angola 29877.0 32053.0 36985.0 38400.0 40573.0 38064.0 \n", + "Antigua and Barbuda 122.0 115.0 114.0 115.0 118.0 113.0 \n", + "\n", + " Y2013 Mean_Produce Rank \n", + "Afghanistan 23007.0 13003.056604 69.0 \n", + "Albania 8271.0 4475.509434 104.0 \n", + "Algeria 72161.0 28879.490566 38.0 \n", + "Angola 48639.0 13321.056604 68.0 \n", + "Antigua and Barbuda 119.0 83.886792 172.0 \n", + "\n", + "[5 rows x 55 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "_cell_guid": "66964df2-892d-4e55-a4b1-f94d10e4c7dd", + "_uuid": "19bdd89a3ad9df962959ad6b996946f6f3916d58" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: FutureWarning: convert_objects is deprecated. To re-infer data dtypes for object columns, use DataFrame.infer_objects()\n", + "For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n", + " after removing the cwd from sys.path.\n" + ] + } + ], + "source": [ + "X = new_df.iloc[:,:-2].values\n", + "\n", + "X = pd.DataFrame(X)\n", + "X = X.convert_objects(convert_numeric=True)\n", + "X.columns = year_list" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "461e5bcc-0101-4ea1-ae13-20600f883929", + "_uuid": "0d3e50235c9505ebc255053d4a5aae547fc17d8d" + }, + "source": [ + "# Elbow method to select number of clusters\n", + "This method looks at the percentage of variance explained as a function of the number of clusters: One should choose a number of clusters so that adding another cluster doesn't give much better modeling of the data. More precisely, if one plots the percentage of variance explained by the clusters against the number of clusters, the first clusters will add much information (explain a lot of variance), but at some point the marginal gain will drop, giving an angle in the graph. The number of clusters is chosen at this point, hence the \"elbow criterion\". This \"elbow\" cannot always be unambiguously identified. Percentage of variance explained is the ratio of the between-group variance to the total variance, also known as an F-test. A slight variation of this method plots the curvature of the within group variance.\n", + "# Basically, number of clusters = the x-axis value of the point that is the corner of the \"elbow\"(the plot looks often looks like an elbow)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "_cell_guid": "06271223-bd32-48ac-a373-6c1e6bbf7c7b", + "_uuid": "c57d7277510a8c11fdc3d311e4d8a22539617ed9" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.cluster import KMeans\n", + "wcss = []\n", + "for i in range(1,11):\n", + " kmeans = KMeans(n_clusters=i,init='k-means++',max_iter=300,n_init=10,random_state=0)\n", + " kmeans.fit(X)\n", + " wcss.append(kmeans.inertia_)\n", + "plt.plot(range(1,11),wcss)\n", + "plt.title('The Elbow Method')\n", + "plt.xlabel('Number of clusters')\n", + "plt.ylabel('WCSS')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "ad4bc40a-9540-497d-95e3-3fee6088ea95", + "_uuid": "6450dd1c3d7a8114931dc358d2f09a0424b52fd7" + }, + "source": [ + "As the elbow corner coincides with x=2, we will have to form **2 clusters**. Personally, I would have liked to select 3 to 4 clusters. But trust me, only selecting 2 clusters can lead to best results.\n", + "Now, we apply k-means algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "_cell_guid": "eed3f672-e089-4dbb-aad8-b9618967abf3", + "_uuid": "d92d758ee7213ddcd84e9b8b2f61c9e260ed6ba2" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", + " after removing the cwd from sys.path.\n" + ] + } + ], + "source": [ + "kmeans = KMeans(n_clusters=2,init='k-means++',max_iter=300,n_init=10,random_state=0) \n", + "y_kmeans = kmeans.fit_predict(X)\n", + "\n", + "X = X.as_matrix(columns=None)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "ef07bd6d-679d-4375-b7b3-abeca3421e37", + "_uuid": "6f93a4bd3f17427f4b2dbe08af8e015a1e4a2f89" + }, + "source": [ + "Now, let's visualize the results." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "_cell_guid": "5a7fe139-13df-453b-8c16-891929bc595e", + "_uuid": "a57e0a38f4c0f0385be75fd9f71d4a2d8213aea3" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0,1],s=100,c='red',label='Others')\n", + "plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1,1],s=100,c='blue',label='China(mainland),USA,India')\n", + "plt.scatter(kmeans.cluster_centers_[:,0],kmeans.cluster_centers_[:,1],s=300,c='yellow',label='Centroids')\n", + "plt.title('Clusters of countries by Productivity')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "923d4536-2bce-4b99-b98a-33b801a56a8b", + "_uuid": "fe531e8c41eec0eb5dc52a9890871841f5d27211" + }, + "source": [ + "So, the blue cluster represents China(Mainland), USA and India while the red cluster represents all the other countries.\n", + "This result was highly probable. Just take a look at the plot of cell 3 above. See how China, USA and India stand out. That has been observed here in clustering too.\n", + "\n", + "You should try this algorithm for 3 or 4 clusters. Looking at the distribution, you will realise why 2 clusters is the best choice for the given data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "6dee7acb-0f08-4ae1-85b4-f4704026694a", + "_uuid": "179a1ede21ae330664a0b7c63e36574acdc0428c" + }, + "source": [ + "This is not the end! More is yet to come." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Now, lets try to predict the production using regression for 2020. We will predict the production for USA,India and Pakistan.**\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "Expected 2D array, got scalar array instead:\narray=2020.\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2020\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mArea\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m'India'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mElement\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m'Food'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Y1961'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[0mReturns\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 212\u001b[0m \"\"\"\n\u001b[0;32m--> 213\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[0m_preprocess_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstaticmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_preprocess_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36m_decision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"coef_\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'csc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'coo'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 197\u001b[0m return safe_sparse_dot(X, self.coef_.T,\n\u001b[1;32m 198\u001b[0m dense_output=True) + self.intercept_\n", + "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m 543\u001b[0m \u001b[0;34m\"Reshape your data either using array.reshape(-1, 1) if \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 544\u001b[0m \u001b[0;34m\"your data has a single feature or array.reshape(1, -1) \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 545\u001b[0;31m \"if it contains a single sample.\".format(array))\n\u001b[0m\u001b[1;32m 546\u001b[0m \u001b[0;31m# If input is 1D raise error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 547\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: Expected 2D array, got scalar array instead:\narray=2020.\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample." + ] + } + ], + "source": [ + "india_list=[]\n", + "year_list = list(df.iloc[:,10:].columns)\n", + "for i in year_list:\n", + " x=df[(df.Area=='India') & (df.Element=='Food')][i].mean()\n", + " india_list.append(x) \n", + "\n", + "reset=[]\n", + "for i in year_list:\n", + " reset.append(int(i[1:]))\n", + "\n", + "\n", + "reset=np.array(reset)\n", + "reset=reset.reshape(-1,1)\n", + "\n", + "\n", + "india_list=np.array(india_list)\n", + "india_list=india_list.reshape(-1,1)\n", + "\n", + "\n", + "reg = LinearRegression()\n", + "reg.fit(reset,india_list)\n", + "predictions = reg.predict(reset)\n", + "plt.title(\"India\")\n", + "plt.xlabel(\"Year\")\n", + "plt.ylabel(\"Production\")\n", + "plt.scatter(reset,india_list)\n", + "plt.plot(reset,predictions)\n", + "plt.show()\n", + "print(reg.predict(2020))\n", + "\n", + "df[(df.Area=='India') & (df.Element=='Food')]['Y1961'].mean()\n", + "\n", + "df[(df.Area=='Pakistan') & (df.Element=='Food')]\n", + "\n", + "Pak_list=[]\n", + "year_list = list(df.iloc[:,10:].columns)\n", + "for i in year_list:\n", + " yx=df[(df.Area=='Pakistan') & (df.Element=='Food')][i].mean()\n", + " Pak_list.append(yx) \n", + "\n", + "Pak_list=np.array(Pak_list)\n", + "Pak_list=Pak_list.reshape(-1,1)\n", + "Pak_list\n", + "reg = LinearRegression()\n", + "reg.fit(reset,Pak_list)\n", + "predictions = reg.predict(reset)\n", + "plt.title(\"Pakistan\")\n", + "plt.xlabel(\"Year\")\n", + "plt.ylabel(\"Production\")\n", + "plt.scatter(reset,Pak_list)\n", + "plt.plot(reset,predictions)\n", + "plt.show()\n", + "print(reg.predict(2020))\n", + "\n", + "\n", + "\n", + "usa_list=[]\n", + "year_list = list(df.iloc[:,10:].columns)\n", + "for i in year_list:\n", + " xu=df[(df.Area=='United States of America') & (df.Element=='Food')][i].mean()\n", + " usa_list.append(xu)\n", + "\n", + "usa_list=np.array(usa_list)\n", + "usa_list=india_list.reshape(-1,1)\n", + "\n", + "\n", + "reg = LinearRegression()\n", + "reg.fit(reset,usa_list)\n", + "predictions = reg.predict(reset)\n", + "plt.title(\"USA\")\n", + "plt.xlabel(\"Year\")\n", + "plt.ylabel(\"Production\")\n", + "plt.scatter(reset,usa_list)\n", + "plt.plot(reset,predictions)\n", + "plt.show()\n", + "print(reg.predict(2020))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/other/frequency_finder.py b/other/frequency_finder.py index 6264b25bf303..48760a9deb09 100644 --- a/other/frequency_finder.py +++ b/other/frequency_finder.py @@ -1,29 +1,78 @@ # Frequency Finder # frequency taken from http://en.wikipedia.org/wiki/Letter_frequency -englishLetterFreq = {'E': 12.70, 'T': 9.06, 'A': 8.17, 'O': 7.51, 'I': 6.97, - 'N': 6.75, 'S': 6.33, 'H': 6.09, 'R': 5.99, 'D': 4.25, - 'L': 4.03, 'C': 2.78, 'U': 2.76, 'M': 2.41, 'W': 2.36, - 'F': 2.23, 'G': 2.02, 'Y': 1.97, 'P': 1.93, 'B': 1.29, - 'V': 0.98, 'K': 0.77, 'J': 0.15, 'X': 0.15, 'Q': 0.10, - 'Z': 0.07} -ETAOIN = 'ETAOINSHRDLCUMWFGYPBVKJXQZ' -LETTERS = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' +englishLetterFreq = { + "E": 12.70, + "T": 9.06, + "A": 8.17, + "O": 7.51, + "I": 6.97, + "N": 6.75, + "S": 6.33, + "H": 6.09, + "R": 5.99, + "D": 4.25, + "L": 4.03, + "C": 2.78, + "U": 2.76, + "M": 2.41, + "W": 2.36, + "F": 2.23, + "G": 2.02, + "Y": 1.97, + "P": 1.93, + "B": 1.29, + "V": 0.98, + "K": 0.77, + "J": 0.15, + "X": 0.15, + "Q": 0.10, + "Z": 0.07, +} +ETAOIN = "ETAOINSHRDLCUMWFGYPBVKJXQZ" +LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" + def getLetterCount(message): - letterCount = {'A': 0, 'B': 0, 'C': 0, 'D': 0, 'E': 0, 'F': 0, 'G': 0, 'H': 0, - 'I': 0, 'J': 0, 'K': 0, 'L': 0, 'M': 0, 'N': 0, 'O': 0, 'P': 0, - 'Q': 0, 'R': 0, 'S': 0, 'T': 0, 'U': 0, 'V': 0, 'W': 0, 'X': 0, - 'Y': 0, 'Z': 0} + letterCount = { + "A": 0, + "B": 0, + "C": 0, + "D": 0, + "E": 0, + "F": 0, + "G": 0, + "H": 0, + "I": 0, + "J": 0, + "K": 0, + "L": 0, + "M": 0, + "N": 0, + "O": 0, + "P": 0, + "Q": 0, + "R": 0, + "S": 0, + "T": 0, + "U": 0, + "V": 0, + "W": 0, + "X": 0, + "Y": 0, + "Z": 0, + } for letter in message.upper(): if letter in LETTERS: letterCount[letter] += 1 return letterCount + def getItemAtIndexZero(x): return x[0] + def getFrequencyOrder(message): letterToFreq = getLetterCount(message) freqToLetter = {} @@ -34,23 +83,24 @@ def getFrequencyOrder(message): freqToLetter[letterToFreq[letter]].append(letter) for freq in freqToLetter: - freqToLetter[freq].sort(key = ETAOIN.find, reverse = True) - freqToLetter[freq] = ''.join(freqToLetter[freq]) + freqToLetter[freq].sort(key=ETAOIN.find, reverse=True) + freqToLetter[freq] = "".join(freqToLetter[freq]) freqPairs = list(freqToLetter.items()) - freqPairs.sort(key = getItemAtIndexZero, reverse = True) + freqPairs.sort(key=getItemAtIndexZero, reverse=True) freqOrder = [] for freqPair in freqPairs: freqOrder.append(freqPair[1]) - return ''.join(freqOrder) + return "".join(freqOrder) + def englishFreqMatchScore(message): - ''' + """ >>> englishFreqMatchScore('Hello World') 1 - ''' + """ freqOrder = getFrequencyOrder(message) matchScore = 0 for commonLetter in ETAOIN[:6]: @@ -63,6 +113,8 @@ def englishFreqMatchScore(message): return matchScore -if __name__ == '__main__': + +if __name__ == "__main__": import doctest + doctest.testmod() diff --git a/other/game_of_life/game_o_life.py b/other/game_of_life.py similarity index 55% rename from other/game_of_life/game_o_life.py rename to other/game_of_life.py index 1fdaa21b4a7b..2b4d1116fa8c 100644 --- a/other/game_of_life/game_o_life.py +++ b/other/game_of_life.py @@ -1,4 +1,4 @@ -'''Conway's Game Of Life, Author Anurag Kumar(mailto:anuragkumarak95@gmail.com) +"""Conway's Game Of Life, Author Anurag Kumar(mailto:anuragkumarak95@gmail.com) Requirements: - numpy @@ -26,28 +26,31 @@ 4. Any dead cell with exactly three live neighbours be- comes a live cell, as if by reproduction. - ''' + """ import numpy as np import random, sys from matplotlib import pyplot as plt from matplotlib.colors import ListedColormap -usage_doc='Usage of script: script_nama ' +usage_doc = "Usage of script: script_nama " -choice = [0]*100 + [1]*10 +choice = [0] * 100 + [1] * 10 random.shuffle(choice) + def create_canvas(size): - canvas = [ [False for i in range(size)] for j in range(size)] + canvas = [[False for i in range(size)] for j in range(size)] return canvas + def seed(canvas): - for i,row in enumerate(canvas): - for j,_ in enumerate(row): - canvas[i][j]=bool(random.getrandbits(1)) + for i, row in enumerate(canvas): + for j, _ in enumerate(row): + canvas[i][j] = bool(random.getrandbits(1)) + def run(canvas): - ''' This function runs the rules of game through all points, and changes their status accordingly.(in the same canvas) + """ This function runs the rules of game through all points, and changes their status accordingly.(in the same canvas) @Args: -- canvas : canvas of population to run the rules on. @@ -55,63 +58,71 @@ def run(canvas): @returns: -- None - ''' + """ canvas = np.array(canvas) next_gen_canvas = np.array(create_canvas(canvas.shape[0])) for r, row in enumerate(canvas): for c, pt in enumerate(row): # print(r-1,r+2,c-1,c+2) - next_gen_canvas[r][c] = __judge_point(pt,canvas[r-1:r+2,c-1:c+2]) - + next_gen_canvas[r][c] = __judge_point( + pt, canvas[r - 1 : r + 2, c - 1 : c + 2] + ) + canvas = next_gen_canvas - del next_gen_canvas # cleaning memory as we move on. - return canvas.tolist() + del next_gen_canvas # cleaning memory as we move on. + return canvas.tolist() + -def __judge_point(pt,neighbours): - dead = 0 +def __judge_point(pt, neighbours): + dead = 0 alive = 0 # finding dead or alive neighbours count. for i in neighbours: for status in i: - if status: alive+=1 - else: dead+=1 + if status: + alive += 1 + else: + dead += 1 # handling duplicate entry for focus pt. - if pt : alive-=1 - else : dead-=1 - + if pt: + alive -= 1 + else: + dead -= 1 + # running the rules of game here. state = pt if pt: - if alive<2: - state=False - elif alive==2 or alive==3: - state=True - elif alive>3: - state=False + if alive < 2: + state = False + elif alive == 2 or alive == 3: + state = True + elif alive > 3: + state = False else: - if alive==3: - state=True + if alive == 3: + state = True return state -if __name__=='__main__': - if len(sys.argv) != 2: raise Exception(usage_doc) - +if __name__ == "__main__": + if len(sys.argv) != 2: + raise Exception(usage_doc) + canvas_size = int(sys.argv[1]) # main working structure of this module. - c=create_canvas(canvas_size) + c = create_canvas(canvas_size) seed(c) fig, ax = plt.subplots() - fig.show() - cmap = ListedColormap(['w','k']) + fig.show() + cmap = ListedColormap(["w", "k"]) try: while True: - c = run(c) - ax.matshow(c,cmap=cmap) + c = run(c) + ax.matshow(c, cmap=cmap) fig.canvas.draw() - ax.cla() + ax.cla() except KeyboardInterrupt: # do nothing. pass diff --git a/other/game_of_life/sample.gif b/other/game_of_life/sample.gif deleted file mode 100644 index 0bf2ae1f95e4..000000000000 Binary files a/other/game_of_life/sample.gif and /dev/null differ diff --git a/other/greedy.py b/other/greedy.py new file mode 100644 index 000000000000..d1bc156304b0 --- /dev/null +++ b/other/greedy.py @@ -0,0 +1,63 @@ +class things: + def __init__(self, n, v, w): + self.name = n + self.value = v + self.weight = w + + def __repr__(self): + return f"{self.__class__.__name__}({self.name}, {self.value}, {self.weight})" + + def get_value(self): + return self.value + + def get_name(self): + return self.name + + def get_weight(self): + return self.weight + + def value_Weight(self): + return self.value / self.weight + + +def build_menu(name, value, weight): + menu = [] + for i in range(len(value)): + menu.append(things(name[i], value[i], weight[i])) + return menu + + +def greedy(item, maxCost, keyFunc): + itemsCopy = sorted(item, key=keyFunc, reverse=True) + result = [] + totalValue, total_cost = 0.0, 0.0 + for i in range(len(itemsCopy)): + if (total_cost + itemsCopy[i].get_weight()) <= maxCost: + result.append(itemsCopy[i]) + total_cost += itemsCopy[i].get_weight() + totalValue += itemsCopy[i].get_value() + return (result, totalValue) + + +def test_greedy(): + """ + >>> food = ["Burger", "Pizza", "Coca Cola", "Rice", + ... "Sambhar", "Chicken", "Fries", "Milk"] + >>> value = [80, 100, 60, 70, 50, 110, 90, 60] + >>> weight = [40, 60, 40, 70, 100, 85, 55, 70] + >>> foods = build_menu(food, value, weight) + >>> foods # doctest: +NORMALIZE_WHITESPACE + [things(Burger, 80, 40), things(Pizza, 100, 60), things(Coca Cola, 60, 40), + things(Rice, 70, 70), things(Sambhar, 50, 100), things(Chicken, 110, 85), + things(Fries, 90, 55), things(Milk, 60, 70)] + >>> greedy(foods, 500, things.get_value) # doctest: +NORMALIZE_WHITESPACE + ([things(Chicken, 110, 85), things(Pizza, 100, 60), things(Fries, 90, 55), + things(Burger, 80, 40), things(Rice, 70, 70), things(Coca Cola, 60, 40), + things(Milk, 60, 70)], 570.0) + """ + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/other/largest_subarray_sum.py b/other/largest_subarray_sum.py new file mode 100644 index 000000000000..0449e72e64e3 --- /dev/null +++ b/other/largest_subarray_sum.py @@ -0,0 +1,23 @@ +from sys import maxsize + + +def max_sub_array_sum(a: list, size: int = 0): + """ + >>> max_sub_array_sum([-13, -3, -25, -20, -3, -16, -23, -12, -5, -22, -15, -4, -7]) + -3 + """ + size = size or len(a) + max_so_far = -maxsize - 1 + max_ending_here = 0 + for i in range(0, size): + max_ending_here = max_ending_here + a[i] + if max_so_far < max_ending_here: + max_so_far = max_ending_here + if max_ending_here < 0: + max_ending_here = 0 + return max_so_far + + +if __name__ == "__main__": + a = [-13, -3, -25, -20, 1, -16, -23, -12, -5, -22, -15, -4, -7] + print(("Maximum contiguous sum is", max_sub_array_sum(a, len(a)))) diff --git a/other/least_recently_used.py b/other/least_recently_used.py new file mode 100644 index 000000000000..2932e9c185e8 --- /dev/null +++ b/other/least_recently_used.py @@ -0,0 +1,62 @@ +from abc import abstractmethod +import sys +from collections import deque + +class LRUCache: + """ Page Replacement Algorithm, Least Recently Used (LRU) Caching.""" + + dq_store = object() # Cache store of keys + key_reference_map = object() # References of the keys in cache + _MAX_CAPACITY: int = 10 # Maximum capacity of cache + + @abstractmethod + def __init__(self, n: int): + """ Creates an empty store and map for the keys. + The LRUCache is set the size n. + """ + self.dq_store = deque() + self.key_reference_map = set() + if not n: + LRUCache._MAX_CAPACITY = sys.maxsize + elif n < 0: + raise ValueError('n should be an integer greater than 0.') + else: + LRUCache._MAX_CAPACITY = n + + def refer(self, x): + """ + Looks for a page in the cache store and adds reference to the set. + Remove the least recently used key if the store is full. + Update store to reflect recent access. + """ + if x not in self.key_reference_map: + if len(self.dq_store) == LRUCache._MAX_CAPACITY: + last_element = self.dq_store.pop() + self.key_reference_map.remove(last_element) + else: + index_remove = 0 + for idx, key in enumerate(self.dq_store): + if key == x: + index_remove = idx + break + self.dq_store.remove(index_remove) + + self.dq_store.appendleft(x) + self.key_reference_map.add(x) + + def display(self): + """ + Prints all the elements in the store. + """ + for k in self.dq_store: + print(k) + +if __name__ == "__main__": + lru_cache = LRUCache(4) + lru_cache.refer(1) + lru_cache.refer(2) + lru_cache.refer(3) + lru_cache.refer(1) + lru_cache.refer(4) + lru_cache.refer(5) + lru_cache.display() diff --git a/other/linear_congruential_generator.py b/other/linear_congruential_generator.py index 34abdf34eaf3..3b150f422e4f 100644 --- a/other/linear_congruential_generator.py +++ b/other/linear_congruential_generator.py @@ -1,17 +1,17 @@ -from __future__ import print_function __author__ = "Tobias Carryer" from time import time + class LinearCongruentialGenerator(object): """ A pseudorandom number generator. """ - - def __init__( self, multiplier, increment, modulo, seed=int(time()) ): + + def __init__(self, multiplier, increment, modulo, seed=int(time())): """ These parameters are saved and used when nextNumber() is called. - + modulo is the largest number that can be generated (exclusive). The most efficent values are powers of 2. 2^32 is a common value. """ @@ -19,8 +19,8 @@ def __init__( self, multiplier, increment, modulo, seed=int(time()) ): self.increment = increment self.modulo = modulo self.seed = seed - - def next_number( self ): + + def next_number(self): """ The smallest number that can be generated is zero. The largest number that can be generated is modulo-1. modulo is set in the constructor. @@ -28,8 +28,9 @@ def next_number( self ): self.seed = (self.multiplier * self.seed + self.increment) % self.modulo return self.seed + if __name__ == "__main__": # Show the LCG in action. - lcg = LinearCongruentialGenerator(1664525, 1013904223, 2<<31) - while True : - print(lcg.next_number()) \ No newline at end of file + lcg = LinearCongruentialGenerator(1664525, 1013904223, 2 << 31) + while True: + print(lcg.next_number()) diff --git a/other/magicdiamondpattern.py b/other/magicdiamondpattern.py new file mode 100644 index 000000000000..9b434a7b6e0b --- /dev/null +++ b/other/magicdiamondpattern.py @@ -0,0 +1,54 @@ +# Python program for generating diamond pattern in python 3.7+ + +# Function to print upper half of diamond (pyramid) +def floyd(n): + """ + Parameters: + n : size of pattern + """ + for i in range(0, n): + for j in range(0, n - i - 1): # printing spaces + print(" ", end="") + for k in range(0, i + 1): # printing stars + print("* ", end="") + print() + + +# Function to print lower half of diamond (pyramid) +def reverse_floyd(n): + """ + Parameters: + n : size of pattern + """ + for i in range(n, 0, -1): + for j in range(i, 0, -1): # printing stars + print("* ", end="") + print() + for k in range(n - i + 1, 0, -1): # printing spaces + print(" ", end="") + + +# Function to print complete diamond pattern of "*" +def pretty_print(n): + """ + Parameters: + n : size of pattern + """ + if n <= 0: + print(" ... .... nothing printing :(") + return + floyd(n) # upper half + reverse_floyd(n) # lower half + + +if __name__ == "__main__": + print(r"| /\ | |- | |- |--| |\ /| |-") + print(r"|/ \| |- |_ |_ |__| | \/ | |_") + K = 1 + while K: + user_number = int(input("enter the number and , and see the magic : ")) + print() + pretty_print(user_number) + K = int(input("press 0 to exit... and 1 to continue...")) + + print("Good Bye...") diff --git a/other/nested_brackets.py b/other/nested_brackets.py index 76677d56439a..011e94b92928 100644 --- a/other/nested_brackets.py +++ b/other/nested_brackets.py @@ -1,4 +1,4 @@ -''' +""" The nested brackets problem is a problem that determines if a sequence of brackets are properly nested. A sequence of brackets s is considered properly nested if any of the following conditions are true: @@ -12,16 +12,15 @@ The function called is_balanced takes as input a string S which is a sequence of brackets and returns true if S is nested and false otherwise. -''' -from __future__ import print_function +""" def is_balanced(S): stack = [] - open_brackets = set({'(', '[', '{'}) - closed_brackets = set({')', ']', '}'}) - open_to_closed = dict({'{':'}', '[':']', '(':')'}) + open_brackets = set({"(", "[", "{"}) + closed_brackets = set({")", "]", "}"}) + open_to_closed = dict({"{": "}", "[": "]", "(": ")"}) for i in range(len(S)): @@ -29,7 +28,9 @@ def is_balanced(S): stack.append(S[i]) elif S[i] in closed_brackets: - if len(stack) == 0 or (len(stack) > 0 and open_to_closed[stack.pop()] != S[i]): + if len(stack) == 0 or ( + len(stack) > 0 and open_to_closed[stack.pop()] != S[i] + ): return False return len(stack) == 0 diff --git a/other/palindrome.py b/other/palindrome.py index 990ec844f9fb..dd1fe316f479 100644 --- a/other/palindrome.py +++ b/other/palindrome.py @@ -1,9 +1,31 @@ -# Program to find whether given string is palindrome or not -def is_palindrome(str): +# Algorithms to determine if a string is palindrome + +test_data = { + "MALAYALAM": True, + "String": False, + "rotor": True, + "level": True, + "A": True, + "BB": True, + "ABC": False, + "amanaplanacanalpanama": True, # "a man a plan a canal panama" +} +# Ensure our test data is valid +assert all((key == key[::-1]) is value for key, value in test_data.items()) + + +def is_palindrome(s: str) -> bool: + """ + Return True if s is a palindrome otherwise return False. + + >>> all(is_palindrome(key) is value for key, value in test_data.items()) + True + """ + start_i = 0 - end_i = len(str) - 1 + end_i = len(s) - 1 while start_i < end_i: - if str[start_i] == str[end_i]: + if s[start_i] == s[end_i]: start_i += 1 end_i -= 1 else: @@ -11,21 +33,34 @@ def is_palindrome(str): return True -# Recursive method -def recursive_palindrome(str): - if len(str) <= 1: +def is_palindrome_recursive(s: str) -> bool: + """ + Return True if s is a palindrome otherwise return False. + + >>> all(is_palindrome_recursive(key) is value for key, value in test_data.items()) + True + """ + if len(s) <= 1: return True - if str[0] == str[len(str) - 1]: - return recursive_palindrome(str[1:-1]) + if s[0] == s[len(s) - 1]: + return is_palindrome_recursive(s[1:-1]) else: return False -def main(): - str = 'ama' - print(recursive_palindrome(str.lower())) - print(is_palindrome(str.lower())) +def is_palindrome_slice(s: str) -> bool: + """ + Return True if s is a palindrome otherwise return False. + + >>> all(is_palindrome_slice(key) is value for key, value in test_data.items()) + True + """ + return s == s[::-1] -if __name__ == '__main__': - main() +if __name__ == "__main__": + for key, value in test_data.items(): + assert is_palindrome(key) is is_palindrome_recursive(key) + assert is_palindrome(key) is is_palindrome_slice(key) + print(f"{key:21} {value}") + print("a man a plan a canal panama") diff --git a/other/password_generator.py b/other/password_generator.py index 8916079fc758..598f8d0eeade 100644 --- a/other/password_generator.py +++ b/other/password_generator.py @@ -1,35 +1,76 @@ -from __future__ import print_function -import string -import random +"""Password generator allows you to generate a random password of length N.""" +from random import choice, shuffle +from string import ascii_letters, digits, punctuation -letters = [letter for letter in string.ascii_letters] -digits = [digit for digit in string.digits] -symbols = [symbol for symbol in string.punctuation] -chars = letters + digits + symbols -random.shuffle(chars) -min_length = 8 -max_length = 16 -password = ''.join(random.choice(chars) for x in range(random.randint(min_length, max_length))) -print('Password: ' + password) -print('[ If you are thinking of using this passsword, You better save it. ]') +def password_generator(length=8): + """ + >>> len(password_generator()) + 8 + >>> len(password_generator(length=16)) + 16 + >>> len(password_generator(257)) + 257 + >>> len(password_generator(length=0)) + 0 + >>> len(password_generator(-1)) + 0 + """ + chars = tuple(ascii_letters) + tuple(digits) + tuple(punctuation) + return "".join(choice(chars) for x in range(length)) -# ALTERNATIVE METHODS +# ALTERNATIVE METHODS # ctbi= characters that must be in password -# i= how many letters or characters the password length will be -def password_generator(ctbi, i): - # Password generator = full boot with random_number, random_letters, and random_character FUNCTIONS - pass # Put your code here... +# i= how many letters or characters the password length will be +def alternative_password_generator(ctbi, i): + # Password generator = full boot with random_number, random_letters, and + # random_character FUNCTIONS + # Put your code here... + i = i - len(ctbi) + quotient = int(i / 3) + remainder = i % 3 + # chars = ctbi + random_letters(ascii_letters, i / 3 + remainder) + random_number(digits, i / 3) + random_characters(punctuation, i / 3) + chars = ( + ctbi + + random(ascii_letters, quotient + remainder) + + random(digits, quotient) + + random(punctuation, quotient) + ) + chars = list(chars) + shuffle(chars) + return "".join(chars) + + # random is a generalised function for letters, characters and numbers + + +def random(ctbi, i): + return "".join(choice(ctbi) for x in range(i)) def random_number(ctbi, i): - pass # Put your code here... + pass # Put your code here... def random_letters(ctbi, i): - pass # Put your code here... + pass # Put your code here... def random_characters(ctbi, i): - pass # Put your code here... + pass # Put your code here... + + +def main(): + length = int(input("Please indicate the max length of your password: ").strip()) + ctbi = input( + "Please indicate the characters that must be in your password: " + ).strip() + print("Password generated:", password_generator(length)) + print( + "Alternative Password generated:", alternative_password_generator(ctbi, length) + ) + print("[If you are thinking of using this passsword, You better save it.]") + + +if __name__ == "__main__": + main() diff --git a/other/primelib.py b/other/primelib.py index 19572f8611cb..6fc5eddeb257 100644 --- a/other/primelib.py +++ b/other/primelib.py @@ -16,7 +16,7 @@ greatestPrimeFactor(number) smallestPrimeFactor(number) getPrime(n) -getPrimesBetween(pNumber1, pNumber2) +getPrimesBetween(pNumber1, pNumber2) ---- @@ -39,479 +39,507 @@ """ +from math import sqrt + + def isPrime(number): """ input: positive integer 'number' returns true if 'number' is prime otherwise false. """ - import math # for function sqrt - + # precondition - assert isinstance(number,int) and (number >= 0) , \ - "'number' must been an int and positive" - + assert isinstance(number, int) and ( + number >= 0 + ), "'number' must been an int and positive" + status = True - - # 0 and 1 are none primes. + + # 0 and 1 are none primes. if number <= 1: status = False - - for divisor in range(2,int(round(math.sqrt(number)))+1): - + + for divisor in range(2, int(round(sqrt(number))) + 1): + # if 'number' divisible by 'divisor' then sets 'status' - # of false and break up the loop. + # of false and break up the loop. if number % divisor == 0: status = False break - + # precondition - assert isinstance(status,bool), "'status' must been from type bool" - + assert isinstance(status, bool), "'status' must been from type bool" + return status + # ------------------------------------------ + def sieveEr(N): """ input: positive integer 'N' > 2 returns a list of prime numbers from 2 up to N. - + This function implements the algorithm called - sieve of erathostenes. - + sieve of erathostenes. + """ - + # precondition - assert isinstance(N,int) and (N > 2), "'N' must been an int and > 2" - + assert isinstance(N, int) and (N > 2), "'N' must been an int and > 2" + # beginList: conatins all natural numbers from 2 upt to N - beginList = [x for x in range(2,N+1)] + beginList = [x for x in range(2, N + 1)] + + ans = [] # this list will be returns. - ans = [] # this list will be returns. - # actual sieve of erathostenes for i in range(len(beginList)): - - for j in range(i+1,len(beginList)): - - if (beginList[i] != 0) and \ - (beginList[j] % beginList[i] == 0): + + for j in range(i + 1, len(beginList)): + + if (beginList[i] != 0) and (beginList[j] % beginList[i] == 0): beginList[j] = 0 - - # filters actual prime numbers. + + # filters actual prime numbers. ans = [x for x in beginList if x != 0] - + # precondition - assert isinstance(ans,list), "'ans' must been from type list" - + assert isinstance(ans, list), "'ans' must been from type list" + return ans - + # -------------------------------- + def getPrimeNumbers(N): """ input: positive integer 'N' > 2 returns a list of prime numbers from 2 up to N (inclusive) This function is more efficient as function 'sieveEr(...)' - """ - - # precondition - assert isinstance(N,int) and (N > 2), "'N' must been an int and > 2" - - ans = [] - - # iterates over all numbers between 2 up to N+1 + """ + + # precondition + assert isinstance(N, int) and (N > 2), "'N' must been an int and > 2" + + ans = [] + + # iterates over all numbers between 2 up to N+1 # if a number is prime then appends to list 'ans' - for number in range(2,N+1): - + for number in range(2, N + 1): + if isPrime(number): - + ans.append(number) - + # precondition - assert isinstance(ans,list), "'ans' must been from type list" - + assert isinstance(ans, list), "'ans' must been from type list" + return ans # ----------------------------------------- - + + def primeFactorization(number): """ - input: positive integer 'number' + input: positive integer 'number' returns a list of the prime number factors of 'number' """ - import math # for function sqrt - # precondition - assert isinstance(number,int) and number >= 0, \ - "'number' must been an int and >= 0" - - ans = [] # this list will be returns of the function. + assert isinstance(number, int) and number >= 0, "'number' must been an int and >= 0" + + ans = [] # this list will be returns of the function. # potential prime number factors. - factor = 2 + factor = 2 quotient = number - - + if number == 0 or number == 1: - + ans.append(number) - - # if 'number' not prime then builds the prime factorization of 'number' + + # if 'number' not prime then builds the prime factorization of 'number' elif not isPrime(number): - - while (quotient != 1): - + + while quotient != 1: + if isPrime(factor) and (quotient % factor == 0): - ans.append(factor) - quotient /= factor + ans.append(factor) + quotient /= factor else: - factor += 1 - + factor += 1 + else: ans.append(number) - + # precondition - assert isinstance(ans,list), "'ans' must been from type list" - + assert isinstance(ans, list), "'ans' must been from type list" + return ans - + # ----------------------------------------- - + + def greatestPrimeFactor(number): """ input: positive integer 'number' >= 0 returns the greatest prime number factor of 'number' """ - + # precondition - assert isinstance(number,int) and (number >= 0), \ - "'number' bust been an int and >= 0" - - ans = 0 - + assert isinstance(number, int) and ( + number >= 0 + ), "'number' bust been an int and >= 0" + + ans = 0 + # prime factorization of 'number' primeFactors = primeFactorization(number) - ans = max(primeFactors) - + ans = max(primeFactors) + # precondition - assert isinstance(ans,int), "'ans' must been from type int" - + assert isinstance(ans, int), "'ans' must been from type int" + return ans - + # ---------------------------------------------- - - + + def smallestPrimeFactor(number): """ input: integer 'number' >= 0 returns the smallest prime number factor of 'number' """ - + # precondition - assert isinstance(number,int) and (number >= 0), \ - "'number' bust been an int and >= 0" - - ans = 0 - + assert isinstance(number, int) and ( + number >= 0 + ), "'number' bust been an int and >= 0" + + ans = 0 + # prime factorization of 'number' primeFactors = primeFactorization(number) - + ans = min(primeFactors) # precondition - assert isinstance(ans,int), "'ans' must been from type int" - + assert isinstance(ans, int), "'ans' must been from type int" + return ans - - + + # ---------------------- - + + def isEven(number): """ input: integer 'number' returns true if 'number' is even, otherwise false. - """ + """ # precondition - assert isinstance(number, int), "'number' must been an int" + assert isinstance(number, int), "'number' must been an int" assert isinstance(number % 2 == 0, bool), "compare bust been from type bool" - + return number % 2 == 0 - + + # ------------------------ - + + def isOdd(number): """ input: integer 'number' returns true if 'number' is odd, otherwise false. - """ + """ # precondition - assert isinstance(number, int), "'number' must been an int" + assert isinstance(number, int), "'number' must been an int" assert isinstance(number % 2 != 0, bool), "compare bust been from type bool" - + return number % 2 != 0 - + + # ------------------------ - - + + def goldbach(number): """ Goldbach's assumption input: a even positive integer 'number' > 2 returns a list of two prime numbers whose sum is equal to 'number' """ - + # precondition - assert isinstance(number,int) and (number > 2) and isEven(number), \ - "'number' must been an int, even and > 2" - - ans = [] # this list will returned - + assert ( + isinstance(number, int) and (number > 2) and isEven(number) + ), "'number' must been an int, even and > 2" + + ans = [] # this list will returned + # creates a list of prime numbers between 2 up to 'number' primeNumbers = getPrimeNumbers(number) - lenPN = len(primeNumbers) + lenPN = len(primeNumbers) # run variable for while-loops. i = 0 - j = 1 - + j = None + # exit variable. for break up the loops loop = True - - while (i < lenPN and loop): - - j = i+1 - - - while (j < lenPN and loop): - + + while i < lenPN and loop: + + j = i + 1 + + while j < lenPN and loop: + if primeNumbers[i] + primeNumbers[j] == number: loop = False ans.append(primeNumbers[i]) ans.append(primeNumbers[j]) - + j += 1 i += 1 - + # precondition - assert isinstance(ans,list) and (len(ans) == 2) and \ - (ans[0] + ans[1] == number) and isPrime(ans[0]) and isPrime(ans[1]), \ - "'ans' must contains two primes. And sum of elements must been eq 'number'" - + assert ( + isinstance(ans, list) + and (len(ans) == 2) + and (ans[0] + ans[1] == number) + and isPrime(ans[0]) + and isPrime(ans[1]) + ), "'ans' must contains two primes. And sum of elements must been eq 'number'" + return ans - + + # ---------------------------------------------- -def gcd(number1,number2): + +def gcd(number1, number2): """ Greatest common divisor input: two positive integer 'number1' and 'number2' returns the greatest common divisor of 'number1' and 'number2' """ - + # precondition - assert isinstance(number1,int) and isinstance(number2,int) \ - and (number1 >= 0) and (number2 >= 0), \ - "'number1' and 'number2' must been positive integer." + assert ( + isinstance(number1, int) + and isinstance(number2, int) + and (number1 >= 0) + and (number2 >= 0) + ), "'number1' and 'number2' must been positive integer." + + rest = 0 - rest = 0 - while number2 != 0: - + rest = number1 % number2 number1 = number2 number2 = rest # precondition - assert isinstance(number1,int) and (number1 >= 0), \ - "'number' must been from type int and positive" - + assert isinstance(number1, int) and ( + number1 >= 0 + ), "'number' must been from type int and positive" + return number1 - + + # ---------------------------------------------------- - + + def kgV(number1, number2): """ Least common multiple input: two positive integer 'number1' and 'number2' returns the least common multiple of 'number1' and 'number2' """ - + # precondition - assert isinstance(number1,int) and isinstance(number2,int) \ - and (number1 >= 1) and (number2 >= 1), \ - "'number1' and 'number2' must been positive integer." - - ans = 1 # actual answer that will be return. - + assert ( + isinstance(number1, int) + and isinstance(number2, int) + and (number1 >= 1) + and (number2 >= 1) + ), "'number1' and 'number2' must been positive integer." + + ans = 1 # actual answer that will be return. + # for kgV (x,1) if number1 > 1 and number2 > 1: - + # builds the prime factorization of 'number1' and 'number2' primeFac1 = primeFactorization(number1) primeFac2 = primeFactorization(number2) - + elif number1 == 1 or number2 == 1: - + primeFac1 = [] primeFac2 = [] - ans = max(number1,number2) - + ans = max(number1, number2) + count1 = 0 count2 = 0 - - done = [] # captured numbers int both 'primeFac1' and 'primeFac2' - + + done = [] # captured numbers int both 'primeFac1' and 'primeFac2' + # iterates through primeFac1 for n in primeFac1: - + if n not in done: - + if n in primeFac2: - + count1 = primeFac1.count(n) count2 = primeFac2.count(n) - - for i in range(max(count1,count2)): + + for i in range(max(count1, count2)): ans *= n - + else: - + count1 = primeFac1.count(n) - + for i in range(count1): ans *= n - + done.append(n) - + # iterates through primeFac2 for n in primeFac2: - + if n not in done: - + count2 = primeFac2.count(n) - + for i in range(count2): ans *= n - + done.append(n) - + # precondition - assert isinstance(ans,int) and (ans >= 0), \ - "'ans' must been from type int and positive" - + assert isinstance(ans, int) and ( + ans >= 0 + ), "'ans' must been from type int and positive" + return ans - + + # ---------------------------------- - + + def getPrime(n): """ Gets the n-th prime number. input: positive integer 'n' >= 0 returns the n-th prime number, beginning at index 0 """ - + # precondition - assert isinstance(n,int) and (n >= 0), "'number' must been a positive int" - + assert isinstance(n, int) and (n >= 0), "'number' must been a positive int" + index = 0 - ans = 2 # this variable holds the answer - + ans = 2 # this variable holds the answer + while index < n: - + index += 1 - - ans += 1 # counts to the next number - + + ans += 1 # counts to the next number + # if ans not prime then - # runs to the next prime number. + # runs to the next prime number. while not isPrime(ans): ans += 1 - + # precondition - assert isinstance(ans,int) and isPrime(ans), \ - "'ans' must been a prime number and from type int" - + assert isinstance(ans, int) and isPrime( + ans + ), "'ans' must been a prime number and from type int" + return ans - + + # --------------------------------------------------- - + + def getPrimesBetween(pNumber1, pNumber2): """ input: prime numbers 'pNumber1' and 'pNumber2' pNumber1 < pNumber2 returns a list of all prime numbers between 'pNumber1' (exclusiv) - and 'pNumber2' (exclusiv) + and 'pNumber2' (exclusiv) """ - + # precondition - assert isPrime(pNumber1) and isPrime(pNumber2) and (pNumber1 < pNumber2), \ - "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'" - - number = pNumber1 + 1 # jump to the next number - - ans = [] # this list will be returns. - + assert ( + isPrime(pNumber1) and isPrime(pNumber2) and (pNumber1 < pNumber2) + ), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'" + + number = pNumber1 + 1 # jump to the next number + + ans = [] # this list will be returns. + # if number is not prime then - # fetch the next prime number. + # fetch the next prime number. while not isPrime(number): number += 1 - + while number < pNumber2: - + ans.append(number) - + number += 1 - - # fetch the next prime number. + + # fetch the next prime number. while not isPrime(number): number += 1 - + # precondition - assert isinstance(ans,list) and ans[0] != pNumber1 \ - and ans[len(ans)-1] != pNumber2, \ - "'ans' must been a list without the arguments" - + assert ( + isinstance(ans, list) and ans[0] != pNumber1 and ans[len(ans) - 1] != pNumber2 + ), "'ans' must been a list without the arguments" + # 'ans' contains not 'pNumber1' and 'pNumber2' ! return ans - + + # ---------------------------------------------------- + def getDivisors(n): """ input: positive integer 'n' >= 1 returns all divisors of n (inclusive 1 and 'n') """ - + # precondition - assert isinstance(n,int) and (n >= 1), "'n' must been int and >= 1" + assert isinstance(n, int) and (n >= 1), "'n' must been int and >= 1" + + ans = [] # will be returned. + + for divisor in range(1, n + 1): - from math import sqrt - - ans = [] # will be returned. - - for divisor in range(1,n+1): - if n % divisor == 0: ans.append(divisor) - - - #precondition - assert ans[0] == 1 and ans[len(ans)-1] == n, \ - "Error in function getDivisiors(...)" - - + + # precondition + assert ans[0] == 1 and ans[len(ans) - 1] == n, "Error in function getDivisiors(...)" + return ans @@ -523,82 +551,95 @@ def isPerfectNumber(number): input: positive integer 'number' > 1 returns true if 'number' is a perfect number otherwise false. """ - + # precondition - assert isinstance(number,int) and (number > 1), \ - "'number' must been an int and >= 1" - + assert isinstance(number, int) and ( + number > 1 + ), "'number' must been an int and >= 1" + divisors = getDivisors(number) - + # precondition - assert isinstance(divisors,list) and(divisors[0] == 1) and \ - (divisors[len(divisors)-1] == number), \ - "Error in help-function getDivisiors(...)" - + assert ( + isinstance(divisors, list) + and (divisors[0] == 1) + and (divisors[len(divisors) - 1] == number) + ), "Error in help-function getDivisiors(...)" + # summed all divisors up to 'number' (exclusive), hence [:-1] return sum(divisors[:-1]) == number + # ------------------------------------------------------------ + def simplifyFraction(numerator, denominator): """ input: two integer 'numerator' and 'denominator' assumes: 'denominator' != 0 returns: a tuple with simplify numerator and denominator. - """ - + """ + # precondition - assert isinstance(numerator, int) and isinstance(denominator,int) \ - and (denominator != 0), \ - "The arguments must been from type int and 'denominator' != 0" - + assert ( + isinstance(numerator, int) + and isinstance(denominator, int) + and (denominator != 0) + ), "The arguments must been from type int and 'denominator' != 0" + # build the greatest common divisor of numerator and denominator. gcdOfFraction = gcd(abs(numerator), abs(denominator)) # precondition - assert isinstance(gcdOfFraction, int) and (numerator % gcdOfFraction == 0) \ - and (denominator % gcdOfFraction == 0), \ - "Error in function gcd(...,...)" - + assert ( + isinstance(gcdOfFraction, int) + and (numerator % gcdOfFraction == 0) + and (denominator % gcdOfFraction == 0) + ), "Error in function gcd(...,...)" + return (numerator // gcdOfFraction, denominator // gcdOfFraction) - + + # ----------------------------------------------------------------- - + + def factorial(n): """ input: positive integer 'n' returns the factorial of 'n' (n!) """ - + # precondition - assert isinstance(n,int) and (n >= 0), "'n' must been a int and >= 0" - - ans = 1 # this will be return. - - for factor in range(1,n+1): + assert isinstance(n, int) and (n >= 0), "'n' must been a int and >= 0" + + ans = 1 # this will be return. + + for factor in range(1, n + 1): ans *= factor - + return ans - + + # ------------------------------------------------------------------- - + + def fib(n): """ input: positive integer 'n' returns the n-th fibonacci term , indexing by 0 - """ - + """ + # precondition assert isinstance(n, int) and (n >= 0), "'n' must been an int and >= 0" - + tmp = 0 fib1 = 1 - ans = 1 # this will be return - - for i in range(n-1): - + ans = 1 # this will be return + + for i in range(n - 1): + tmp = ans ans += fib1 fib1 = tmp - + return ans diff --git a/other/sdes.py b/other/sdes.py new file mode 100644 index 000000000000..3038ff193ae9 --- /dev/null +++ b/other/sdes.py @@ -0,0 +1,97 @@ +def apply_table(inp, table): + """ + >>> apply_table("0123456789", list(range(10))) + '9012345678' + >>> apply_table("0123456789", list(range(9, -1, -1))) + '8765432109' + """ + res = "" + for i in table: + res += inp[i - 1] + return res + + +def left_shift(data): + """ + >>> left_shift("0123456789") + '1234567890' + """ + return data[1:] + data[0] + + +def XOR(a, b): + """ + >>> XOR("01010101", "00001111") + '01011010' + """ + res = "" + for i in range(len(a)): + if a[i] == b[i]: + res += "0" + else: + res += "1" + return res + + +def apply_sbox(s, data): + row = int("0b" + data[0] + data[-1], 2) + col = int("0b" + data[1:3], 2) + return bin(s[row][col])[2:] + + +def function(expansion, s0, s1, key, message): + left = message[:4] + right = message[4:] + temp = apply_table(right, expansion) + temp = XOR(temp, key) + l = apply_sbox(s0, temp[:4]) + r = apply_sbox(s1, temp[4:]) + l = "0" * (2 - len(l)) + l + r = "0" * (2 - len(r)) + r + temp = apply_table(l + r, p4_table) + temp = XOR(left, temp) + return temp + right + + +if __name__ == "__main__": + + key = input("Enter 10 bit key: ") + message = input("Enter 8 bit message: ") + + p8_table = [6, 3, 7, 4, 8, 5, 10, 9] + p10_table = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6] + p4_table = [2, 4, 3, 1] + IP = [2, 6, 3, 1, 4, 8, 5, 7] + IP_inv = [4, 1, 3, 5, 7, 2, 8, 6] + expansion = [4, 1, 2, 3, 2, 3, 4, 1] + s0 = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]] + s1 = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]] + + # key generation + temp = apply_table(key, p10_table) + left = temp[:5] + right = temp[5:] + left = left_shift(left) + right = left_shift(right) + key1 = apply_table(left + right, p8_table) + left = left_shift(left) + right = left_shift(right) + left = left_shift(left) + right = left_shift(right) + key2 = apply_table(left + right, p8_table) + + # encryption + temp = apply_table(message, IP) + temp = function(expansion, s0, s1, key1, temp) + temp = temp[4:] + temp[:4] + temp = function(expansion, s0, s1, key2, temp) + CT = apply_table(temp, IP_inv) + print("Cipher text is:", CT) + + # decryption + temp = apply_table(CT, IP) + temp = function(expansion, s0, s1, key2, temp) + temp = temp[4:] + temp[:4] + temp = function(expansion, s0, s1, key1, temp) + PT = apply_table(temp, IP_inv) + print("Plain text after decypting is:", PT) diff --git a/other/sierpinski_triangle.py b/other/sierpinski_triangle.py index 6a06058fe03e..0e6ce43e35d3 100644 --- a/other/sierpinski_triangle.py +++ b/other/sierpinski_triangle.py @@ -1,7 +1,7 @@ #!/usr/bin/python # encoding=utf8 -'''Author Anurag Kumar | anuragkumarak95@gmail.com | git/anuragkumarak95 +"""Author Anurag Kumar | anuragkumarak95@gmail.com | git/anuragkumarak95 Simple example of Fractal generation using recursive function. @@ -21,47 +21,53 @@ Usage: - $python sierpinski_triangle.py -Credits: This code was written by editing the code from http://www.lpb-riannetrujillo.com/blog/python-fractal/ +Credits: This code was written by editing the code from http://www.riannetrujillo.com/blog/python-fractal/ -''' +""" import turtle import sys -PROGNAME = 'Sierpinski Triangle' -if len(sys.argv) !=2: - raise Exception('right format for using this script: $python fractals.py ') -myPen = turtle.Turtle() -myPen.ht() -myPen.speed(5) -myPen.pencolor('red') +PROGNAME = "Sierpinski Triangle" -points = [[-175,-125],[0,175],[175,-125]] #size of triangle +points = [[-175, -125], [0, 175], [175, -125]] # size of triangle -def getMid(p1,p2): - return ( (p1[0]+p2[0]) / 2, (p1[1] + p2[1]) / 2) #find midpoint -def triangle(points,depth): +def getMid(p1, p2): + return ((p1[0] + p2[0]) / 2, (p1[1] + p2[1]) / 2) # find midpoint + + +def triangle(points, depth): myPen.up() - myPen.goto(points[0][0],points[0][1]) + myPen.goto(points[0][0], points[0][1]) myPen.down() - myPen.goto(points[1][0],points[1][1]) - myPen.goto(points[2][0],points[2][1]) - myPen.goto(points[0][0],points[0][1]) - - if depth>0: - triangle([points[0], - getMid(points[0], points[1]), - getMid(points[0], points[2])], - depth-1) - triangle([points[1], - getMid(points[0], points[1]), - getMid(points[1], points[2])], - depth-1) - triangle([points[2], - getMid(points[2], points[1]), - getMid(points[0], points[2])], - depth-1) - - -triangle(points,int(sys.argv[1])) \ No newline at end of file + myPen.goto(points[1][0], points[1][1]) + myPen.goto(points[2][0], points[2][1]) + myPen.goto(points[0][0], points[0][1]) + + if depth > 0: + triangle( + [points[0], getMid(points[0], points[1]), getMid(points[0], points[2])], + depth - 1, + ) + triangle( + [points[1], getMid(points[0], points[1]), getMid(points[1], points[2])], + depth - 1, + ) + triangle( + [points[2], getMid(points[2], points[1]), getMid(points[0], points[2])], + depth - 1, + ) + + +if __name__ == "__main__": + if len(sys.argv) != 2: + raise ValueError( + "right format for using this script: " + "$python fractals.py " + ) + myPen = turtle.Turtle() + myPen.ht() + myPen.speed(5) + myPen.pencolor("red") + triangle(points, int(sys.argv[1])) diff --git a/other/tower_of_hanoi.py b/other/tower_of_hanoi.py index dc15b2ce8e58..3cc0e40b369f 100644 --- a/other/tower_of_hanoi.py +++ b/other/tower_of_hanoi.py @@ -1,6 +1,5 @@ -from __future__ import print_function -def moveTower(height, fromPole, toPole, withPole): - ''' +def moveTower(height, fromPole, toPole, withPole): + """ >>> moveTower(3, 'A', 'B', 'C') moving disk from A to B moving disk from A to C @@ -9,18 +8,21 @@ def moveTower(height, fromPole, toPole, withPole): moving disk from C to A moving disk from C to B moving disk from A to B - ''' + """ if height >= 1: - moveTower(height-1, fromPole, withPole, toPole) + moveTower(height - 1, fromPole, withPole, toPole) moveDisk(fromPole, toPole) - moveTower(height-1, withPole, toPole, fromPole) + moveTower(height - 1, withPole, toPole, fromPole) + + +def moveDisk(fp, tp): + print("moving disk from", fp, "to", tp) -def moveDisk(fp,tp): - print(('moving disk from', fp, 'to', tp)) def main(): - height = int(input('Height of hanoi: ')) - moveTower(height, 'A', 'B', 'C') + height = int(input("Height of hanoi: ").strip()) + moveTower(height, "A", "B", "C") + -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/other/two_sum.py b/other/two_sum.py index d4484aa85505..70d5c5375026 100644 --- a/other/two_sum.py +++ b/other/two_sum.py @@ -9,7 +9,7 @@ Because nums[0] + nums[1] = 2 + 7 = 9, return [0, 1]. """ -from __future__ import print_function + def twoSum(nums, target): """ @@ -19,11 +19,11 @@ def twoSum(nums, target): """ chk_map = {} for index, val in enumerate(nums): - compl = target - val - if compl in chk_map: - indices = [chk_map[compl], index] - print(indices) - return [indices] - else: - chk_map[val] = index + compl = target - val + if compl in chk_map: + indices = [chk_map[compl], index] + print(indices) + return [indices] + else: + chk_map[val] = index return False diff --git a/other/word_patterns.py b/other/word_patterns.py index c33d520087f7..d229954dea93 100644 --- a/other/word_patterns.py +++ b/other/word_patterns.py @@ -1,39 +1,44 @@ -from __future__ import print_function -import pprint, time - -def getWordPattern(word): +def get_word_pattern(word: str) -> str: + """ + >>> get_word_pattern("pattern") + '0.1.2.2.3.4.5' + >>> get_word_pattern("word pattern") + '0.1.2.3.4.5.6.7.7.8.2.9' + >>> get_word_pattern("get word pattern") + '0.1.2.3.4.5.6.7.3.8.9.2.2.1.6.10' + """ word = word.upper() - nextNum = 0 - letterNums = {} - wordPattern = [] + next_num = 0 + letter_nums = {} + word_pattern = [] for letter in word: - if letter not in letterNums: - letterNums[letter] = str(nextNum) - nextNum += 1 - wordPattern.append(letterNums[letter]) - return '.'.join(wordPattern) + if letter not in letter_nums: + letter_nums[letter] = str(next_num) + next_num += 1 + word_pattern.append(letter_nums[letter]) + return ".".join(word_pattern) -def main(): - startTime = time.time() - allPatterns = {} - with open('Dictionary.txt') as fo: - wordList = fo.read().split('\n') +if __name__ == "__main__": + import pprint + import time - for word in wordList: - pattern = getWordPattern(word) + start_time = time.time() + with open("dictionary.txt") as in_file: + wordList = in_file.read().splitlines() - if pattern not in allPatterns: - allPatterns[pattern] = [word] + all_patterns = {} + for word in wordList: + pattern = get_word_pattern(word) + if pattern in all_patterns: + all_patterns[pattern].append(word) else: - allPatterns[pattern].append(word) - - with open('Word Patterns.txt', 'w') as fo: - fo.write(pprint.pformat(allPatterns)) + all_patterns[pattern] = [word] - totalTime = round(time.time() - startTime, 2) - print(('Done! [', totalTime, 'seconds ]')) + with open("word_patterns.txt", "w") as out_file: + out_file.write(pprint.pformat(all_patterns)) -if __name__ == '__main__': - main() + totalTime = round(time.time() - start_time, 2) + print(f"Done! {len(all_patterns):,} word patterns found in {totalTime} seconds.") + # Done! 9,581 word patterns found in 0.58 seconds. diff --git a/project_euler/README.md b/project_euler/README.md index 9f77f719f0f1..f80d58ea0038 100644 --- a/project_euler/README.md +++ b/project_euler/README.md @@ -8,51 +8,4 @@ insights to solve. Project Euler is ideal for mathematicians who are learning to Here the efficiency of your code is also checked. I've tried to provide all the best possible solutions. -PROBLEMS: - -1. If we list all the natural numbers below 10 that are multiples of 3 or 5, we get 3,5,6 and 9. The sum of these multiples is 23. - Find the sum of all the multiples of 3 or 5 below N. - -2. Each new term in the Fibonacci sequence is generated by adding the previous two terms. By starting with 1 and 2, - the first 10 terms will be: - 1,2,3,5,8,13,21,34,55,89,.. - By considering the terms in the Fibonacci sequence whose values do not exceed n, find the sum of the even-valued terms. - e.g. for n=10, we have {2,8}, sum is 10. - -3. The prime factors of 13195 are 5,7,13 and 29. What is the largest prime factor of a given number N? - e.g. for 10, largest prime factor = 5. For 17, largest prime factor = 17. - -4. A palindromic number reads the same both ways. The largest palindrome made from the product of two 2-digit numbers is 9009 = 91 × 99. - Find the largest palindrome made from the product of two 3-digit numbers which is less than N. - -5. 2520 is the smallest number that can be divided by each of the numbers from 1 to 10 without any remainder. - What is the smallest positive number that is evenly divisible(divisible with no remainder) by all of the numbers from 1 to N? - -6. The sum of the squares of the first ten natural numbers is, - 1^2 + 2^2 + ... + 10^2 = 385 - The square of the sum of the first ten natural numbers is, - (1 + 2 + ... + 10)^2 = 552 = 3025 - Hence the difference between the sum of the squares of the first ten natural numbers and the square of the sum is 3025 − 385 = 2640. - Find the difference between the sum of the squares of the first N natural numbers and the square of the sum. - -7. By listing the first six prime numbers: 2, 3, 5, 7, 11, and 13, we can see that the 6th prime is 13. - What is the Nth prime number? - -9. A Pythagorean triplet is a set of three natural numbers, a < b < c, for which, - a^2 + b^2 = c^2 - There exists exactly one Pythagorean triplet for which a + b + c = 1000. - Find the product abc. - -14. The following iterative sequence is defined for the set of positive integers: - n → n/2 (n is even) - n → 3n + 1 (n is odd) - Using the rule above and starting with 13, we generate the following sequence: - 13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1 - Which starting number, under one million, produces the longest chain? - -16. 2^15 = 32768 and the sum of its digits is 3 + 2 + 7 + 6 + 8 = 26. - What is the sum of the digits of the number 2^1000? -20. n! means n × (n − 1) × ... × 3 × 2 × 1 - For example, 10! = 10 × 9 × ... × 3 × 2 × 1 = 3628800, - and the sum of the digits in the number 10! is 3 + 6 + 2 + 8 + 8 + 0 + 0 = 27. - Find the sum of the digits in the number 100! +For description of the problem statements, kindly visit https://projecteuler.net/show=all diff --git a/data_structures/union_find/__init__.py b/project_euler/problem_01/__init__.py similarity index 100% rename from data_structures/union_find/__init__.py rename to project_euler/problem_01/__init__.py diff --git a/project_euler/problem_01/sol1.py b/project_euler/problem_01/sol1.py index 27031c3cfa9a..e81156edaee4 100644 --- a/project_euler/problem_01/sol1.py +++ b/project_euler/problem_01/sol1.py @@ -1,17 +1,28 @@ -''' +""" Problem Statement: If we list all the natural numbers below 10 that are multiples of 3 or 5, we get 3,5,6 and 9. The sum of these multiples is 23. Find the sum of all the multiples of 3 or 5 below N. -''' -from __future__ import print_function -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 -n = int(raw_input().strip()) -sum=0 -for a in range(3,n): - if(a%3==0 or a%5==0): - sum+=a -print(sum) +""" + + +def solution(n): + """Returns the sum of all the multiples of 3 or 5 below n. + + >>> solution(3) + 0 + >>> solution(4) + 3 + >>> solution(10) + 23 + >>> solution(600) + 83700 + >>> solution(-7) + 0 + """ + + return sum([e for e in range(3, n) if e % 3 == 0 or e % 5 == 0]) + + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_01/sol2.py b/project_euler/problem_01/sol2.py index 2b7760e0bfff..8041c7ffa589 100644 --- a/project_euler/problem_01/sol2.py +++ b/project_euler/problem_01/sol2.py @@ -1,20 +1,33 @@ -''' +""" Problem Statement: If we list all the natural numbers below 10 that are multiples of 3 or 5, we get 3,5,6 and 9. The sum of these multiples is 23. Find the sum of all the multiples of 3 or 5 below N. -''' -from __future__ import print_function -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 -n = int(raw_input().strip()) -sum = 0 -terms = (n-1)//3 -sum+= ((terms)*(6+(terms-1)*3))//2 #sum of an A.P. -terms = (n-1)//5 -sum+= ((terms)*(10+(terms-1)*5))//2 -terms = (n-1)//15 -sum-= ((terms)*(30+(terms-1)*15))//2 -print(sum) +""" + + +def solution(n): + """Returns the sum of all the multiples of 3 or 5 below n. + + >>> solution(3) + 0 + >>> solution(4) + 3 + >>> solution(10) + 23 + >>> solution(600) + 83700 + """ + + sum = 0 + terms = (n - 1) // 3 + sum += ((terms) * (6 + (terms - 1) * 3)) // 2 # sum of an A.P. + terms = (n - 1) // 5 + sum += ((terms) * (10 + (terms - 1) * 5)) // 2 + terms = (n - 1) // 15 + sum -= ((terms) * (30 + (terms - 1) * 15)) // 2 + return sum + + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_01/sol3.py b/project_euler/problem_01/sol3.py index f4f3aefcc5de..c0bcbc06ec83 100644 --- a/project_euler/problem_01/sol3.py +++ b/project_euler/problem_01/sol3.py @@ -1,50 +1,60 @@ -from __future__ import print_function - -''' +""" Problem Statement: If we list all the natural numbers below 10 that are multiples of 3 or 5, we get 3,5,6 and 9. The sum of these multiples is 23. Find the sum of all the multiples of 3 or 5 below N. -''' -''' -This solution is based on the pattern that the successive numbers in the series follow: 0+3,+2,+1,+3,+1,+2,+3. -''' +""" + + +def solution(n): + """ + This solution is based on the pattern that the successive numbers in the + series follow: 0+3,+2,+1,+3,+1,+2,+3. + Returns the sum of all the multiples of 3 or 5 below n. + + >>> solution(3) + 0 + >>> solution(4) + 3 + >>> solution(10) + 23 + >>> solution(600) + 83700 + """ + + sum = 0 + num = 0 + while 1: + num += 3 + if num >= n: + break + sum += num + num += 2 + if num >= n: + break + sum += num + num += 1 + if num >= n: + break + sum += num + num += 3 + if num >= n: + break + sum += num + num += 1 + if num >= n: + break + sum += num + num += 2 + if num >= n: + break + sum += num + num += 3 + if num >= n: + break + sum += num + return sum -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 -n = int(raw_input().strip()) -sum=0 -num=0 -while(1): - num+=3 - if(num>=n): - break - sum+=num - num+=2 - if(num>=n): - break - sum+=num - num+=1 - if(num>=n): - break - sum+=num - num+=3 - if(num>=n): - break - sum+=num - num+=1 - if(num>=n): - break - sum+=num - num+=2 - if(num>=n): - break - sum+=num - num+=3 - if(num>=n): - break - sum+=num -print(sum); +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_01/sol4.py b/project_euler/problem_01/sol4.py index 7941f5fcd3fe..e01dc977d8cf 100644 --- a/project_euler/problem_01/sol4.py +++ b/project_euler/problem_01/sol4.py @@ -1,4 +1,24 @@ -def mulitples(limit): +""" +Problem Statement: +If we list all the natural numbers below 10 that are multiples of 3 or 5, +we get 3,5,6 and 9. The sum of these multiples is 23. +Find the sum of all the multiples of 3 or 5 below N. +""" + + +def solution(n): + """Returns the sum of all the multiples of 3 or 5 below n. + + >>> solution(3) + 0 + >>> solution(4) + 3 + >>> solution(10) + 23 + >>> solution(600) + 83700 + """ + xmulti = [] zmulti = [] z = 3 @@ -6,7 +26,7 @@ def mulitples(limit): temp = 1 while True: result = z * temp - if (result < limit): + if result < n: zmulti.append(result) temp += 1 else: @@ -14,17 +34,14 @@ def mulitples(limit): break while True: result = x * temp - if (result < limit): + if result < n: xmulti.append(result) temp += 1 else: break - collection = list(set(xmulti+zmulti)) - return (sum(collection)) - - - - - - -print (mulitples(1000)) + collection = list(set(xmulti + zmulti)) + return sum(collection) + + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_01/sol5.py b/project_euler/problem_01/sol5.py index e261cc8fc729..bd96d965f92d 100644 --- a/project_euler/problem_01/sol5.py +++ b/project_euler/problem_01/sol5.py @@ -1,16 +1,28 @@ -''' +""" Problem Statement: If we list all the natural numbers below 10 that are multiples of 3 or 5, we get 3,5,6 and 9. The sum of these multiples is 23. Find the sum of all the multiples of 3 or 5 below N. -''' -from __future__ import print_function -try: - input = raw_input #python3 -except NameError: - pass #python 2 +""" """A straightforward pythonic solution using list comprehension""" -n = int(input().strip()) -print(sum([i for i in range(n) if i%3==0 or i%5==0])) + +def solution(n): + """Returns the sum of all the multiples of 3 or 5 below n. + + >>> solution(3) + 0 + >>> solution(4) + 3 + >>> solution(10) + 23 + >>> solution(600) + 83700 + """ + + return sum([i for i in range(n) if i % 3 == 0 or i % 5 == 0]) + + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_01/sol6.py b/project_euler/problem_01/sol6.py new file mode 100644 index 000000000000..c9f94b9f77c8 --- /dev/null +++ b/project_euler/problem_01/sol6.py @@ -0,0 +1,34 @@ +""" +Problem Statement: +If we list all the natural numbers below 10 that are multiples of 3 or 5, +we get 3,5,6 and 9. The sum of these multiples is 23. +Find the sum of all the multiples of 3 or 5 below N. +""" + + +def solution(n): + """Returns the sum of all the multiples of 3 or 5 below n. + + >>> solution(3) + 0 + >>> solution(4) + 3 + >>> solution(10) + 23 + >>> solution(600) + 83700 + """ + + a = 3 + result = 0 + while a < n: + if a % 3 == 0 or a % 5 == 0: + result += a + elif a % 15 == 0: + result -= a + a += 1 + return result + + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_01/sol7.py b/project_euler/problem_01/sol7.py new file mode 100644 index 000000000000..a0510b54c409 --- /dev/null +++ b/project_euler/problem_01/sol7.py @@ -0,0 +1,32 @@ +""" +Problem Statement: +If we list all the natural numbers below 10 that are multiples of 3 or 5, +we get 3,5,6 and 9. The sum of these multiples is 23. +Find the sum of all the multiples of 3 or 5 below N. +""" + + +def solution(n): + """Returns the sum of all the multiples of 3 or 5 below n. + + >>> solution(3) + 0 + >>> solution(4) + 3 + >>> solution(10) + 23 + >>> solution(600) + 83700 + """ + + result = 0 + for i in range(n): + if i % 3 == 0: + result += i + elif i % 5 == 0: + result += i + return result + + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_02/__init__.py b/project_euler/problem_02/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_02/sol1.py b/project_euler/problem_02/sol1.py index 44ea980f2df0..ec89ddaeb2b5 100644 --- a/project_euler/problem_02/sol1.py +++ b/project_euler/problem_02/sol1.py @@ -1,24 +1,41 @@ -''' +""" Problem: -Each new term in the Fibonacci sequence is generated by adding the previous two terms. By starting with 1 and 2, -the first 10 terms will be: - 1,2,3,5,8,13,21,34,55,89,.. -By considering the terms in the Fibonacci sequence whose values do not exceed n, find the sum of the even-valued terms. -e.g. for n=10, we have {2,8}, sum is 10. -''' -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - -n = int(raw_input().strip()) -i=1 -j=2 -sum=0 -while(j<=n): - if j%2 == 0: - sum+=j - i , j = j, i+j -print(sum) +Each new term in the Fibonacci sequence is generated by adding the previous two +terms. By starting with 1 and 2, the first 10 terms will be: + + 1,2,3,5,8,13,21,34,55,89,.. + +By considering the terms in the Fibonacci sequence whose values do not exceed +n, find the sum of the even-valued terms. e.g. for n=10, we have {2,8}, sum is +10. +""" + + +def solution(n): + """Returns the sum of all fibonacci sequence even elements that are lower + or equals to n. + + >>> solution(10) + 10 + >>> solution(15) + 10 + >>> solution(2) + 2 + >>> solution(1) + 0 + >>> solution(34) + 44 + """ + i = 1 + j = 2 + sum = 0 + while j <= n: + if j % 2 == 0: + sum += j + i, j = j, i + j + + return sum + + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_02/sol2.py b/project_euler/problem_02/sol2.py index a2772697bb79..bc5040cc6b3b 100644 --- a/project_euler/problem_02/sol2.py +++ b/project_euler/problem_02/sol2.py @@ -1,15 +1,39 @@ -def fib(n): - """ - Returns a list of all the even terms in the Fibonacci sequence that are less than n. +""" +Problem: +Each new term in the Fibonacci sequence is generated by adding the previous two +terms. By starting with 1 and 2, the first 10 terms will be: + + 1,2,3,5,8,13,21,34,55,89,.. + +By considering the terms in the Fibonacci sequence whose values do not exceed +n, find the sum of the even-valued terms. e.g. for n=10, we have {2,8}, sum is +10. +""" + + +def solution(n): + """Returns the sum of all fibonacci sequence even elements that are lower + or equals to n. + + >>> solution(10) + [2, 8] + >>> solution(15) + [2, 8] + >>> solution(2) + [2] + >>> solution(1) + [] + >>> solution(34) + [2, 8, 34] """ ls = [] a, b = 0, 1 - while b < n: + while b <= n: if b % 2 == 0: ls.append(b) - a, b = b, a+b + a, b = b, a + b return ls -if __name__ == '__main__': - n = int(input("Enter max number: ").strip()) - print(sum(fib(n))) + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_02/sol3.py b/project_euler/problem_02/sol3.py index 0eb46d879704..f29f21c287e5 100644 --- a/project_euler/problem_02/sol3.py +++ b/project_euler/problem_02/sol3.py @@ -1,18 +1,41 @@ -''' +""" Problem: -Each new term in the Fibonacci sequence is generated by adding the previous two terms. - 0,1,1,2,3,5,8,13,21,34,55,89,.. -Every third term from 0 is even So using this I have written a simple code -By considering the terms in the Fibonacci sequence whose values do not exceed n, find the sum of the even-valued terms. -e.g. for n=10, we have {2,8}, sum is 10. -''' -"""Python 3""" -n = int(input()) -a=0 -b=2 -count=0 -while 4*b+a>> solution(10) + 10 + >>> solution(15) + 10 + >>> solution(2) + 2 + >>> solution(1) + 0 + >>> solution(34) + 44 + """ + if n <= 1: + return 0 + a = 0 + b = 2 + count = 0 + while 4 * b + a <= n: + a, b = b, 4 * b + a + count += a + return count + b + + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_02/sol4.py b/project_euler/problem_02/sol4.py index 64bae65f49b4..92ea0a51e026 100644 --- a/project_euler/problem_02/sol4.py +++ b/project_euler/problem_02/sol4.py @@ -1,13 +1,65 @@ +""" +Problem: +Each new term in the Fibonacci sequence is generated by adding the previous two +terms. By starting with 1 and 2, the first 10 terms will be: + + 1,2,3,5,8,13,21,34,55,89,.. + +By considering the terms in the Fibonacci sequence whose values do not exceed +n, find the sum of the even-valued terms. e.g. for n=10, we have {2,8}, sum is +10. +""" import math -from decimal import * +from decimal import Decimal, getcontext + + +def solution(n): + """Returns the sum of all fibonacci sequence even elements that are lower + or equals to n. -getcontext().prec = 100 -phi = (Decimal(5) ** Decimal(0.5) + 1) / Decimal(2) + >>> solution(10) + 10 + >>> solution(15) + 10 + >>> solution(2) + 2 + >>> solution(1) + 0 + >>> solution(34) + 44 + >>> solution(3.4) + 2 + >>> solution(0) + Traceback (most recent call last): + ... + ValueError: Parameter n must be greater or equal to one. + >>> solution(-17) + Traceback (most recent call last): + ... + ValueError: Parameter n must be greater or equal to one. + >>> solution([]) + Traceback (most recent call last): + ... + TypeError: Parameter n must be int or passive of cast to int. + >>> solution("asd") + Traceback (most recent call last): + ... + TypeError: Parameter n must be int or passive of cast to int. + """ + try: + n = int(n) + except (TypeError, ValueError) as e: + raise TypeError("Parameter n must be int or passive of cast to int.") + if n <= 0: + raise ValueError("Parameter n must be greater or equal to one.") + getcontext().prec = 100 + phi = (Decimal(5) ** Decimal(0.5) + 1) / Decimal(2) -n = Decimal(int(input()) - 1) + index = (math.floor(math.log(n * (phi + 2), phi) - 1) // 3) * 3 + 2 + num = Decimal(round(phi ** Decimal(index + 1))) / (phi + 2) + sum = num // 2 + return int(sum) -index = (math.floor(math.log(n * (phi + 2), phi) - 1) // 3) * 3 + 2 -num = round(phi ** Decimal(index + 1)) / (phi + 2) -sum = num // 2 -print(int(sum)) +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_02/sol5.py b/project_euler/problem_02/sol5.py new file mode 100644 index 000000000000..180906cf8717 --- /dev/null +++ b/project_euler/problem_02/sol5.py @@ -0,0 +1,46 @@ +""" +Problem: +Each new term in the Fibonacci sequence is generated by adding the previous two +terms. By starting with 1 and 2, the first 10 terms will be: + + 1,2,3,5,8,13,21,34,55,89,.. + +By considering the terms in the Fibonacci sequence whose values do not exceed +n, find the sum of the even-valued terms. e.g. for n=10, we have {2,8}, sum is +10. +""" + + +def solution(n): + """Returns the sum of all fibonacci sequence even elements that are lower + or equals to n. + + >>> solution(10) + 10 + >>> solution(15) + 10 + >>> solution(2) + 2 + >>> solution(1) + 0 + >>> solution(34) + 44 + """ + + a = [0, 1] + i = 0 + while a[i] <= n: + a.append(a[i] + a[i + 1]) + if a[i + 2] > n: + break + i += 1 + sum = 0 + for j in range(len(a) - 1): + if a[j] % 2 == 0: + sum += a[j] + + return sum + + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_03/__init__.py b/project_euler/problem_03/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_03/sol1.py b/project_euler/problem_03/sol1.py index bb9f8ca9ad12..9f8ecc5e6565 100644 --- a/project_euler/problem_03/sol1.py +++ b/project_euler/problem_03/sol1.py @@ -1,39 +1,78 @@ -''' +""" Problem: -The prime factors of 13195 are 5,7,13 and 29. What is the largest prime factor of a given number N? -e.g. for 10, largest prime factor = 5. For 17, largest prime factor = 17. -''' -from __future__ import print_function, division +The prime factors of 13195 are 5,7,13 and 29. What is the largest prime factor +of a given number N? +e.g. for 10, largest prime factor = 5. For 17, largest prime factor = 17. +""" import math + def isprime(no): - if(no==2): + if no == 2: return True - elif (no%2==0): + elif no % 2 == 0: return False - sq = int(math.sqrt(no))+1 - for i in range(3,sq,2): - if(no%i==0): + sq = int(math.sqrt(no)) + 1 + for i in range(3, sq, 2): + if no % i == 0: return False return True -maxNumber = 0 -n=int(input()) -if(isprime(n)): - print(n) -else: - while (n%2==0): - n=n/2 - if(isprime(n)): - print(n) + +def solution(n): + """Returns the largest prime factor of a given number n. + + >>> solution(13195) + 29 + >>> solution(10) + 5 + >>> solution(17) + 17 + >>> solution(3.4) + 3 + >>> solution(0) + Traceback (most recent call last): + ... + ValueError: Parameter n must be greater or equal to one. + >>> solution(-17) + Traceback (most recent call last): + ... + ValueError: Parameter n must be greater or equal to one. + >>> solution([]) + Traceback (most recent call last): + ... + TypeError: Parameter n must be int or passive of cast to int. + >>> solution("asd") + Traceback (most recent call last): + ... + TypeError: Parameter n must be int or passive of cast to int. + """ + try: + n = int(n) + except (TypeError, ValueError) as e: + raise TypeError("Parameter n must be int or passive of cast to int.") + if n <= 0: + raise ValueError("Parameter n must be greater or equal to one.") + maxNumber = 0 + if isprime(n): + return n else: - n1 = int(math.sqrt(n))+1 - for i in range(3,n1,2): - if(n%i==0): - if(isprime(n/i)): - maxNumber = n/i - break - elif(isprime(i)): - maxNumber = i - print(maxNumber) + while n % 2 == 0: + n = n / 2 + if isprime(n): + return int(n) + else: + n1 = int(math.sqrt(n)) + 1 + for i in range(3, n1, 2): + if n % i == 0: + if isprime(n / i): + maxNumber = n / i + break + elif isprime(i): + maxNumber = i + return maxNumber + + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_03/sol2.py b/project_euler/problem_03/sol2.py index 44f9c63dfb6a..b6fad079fa31 100644 --- a/project_euler/problem_03/sol2.py +++ b/project_euler/problem_03/sol2.py @@ -1,18 +1,57 @@ -''' +""" Problem: -The prime factors of 13195 are 5,7,13 and 29. What is the largest prime factor of a given number N? +The prime factors of 13195 are 5,7,13 and 29. What is the largest prime factor +of a given number N? + e.g. for 10, largest prime factor = 5. For 17, largest prime factor = 17. -''' +""" + + +def solution(n): + """Returns the largest prime factor of a given number n. + + >>> solution(13195) + 29 + >>> solution(10) + 5 + >>> solution(17) + 17 + >>> solution(3.4) + 3 + >>> solution(0) + Traceback (most recent call last): + ... + ValueError: Parameter n must be greater or equal to one. + >>> solution(-17) + Traceback (most recent call last): + ... + ValueError: Parameter n must be greater or equal to one. + >>> solution([]) + Traceback (most recent call last): + ... + TypeError: Parameter n must be int or passive of cast to int. + >>> solution("asd") + Traceback (most recent call last): + ... + TypeError: Parameter n must be int or passive of cast to int. + """ + try: + n = int(n) + except (TypeError, ValueError) as e: + raise TypeError("Parameter n must be int or passive of cast to int.") + if n <= 0: + raise ValueError("Parameter n must be greater or equal to one.") + prime = 1 + i = 2 + while i * i <= n: + while n % i == 0: + prime = i + n //= i + i += 1 + if n > 1: + prime = n + return int(prime) + -from __future__ import print_function -n=int(input()) -prime=1 -i=2 -while(i*i<=n): - while(n%i==0): - prime=i - n//=i - i+=1 -if(n>1): - prime=n -print(prime) +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_04/__init__.py b/project_euler/problem_04/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_04/sol1.py b/project_euler/problem_04/sol1.py index 05fdd9ebab55..53fff8bed4d4 100644 --- a/project_euler/problem_04/sol1.py +++ b/project_euler/problem_04/sol1.py @@ -1,29 +1,42 @@ -''' +""" Problem: -A palindromic number reads the same both ways. The largest palindrome made from the product of two 2-digit numbers is 9009 = 91 x 99. -Find the largest palindrome made from the product of two 3-digit numbers which is less than N. -''' -from __future__ import print_function -limit = int(input("limit? ")) +A palindromic number reads the same both ways. The largest palindrome made from +the product of two 2-digit numbers is 9009 = 91 x 99. -# fetchs the next number -for number in range(limit-1,10000,-1): +Find the largest palindrome made from the product of two 3-digit numbers which +is less than N. +""" - # converts number into string. - strNumber = str(number) - # checks whether 'strNumber' is a palindrome. - if(strNumber == strNumber[::-1]): +def solution(n): + """Returns the largest palindrome made from the product of two 3-digit + numbers which is less than n. - divisor = 999 + >>> solution(20000) + 19591 + >>> solution(30000) + 29992 + >>> solution(40000) + 39893 + """ + # fetchs the next number + for number in range(n - 1, 10000, -1): - # if 'number' is a product of two 3-digit numbers - # then number is the answer otherwise fetch next number. - while(divisor != 99): - - if((number % divisor == 0) and (len(str(number / divisor)) == 3)): + # converts number into string. + strNumber = str(number) - print(number) - exit(0) + # checks whether 'strNumber' is a palindrome. + if strNumber == strNumber[::-1]: - divisor -=1 + divisor = 999 + + # if 'number' is a product of two 3-digit numbers + # then number is the answer otherwise fetch next number. + while divisor != 99: + if (number % divisor == 0) and (len(str(int(number / divisor))) == 3): + return number + divisor -= 1 + + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_04/sol2.py b/project_euler/problem_04/sol2.py index 70810c38986f..ecc503912c34 100644 --- a/project_euler/problem_04/sol2.py +++ b/project_euler/problem_04/sol2.py @@ -1,17 +1,32 @@ -''' +""" Problem: -A palindromic number reads the same both ways. The largest palindrome made from the product of two 2-digit numbers is 9009 = 91 x 99. -Find the largest palindrome made from the product of two 3-digit numbers which is less than N. -''' -from __future__ import print_function -n = int(input().strip()) -answer = 0 -for i in range(999,99,-1): #3 digit nimbers range from 999 down to 100 - for j in range(999,99,-1): - t = str(i*j) - if t == t[::-1] and i*j < n: - answer = max(answer,i*j) -print(answer) -exit(0) +A palindromic number reads the same both ways. The largest palindrome made from +the product of two 2-digit numbers is 9009 = 91 x 99. +Find the largest palindrome made from the product of two 3-digit numbers which +is less than N. +""" + +def solution(n): + """Returns the largest palindrome made from the product of two 3-digit + numbers which is less than n. + + >>> solution(20000) + 19591 + >>> solution(30000) + 29992 + >>> solution(40000) + 39893 + """ + answer = 0 + for i in range(999, 99, -1): # 3 digit nimbers range from 999 down to 100 + for j in range(999, 99, -1): + t = str(i * j) + if t == t[::-1] and i * j < n: + answer = max(answer, i * j) + return answer + + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_05/__init__.py b/project_euler/problem_05/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_05/sol1.py b/project_euler/problem_05/sol1.py index 7896d75e3456..b3a231f4dcf5 100644 --- a/project_euler/problem_05/sol1.py +++ b/project_euler/problem_05/sol1.py @@ -1,21 +1,63 @@ -''' +""" Problem: -2520 is the smallest number that can be divided by each of the numbers from 1 to 10 without any remainder. -What is the smallest positive number that is evenly divisible(divisible with no remainder) by all of the numbers from 1 to N? -''' -from __future__ import print_function +2520 is the smallest number that can be divided by each of the numbers from 1 +to 10 without any remainder. -n = int(input()) -i = 0 -while 1: - i+=n*(n-1) - nfound=0 - for j in range(2,n): - if (i%j != 0): - nfound=1 - break - if(nfound==0): - if(i==0): - i=1 - print(i) - break +What is the smallest positive number that is evenly divisible(divisible with no +remainder) by all of the numbers from 1 to N? +""" + + +def solution(n): + """Returns the smallest positive number that is evenly divisible(divisible + with no remainder) by all of the numbers from 1 to n. + + >>> solution(10) + 2520 + >>> solution(15) + 360360 + >>> solution(20) + 232792560 + >>> solution(22) + 232792560 + >>> solution(3.4) + 6 + >>> solution(0) + Traceback (most recent call last): + ... + ValueError: Parameter n must be greater or equal to one. + >>> solution(-17) + Traceback (most recent call last): + ... + ValueError: Parameter n must be greater or equal to one. + >>> solution([]) + Traceback (most recent call last): + ... + TypeError: Parameter n must be int or passive of cast to int. + >>> solution("asd") + Traceback (most recent call last): + ... + TypeError: Parameter n must be int or passive of cast to int. + """ + try: + n = int(n) + except (TypeError, ValueError) as e: + raise TypeError("Parameter n must be int or passive of cast to int.") + if n <= 0: + raise ValueError("Parameter n must be greater or equal to one.") + i = 0 + while 1: + i += n * (n - 1) + nfound = 0 + for j in range(2, n): + if i % j != 0: + nfound = 1 + break + if nfound == 0: + if i == 0: + i = 1 + return i + + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_05/sol2.py b/project_euler/problem_05/sol2.py index cd11437f30db..5aa84d21c8e8 100644 --- a/project_euler/problem_05/sol2.py +++ b/project_euler/problem_05/sol2.py @@ -1,20 +1,43 @@ -#!/bin/python3 -''' +""" Problem: -2520 is the smallest number that can be divided by each of the numbers from 1 to 10 without any remainder. -What is the smallest positive number that is evenly divisible(divisible with no remainder) by all of the numbers from 1 to N? -''' +2520 is the smallest number that can be divided by each of the numbers from 1 +to 10 without any remainder. +What is the smallest positive number that is evenly divisible(divisible with no +remainder) by all of the numbers from 1 to N? +""" """ Euclidean GCD Algorithm """ -def gcd(x,y): - return x if y==0 else gcd(y,x%y) + + +def gcd(x, y): + return x if y == 0 else gcd(y, x % y) + """ Using the property lcm*gcd of two numbers = product of them """ -def lcm(x,y): - return (x*y)//gcd(x,y) - -n = int(input()) -g=1 -for i in range(1,n+1): - g=lcm(g,i) -print(g) + + +def lcm(x, y): + return (x * y) // gcd(x, y) + + +def solution(n): + """Returns the smallest positive number that is evenly divisible(divisible + with no remainder) by all of the numbers from 1 to n. + + >>> solution(10) + 2520 + >>> solution(15) + 360360 + >>> solution(20) + 232792560 + >>> solution(22) + 232792560 + """ + g = 1 + for i in range(1, n + 1): + g = lcm(g, i) + return g + + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_06/__init__.py b/project_euler/problem_06/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_06/sol1.py b/project_euler/problem_06/sol1.py index 852d4e2f9fc4..c69b6c89e35a 100644 --- a/project_euler/problem_06/sol1.py +++ b/project_euler/problem_06/sol1.py @@ -1,20 +1,42 @@ # -*- coding: utf-8 -*- -''' +""" Problem: + The sum of the squares of the first ten natural numbers is, 1^2 + 2^2 + ... + 10^2 = 385 + The square of the sum of the first ten natural numbers is, (1 + 2 + ... + 10)^2 = 552 = 3025 -Hence the difference between the sum of the squares of the first ten natural numbers and the square of the sum is 3025 − 385 = 2640. -Find the difference between the sum of the squares of the first N natural numbers and the square of the sum. -''' -from __future__ import print_function - -suma = 0 -sumb = 0 -n = int(input()) -for i in range(1,n+1): - suma += i**2 - sumb += i -sum = sumb**2 - suma -print(sum) + +Hence the difference between the sum of the squares of the first ten natural +numbers and the square of the sum is 3025 − 385 = 2640. + +Find the difference between the sum of the squares of the first N natural +numbers and the square of the sum. +""" + + +def solution(n): + """Returns the difference between the sum of the squares of the first n + natural numbers and the square of the sum. + + >>> solution(10) + 2640 + >>> solution(15) + 13160 + >>> solution(20) + 41230 + >>> solution(50) + 1582700 + """ + suma = 0 + sumb = 0 + for i in range(1, n + 1): + suma += i ** 2 + sumb += i + sum = sumb ** 2 - suma + return sum + + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_06/sol2.py b/project_euler/problem_06/sol2.py index aa8aea58fd7b..1698a3fb61fd 100644 --- a/project_euler/problem_06/sol2.py +++ b/project_euler/problem_06/sol2.py @@ -1,16 +1,39 @@ # -*- coding: utf-8 -*- -''' +""" Problem: + The sum of the squares of the first ten natural numbers is, 1^2 + 2^2 + ... + 10^2 = 385 + The square of the sum of the first ten natural numbers is, (1 + 2 + ... + 10)^2 = 552 = 3025 -Hence the difference between the sum of the squares of the first ten natural numbers and the square of the sum is 3025 − 385 = 2640. -Find the difference between the sum of the squares of the first N natural numbers and the square of the sum. -''' -from __future__ import print_function -n = int(input()) -suma = n*(n+1)/2 -suma **= 2 -sumb = n*(n+1)*(2*n+1)/6 -print(suma-sumb) + +Hence the difference between the sum of the squares of the first ten natural +numbers and the square of the sum is 3025 − 385 = 2640. + +Find the difference between the sum of the squares of the first N natural +numbers and the square of the sum. +""" + + +def solution(n): + """Returns the difference between the sum of the squares of the first n + natural numbers and the square of the sum. + + >>> solution(10) + 2640 + >>> solution(15) + 13160 + >>> solution(20) + 41230 + >>> solution(50) + 1582700 + """ + suma = n * (n + 1) / 2 + suma **= 2 + sumb = n * (n + 1) * (2 * n + 1) / 6 + return int(suma - sumb) + + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_06/sol3.py b/project_euler/problem_06/sol3.py index b2d9f444d9a9..f9c5dacb3777 100644 --- a/project_euler/problem_06/sol3.py +++ b/project_euler/problem_06/sol3.py @@ -1,20 +1,39 @@ -''' +# -*- coding: utf-8 -*- +""" Problem: + The sum of the squares of the first ten natural numbers is, 1^2 + 2^2 + ... + 10^2 = 385 + The square of the sum of the first ten natural numbers is, (1 + 2 + ... + 10)^2 = 552 = 3025 -Hence the difference between the sum of the squares of the first ten natural numbers and the square of the sum is 3025 − 385 = 2640. -Find the difference between the sum of the squares of the first N natural numbers and the square of the sum. -''' -from __future__ import print_function + +Hence the difference between the sum of the squares of the first ten natural +numbers and the square of the sum is 3025 − 385 = 2640. + +Find the difference between the sum of the squares of the first N natural +numbers and the square of the sum. +""" import math -def problem6(number=100): - sum_of_squares = sum([i*i for i in range(1,number+1)]) - square_of_sum = int(math.pow(sum(range(1,number+1)),2)) + + +def solution(n): + """Returns the difference between the sum of the squares of the first n + natural numbers and the square of the sum. + + >>> solution(10) + 2640 + >>> solution(15) + 13160 + >>> solution(20) + 41230 + >>> solution(50) + 1582700 + """ + sum_of_squares = sum([i * i for i in range(1, n + 1)]) + square_of_sum = int(math.pow(sum(range(1, n + 1)), 2)) return square_of_sum - sum_of_squares -def main(): - print(problem6()) -if __name__ == '__main__': - main() \ No newline at end of file + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_06/sol4.py b/project_euler/problem_06/sol4.py new file mode 100644 index 000000000000..1e1de5570e7d --- /dev/null +++ b/project_euler/problem_06/sol4.py @@ -0,0 +1,40 @@ +# -*- coding: utf-8 -*- +""" +Problem: + +The sum of the squares of the first ten natural numbers is, + 1^2 + 2^2 + ... + 10^2 = 385 + +The square of the sum of the first ten natural numbers is, + (1 + 2 + ... + 10)^2 = 552 = 3025 + +Hence the difference between the sum of the squares of the first ten natural +numbers and the square of the sum is 3025 − 385 = 2640. + +Find the difference between the sum of the squares of the first N natural +numbers and the square of the sum. +""" + + +def solution(n): + """Returns the difference between the sum of the squares of the first n + natural numbers and the square of the sum. + + >>> solution(10) + 2640 + >>> solution(15) + 13160 + >>> solution(20) + 41230 + >>> solution(50) + 1582700 + >>> solution(100) + 25164150 + """ + sum_of_squares = n * (n + 1) * (2 * n + 1) / 6 + square_of_sum = (n * (n + 1) / 2) ** 2 + return int(square_of_sum - sum_of_squares) + + +if __name__ == "__main__": + print(solution(int(input("Enter a number: ").strip()))) diff --git a/project_euler/problem_07/__init__.py b/project_euler/problem_07/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_07/sol1.py b/project_euler/problem_07/sol1.py index ea31d0b2bb2c..d8d67e157860 100644 --- a/project_euler/problem_07/sol1.py +++ b/project_euler/problem_07/sol1.py @@ -1,30 +1,55 @@ -''' +# -*- coding: utf-8 -*- +""" By listing the first six prime numbers: -2, 3, 5, 7, 11, and 13, we can see that the 6th prime is 13. -What is the Nth prime number? -''' -from __future__ import print_function + + 2, 3, 5, 7, 11, and 13 + +We can see that the 6th prime is 13. What is the Nth prime number? +""" from math import sqrt + + def isprime(n): - if (n==2): + if n == 2: return True - elif (n%2==0): + elif n % 2 == 0: return False else: - sq = int(sqrt(n))+1 - for i in range(3,sq,2): - if(n%i==0): + sq = int(sqrt(n)) + 1 + for i in range(3, sq, 2): + if n % i == 0: return False return True -n = int(input()) -i=0 -j=1 -while(i!=n and j<3): - j+=1 - if (isprime(j)): - i+=1 -while(i!=n): - j+=2 - if(isprime(j)): - i+=1 -print(j) + + +def solution(n): + """Returns the n-th prime number. + + >>> solution(6) + 13 + >>> solution(1) + 2 + >>> solution(3) + 5 + >>> solution(20) + 71 + >>> solution(50) + 229 + >>> solution(100) + 541 + """ + i = 0 + j = 1 + while i != n and j < 3: + j += 1 + if isprime(j): + i += 1 + while i != n: + j += 2 + if isprime(j): + i += 1 + return j + + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_07/sol2.py b/project_euler/problem_07/sol2.py index fdf39cbc4d26..5d30e540b3e7 100644 --- a/project_euler/problem_07/sol2.py +++ b/project_euler/problem_07/sol2.py @@ -1,16 +1,70 @@ -# By listing the first six prime numbers: 2, 3, 5, 7, 11, and 13, we can see that the 6th prime is 13. What is the Nth prime number? +# -*- coding: utf-8 -*- +""" +By listing the first six prime numbers: + + 2, 3, 5, 7, 11, and 13 + +We can see that the 6th prime is 13. What is the Nth prime number? +""" + + def isprime(number): - for i in range(2,int(number**0.5)+1): - if number%i==0: - return False - return True -n = int(input('Enter The N\'th Prime Number You Want To Get: ')) # Ask For The N'th Prime Number Wanted -primes = [] -num = 2 -while len(primes) < n: - if isprime(num): - primes.append(num) - num += 1 - else: - num += 1 -print(primes[len(primes) - 1]) + for i in range(2, int(number ** 0.5) + 1): + if number % i == 0: + return False + return True + + +def solution(n): + """Returns the n-th prime number. + + >>> solution(6) + 13 + >>> solution(1) + 2 + >>> solution(3) + 5 + >>> solution(20) + 71 + >>> solution(50) + 229 + >>> solution(100) + 541 + >>> solution(3.4) + 5 + >>> solution(0) + Traceback (most recent call last): + ... + ValueError: Parameter n must be greater or equal to one. + >>> solution(-17) + Traceback (most recent call last): + ... + ValueError: Parameter n must be greater or equal to one. + >>> solution([]) + Traceback (most recent call last): + ... + TypeError: Parameter n must be int or passive of cast to int. + >>> solution("asd") + Traceback (most recent call last): + ... + TypeError: Parameter n must be int or passive of cast to int. + """ + try: + n = int(n) + except (TypeError, ValueError) as e: + raise TypeError("Parameter n must be int or passive of cast to int.") + if n <= 0: + raise ValueError("Parameter n must be greater or equal to one.") + primes = [] + num = 2 + while len(primes) < n: + if isprime(num): + primes.append(num) + num += 1 + else: + num += 1 + return primes[len(primes) - 1] + + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_07/sol3.py b/project_euler/problem_07/sol3.py index 0001e4318cc9..3c28ecf7fb34 100644 --- a/project_euler/problem_07/sol3.py +++ b/project_euler/problem_07/sol3.py @@ -1,28 +1,47 @@ -''' +# -*- coding: utf-8 -*- +""" By listing the first six prime numbers: -2, 3, 5, 7, 11, and 13, we can see that the 6th prime is 13. -What is the Nth prime number? -''' -from __future__ import print_function -# from Python.Math import PrimeCheck + + 2, 3, 5, 7, 11, and 13 + +We can see that the 6th prime is 13. What is the Nth prime number? +""" import math import itertools + + def primeCheck(number): if number % 2 == 0 and number > 2: return False return all(number % i for i in range(3, int(math.sqrt(number)) + 1, 2)) + def prime_generator(): num = 2 while True: if primeCheck(num): yield num - num+=1 + num += 1 + + +def solution(n): + """Returns the n-th prime number. -def main(): - n = int(input('Enter The N\'th Prime Number You Want To Get: ')) # Ask For The N'th Prime Number Wanted - print(next(itertools.islice(prime_generator(),n-1,n))) + >>> solution(6) + 13 + >>> solution(1) + 2 + >>> solution(3) + 5 + >>> solution(20) + 71 + >>> solution(50) + 229 + >>> solution(100) + 541 + """ + return next(itertools.islice(prime_generator(), n - 1, n)) -if __name__ == '__main__': - main() \ No newline at end of file +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_08/__init__.py b/project_euler/problem_08/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_08/sol1.py b/project_euler/problem_08/sol1.py index 817fd3f87507..6752fae3de60 100644 --- a/project_euler/problem_08/sol1.py +++ b/project_euler/problem_08/sol1.py @@ -1,15 +1,72 @@ +# -*- coding: utf-8 -*- +""" +The four adjacent digits in the 1000-digit number that have the greatest +product are 9 × 9 × 8 × 9 = 5832. + +73167176531330624919225119674426574742355349194934 +96983520312774506326239578318016984801869478851843 +85861560789112949495459501737958331952853208805511 +12540698747158523863050715693290963295227443043557 +66896648950445244523161731856403098711121722383113 +62229893423380308135336276614282806444486645238749 +30358907296290491560440772390713810515859307960866 +70172427121883998797908792274921901699720888093776 +65727333001053367881220235421809751254540594752243 +52584907711670556013604839586446706324415722155397 +53697817977846174064955149290862569321978468622482 +83972241375657056057490261407972968652414535100474 +82166370484403199890008895243450658541227588666881 +16427171479924442928230863465674813919123162824586 +17866458359124566529476545682848912883142607690042 +24219022671055626321111109370544217506941658960408 +07198403850962455444362981230987879927244284909188 +84580156166097919133875499200524063689912560717606 +05886116467109405077541002256983155200055935729725 +71636269561882670428252483600823257530420752963450 + +Find the thirteen adjacent digits in the 1000-digit number that have the +greatest product. What is the value of this product? +""" import sys -def main(): - LargestProduct = -sys.maxsize-1 - number=input().strip() - for i in range(len(number)-12): - product=1 + +N = """73167176531330624919225119674426574742355349194934\ +96983520312774506326239578318016984801869478851843\ +85861560789112949495459501737958331952853208805511\ +12540698747158523863050715693290963295227443043557\ +66896648950445244523161731856403098711121722383113\ +62229893423380308135336276614282806444486645238749\ +30358907296290491560440772390713810515859307960866\ +70172427121883998797908792274921901699720888093776\ +65727333001053367881220235421809751254540594752243\ +52584907711670556013604839586446706324415722155397\ +53697817977846174064955149290862569321978468622482\ +83972241375657056057490261407972968652414535100474\ +82166370484403199890008895243450658541227588666881\ +16427171479924442928230863465674813919123162824586\ +17866458359124566529476545682848912883142607690042\ +24219022671055626321111109370544217506941658960408\ +07198403850962455444362981230987879927244284909188\ +84580156166097919133875499200524063689912560717606\ +05886116467109405077541002256983155200055935729725\ +71636269561882670428252483600823257530420752963450""" + + +def solution(n): + """Find the thirteen adjacent digits in the 1000-digit number n that have + the greatest product and returns it. + + >>> solution(N) + 23514624000 + """ + LargestProduct = -sys.maxsize - 1 + for i in range(len(n) - 12): + product = 1 for j in range(13): - product *= int(number[i+j]) + product *= int(n[i + j]) if product > LargestProduct: LargestProduct = product - print(LargestProduct) + return LargestProduct -if __name__ == '__main__': - main() +if __name__ == "__main__": + print(solution(N)) diff --git a/project_euler/problem_08/sol2.py b/project_euler/problem_08/sol2.py index ae03f3ad0aa6..bae96e373d6c 100644 --- a/project_euler/problem_08/sol2.py +++ b/project_euler/problem_08/sol2.py @@ -1,8 +1,73 @@ +# -*- coding: utf-8 -*- +""" +The four adjacent digits in the 1000-digit number that have the greatest +product are 9 × 9 × 8 × 9 = 5832. + +73167176531330624919225119674426574742355349194934 +96983520312774506326239578318016984801869478851843 +85861560789112949495459501737958331952853208805511 +12540698747158523863050715693290963295227443043557 +66896648950445244523161731856403098711121722383113 +62229893423380308135336276614282806444486645238749 +30358907296290491560440772390713810515859307960866 +70172427121883998797908792274921901699720888093776 +65727333001053367881220235421809751254540594752243 +52584907711670556013604839586446706324415722155397 +53697817977846174064955149290862569321978468622482 +83972241375657056057490261407972968652414535100474 +82166370484403199890008895243450658541227588666881 +16427171479924442928230863465674813919123162824586 +17866458359124566529476545682848912883142607690042 +24219022671055626321111109370544217506941658960408 +07198403850962455444362981230987879927244284909188 +84580156166097919133875499200524063689912560717606 +05886116467109405077541002256983155200055935729725 +71636269561882670428252483600823257530420752963450 + +Find the thirteen adjacent digits in the 1000-digit number that have the +greatest product. What is the value of this product? +""" + from functools import reduce -def main(): - number=input().strip() - print(max([reduce(lambda x,y: int(x)*int(y),number[i:i+13]) for i in range(len(number)-12)])) - -if __name__ == '__main__': - main() +N = ( + "73167176531330624919225119674426574742355349194934" + "96983520312774506326239578318016984801869478851843" + "85861560789112949495459501737958331952853208805511" + "12540698747158523863050715693290963295227443043557" + "66896648950445244523161731856403098711121722383113" + "62229893423380308135336276614282806444486645238749" + "30358907296290491560440772390713810515859307960866" + "70172427121883998797908792274921901699720888093776" + "65727333001053367881220235421809751254540594752243" + "52584907711670556013604839586446706324415722155397" + "53697817977846174064955149290862569321978468622482" + "83972241375657056057490261407972968652414535100474" + "82166370484403199890008895243450658541227588666881" + "16427171479924442928230863465674813919123162824586" + "17866458359124566529476545682848912883142607690042" + "24219022671055626321111109370544217506941658960408" + "07198403850962455444362981230987879927244284909188" + "84580156166097919133875499200524063689912560717606" + "05886116467109405077541002256983155200055935729725" + "71636269561882670428252483600823257530420752963450" +) + + +def solution(n): + """Find the thirteen adjacent digits in the 1000-digit number n that have + the greatest product and returns it. + + >>> solution(N) + 23514624000 + """ + return max( + [ + reduce(lambda x, y: int(x) * int(y), n[i : i + 13]) + for i in range(len(n) - 12) + ] + ) + + +if __name__ == "__main__": + print(solution(str(N))) diff --git a/project_euler/problem_09/__init__.py b/project_euler/problem_09/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_09/sol1.py b/project_euler/problem_09/sol1.py index e54c543b4721..3bb5c968115d 100644 --- a/project_euler/problem_09/sol1.py +++ b/project_euler/problem_09/sol1.py @@ -1,15 +1,35 @@ -from __future__ import print_function -# Program to find the product of a,b,c which are Pythagorean Triplet that satisfice the following: -# 1. a < b < c -# 2. a**2 + b**2 = c**2 -# 3. a + b + c = 1000 - -print("Please Wait...") -for a in range(300): - for b in range(400): - for c in range(500): - if(a < b < c): - if((a**2) + (b**2) == (c**2)): - if((a+b+c) == 1000): - print(("Product of",a,"*",b,"*",c,"=",(a*b*c))) - break +""" +Problem Statement: +A Pythagorean triplet is a set of three natural numbers, a < b < c, for which, + a^2 + b^2 = c^2 +For example, 3^2 + 4^2 = 9 + 16 = 25 = 5^2. + +There exists exactly one Pythagorean triplet for which a + b + c = 1000. +Find the product abc. +""" + + +def solution(): + """ + Returns the product of a,b,c which are Pythagorean Triplet that satisfies + the following: + 1. a < b < c + 2. a**2 + b**2 = c**2 + 3. a + b + c = 1000 + + # The code below has been commented due to slow execution affecting Travis. + # >>> solution() + # 31875000 + """ + for a in range(300): + for b in range(400): + for c in range(500): + if a < b < c: + if (a ** 2) + (b ** 2) == (c ** 2): + if (a + b + c) == 1000: + return a * b * c + + +if __name__ == "__main__": + print("Please Wait...") + print(solution()) diff --git a/project_euler/problem_09/sol2.py b/project_euler/problem_09/sol2.py index 933f5c557d71..de7b12d40c09 100644 --- a/project_euler/problem_09/sol2.py +++ b/project_euler/problem_09/sol2.py @@ -1,18 +1,38 @@ -"""A Pythagorean triplet is a set of three natural numbers, for which, -a^2+b^2=c^2 -Given N, Check if there exists any Pythagorean triplet for which a+b+c=N -Find maximum possible value of product of a,b,c among all such Pythagorean triplets, If there is no such Pythagorean triplet print -1.""" -#!/bin/python3 +""" +Problem Statement: +A Pythagorean triplet is a set of three natural numbers, a < b < c, for which, + a^2 + b^2 = c^2 +For example, 3^2 + 4^2 = 9 + 16 = 25 = 5^2. -product=-1 -d=0 -N = int(input()) -for a in range(1,N//3): - """Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c """ - b=(N*N-2*a*N)//(2*N-2*a) - c=N-a-b - if c*c==(a*a+b*b): - d=(a*b*c) - if d>=product: - product=d -print(product) +There exists exactly one Pythagorean triplet for which a + b + c = 1000. +Find the product abc. +""" + + +def solution(n): + """ + Return the product of a,b,c which are Pythagorean Triplet that satisfies + the following: + 1. a < b < c + 2. a**2 + b**2 = c**2 + 3. a + b + c = 1000 + + >>> solution(1000) + 31875000 + """ + product = -1 + d = 0 + for a in range(1, n // 3): + """Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c + """ + b = (n * n - 2 * a * n) // (2 * n - 2 * a) + c = n - a - b + if c * c == (a * a + b * b): + d = a * b * c + if d >= product: + product = d + return product + + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_09/sol3.py b/project_euler/problem_09/sol3.py index 5ebf38e76e1a..a6df46a3a66b 100644 --- a/project_euler/problem_09/sol3.py +++ b/project_euler/problem_09/sol3.py @@ -1,6 +1,37 @@ -def main(): - print([a*b*c for a in range(1,999) for b in range(a,999) for c in range(b,999) - if (a*a+b*b==c*c) and (a+b+c==1000 ) ][0]) - -if __name__ == '__main__': - main() +""" +Problem Statement: + +A Pythagorean triplet is a set of three natural numbers, a < b < c, for which, + + a^2 + b^2 = c^2 + +For example, 3^2 + 4^2 = 9 + 16 = 25 = 5^2. + +There exists exactly one Pythagorean triplet for which a + b + c = 1000. +Find the product abc. +""" + + +def solution(): + """ + Returns the product of a,b,c which are Pythagorean Triplet that satisfies + the following: + + 1. a**2 + b**2 = c**2 + 2. a + b + c = 1000 + + # The code below has been commented due to slow execution affecting Travis. + # >>> solution() + # 31875000 + """ + return [ + a * b * c + for a in range(1, 999) + for b in range(a, 999) + for c in range(b, 999) + if (a * a + b * b == c * c) and (a + b + c == 1000) + ][0] + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_10/__init__.py b/project_euler/problem_10/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_10/sol1.py b/project_euler/problem_10/sol1.py index 94e5b7362114..c81085951ecf 100644 --- a/project_euler/problem_10/sol1.py +++ b/project_euler/problem_10/sol1.py @@ -1,38 +1,50 @@ -from __future__ import print_function +""" +Problem Statement: +The sum of the primes below 10 is 2 + 3 + 5 + 7 = 17. + +Find the sum of all the primes below two million. +""" from math import sqrt -try: - xrange #Python 2 -except NameError: - xrange = range #Python 3 def is_prime(n): - for i in xrange(2, int(sqrt(n))+1): - if n%i == 0: - return False + for i in range(2, int(sqrt(n)) + 1): + if n % i == 0: + return False + + return True - return True def sum_of_primes(n): - if n > 2: - sumOfPrimes = 2 - else: - return 0 - - for i in xrange(3, n, 2): - if is_prime(i): - sumOfPrimes += i - - return sumOfPrimes - -if __name__ == '__main__': - import sys - - if len(sys.argv) == 1: - print(sum_of_primes(2000000)) - else: - try: - n = int(sys.argv[1]) - print(sum_of_primes(n)) - except ValueError: - print('Invalid entry - please enter a number.') + if n > 2: + sumOfPrimes = 2 + else: + return 0 + + for i in range(3, n, 2): + if is_prime(i): + sumOfPrimes += i + + return sumOfPrimes + + +def solution(n): + """Returns the sum of all the primes below n. + + # The code below has been commented due to slow execution affecting Travis. + # >>> solution(2000000) + # 142913828922 + >>> solution(1000) + 76127 + >>> solution(5000) + 1548136 + >>> solution(10000) + 5736396 + >>> solution(7) + 10 + """ + return sum_of_primes(n) + + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_10/sol2.py b/project_euler/problem_10/sol2.py index 22df95c063e2..b2e2b6e1adf3 100644 --- a/project_euler/problem_10/sol2.py +++ b/project_euler/problem_10/sol2.py @@ -1,22 +1,44 @@ -#from Python.Math import prime_generator -import math -from itertools import takewhile +""" +Problem Statement: +The sum of the primes below 10 is 2 + 3 + 5 + 7 = 17. + +Find the sum of all the primes below two million. +""" +import math +from itertools import takewhile + def primeCheck(number): if number % 2 == 0 and number > 2: return False return all(number % i for i in range(3, int(math.sqrt(number)) + 1, 2)) - + + def prime_generator(): num = 2 while True: if primeCheck(num): yield num - num+=1 - -def main(): - n = int(input('Enter The upper limit of prime numbers: ')) - print(sum(takewhile(lambda x: x < n,prime_generator()))) - -if __name__ == '__main__': - main() + num += 1 + + +def solution(n): + """Returns the sum of all the primes below n. + + # The code below has been commented due to slow execution affecting Travis. + # >>> solution(2000000) + # 142913828922 + >>> solution(1000) + 76127 + >>> solution(5000) + 1548136 + >>> solution(10000) + 5736396 + >>> solution(7) + 10 + """ + return sum(takewhile(lambda x: x < n, prime_generator())) + + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_10/sol3.py b/project_euler/problem_10/sol3.py new file mode 100644 index 000000000000..e5bc0731d8ab --- /dev/null +++ b/project_euler/problem_10/sol3.py @@ -0,0 +1,58 @@ +""" +https://projecteuler.net/problem=10 + +Problem Statement: +The sum of the primes below 10 is 2 + 3 + 5 + 7 = 17. + +Find the sum of all the primes below two million using Sieve_of_Eratosthenes: + +The sieve of Eratosthenes is one of the most efficient ways to find all primes +smaller than n when n is smaller than 10 million. Only for positive numbers. +""" + + +def prime_sum(n: int) -> int: + """ Returns the sum of all the primes below n. + + >>> prime_sum(2_000_000) + 142913828922 + >>> prime_sum(1_000) + 76127 + >>> prime_sum(5_000) + 1548136 + >>> prime_sum(10_000) + 5736396 + >>> prime_sum(7) + 10 + >>> prime_sum(7.1) # doctest: +ELLIPSIS + Traceback (most recent call last): + ... + TypeError: 'float' object cannot be interpreted as an integer + >>> prime_sum(-7) # doctest: +ELLIPSIS + Traceback (most recent call last): + ... + IndexError: list assignment index out of range + >>> prime_sum("seven") # doctest: +ELLIPSIS + Traceback (most recent call last): + ... + TypeError: can only concatenate str (not "int") to str + """ + list_ = [0 for i in range(n + 1)] + list_[0] = 1 + list_[1] = 1 + + for i in range(2, int(n ** 0.5) + 1): + if list_[i] == 0: + for j in range(i * i, n + 1, i): + list_[j] = 1 + s = 0 + for i in range(n): + if list_[i] == 0: + s += i + return s + + +if __name__ == "__main__": + # import doctest + # doctest.testmod() + print(prime_sum(int(input().strip()))) diff --git a/project_euler/problem_11/__init__.py b/project_euler/problem_11/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_11/sol1.py b/project_euler/problem_11/sol1.py index b882dc449156..4e49013c8210 100644 --- a/project_euler/problem_11/sol1.py +++ b/project_euler/problem_11/sol1.py @@ -1,6 +1,6 @@ -from __future__ import print_function -''' -What is the greatest product of four adjacent numbers (horizontally, vertically, or diagonally) in this 20x20 array? +""" +What is the greatest product of four adjacent numbers (horizontally, +vertically, or diagonally) in this 20x20 array? 08 02 22 97 38 15 00 40 00 75 04 05 07 78 52 12 50 77 91 08 49 49 99 40 17 81 18 57 60 87 17 40 98 43 69 48 04 56 62 00 @@ -22,47 +22,66 @@ 20 69 36 41 72 30 23 88 34 62 99 69 82 67 59 85 74 04 36 16 20 73 35 29 78 31 90 01 74 31 49 71 48 86 81 16 23 57 05 54 01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48 -''' +""" + +import os -try: - xrange #Python 2 -except NameError: - xrange = range #Python 2 def largest_product(grid): - nColumns = len(grid[0]) - nRows = len(grid) + nColumns = len(grid[0]) + nRows = len(grid) + + largest = 0 + lrDiagProduct = 0 + rlDiagProduct = 0 + + # Check vertically, horizontally, diagonally at the same time (only works + # for nxn grid) + for i in range(nColumns): + for j in range(nRows - 3): + vertProduct = grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i] + horzProduct = grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3] + + # Left-to-right diagonal (\) product + if i < nColumns - 3: + lrDiagProduct = ( + grid[i][j] + * grid[i + 1][j + 1] + * grid[i + 2][j + 2] + * grid[i + 3][j + 3] + ) + + # Right-to-left diagonal(/) product + if i > 2: + rlDiagProduct = ( + grid[i][j] + * grid[i - 1][j + 1] + * grid[i - 2][j + 2] + * grid[i - 3][j + 3] + ) - largest = 0 - lrDiagProduct = 0 - rlDiagProduct = 0 + maxProduct = max(vertProduct, horzProduct, lrDiagProduct, rlDiagProduct) + if maxProduct > largest: + largest = maxProduct - #Check vertically, horizontally, diagonally at the same time (only works for nxn grid) - for i in xrange(nColumns): - for j in xrange(nRows-3): - vertProduct = grid[j][i]*grid[j+1][i]*grid[j+2][i]*grid[j+3][i] - horzProduct = grid[i][j]*grid[i][j+1]*grid[i][j+2]*grid[i][j+3] + return largest - #Left-to-right diagonal (\) product - if (i < nColumns-3): - lrDiagProduct = grid[i][j]*grid[i+1][j+1]*grid[i+2][j+2]*grid[i+3][j+3] - #Right-to-left diagonal(/) product - if (i > 2): - rlDiagProduct = grid[i][j]*grid[i-1][j+1]*grid[i-2][j+2]*grid[i-3][j+3] +def solution(): + """Returns the sum of all the multiples of 3 or 5 below n. - maxProduct = max(vertProduct, horzProduct, lrDiagProduct, rlDiagProduct) - if maxProduct > largest: - largest = maxProduct + >>> solution() + 70600674 + """ + grid = [] + with open(os.path.dirname(__file__) + "/grid.txt") as file: + for line in file: + grid.append(line.strip("\n").split(" ")) - return largest + grid = [[int(i) for i in grid[j]] for j in range(len(grid))] -if __name__ == '__main__': - grid = [] - with open('grid.txt') as file: - for line in file: - grid.append(line.strip('\n').split(' ')) + return largest_product(grid) - grid = [[int(i) for i in grid[j]] for j in xrange(len(grid))] - print(largest_product(grid)) \ No newline at end of file +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_11/sol2.py b/project_euler/problem_11/sol2.py index b03395f01697..64702e852b0f 100644 --- a/project_euler/problem_11/sol2.py +++ b/project_euler/problem_11/sol2.py @@ -1,39 +1,74 @@ -def main(): - with open ("grid.txt", "r") as f: - l = [] - for i in range(20): - l.append([int(x) for x in f.readline().split()]) - - maximum = 0 - - # right - for i in range(20): - for j in range(17): - temp = l[i][j] * l[i][j+1] * l[i][j+2] * l[i][j+3] - if temp > maximum: - maximum = temp - - # down - for i in range(17): - for j in range(20): - temp = l[i][j] * l[i+1][j] * l[i+2][j] * l[i+3][j] - if temp > maximum: - maximum = temp - - #diagonal 1 - for i in range(17): - for j in range(17): - temp = l[i][j] * l[i+1][j+1] * l[i+2][j+2] * l[i+3][j+3] - if temp > maximum: - maximum = temp - - #diagonal 2 - for i in range(17): - for j in range(3, 20): - temp = l[i][j] * l[i+1][j-1] * l[i+2][j-2] * l[i+3][j-3] - if temp > maximum: - maximum = temp - print(maximum) - -if __name__ == '__main__': - main() \ No newline at end of file +""" +What is the greatest product of four adjacent numbers (horizontally, +vertically, or diagonally) in this 20x20 array? + +08 02 22 97 38 15 00 40 00 75 04 05 07 78 52 12 50 77 91 08 +49 49 99 40 17 81 18 57 60 87 17 40 98 43 69 48 04 56 62 00 +81 49 31 73 55 79 14 29 93 71 40 67 53 88 30 03 49 13 36 65 +52 70 95 23 04 60 11 42 69 24 68 56 01 32 56 71 37 02 36 91 +22 31 16 71 51 67 63 89 41 92 36 54 22 40 40 28 66 33 13 80 +24 47 32 60 99 03 45 02 44 75 33 53 78 36 84 20 35 17 12 50 +32 98 81 28 64 23 67 10 26 38 40 67 59 54 70 66 18 38 64 70 +67 26 20 68 02 62 12 20 95 63 94 39 63 08 40 91 66 49 94 21 +24 55 58 05 66 73 99 26 97 17 78 78 96 83 14 88 34 89 63 72 +21 36 23 09 75 00 76 44 20 45 35 14 00 61 33 97 34 31 33 95 +78 17 53 28 22 75 31 67 15 94 03 80 04 62 16 14 09 53 56 92 +16 39 05 42 96 35 31 47 55 58 88 24 00 17 54 24 36 29 85 57 +86 56 00 48 35 71 89 07 05 44 44 37 44 60 21 58 51 54 17 58 +19 80 81 68 05 94 47 69 28 73 92 13 86 52 17 77 04 89 55 40 +04 52 08 83 97 35 99 16 07 97 57 32 16 26 26 79 33 27 98 66 +88 36 68 87 57 62 20 72 03 46 33 67 46 55 12 32 63 93 53 69 +04 42 16 73 38 25 39 11 24 94 72 18 08 46 29 32 40 62 76 36 +20 69 36 41 72 30 23 88 34 62 99 69 82 67 59 85 74 04 36 16 +20 73 35 29 78 31 90 01 74 31 49 71 48 86 81 16 23 57 05 54 +01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48 +""" + +import os + + +def solution(): + """Returns the sum of all the multiples of 3 or 5 below n. + + >>> solution() + 70600674 + """ + with open(os.path.dirname(__file__) + "/grid.txt") as f: + l = [] + for i in range(20): + l.append([int(x) for x in f.readline().split()]) + + maximum = 0 + + # right + for i in range(20): + for j in range(17): + temp = l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3] + if temp > maximum: + maximum = temp + + # down + for i in range(17): + for j in range(20): + temp = l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j] + if temp > maximum: + maximum = temp + + # diagonal 1 + for i in range(17): + for j in range(17): + temp = l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3] + if temp > maximum: + maximum = temp + + # diagonal 2 + for i in range(17): + for j in range(3, 20): + temp = l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3] + if temp > maximum: + maximum = temp + return maximum + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_12/__init__.py b/project_euler/problem_12/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_12/sol1.py b/project_euler/problem_12/sol1.py index 73d48a2ec897..7e080c4e45a1 100644 --- a/project_euler/problem_12/sol1.py +++ b/project_euler/problem_12/sol1.py @@ -1,9 +1,9 @@ -from __future__ import print_function -from math import sqrt -''' +""" Highly divisible triangular numbers Problem 12 -The sequence of triangle numbers is generated by adding the natural numbers. So the 7th triangle number would be 1 + 2 + 3 + 4 + 5 + 6 + 7 = 28. The first ten terms would be: +The sequence of triangle numbers is generated by adding the natural numbers. So +the 7th triangle number would be 1 + 2 + 3 + 4 + 5 + 6 + 7 = 28. The first ten +terms would be: 1, 3, 6, 10, 15, 21, 28, 36, 45, 55, ... @@ -18,31 +18,43 @@ 28: 1,2,4,7,14,28 We can see that 28 is the first triangle number to have over five divisors. -What is the value of the first triangle number to have over five hundred divisors? -''' -try: - xrange #Python 2 -except NameError: - xrange = range #Python 3 +What is the value of the first triangle number to have over five hundred +divisors? +""" +from math import sqrt + def count_divisors(n): - nDivisors = 0 - for i in xrange(1, int(sqrt(n))+1): - if n%i == 0: - nDivisors += 2 - #check if n is perfect square - if n**0.5 == int(n**0.5): - nDivisors -= 1 - return nDivisors - -tNum = 1 -i = 1 - -while True: - i += 1 - tNum += i - - if count_divisors(tNum) > 500: - break - -print(tNum) + nDivisors = 0 + for i in range(1, int(sqrt(n)) + 1): + if n % i == 0: + nDivisors += 2 + # check if n is perfect square + if n ** 0.5 == int(n ** 0.5): + nDivisors -= 1 + return nDivisors + + +def solution(): + """Returns the value of the first triangle number to have over five hundred + divisors. + + # The code below has been commented due to slow execution affecting Travis. + # >>> solution() + # 76576500 + """ + tNum = 1 + i = 1 + + while True: + i += 1 + tNum += i + + if count_divisors(tNum) > 500: + break + + return tNum + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_12/sol2.py b/project_euler/problem_12/sol2.py index 479ab2b900cb..5ff0d8349b90 100644 --- a/project_euler/problem_12/sol2.py +++ b/project_euler/problem_12/sol2.py @@ -1,8 +1,47 @@ -def triangle_number_generator(): - for n in range(1,1000000): - yield n*(n+1)//2 - -def count_divisors(n): - return sum([2 for i in range(1,int(n**0.5)+1) if n%i==0 and i*i != n]) - -print(next(i for i in triangle_number_generator() if count_divisors(i) > 500)) +""" +Highly divisible triangular numbers +Problem 12 +The sequence of triangle numbers is generated by adding the natural numbers. So +the 7th triangle number would be 1 + 2 + 3 + 4 + 5 + 6 + 7 = 28. The first ten +terms would be: + +1, 3, 6, 10, 15, 21, 28, 36, 45, 55, ... + +Let us list the factors of the first seven triangle numbers: + + 1: 1 + 3: 1,3 + 6: 1,2,3,6 +10: 1,2,5,10 +15: 1,3,5,15 +21: 1,3,7,21 +28: 1,2,4,7,14,28 +We can see that 28 is the first triangle number to have over five divisors. + +What is the value of the first triangle number to have over five hundred +divisors? +""" + + +def triangle_number_generator(): + for n in range(1, 1000000): + yield n * (n + 1) // 2 + + +def count_divisors(n): + return sum([2 for i in range(1, int(n ** 0.5) + 1) if n % i == 0 and i * i != n]) + + +def solution(): + """Returns the value of the first triangle number to have over five hundred + divisors. + + # The code below has been commented due to slow execution affecting Travis. + # >>> solution() + # 76576500 + """ + return next(i for i in triangle_number_generator() if count_divisors(i) > 500) + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_13/__init__.py b/project_euler/problem_13/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_13/num.txt b/project_euler/problem_13/num.txt new file mode 100644 index 000000000000..43b568e812a8 --- /dev/null +++ b/project_euler/problem_13/num.txt @@ -0,0 +1,100 @@ +37107287533902102798797998220837590246510135740250 +46376937677490009712648124896970078050417018260538 +74324986199524741059474233309513058123726617309629 +91942213363574161572522430563301811072406154908250 +23067588207539346171171980310421047513778063246676 +89261670696623633820136378418383684178734361726757 +28112879812849979408065481931592621691275889832738 +44274228917432520321923589422876796487670272189318 +47451445736001306439091167216856844588711603153276 +70386486105843025439939619828917593665686757934951 +62176457141856560629502157223196586755079324193331 +64906352462741904929101432445813822663347944758178 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+32238195734329339946437501907836945765883352399886 +75506164965184775180738168837861091527357929701337 +62177842752192623401942399639168044983993173312731 +32924185707147349566916674687634660915035914677504 +99518671430235219628894890102423325116913619626622 +73267460800591547471830798392868535206946944540724 +76841822524674417161514036427982273348055556214818 +97142617910342598647204516893989422179826088076852 +87783646182799346313767754307809363333018982642090 +10848802521674670883215120185883543223812876952786 +71329612474782464538636993009049310363619763878039 +62184073572399794223406235393808339651327408011116 +66627891981488087797941876876144230030984490851411 +60661826293682836764744779239180335110989069790714 +85786944089552990653640447425576083659976645795096 +66024396409905389607120198219976047599490197230297 +64913982680032973156037120041377903785566085089252 +16730939319872750275468906903707539413042652315011 +94809377245048795150954100921645863754710598436791 +78639167021187492431995700641917969777599028300699 +15368713711936614952811305876380278410754449733078 +40789923115535562561142322423255033685442488917353 +44889911501440648020369068063960672322193204149535 +41503128880339536053299340368006977710650566631954 +81234880673210146739058568557934581403627822703280 +82616570773948327592232845941706525094512325230608 +22918802058777319719839450180888072429661980811197 +77158542502016545090413245809786882778948721859617 +72107838435069186155435662884062257473692284509516 +20849603980134001723930671666823555245252804609722 +53503534226472524250874054075591789781264330331690 diff --git a/project_euler/problem_13/sol1.py b/project_euler/problem_13/sol1.py index faaaad5e88c1..e36065ec8e11 100644 --- a/project_euler/problem_13/sol1.py +++ b/project_euler/problem_13/sol1.py @@ -1,14 +1,30 @@ -''' +""" Problem Statement: -Work out the first ten digits of the sum of the N 50-digit numbers. -''' -from __future__ import print_function +Work out the first ten digits of the sum of the following one-hundred 50-digit +numbers. +""" -n = int(input().strip()) -array = [] -for i in range(n): - array.append(int(input().strip())) +def solution(array): + """Returns the first ten digits of the sum of the array elements. -print(str(sum(array))[:10]) + >>> import os + >>> sum = 0 + >>> array = [] + >>> with open(os.path.dirname(__file__) + "/num.txt","r") as f: + ... for line in f: + ... array.append(int(line)) + ... + >>> solution(array) + '5537376230' + """ + return str(sum(array))[:10] + +if __name__ == "__main__": + n = int(input().strip()) + + array = [] + for i in range(n): + array.append(int(input().strip())) + print(solution(array)) diff --git a/project_euler/problem_14/__init__.py b/project_euler/problem_14/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_14/sol1.py b/project_euler/problem_14/sol1.py index 9037f6eb8bd5..ab09937fb315 100644 --- a/project_euler/problem_14/sol1.py +++ b/project_euler/problem_14/sol1.py @@ -1,21 +1,62 @@ -from __future__ import print_function -largest_number = 0 -pre_counter = 0 - -for input1 in range(750000,1000000): - counter = 1 - number = input1 - - while number > 1: - if number % 2 == 0: - number /=2 - counter += 1 - else: - number = (3*number)+1 - counter += 1 - - if counter > pre_counter: - largest_number = input1 - pre_counter = counter - -print(('Largest Number:',largest_number,'->',pre_counter,'digits')) +# -*- coding: utf-8 -*- +""" +Problem Statement: +The following iterative sequence is defined for the set of positive integers: + + n → n/2 (n is even) + n → 3n + 1 (n is odd) + +Using the rule above and starting with 13, we generate the following sequence: + + 13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1 + +It can be seen that this sequence (starting at 13 and finishing at 1) contains +10 terms. Although it has not been proved yet (Collatz Problem), it is thought +that all starting numbers finish at 1. + +Which starting number, under one million, produces the longest chain? +""" + + +def solution(n): + """Returns the number under n that generates the longest sequence using the + formula: + n → n/2 (n is even) + n → 3n + 1 (n is odd) + + # The code below has been commented due to slow execution affecting Travis. + # >>> solution(1000000) + # {'counter': 525, 'largest_number': 837799} + >>> solution(200) + {'counter': 125, 'largest_number': 171} + >>> solution(5000) + {'counter': 238, 'largest_number': 3711} + >>> solution(15000) + {'counter': 276, 'largest_number': 13255} + """ + largest_number = 0 + pre_counter = 0 + + for input1 in range(n): + counter = 1 + number = input1 + + while number > 1: + if number % 2 == 0: + number /= 2 + counter += 1 + else: + number = (3 * number) + 1 + counter += 1 + + if counter > pre_counter: + largest_number = input1 + pre_counter = counter + return {"counter": pre_counter, "largest_number": largest_number} + + +if __name__ == "__main__": + result = solution(int(input().strip())) + print( + ("Largest Number:", result["largest_number"], "->", result["counter"], "digits") + ) diff --git a/project_euler/problem_14/sol2.py b/project_euler/problem_14/sol2.py index b9de42be1108..9b8857e710b4 100644 --- a/project_euler/problem_14/sol2.py +++ b/project_euler/problem_14/sol2.py @@ -1,16 +1,64 @@ +# -*- coding: utf-8 -*- +""" +Collatz conjecture: start with any positive integer n. Next term obtained from +the previous term as follows: + +If the previous term is even, the next term is one half the previous term. +If the previous term is odd, the next term is 3 times the previous term plus 1. +The conjecture states the sequence will always reach 1 regardless of starting +n. + +Problem Statement: +The following iterative sequence is defined for the set of positive integers: + + n → n/2 (n is even) + n → 3n + 1 (n is odd) + +Using the rule above and starting with 13, we generate the following sequence: + + 13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1 + +It can be seen that this sequence (starting at 13 and finishing at 1) contains +10 terms. Although it has not been proved yet (Collatz Problem), it is thought +that all starting numbers finish at 1. + +Which starting number, under one million, produces the longest chain? +""" + + def collatz_sequence(n): - """Collatz conjecture: start with any positive integer n.Next termis obtained from the previous term as follows: - if the previous term is even, the next term is one half the previous term. - If the previous term is odd, the next term is 3 times the previous term plus 1. - The conjecture states the sequence will always reach 1 regaardess of starting n.""" - sequence = [n] - while n != 1: - if n % 2 == 0:# even - n //= 2 - else: - n = 3*n +1 - sequence.append(n) - return sequence - -answer = max([(len(collatz_sequence(i)), i) for i in range(1,1000000)]) -print("Longest Collatz sequence under one million is %d with length %d" % (answer[1],answer[0])) \ No newline at end of file + """Returns the Collatz sequence for n.""" + sequence = [n] + while n != 1: + if n % 2 == 0: + n //= 2 + else: + n = 3 * n + 1 + sequence.append(n) + return sequence + + +def solution(n): + """Returns the number under n that generates the longest Collatz sequence. + + # The code below has been commented due to slow execution affecting Travis. + # >>> solution(1000000) + # {'counter': 525, 'largest_number': 837799} + >>> solution(200) + {'counter': 125, 'largest_number': 171} + >>> solution(5000) + {'counter': 238, 'largest_number': 3711} + >>> solution(15000) + {'counter': 276, 'largest_number': 13255} + """ + + result = max([(len(collatz_sequence(i)), i) for i in range(1, n)]) + return {"counter": result[0], "largest_number": result[1]} + + +if __name__ == "__main__": + result = solution(int(input().strip())) + print( + "Longest Collatz sequence under one million is %d with length %d" + % (result["largest_number"], result["counter"]) + ) diff --git a/project_euler/problem_15/__init__.py b/project_euler/problem_15/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_15/sol1.py b/project_euler/problem_15/sol1.py index d24748011ef9..1be7d10ed674 100644 --- a/project_euler/problem_15/sol1.py +++ b/project_euler/problem_15/sol1.py @@ -1,20 +1,55 @@ -from __future__ import print_function +""" +Starting in the top left corner of a 2×2 grid, and only being able to move to +the right and down, there are exactly 6 routes to the bottom right corner. +How many such routes are there through a 20×20 grid? +""" from math import factorial + def lattice_paths(n): - n = 2*n #middle entry of odd rows starting at row 3 is the solution for n = 1, 2, 3,... - k = n/2 - - return factorial(n)/(factorial(k)*factorial(n-k)) - -if __name__ == '__main__': - import sys - - if len(sys.argv) == 1: - print(lattice_paths(20)) - else: - try: - n = int(sys.argv[1]) - print(lattice_paths(n)) - except ValueError: - print('Invalid entry - please enter a number.') + """ + Returns the number of paths possible in a n x n grid starting at top left + corner going to bottom right corner and being able to move right and down + only. + +bruno@bruno-laptop:~/git/Python/project_euler/problem_15$ python3 sol1.py 50 +1.008913445455642e+29 +bruno@bruno-laptop:~/git/Python/project_euler/problem_15$ python3 sol1.py 25 +126410606437752.0 +bruno@bruno-laptop:~/git/Python/project_euler/problem_15$ python3 sol1.py 23 +8233430727600.0 +bruno@bruno-laptop:~/git/Python/project_euler/problem_15$ python3 sol1.py 15 +155117520.0 +bruno@bruno-laptop:~/git/Python/project_euler/problem_15$ python3 sol1.py 1 +2.0 + + >>> lattice_paths(25) + 126410606437752 + >>> lattice_paths(23) + 8233430727600 + >>> lattice_paths(20) + 137846528820 + >>> lattice_paths(15) + 155117520 + >>> lattice_paths(1) + 2 + + """ + n = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1, + # 2, 3,... + k = n / 2 + + return int(factorial(n) / (factorial(k) * factorial(n - k))) + + +if __name__ == "__main__": + import sys + + if len(sys.argv) == 1: + print(lattice_paths(20)) + else: + try: + n = int(sys.argv[1]) + print(lattice_paths(n)) + except ValueError: + print("Invalid entry - please enter a number.") diff --git a/project_euler/problem_16/__init__.py b/project_euler/problem_16/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_16/sol1.py b/project_euler/problem_16/sol1.py index 05c7916bd10a..67c50ac87876 100644 --- a/project_euler/problem_16/sol1.py +++ b/project_euler/problem_16/sol1.py @@ -1,15 +1,34 @@ -power = int(input("Enter the power of 2: ")) -num = 2**power +""" +2^15 = 32768 and the sum of its digits is 3 + 2 + 7 + 6 + 8 = 26. -string_num = str(num) +What is the sum of the digits of the number 2^1000? +""" -list_num = list(string_num) -sum_of_num = 0 +def solution(power): + """Returns the sum of the digits of the number 2^power. + >>> solution(1000) + 1366 + >>> solution(50) + 76 + >>> solution(20) + 31 + >>> solution(15) + 26 + """ + num = 2 ** power + string_num = str(num) + list_num = list(string_num) + sum_of_num = 0 -print("2 ^",power,"=",num) + for i in list_num: + sum_of_num += int(i) -for i in list_num: - sum_of_num += int(i) + return sum_of_num -print("Sum of the digits are:",sum_of_num) + +if __name__ == "__main__": + power = int(input("Enter the power of 2: ").strip()) + print("2 ^ ", power, " = ", 2 ** power) + result = solution(power) + print("Sum of the digits is: ", result) diff --git a/project_euler/problem_16/sol2.py b/project_euler/problem_16/sol2.py new file mode 100644 index 000000000000..88672e9a9e54 --- /dev/null +++ b/project_euler/problem_16/sol2.py @@ -0,0 +1,28 @@ +""" +2^15 = 32768 and the sum of its digits is 3 + 2 + 7 + 6 + 8 = 26. + +What is the sum of the digits of the number 2^1000? +""" + + +def solution(power): + """Returns the sum of the digits of the number 2^power. + + >>> solution(1000) + 1366 + >>> solution(50) + 76 + >>> solution(20) + 31 + >>> solution(15) + 26 + """ + n = 2 ** power + r = 0 + while n: + r, n = r + n % 10, n // 10 + return r + + +if __name__ == "__main__": + print(solution(int(str(input()).strip()))) diff --git a/project_euler/problem_17/__init__.py b/project_euler/problem_17/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_17/sol1.py b/project_euler/problem_17/sol1.py index 9de5d80b9b29..d585d81a0825 100644 --- a/project_euler/problem_17/sol1.py +++ b/project_euler/problem_17/sol1.py @@ -1,35 +1,63 @@ -from __future__ import print_function -''' +""" Number letter counts Problem 17 -If the numbers 1 to 5 are written out in words: one, two, three, four, five, then there are 3 + 3 + 5 + 4 + 4 = 19 letters used in total. - -If all the numbers from 1 to 1000 (one thousand) inclusive were written out in words, how many letters would be used? - - -NOTE: Do not count spaces or hyphens. For example, 342 (three hundred and forty-two) contains 23 letters and 115 (one hundred and fifteen) -contains 20 letters. The use of "and" when writing out numbers is in compliance with British usage. -''' - -ones_counts = [0, 3, 3, 5, 4, 4, 3, 5, 5, 4, 3, 6, 6, 8, 8, 7, 7, 9, 8, 8] #number of letters in zero, one, two, ..., nineteen (0 for zero since it's never said aloud) -tens_counts = [0, 0, 6, 6, 5, 5, 5, 7, 6, 6] #number of letters in twenty, thirty, ..., ninety (0 for numbers less than 20 due to inconsistency in teens) - -count = 0 - -for i in range(1, 1001): - if i < 1000: - if i >= 100: - count += ones_counts[i/100] + 7 #add number of letters for "n hundred" - - if i%100 is not 0: - count += 3 #add number of letters for "and" if number is not multiple of 100 - - if 0 < i%100 < 20: - count += ones_counts[i%100] #add number of letters for one, two, three, ..., nineteen (could be combined with below if not for inconsistency in teens) - else: - count += ones_counts[i%10] + tens_counts[(i%100-i%10)/10] #add number of letters for twenty, twenty one, ..., ninety nine - else: - count += ones_counts[i/1000] + 8 - -print(count) \ No newline at end of file +If the numbers 1 to 5 are written out in words: one, two, three, four, five, +then there are 3 + 3 + 5 + 4 + 4 = 19 letters used in total. + +If all the numbers from 1 to 1000 (one thousand) inclusive were written out in +words, how many letters would be used? + + +NOTE: Do not count spaces or hyphens. For example, 342 (three hundred and +forty-two) contains 23 letters and 115 (one hundred and fifteen) contains 20 +letters. The use of "and" when writing out numbers is in compliance withBritish +usage. +""" + + +def solution(n): + """Returns the number of letters used to write all numbers from 1 to n. + where n is lower or equals to 1000. + >>> solution(1000) + 21124 + >>> solution(5) + 19 + """ + # number of letters in zero, one, two, ..., nineteen (0 for zero since it's + # never said aloud) + ones_counts = [0, 3, 3, 5, 4, 4, 3, 5, 5, 4, 3, 6, 6, 8, 8, 7, 7, 9, 8, 8] + # number of letters in twenty, thirty, ..., ninety (0 for numbers less than + # 20 due to inconsistency in teens) + tens_counts = [0, 0, 6, 6, 5, 5, 5, 7, 6, 6] + + count = 0 + + for i in range(1, n + 1): + if i < 1000: + if i >= 100: + # add number of letters for "n hundred" + count += ones_counts[i // 100] + 7 + + if i % 100 != 0: + # add number of letters for "and" if number is not multiple + # of 100 + count += 3 + + if 0 < i % 100 < 20: + # add number of letters for one, two, three, ..., nineteen + # (could be combined with below if not for inconsistency in + # teens) + count += ones_counts[i % 100] + else: + # add number of letters for twenty, twenty one, ..., ninety + # nine + count += ones_counts[i % 10] + count += tens_counts[(i % 100 - i % 10) // 10] + else: + count += ones_counts[i // 1000] + 8 + return count + + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_18/solution.py b/project_euler/problem_18/solution.py new file mode 100644 index 000000000000..38593813901e --- /dev/null +++ b/project_euler/problem_18/solution.py @@ -0,0 +1,64 @@ +""" +By starting at the top of the triangle below and moving to adjacent numbers on +the row below, the maximum total from top to bottom is 23. + +3 +7 4 +2 4 6 +8 5 9 3 + +That is, 3 + 7 + 4 + 9 = 23. + +Find the maximum total from top to bottom of the triangle below: + +75 +95 64 +17 47 82 +18 35 87 10 +20 04 82 47 65 +19 01 23 75 03 34 +88 02 77 73 07 63 67 +99 65 04 28 06 16 70 92 +41 41 26 56 83 40 80 70 33 +41 48 72 33 47 32 37 16 94 29 +53 71 44 65 25 43 91 52 97 51 14 +70 11 33 28 77 73 17 78 39 68 17 57 +91 71 52 38 17 14 91 43 58 50 27 29 48 +63 66 04 68 89 53 67 30 73 16 69 87 40 31 +04 62 98 27 23 09 70 98 73 93 38 53 60 04 23 +""" +import os + + +def solution(): + """ + Finds the maximum total in a triangle as described by the problem statement + above. + + >>> solution() + 1074 + """ + script_dir = os.path.dirname(os.path.realpath(__file__)) + triangle = os.path.join(script_dir, "triangle.txt") + + with open(triangle, "r") as f: + triangle = f.readlines() + + a = [[int(y) for y in x.rstrip("\r\n").split(" ")] for x in triangle] + + for i in range(1, len(a)): + for j in range(len(a[i])): + if j != len(a[i - 1]): + number1 = a[i - 1][j] + else: + number1 = 0 + if j > 0: + number2 = a[i - 1][j - 1] + else: + number2 = 0 + a[i][j] += max(number1, number2) + return max(a[-1]) + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_18/triangle.txt b/project_euler/problem_18/triangle.txt new file mode 100644 index 000000000000..e236c2ff7ee2 --- /dev/null +++ b/project_euler/problem_18/triangle.txt @@ -0,0 +1,15 @@ +75 +95 64 +17 47 82 +18 35 87 10 +20 04 82 47 65 +19 01 23 75 03 34 +88 02 77 73 07 63 67 +99 65 04 28 06 16 70 92 +41 41 26 56 83 40 80 70 33 +41 48 72 33 47 32 37 16 94 29 +53 71 44 65 25 43 91 52 97 51 14 +70 11 33 28 77 73 17 78 39 68 17 57 +91 71 52 38 17 14 91 43 58 50 27 29 48 +63 66 04 68 89 53 67 30 73 16 69 87 40 31 +04 62 98 27 23 09 70 98 73 93 38 53 60 04 23 diff --git a/project_euler/problem_19/__init__.py b/project_euler/problem_19/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_19/sol1.py b/project_euler/problem_19/sol1.py index 94cf117026a4..ab59365843b2 100644 --- a/project_euler/problem_19/sol1.py +++ b/project_euler/problem_19/sol1.py @@ -1,9 +1,9 @@ -from __future__ import print_function -''' +""" Counting Sundays Problem 19 -You are given the following information, but you may prefer to do some research for yourself. +You are given the following information, but you may prefer to do some research +for yourself. 1 Jan 1900 was a Monday. Thirty days has September, @@ -13,39 +13,52 @@ Which has twenty-eight, rain or shine. And on leap years, twenty-nine. -A leap year occurs on any year evenly divisible by 4, but not on a century unless it is divisible by 400. +A leap year occurs on any year evenly divisible by 4, but not on a century +unless it is divisible by 400. -How many Sundays fell on the first of the month during the twentieth century (1 Jan 1901 to 31 Dec 2000)? -''' +How many Sundays fell on the first of the month during the twentieth century +(1 Jan 1901 to 31 Dec 2000)? +""" -days_per_month = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] -day = 6 -month = 1 -year = 1901 +def solution(): + """Returns the number of mondays that fall on the first of the month during + the twentieth century (1 Jan 1901 to 31 Dec 2000)? -sundays = 0 + >>> solution() + 171 + """ + days_per_month = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] -while year < 2001: - day += 7 + day = 6 + month = 1 + year = 1901 - if (year%4 == 0 and not year%100 == 0) or (year%400 == 0): - if day > days_per_month[month-1] and month is not 2: - month += 1 - day = day-days_per_month[month-2] - elif day > 29 and month is 2: - month += 1 - day = day-29 - else: - if day > days_per_month[month-1]: - month += 1 - day = day-days_per_month[month-2] - - if month > 12: - year += 1 - month = 1 + sundays = 0 - if year < 2001 and day is 1: - sundays += 1 + while year < 2001: + day += 7 -print(sundays) \ No newline at end of file + if (year % 4 == 0 and not year % 100 == 0) or (year % 400 == 0): + if day > days_per_month[month - 1] and month != 2: + month += 1 + day = day - days_per_month[month - 2] + elif day > 29 and month == 2: + month += 1 + day = day - 29 + else: + if day > days_per_month[month - 1]: + month += 1 + day = day - days_per_month[month - 2] + + if month > 12: + year += 1 + month = 1 + + if year < 2001 and day == 1: + sundays += 1 + return sundays + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_20/__init__.py b/project_euler/problem_20/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_20/sol1.py b/project_euler/problem_20/sol1.py index 73e41d5cc8fa..13b3c987f046 100644 --- a/project_euler/problem_20/sol1.py +++ b/project_euler/problem_20/sol1.py @@ -1,27 +1,51 @@ -# Finding the factorial. +""" +n! means n × (n − 1) × ... × 3 × 2 × 1 + +For example, 10! = 10 × 9 × ... × 3 × 2 × 1 = 3628800, +and the sum of the digits in the number 10! is 3 + 6 + 2 + 8 + 8 + 0 + 0 = 27. + +Find the sum of the digits in the number 100! +""" + + def factorial(n): fact = 1 - for i in range(1,n+1): + for i in range(1, n + 1): fact *= i return fact -# Spliting the digits and adding it. + def split_and_add(number): + """Split number digits and add them.""" sum_of_digits = 0 - while(number>0): + while number > 0: last_digit = number % 10 sum_of_digits += last_digit - number = int(number/10) # Removing the last_digit from the given number. + number = number // 10 # Removing the last_digit from the given number return sum_of_digits -# Taking the user input. -number = int(input("Enter the Number: ")) -# Assigning the factorial from the factorial function. -factorial = factorial(number) +def solution(n): + """Returns the sum of the digits in the number 100! + >>> solution(100) + 648 + >>> solution(50) + 216 + >>> solution(10) + 27 + >>> solution(5) + 3 + >>> solution(3) + 6 + >>> solution(2) + 2 + >>> solution(1) + 1 + """ + f = factorial(n) + result = split_and_add(f) + return result -# Spliting and adding the factorial into answer. -answer = split_and_add(factorial) -# Printing the answer. -print(answer) +if __name__ == "__main__": + print(solution(int(input("Enter the Number: ").strip()))) diff --git a/project_euler/problem_20/sol2.py b/project_euler/problem_20/sol2.py index bca9af9cb9ef..14e591795292 100644 --- a/project_euler/problem_20/sol2.py +++ b/project_euler/problem_20/sol2.py @@ -1,5 +1,33 @@ +""" +n! means n × (n − 1) × ... × 3 × 2 × 1 + +For example, 10! = 10 × 9 × ... × 3 × 2 × 1 = 3628800, +and the sum of the digits in the number 10! is 3 + 6 + 2 + 8 + 8 + 0 + 0 = 27. + +Find the sum of the digits in the number 100! +""" from math import factorial -def main(): - print(sum([int(x) for x in str(factorial(100))])) -if __name__ == '__main__': - main() \ No newline at end of file + + +def solution(n): + """Returns the sum of the digits in the number 100! + >>> solution(100) + 648 + >>> solution(50) + 216 + >>> solution(10) + 27 + >>> solution(5) + 3 + >>> solution(3) + 6 + >>> solution(2) + 2 + >>> solution(1) + 1 + """ + return sum([int(x) for x in str(factorial(n))]) + + +if __name__ == "__main__": + print(solution(int(input("Enter the Number: ").strip()))) diff --git a/project_euler/problem_20/sol3.py b/project_euler/problem_20/sol3.py new file mode 100644 index 000000000000..13f9d7831c47 --- /dev/null +++ b/project_euler/problem_20/sol3.py @@ -0,0 +1,39 @@ +""" +n! means n × (n − 1) × ... × 3 × 2 × 1 + +For example, 10! = 10 × 9 × ... × 3 × 2 × 1 = 3628800, +and the sum of the digits in the number 10! is 3 + 6 + 2 + 8 + 8 + 0 + 0 = 27. + +Find the sum of the digits in the number 100! +""" +from math import factorial + + +def solution(n): + """Returns the sum of the digits in the number 100! + >>> solution(1000) + 10539 + >>> solution(200) + 1404 + >>> solution(100) + 648 + >>> solution(50) + 216 + >>> solution(10) + 27 + >>> solution(5) + 3 + >>> solution(3) + 6 + >>> solution(2) + 2 + >>> solution(1) + 1 + >>> solution(0) + 1 + """ + return sum(map(int, str(factorial(n)))) + + +if __name__ == "__main__": + print(solution(int(input("Enter the Number: ").strip()))) diff --git a/project_euler/problem_20/sol4.py b/project_euler/problem_20/sol4.py new file mode 100644 index 000000000000..50ebca5a0bf7 --- /dev/null +++ b/project_euler/problem_20/sol4.py @@ -0,0 +1,40 @@ +""" +n! means n × (n − 1) × ... × 3 × 2 × 1 + +For example, 10! = 10 × 9 × ... × 3 × 2 × 1 = 3628800, +and the sum of the digits in the number 10! is 3 + 6 + 2 + 8 + 8 + 0 + 0 = 27. + +Find the sum of the digits in the number 100! +""" + + +def solution(n): + """Returns the sum of the digits in the number 100! + >>> solution(100) + 648 + >>> solution(50) + 216 + >>> solution(10) + 27 + >>> solution(5) + 3 + >>> solution(3) + 6 + >>> solution(2) + 2 + >>> solution(1) + 1 + """ + fact = 1 + result = 0 + for i in range(1,n + 1): + fact *= i + + for j in str(fact): + result += int(j) + + return result + + +if __name__ == "__main__": + print(solution(int(input("Enter the Number: ").strip()))) diff --git a/project_euler/problem_21/__init__.py b/project_euler/problem_21/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_21/sol1.py b/project_euler/problem_21/sol1.py index da29a5c7b631..49c2db964316 100644 --- a/project_euler/problem_21/sol1.py +++ b/project_euler/problem_21/sol1.py @@ -1,30 +1,56 @@ -#-.- coding: latin-1 -.- -from __future__ import print_function +# -.- coding: latin-1 -.- from math import sqrt -''' + +""" Amicable Numbers Problem 21 -Let d(n) be defined as the sum of proper divisors of n (numbers less than n which divide evenly into n). -If d(a) = b and d(b) = a, where a ≠ b, then a and b are an amicable pair and each of a and b are called amicable numbers. +Let d(n) be defined as the sum of proper divisors of n (numbers less than n +which divide evenly into n). +If d(a) = b and d(b) = a, where a ≠ b, then a and b are an amicable pair and +each of a and b are called amicable numbers. -For example, the proper divisors of 220 are 1, 2, 4, 5, 10, 11, 20, 22, 44, 55 and 110; therefore d(220) = 284. The proper divisors of 284 are 1, 2, 4, 71 and 142; so d(284) = 220. +For example, the proper divisors of 220 are 1, 2, 4, 5, 10, 11, 20, 22, 44, 55 +and 110; therefore d(220) = 284. The proper divisors of 284 are 1, 2, 4, 71 and +142; so d(284) = 220. Evaluate the sum of all the amicable numbers under 10000. -''' -try: - xrange #Python 2 -except NameError: - xrange = range #Python 3 +""" + def sum_of_divisors(n): - total = 0 - for i in xrange(1, int(sqrt(n)+1)): - if n%i == 0 and i != sqrt(n): - total += i + n//i - elif i == sqrt(n): - total += i - return total-n - -total = [i for i in range(1,10000) if sum_of_divisors(sum_of_divisors(i)) == i and sum_of_divisors(i) != i] -print(sum(total)) + total = 0 + for i in range(1, int(sqrt(n) + 1)): + if n % i == 0 and i != sqrt(n): + total += i + n // i + elif i == sqrt(n): + total += i + return total - n + + +def solution(n): + """Returns the sum of all the amicable numbers under n. + + >>> solution(10000) + 31626 + >>> solution(5000) + 8442 + >>> solution(1000) + 504 + >>> solution(100) + 0 + >>> solution(50) + 0 + """ + total = sum( + [ + i + for i in range(1, n) + if sum_of_divisors(sum_of_divisors(i)) == i and sum_of_divisors(i) != i + ] + ) + return total + + +if __name__ == "__main__": + print(solution(int(str(input()).strip()))) diff --git a/project_euler/problem_22/__init__.py b/project_euler/problem_22/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_22/sol1.py b/project_euler/problem_22/sol1.py index 7754306583dc..f6275e2138bb 100644 --- a/project_euler/problem_22/sol1.py +++ b/project_euler/problem_22/sol1.py @@ -1,37 +1,46 @@ # -*- coding: latin-1 -*- -from __future__ import print_function -''' +""" Name scores Problem 22 -Using names.txt (right click and 'Save Link/Target As...'), a 46K text file containing over five-thousand first names, begin by sorting it -into alphabetical order. Then working out the alphabetical value for each name, multiply this value by its alphabetical position in the list -to obtain a name score. +Using names.txt (right click and 'Save Link/Target As...'), a 46K text file +containing over five-thousand first names, begin by sorting it into +alphabetical order. Then working out the alphabetical value for each name, +multiply this value by its alphabetical position in the list to obtain a name +score. -For example, when the list is sorted into alphabetical order, COLIN, which is worth 3 + 15 + 12 + 9 + 14 = 53, is the 938th name in the list. -So, COLIN would obtain a score of 938 × 53 = 49714. +For example, when the list is sorted into alphabetical order, COLIN, which is +worth 3 + 15 + 12 + 9 + 14 = 53, is the 938th name in the list. So, COLIN would +obtain a score of 938 × 53 = 49714. What is the total of all the name scores in the file? -''' -try: - xrange #Python 2 -except NameError: - xrange = range #Python 3 +""" +import os -with open('p022_names.txt') as file: - names = str(file.readlines()[0]) - names = names.replace('"', '').split(',') -names.sort() +def solution(): + """Returns the total of all the name scores in the file. -name_score = 0 -total_score = 0 + >>> solution() + 871198282 + """ + with open(os.path.dirname(__file__) + "/p022_names.txt") as file: + names = str(file.readlines()[0]) + names = names.replace('"', "").split(",") -for i, name in enumerate(names): - for letter in name: - name_score += ord(letter) - 64 + names.sort() - total_score += (i+1)*name_score - name_score = 0 + name_score = 0 + total_score = 0 -print(total_score) \ No newline at end of file + for i, name in enumerate(names): + for letter in name: + name_score += ord(letter) - 64 + + total_score += (i + 1) * name_score + name_score = 0 + return total_score + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_22/sol2.py b/project_euler/problem_22/sol2.py index d7f9abf09d49..69acd2fb8ef3 100644 --- a/project_euler/problem_22/sol2.py +++ b/project_euler/problem_22/sol2.py @@ -1,533 +1,43 @@ -def main(): - name = [ - "MARY", "PATRICIA", "LINDA", "BARBARA", "ELIZABETH", "JENNIFER", "MARIA", "SUSAN", "MARGARET", "DOROTHY", - "LISA", "NANCY", "KAREN", "BETTY", "HELEN", "SANDRA", "DONNA", "CAROL", "RUTH", "SHARON", - "MICHELLE", "LAURA", "SARAH", "KIMBERLY", "DEBORAH", "JESSICA", "SHIRLEY", "CYNTHIA", "ANGELA", "MELISSA", - "BRENDA", "AMY", "ANNA", "REBECCA", "VIRGINIA", "KATHLEEN", "PAMELA", "MARTHA", "DEBRA", "AMANDA", - "STEPHANIE", "CAROLYN", "CHRISTINE", "MARIE", "JANET", "CATHERINE", "FRANCES", "ANN", "JOYCE", "DIANE", - "ALICE", "JULIE", "HEATHER", "TERESA", "DORIS", "GLORIA", "EVELYN", "JEAN", "CHERYL", "MILDRED", - "KATHERINE", "JOAN", "ASHLEY", "JUDITH", "ROSE", "JANICE", "KELLY", "NICOLE", "JUDY", "CHRISTINA", - "KATHY", "THERESA", "BEVERLY", "DENISE", "TAMMY", "IRENE", "JANE", "LORI", "RACHEL", "MARILYN", - "ANDREA", "KATHRYN", "LOUISE", "SARA", "ANNE", "JACQUELINE", "WANDA", "BONNIE", "JULIA", "RUBY", - "LOIS", "TINA", "PHYLLIS", "NORMA", "PAULA", "DIANA", "ANNIE", "LILLIAN", "EMILY", "ROBIN", - "PEGGY", "CRYSTAL", "GLADYS", "RITA", "DAWN", "CONNIE", "FLORENCE", "TRACY", "EDNA", "TIFFANY", - "CARMEN", "ROSA", "CINDY", "GRACE", "WENDY", "VICTORIA", "EDITH", "KIM", "SHERRY", "SYLVIA", - "JOSEPHINE", "THELMA", "SHANNON", "SHEILA", "ETHEL", "ELLEN", "ELAINE", "MARJORIE", "CARRIE", "CHARLOTTE", - "MONICA", "ESTHER", "PAULINE", "EMMA", "JUANITA", "ANITA", "RHONDA", "HAZEL", "AMBER", "EVA", - "DEBBIE", "APRIL", "LESLIE", "CLARA", "LUCILLE", "JAMIE", "JOANNE", "ELEANOR", "VALERIE", "DANIELLE", - "MEGAN", "ALICIA", "SUZANNE", "MICHELE", "GAIL", "BERTHA", "DARLENE", "VERONICA", "JILL", "ERIN", - "GERALDINE", "LAUREN", "CATHY", "JOANN", "LORRAINE", "LYNN", "SALLY", "REGINA", "ERICA", "BEATRICE", - "DOLORES", "BERNICE", "AUDREY", "YVONNE", "ANNETTE", "JUNE", "SAMANTHA", "MARION", "DANA", "STACY", - "ANA", "RENEE", "IDA", "VIVIAN", "ROBERTA", "HOLLY", "BRITTANY", "MELANIE", "LORETTA", "YOLANDA", - "JEANETTE", "LAURIE", "KATIE", "KRISTEN", "VANESSA", "ALMA", "SUE", "ELSIE", "BETH", "JEANNE", - "VICKI", "CARLA", "TARA", "ROSEMARY", "EILEEN", "TERRI", "GERTRUDE", "LUCY", "TONYA", "ELLA", - "STACEY", "WILMA", "GINA", "KRISTIN", "JESSIE", "NATALIE", "AGNES", "VERA", "WILLIE", "CHARLENE", - "BESSIE", "DELORES", "MELINDA", "PEARL", "ARLENE", "MAUREEN", "COLLEEN", "ALLISON", "TAMARA", "JOY", - "GEORGIA", "CONSTANCE", "LILLIE", "CLAUDIA", "JACKIE", "MARCIA", "TANYA", "NELLIE", "MINNIE", "MARLENE", - "HEIDI", "GLENDA", "LYDIA", "VIOLA", "COURTNEY", "MARIAN", "STELLA", "CAROLINE", "DORA", "JO", - "VICKIE", "MATTIE", "TERRY", "MAXINE", "IRMA", "MABEL", "MARSHA", "MYRTLE", "LENA", "CHRISTY", - "DEANNA", "PATSY", "HILDA", "GWENDOLYN", "JENNIE", "NORA", "MARGIE", "NINA", "CASSANDRA", "LEAH", - "PENNY", "KAY", "PRISCILLA", "NAOMI", "CAROLE", "BRANDY", "OLGA", "BILLIE", "DIANNE", "TRACEY", - "LEONA", "JENNY", "FELICIA", "SONIA", "MIRIAM", "VELMA", "BECKY", "BOBBIE", "VIOLET", "KRISTINA", - "TONI", "MISTY", "MAE", "SHELLY", "DAISY", "RAMONA", "SHERRI", "ERIKA", "KATRINA", "CLAIRE", - "LINDSEY", "LINDSAY", "GENEVA", "GUADALUPE", "BELINDA", "MARGARITA", "SHERYL", "CORA", "FAYE", "ADA", - "NATASHA", "SABRINA", "ISABEL", "MARGUERITE", "HATTIE", "HARRIET", "MOLLY", "CECILIA", "KRISTI", "BRANDI", - "BLANCHE", "SANDY", "ROSIE", "JOANNA", "IRIS", "EUNICE", "ANGIE", "INEZ", "LYNDA", "MADELINE", - "AMELIA", "ALBERTA", "GENEVIEVE", "MONIQUE", "JODI", "JANIE", "MAGGIE", "KAYLA", "SONYA", "JAN", - "LEE", "KRISTINE", "CANDACE", "FANNIE", "MARYANN", "OPAL", "ALISON", "YVETTE", "MELODY", "LUZ", - "SUSIE", "OLIVIA", "FLORA", "SHELLEY", "KRISTY", "MAMIE", "LULA", "LOLA", "VERNA", "BEULAH", - "ANTOINETTE", "CANDICE", "JUANA", "JEANNETTE", "PAM", "KELLI", "HANNAH", "WHITNEY", "BRIDGET", "KARLA", - "CELIA", "LATOYA", "PATTY", "SHELIA", "GAYLE", "DELLA", "VICKY", "LYNNE", "SHERI", "MARIANNE", - "KARA", "JACQUELYN", "ERMA", "BLANCA", "MYRA", "LETICIA", "PAT", "KRISTA", "ROXANNE", "ANGELICA", - "JOHNNIE", "ROBYN", "FRANCIS", "ADRIENNE", "ROSALIE", "ALEXANDRA", "BROOKE", "BETHANY", "SADIE", "BERNADETTE", - "TRACI", "JODY", "KENDRA", "JASMINE", "NICHOLE", "RACHAEL", "CHELSEA", "MABLE", "ERNESTINE", "MURIEL", - "MARCELLA", "ELENA", "KRYSTAL", "ANGELINA", "NADINE", "KARI", "ESTELLE", "DIANNA", "PAULETTE", "LORA", - "MONA", "DOREEN", "ROSEMARIE", "ANGEL", "DESIREE", "ANTONIA", "HOPE", "GINGER", "JANIS", "BETSY", - "CHRISTIE", "FREDA", "MERCEDES", "MEREDITH", "LYNETTE", "TERI", "CRISTINA", "EULA", "LEIGH", "MEGHAN", - "SOPHIA", "ELOISE", "ROCHELLE", "GRETCHEN", "CECELIA", "RAQUEL", "HENRIETTA", "ALYSSA", "JANA", "KELLEY", - "GWEN", "KERRY", "JENNA", "TRICIA", "LAVERNE", "OLIVE", "ALEXIS", "TASHA", "SILVIA", "ELVIRA", - "CASEY", "DELIA", "SOPHIE", "KATE", "PATTI", "LORENA", "KELLIE", "SONJA", "LILA", "LANA", - "DARLA", "MAY", "MINDY", "ESSIE", "MANDY", "LORENE", "ELSA", "JOSEFINA", "JEANNIE", "MIRANDA", - "DIXIE", "LUCIA", "MARTA", "FAITH", "LELA", "JOHANNA", "SHARI", "CAMILLE", "TAMI", "SHAWNA", - "ELISA", "EBONY", "MELBA", "ORA", "NETTIE", "TABITHA", "OLLIE", "JAIME", "WINIFRED", "KRISTIE", - "MARINA", "ALISHA", "AIMEE", "RENA", "MYRNA", "MARLA", "TAMMIE", "LATASHA", "BONITA", "PATRICE", - "RONDA", "SHERRIE", "ADDIE", "FRANCINE", "DELORIS", "STACIE", "ADRIANA", "CHERI", "SHELBY", "ABIGAIL", - "CELESTE", "JEWEL", "CARA", "ADELE", "REBEKAH", "LUCINDA", "DORTHY", "CHRIS", "EFFIE", "TRINA", - "REBA", "SHAWN", "SALLIE", "AURORA", "LENORA", "ETTA", "LOTTIE", "KERRI", "TRISHA", "NIKKI", - "ESTELLA", "FRANCISCA", "JOSIE", "TRACIE", "MARISSA", "KARIN", "BRITTNEY", "JANELLE", "LOURDES", "LAUREL", - "HELENE", "FERN", "ELVA", "CORINNE", "KELSEY", "INA", "BETTIE", "ELISABETH", "AIDA", "CAITLIN", - "INGRID", "IVA", "EUGENIA", "CHRISTA", "GOLDIE", "CASSIE", "MAUDE", "JENIFER", "THERESE", "FRANKIE", - "DENA", "LORNA", "JANETTE", "LATONYA", "CANDY", "MORGAN", "CONSUELO", "TAMIKA", "ROSETTA", "DEBORA", - "CHERIE", "POLLY", "DINA", "JEWELL", "FAY", "JILLIAN", "DOROTHEA", "NELL", "TRUDY", "ESPERANZA", - "PATRICA", "KIMBERLEY", "SHANNA", "HELENA", "CAROLINA", "CLEO", "STEFANIE", "ROSARIO", "OLA", "JANINE", - "MOLLIE", "LUPE", "ALISA", "LOU", "MARIBEL", "SUSANNE", "BETTE", "SUSANA", "ELISE", "CECILE", - "ISABELLE", "LESLEY", "JOCELYN", "PAIGE", "JONI", "RACHELLE", "LEOLA", "DAPHNE", "ALTA", "ESTER", - "PETRA", "GRACIELA", "IMOGENE", "JOLENE", "KEISHA", "LACEY", "GLENNA", "GABRIELA", "KERI", "URSULA", - "LIZZIE", "KIRSTEN", "SHANA", "ADELINE", "MAYRA", "JAYNE", "JACLYN", "GRACIE", "SONDRA", "CARMELA", - "MARISA", "ROSALIND", "CHARITY", "TONIA", "BEATRIZ", "MARISOL", "CLARICE", "JEANINE", "SHEENA", "ANGELINE", - "FRIEDA", "LILY", "ROBBIE", "SHAUNA", "MILLIE", "CLAUDETTE", "CATHLEEN", "ANGELIA", "GABRIELLE", "AUTUMN", - "KATHARINE", "SUMMER", "JODIE", "STACI", "LEA", "CHRISTI", "JIMMIE", "JUSTINE", "ELMA", "LUELLA", - "MARGRET", "DOMINIQUE", "SOCORRO", "RENE", "MARTINA", "MARGO", "MAVIS", "CALLIE", "BOBBI", "MARITZA", - "LUCILE", "LEANNE", "JEANNINE", "DEANA", "AILEEN", "LORIE", "LADONNA", "WILLA", "MANUELA", "GALE", - "SELMA", "DOLLY", "SYBIL", "ABBY", "LARA", "DALE", "IVY", "DEE", "WINNIE", "MARCY", - "LUISA", "JERI", "MAGDALENA", "OFELIA", "MEAGAN", "AUDRA", "MATILDA", "LEILA", "CORNELIA", "BIANCA", - "SIMONE", "BETTYE", "RANDI", "VIRGIE", "LATISHA", "BARBRA", "GEORGINA", "ELIZA", "LEANN", "BRIDGETTE", - "RHODA", "HALEY", "ADELA", "NOLA", "BERNADINE", "FLOSSIE", "ILA", "GRETA", "RUTHIE", "NELDA", - "MINERVA", "LILLY", "TERRIE", "LETHA", "HILARY", "ESTELA", "VALARIE", "BRIANNA", "ROSALYN", "EARLINE", - "CATALINA", "AVA", "MIA", "CLARISSA", "LIDIA", "CORRINE", "ALEXANDRIA", "CONCEPCION", "TIA", "SHARRON", - "RAE", "DONA", "ERICKA", "JAMI", "ELNORA", "CHANDRA", "LENORE", "NEVA", "MARYLOU", "MELISA", - "TABATHA", "SERENA", "AVIS", "ALLIE", "SOFIA", "JEANIE", "ODESSA", "NANNIE", "HARRIETT", "LORAINE", - "PENELOPE", "MILAGROS", "EMILIA", "BENITA", "ALLYSON", "ASHLEE", "TANIA", "TOMMIE", "ESMERALDA", "KARINA", - "EVE", "PEARLIE", "ZELMA", "MALINDA", "NOREEN", "TAMEKA", "SAUNDRA", "HILLARY", "AMIE", "ALTHEA", - "ROSALINDA", "JORDAN", "LILIA", "ALANA", "GAY", "CLARE", "ALEJANDRA", "ELINOR", "MICHAEL", "LORRIE", - "JERRI", "DARCY", "EARNESTINE", "CARMELLA", "TAYLOR", "NOEMI", "MARCIE", "LIZA", "ANNABELLE", "LOUISA", - "EARLENE", "MALLORY", "CARLENE", "NITA", "SELENA", "TANISHA", "KATY", "JULIANNE", "JOHN", "LAKISHA", - "EDWINA", "MARICELA", "MARGERY", "KENYA", "DOLLIE", "ROXIE", "ROSLYN", "KATHRINE", "NANETTE", "CHARMAINE", - "LAVONNE", "ILENE", "KRIS", "TAMMI", "SUZETTE", "CORINE", "KAYE", "JERRY", "MERLE", "CHRYSTAL", - "LINA", "DEANNE", "LILIAN", "JULIANA", "ALINE", "LUANN", "KASEY", "MARYANNE", "EVANGELINE", "COLETTE", - "MELVA", "LAWANDA", "YESENIA", "NADIA", "MADGE", "KATHIE", "EDDIE", "OPHELIA", "VALERIA", "NONA", - "MITZI", "MARI", "GEORGETTE", "CLAUDINE", "FRAN", "ALISSA", "ROSEANN", "LAKEISHA", "SUSANNA", "REVA", - "DEIDRE", "CHASITY", "SHEREE", "CARLY", "JAMES", "ELVIA", "ALYCE", "DEIRDRE", "GENA", "BRIANA", - "ARACELI", "KATELYN", "ROSANNE", "WENDI", "TESSA", "BERTA", "MARVA", "IMELDA", "MARIETTA", "MARCI", - "LEONOR", "ARLINE", "SASHA", "MADELYN", "JANNA", "JULIETTE", "DEENA", "AURELIA", "JOSEFA", "AUGUSTA", - "LILIANA", "YOUNG", "CHRISTIAN", "LESSIE", "AMALIA", "SAVANNAH", "ANASTASIA", "VILMA", "NATALIA", "ROSELLA", - "LYNNETTE", "CORINA", "ALFREDA", "LEANNA", "CAREY", "AMPARO", "COLEEN", "TAMRA", "AISHA", "WILDA", - "KARYN", "CHERRY", "QUEEN", "MAURA", "MAI", "EVANGELINA", "ROSANNA", "HALLIE", "ERNA", "ENID", - "MARIANA", "LACY", "JULIET", "JACKLYN", "FREIDA", "MADELEINE", "MARA", "HESTER", "CATHRYN", "LELIA", - "CASANDRA", "BRIDGETT", "ANGELITA", "JANNIE", "DIONNE", "ANNMARIE", "KATINA", "BERYL", "PHOEBE", "MILLICENT", - "KATHERYN", "DIANN", "CARISSA", "MARYELLEN", "LIZ", "LAURI", "HELGA", "GILDA", "ADRIAN", "RHEA", - "MARQUITA", "HOLLIE", "TISHA", "TAMERA", "ANGELIQUE", "FRANCESCA", "BRITNEY", "KAITLIN", "LOLITA", "FLORINE", - "ROWENA", "REYNA", "TWILA", "FANNY", "JANELL", "INES", "CONCETTA", "BERTIE", "ALBA", "BRIGITTE", - "ALYSON", "VONDA", "PANSY", "ELBA", "NOELLE", "LETITIA", "KITTY", "DEANN", "BRANDIE", "LOUELLA", - "LETA", "FELECIA", "SHARLENE", "LESA", "BEVERLEY", "ROBERT", "ISABELLA", "HERMINIA", "TERRA", "CELINA", - "TORI", "OCTAVIA", "JADE", "DENICE", "GERMAINE", "SIERRA", "MICHELL", "CORTNEY", "NELLY", "DORETHA", - "SYDNEY", "DEIDRA", "MONIKA", "LASHONDA", "JUDI", "CHELSEY", "ANTIONETTE", "MARGOT", "BOBBY", "ADELAIDE", - "NAN", "LEEANN", "ELISHA", "DESSIE", "LIBBY", "KATHI", "GAYLA", "LATANYA", "MINA", "MELLISA", - "KIMBERLEE", "JASMIN", "RENAE", "ZELDA", "ELDA", "MA", "JUSTINA", "GUSSIE", "EMILIE", "CAMILLA", - "ABBIE", "ROCIO", "KAITLYN", "JESSE", "EDYTHE", "ASHLEIGH", "SELINA", "LAKESHA", "GERI", "ALLENE", - "PAMALA", "MICHAELA", "DAYNA", "CARYN", "ROSALIA", "SUN", "JACQULINE", "REBECA", "MARYBETH", "KRYSTLE", - "IOLA", "DOTTIE", "BENNIE", "BELLE", "AUBREY", "GRISELDA", "ERNESTINA", "ELIDA", "ADRIANNE", "DEMETRIA", - "DELMA", "CHONG", "JAQUELINE", "DESTINY", "ARLEEN", "VIRGINA", "RETHA", "FATIMA", "TILLIE", "ELEANORE", - "CARI", "TREVA", "BIRDIE", "WILHELMINA", "ROSALEE", "MAURINE", "LATRICE", "YONG", "JENA", "TARYN", - "ELIA", "DEBBY", "MAUDIE", "JEANNA", "DELILAH", "CATRINA", "SHONDA", "HORTENCIA", "THEODORA", "TERESITA", - "ROBBIN", "DANETTE", "MARYJANE", "FREDDIE", "DELPHINE", "BRIANNE", "NILDA", "DANNA", "CINDI", "BESS", - "IONA", "HANNA", "ARIEL", "WINONA", "VIDA", "ROSITA", "MARIANNA", "WILLIAM", "RACHEAL", "GUILLERMINA", - "ELOISA", "CELESTINE", "CAREN", "MALISSA", "LONA", "CHANTEL", "SHELLIE", "MARISELA", "LEORA", "AGATHA", - "SOLEDAD", "MIGDALIA", "IVETTE", "CHRISTEN", "ATHENA", "JANEL", "CHLOE", "VEDA", "PATTIE", "TESSIE", - "TERA", "MARILYNN", "LUCRETIA", "KARRIE", "DINAH", "DANIELA", "ALECIA", "ADELINA", "VERNICE", "SHIELA", - "PORTIA", "MERRY", "LASHAWN", "DEVON", "DARA", "TAWANA", "OMA", "VERDA", "CHRISTIN", "ALENE", - "ZELLA", "SANDI", "RAFAELA", "MAYA", "KIRA", "CANDIDA", "ALVINA", "SUZAN", "SHAYLA", "LYN", - "LETTIE", "ALVA", "SAMATHA", "ORALIA", "MATILDE", "MADONNA", "LARISSA", "VESTA", "RENITA", "INDIA", - "DELOIS", "SHANDA", "PHILLIS", "LORRI", "ERLINDA", "CRUZ", "CATHRINE", "BARB", "ZOE", "ISABELL", - "IONE", "GISELA", "CHARLIE", "VALENCIA", "ROXANNA", "MAYME", "KISHA", "ELLIE", "MELLISSA", "DORRIS", - "DALIA", "BELLA", "ANNETTA", "ZOILA", "RETA", "REINA", "LAURETTA", "KYLIE", "CHRISTAL", "PILAR", - "CHARLA", "ELISSA", "TIFFANI", "TANA", "PAULINA", "LEOTA", "BREANNA", "JAYME", "CARMEL", "VERNELL", - "TOMASA", "MANDI", "DOMINGA", "SANTA", "MELODIE", "LURA", "ALEXA", "TAMELA", "RYAN", "MIRNA", - "KERRIE", "VENUS", "NOEL", "FELICITA", "CRISTY", "CARMELITA", "BERNIECE", "ANNEMARIE", "TIARA", "ROSEANNE", - "MISSY", "CORI", "ROXANA", "PRICILLA", "KRISTAL", "JUNG", "ELYSE", "HAYDEE", "ALETHA", "BETTINA", - "MARGE", "GILLIAN", "FILOMENA", "CHARLES", "ZENAIDA", "HARRIETTE", "CARIDAD", "VADA", "UNA", "ARETHA", - "PEARLINE", "MARJORY", "MARCELA", "FLOR", "EVETTE", "ELOUISE", "ALINA", "TRINIDAD", "DAVID", "DAMARIS", - "CATHARINE", "CARROLL", "BELVA", "NAKIA", "MARLENA", "LUANNE", "LORINE", "KARON", "DORENE", "DANITA", - "BRENNA", "TATIANA", "SAMMIE", "LOUANN", "LOREN", "JULIANNA", "ANDRIA", "PHILOMENA", "LUCILA", "LEONORA", - "DOVIE", "ROMONA", "MIMI", "JACQUELIN", "GAYE", "TONJA", "MISTI", "JOE", "GENE", "CHASTITY", - "STACIA", "ROXANN", "MICAELA", "NIKITA", "MEI", "VELDA", "MARLYS", "JOHNNA", "AURA", "LAVERN", - "IVONNE", "HAYLEY", "NICKI", "MAJORIE", "HERLINDA", "GEORGE", "ALPHA", "YADIRA", "PERLA", "GREGORIA", - "DANIEL", "ANTONETTE", "SHELLI", "MOZELLE", "MARIAH", "JOELLE", "CORDELIA", "JOSETTE", "CHIQUITA", "TRISTA", - "LOUIS", "LAQUITA", "GEORGIANA", "CANDI", "SHANON", "LONNIE", "HILDEGARD", "CECIL", "VALENTINA", "STEPHANY", - "MAGDA", "KAROL", "GERRY", "GABRIELLA", "TIANA", "ROMA", "RICHELLE", "RAY", "PRINCESS", "OLETA", - "JACQUE", "IDELLA", "ALAINA", "SUZANNA", "JOVITA", "BLAIR", "TOSHA", "RAVEN", "NEREIDA", "MARLYN", - "KYLA", "JOSEPH", "DELFINA", "TENA", "STEPHENIE", "SABINA", "NATHALIE", "MARCELLE", "GERTIE", "DARLEEN", - "THEA", "SHARONDA", "SHANTEL", "BELEN", "VENESSA", "ROSALINA", "ONA", "GENOVEVA", "COREY", "CLEMENTINE", - "ROSALBA", "RENATE", "RENATA", "MI", "IVORY", "GEORGIANNA", "FLOY", "DORCAS", "ARIANA", "TYRA", - "THEDA", "MARIAM", "JULI", "JESICA", "DONNIE", "VIKKI", "VERLA", "ROSELYN", "MELVINA", "JANNETTE", - "GINNY", "DEBRAH", "CORRIE", "ASIA", "VIOLETA", "MYRTIS", "LATRICIA", "COLLETTE", "CHARLEEN", "ANISSA", - "VIVIANA", "TWYLA", "PRECIOUS", "NEDRA", "LATONIA", "LAN", "HELLEN", "FABIOLA", "ANNAMARIE", "ADELL", - "SHARYN", "CHANTAL", "NIKI", "MAUD", "LIZETTE", "LINDY", "KIA", "KESHA", "JEANA", "DANELLE", - "CHARLINE", "CHANEL", "CARROL", "VALORIE", "LIA", "DORTHA", "CRISTAL", "SUNNY", "LEONE", "LEILANI", - "GERRI", "DEBI", "ANDRA", "KESHIA", "IMA", "EULALIA", "EASTER", "DULCE", "NATIVIDAD", "LINNIE", - "KAMI", "GEORGIE", "CATINA", "BROOK", "ALDA", "WINNIFRED", "SHARLA", "RUTHANN", "MEAGHAN", "MAGDALENE", - "LISSETTE", "ADELAIDA", "VENITA", "TRENA", "SHIRLENE", "SHAMEKA", "ELIZEBETH", "DIAN", "SHANTA", "MICKEY", - "LATOSHA", "CARLOTTA", "WINDY", "SOON", "ROSINA", "MARIANN", "LEISA", "JONNIE", "DAWNA", "CATHIE", - "BILLY", "ASTRID", "SIDNEY", "LAUREEN", "JANEEN", "HOLLI", "FAWN", "VICKEY", "TERESSA", "SHANTE", - "RUBYE", "MARCELINA", "CHANDA", "CARY", "TERESE", "SCARLETT", "MARTY", "MARNIE", "LULU", "LISETTE", - "JENIFFER", "ELENOR", "DORINDA", "DONITA", "CARMAN", "BERNITA", "ALTAGRACIA", "ALETA", "ADRIANNA", "ZORAIDA", - "RONNIE", "NICOLA", "LYNDSEY", "KENDALL", "JANINA", "CHRISSY", "AMI", "STARLA", "PHYLIS", "PHUONG", - "KYRA", "CHARISSE", "BLANCH", "SANJUANITA", "RONA", "NANCI", "MARILEE", "MARANDA", "CORY", "BRIGETTE", - "SANJUANA", "MARITA", "KASSANDRA", "JOYCELYN", "IRA", "FELIPA", "CHELSIE", "BONNY", "MIREYA", "LORENZA", - "KYONG", "ILEANA", "CANDELARIA", "TONY", "TOBY", "SHERIE", "OK", "MARK", "LUCIE", "LEATRICE", - "LAKESHIA", "GERDA", "EDIE", "BAMBI", "MARYLIN", "LAVON", "HORTENSE", "GARNET", "EVIE", "TRESSA", - "SHAYNA", "LAVINA", "KYUNG", "JEANETTA", "SHERRILL", "SHARA", "PHYLISS", "MITTIE", "ANABEL", "ALESIA", - "THUY", "TAWANDA", "RICHARD", "JOANIE", "TIFFANIE", "LASHANDA", "KARISSA", "ENRIQUETA", "DARIA", "DANIELLA", - "CORINNA", "ALANNA", "ABBEY", "ROXANE", "ROSEANNA", "MAGNOLIA", "LIDA", "KYLE", "JOELLEN", "ERA", - "CORAL", "CARLEEN", "TRESA", "PEGGIE", "NOVELLA", "NILA", "MAYBELLE", "JENELLE", "CARINA", "NOVA", - "MELINA", "MARQUERITE", "MARGARETTE", "JOSEPHINA", "EVONNE", "DEVIN", "CINTHIA", "ALBINA", "TOYA", "TAWNYA", - "SHERITA", "SANTOS", "MYRIAM", "LIZABETH", "LISE", "KEELY", "JENNI", "GISELLE", "CHERYLE", "ARDITH", - "ARDIS", "ALESHA", "ADRIANE", "SHAINA", "LINNEA", "KAROLYN", "HONG", "FLORIDA", "FELISHA", "DORI", - "DARCI", "ARTIE", "ARMIDA", "ZOLA", "XIOMARA", "VERGIE", "SHAMIKA", "NENA", "NANNETTE", "MAXIE", - "LOVIE", "JEANE", "JAIMIE", "INGE", "FARRAH", "ELAINA", "CAITLYN", "STARR", "FELICITAS", "CHERLY", - "CARYL", "YOLONDA", "YASMIN", "TEENA", "PRUDENCE", "PENNIE", "NYDIA", "MACKENZIE", "ORPHA", "MARVEL", - "LIZBETH", "LAURETTE", "JERRIE", "HERMELINDA", "CAROLEE", "TIERRA", "MIRIAN", "META", "MELONY", "KORI", - "JENNETTE", "JAMILA", "ENA", "ANH", "YOSHIKO", "SUSANNAH", "SALINA", "RHIANNON", "JOLEEN", "CRISTINE", - "ASHTON", "ARACELY", "TOMEKA", "SHALONDA", "MARTI", "LACIE", "KALA", "JADA", "ILSE", "HAILEY", - "BRITTANI", "ZONA", "SYBLE", "SHERRYL", "RANDY", "NIDIA", "MARLO", "KANDICE", "KANDI", "DEB", - "DEAN", "AMERICA", "ALYCIA", "TOMMY", "RONNA", "NORENE", "MERCY", "JOSE", "INGEBORG", "GIOVANNA", - "GEMMA", "CHRISTEL", "AUDRY", "ZORA", "VITA", "VAN", "TRISH", "STEPHAINE", "SHIRLEE", "SHANIKA", - "MELONIE", "MAZIE", "JAZMIN", "INGA", "HOA", "HETTIE", "GERALYN", "FONDA", "ESTRELLA", "ADELLA", - "SU", "SARITA", "RINA", "MILISSA", "MARIBETH", "GOLDA", "EVON", "ETHELYN", "ENEDINA", "CHERISE", - "CHANA", "VELVA", "TAWANNA", "SADE", "MIRTA", "LI", "KARIE", "JACINTA", "ELNA", "DAVINA", - "CIERRA", "ASHLIE", "ALBERTHA", "TANESHA", "STEPHANI", "NELLE", "MINDI", "LU", "LORINDA", "LARUE", - "FLORENE", "DEMETRA", "DEDRA", "CIARA", "CHANTELLE", "ASHLY", "SUZY", "ROSALVA", "NOELIA", "LYDA", - "LEATHA", "KRYSTYNA", "KRISTAN", "KARRI", "DARLINE", "DARCIE", "CINDA", "CHEYENNE", "CHERRIE", "AWILDA", - "ALMEDA", "ROLANDA", "LANETTE", "JERILYN", "GISELE", "EVALYN", "CYNDI", "CLETA", "CARIN", "ZINA", - "ZENA", "VELIA", "TANIKA", "PAUL", "CHARISSA", "THOMAS", "TALIA", "MARGARETE", "LAVONDA", "KAYLEE", - "KATHLENE", "JONNA", "IRENA", "ILONA", "IDALIA", "CANDIS", "CANDANCE", "BRANDEE", "ANITRA", "ALIDA", - "SIGRID", "NICOLETTE", "MARYJO", "LINETTE", "HEDWIG", "CHRISTIANA", "CASSIDY", "ALEXIA", "TRESSIE", "MODESTA", - "LUPITA", "LITA", "GLADIS", "EVELIA", "DAVIDA", "CHERRI", "CECILY", "ASHELY", "ANNABEL", "AGUSTINA", - "WANITA", "SHIRLY", "ROSAURA", "HULDA", "EUN", "BAILEY", "YETTA", "VERONA", "THOMASINA", "SIBYL", - "SHANNAN", "MECHELLE", "LUE", "LEANDRA", "LANI", "KYLEE", "KANDY", "JOLYNN", "FERNE", "EBONI", - "CORENE", "ALYSIA", "ZULA", "NADA", "MOIRA", "LYNDSAY", "LORRETTA", "JUAN", "JAMMIE", "HORTENSIA", - "GAYNELL", "CAMERON", "ADRIA", "VINA", "VICENTA", "TANGELA", "STEPHINE", "NORINE", "NELLA", "LIANA", - "LESLEE", "KIMBERELY", "ILIANA", "GLORY", "FELICA", "EMOGENE", "ELFRIEDE", "EDEN", "EARTHA", "CARMA", - "BEA", "OCIE", "MARRY", "LENNIE", "KIARA", "JACALYN", "CARLOTA", "ARIELLE", "YU", "STAR", - "OTILIA", "KIRSTIN", "KACEY", "JOHNETTA", "JOEY", "JOETTA", "JERALDINE", "JAUNITA", "ELANA", "DORTHEA", - "CAMI", "AMADA", "ADELIA", "VERNITA", "TAMAR", "SIOBHAN", "RENEA", "RASHIDA", "OUIDA", "ODELL", - "NILSA", "MERYL", "KRISTYN", "JULIETA", "DANICA", "BREANNE", "AUREA", "ANGLEA", "SHERRON", "ODETTE", - "MALIA", "LORELEI", "LIN", "LEESA", "KENNA", "KATHLYN", "FIONA", "CHARLETTE", "SUZIE", "SHANTELL", - "SABRA", "RACQUEL", "MYONG", "MIRA", "MARTINE", "LUCIENNE", "LAVADA", "JULIANN", "JOHNIE", "ELVERA", - "DELPHIA", "CLAIR", "CHRISTIANE", "CHAROLETTE", "CARRI", "AUGUSTINE", "ASHA", "ANGELLA", "PAOLA", "NINFA", - "LEDA", "LAI", "EDA", "SUNSHINE", "STEFANI", "SHANELL", "PALMA", "MACHELLE", "LISSA", "KECIA", - "KATHRYNE", "KARLENE", "JULISSA", "JETTIE", "JENNIFFER", "HUI", "CORRINA", "CHRISTOPHER", "CAROLANN", "ALENA", - "TESS", "ROSARIA", "MYRTICE", "MARYLEE", "LIANE", "KENYATTA", "JUDIE", "JANEY", "IN", "ELMIRA", - "ELDORA", "DENNA", "CRISTI", "CATHI", "ZAIDA", "VONNIE", "VIVA", "VERNIE", "ROSALINE", "MARIELA", - "LUCIANA", "LESLI", "KARAN", "FELICE", "DENEEN", "ADINA", "WYNONA", "TARSHA", "SHERON", "SHASTA", - "SHANITA", "SHANI", "SHANDRA", "RANDA", "PINKIE", "PARIS", "NELIDA", "MARILOU", "LYLA", "LAURENE", - "LACI", "JOI", "JANENE", "DOROTHA", "DANIELE", "DANI", "CAROLYNN", "CARLYN", "BERENICE", "AYESHA", - "ANNELIESE", "ALETHEA", "THERSA", "TAMIKO", "RUFINA", "OLIVA", "MOZELL", "MARYLYN", "MADISON", "KRISTIAN", - "KATHYRN", "KASANDRA", "KANDACE", "JANAE", "GABRIEL", "DOMENICA", "DEBBRA", "DANNIELLE", "CHUN", "BUFFY", - "BARBIE", "ARCELIA", "AJA", "ZENOBIA", "SHAREN", "SHAREE", "PATRICK", "PAGE", "MY", "LAVINIA", - "KUM", "KACIE", "JACKELINE", "HUONG", "FELISA", "EMELIA", "ELEANORA", "CYTHIA", "CRISTIN", "CLYDE", - "CLARIBEL", "CARON", "ANASTACIA", "ZULMA", "ZANDRA", "YOKO", "TENISHA", "SUSANN", "SHERILYN", "SHAY", - "SHAWANDA", "SABINE", "ROMANA", "MATHILDA", "LINSEY", "KEIKO", "JOANA", "ISELA", "GRETTA", "GEORGETTA", - "EUGENIE", "DUSTY", "DESIRAE", "DELORA", "CORAZON", "ANTONINA", "ANIKA", "WILLENE", "TRACEE", "TAMATHA", - "REGAN", "NICHELLE", "MICKIE", "MAEGAN", "LUANA", "LANITA", "KELSIE", "EDELMIRA", "BREE", "AFTON", - "TEODORA", "TAMIE", "SHENA", "MEG", "LINH", "KELI", "KACI", "DANYELLE", "BRITT", "ARLETTE", - "ALBERTINE", "ADELLE", "TIFFINY", "STORMY", "SIMONA", "NUMBERS", "NICOLASA", "NICHOL", "NIA", "NAKISHA", - "MEE", "MAIRA", "LOREEN", "KIZZY", "JOHNNY", "JAY", "FALLON", "CHRISTENE", "BOBBYE", "ANTHONY", - "YING", "VINCENZA", "TANJA", "RUBIE", "RONI", "QUEENIE", "MARGARETT", "KIMBERLI", "IRMGARD", "IDELL", - "HILMA", "EVELINA", "ESTA", "EMILEE", "DENNISE", "DANIA", "CARL", "CARIE", "ANTONIO", "WAI", - "SANG", "RISA", "RIKKI", "PARTICIA", "MUI", "MASAKO", "MARIO", "LUVENIA", "LOREE", "LONI", - "LIEN", "KEVIN", "GIGI", "FLORENCIA", "DORIAN", "DENITA", "DALLAS", "CHI", "BILLYE", "ALEXANDER", - "TOMIKA", "SHARITA", "RANA", "NIKOLE", "NEOMA", "MARGARITE", "MADALYN", "LUCINA", "LAILA", "KALI", - "JENETTE", "GABRIELE", "EVELYNE", "ELENORA", "CLEMENTINA", "ALEJANDRINA", "ZULEMA", "VIOLETTE", "VANNESSA", "THRESA", - "RETTA", "PIA", "PATIENCE", "NOELLA", "NICKIE", "JONELL", "DELTA", "CHUNG", "CHAYA", "CAMELIA", - "BETHEL", "ANYA", "ANDREW", "THANH", "SUZANN", "SPRING", "SHU", "MILA", "LILLA", "LAVERNA", - "KEESHA", "KATTIE", "GIA", "GEORGENE", "EVELINE", "ESTELL", "ELIZBETH", "VIVIENNE", "VALLIE", "TRUDIE", - "STEPHANE", "MICHEL", "MAGALY", "MADIE", "KENYETTA", "KARREN", "JANETTA", "HERMINE", "HARMONY", "DRUCILLA", - "DEBBI", "CELESTINA", "CANDIE", "BRITNI", "BECKIE", "AMINA", "ZITA", "YUN", "YOLANDE", "VIVIEN", - "VERNETTA", "TRUDI", "SOMMER", "PEARLE", "PATRINA", "OSSIE", "NICOLLE", "LOYCE", "LETTY", "LARISA", - "KATHARINA", "JOSELYN", "JONELLE", "JENELL", "IESHA", "HEIDE", "FLORINDA", "FLORENTINA", "FLO", "ELODIA", - "DORINE", "BRUNILDA", "BRIGID", "ASHLI", "ARDELLA", "TWANA", "THU", "TARAH", "SUNG", "SHEA", - "SHAVON", "SHANE", "SERINA", "RAYNA", "RAMONITA", "NGA", "MARGURITE", "LUCRECIA", "KOURTNEY", "KATI", - "JESUS", "JESENIA", "DIAMOND", "CRISTA", "AYANA", "ALICA", "ALIA", "VINNIE", "SUELLEN", "ROMELIA", - "RACHELL", "PIPER", "OLYMPIA", "MICHIKO", "KATHALEEN", "JOLIE", "JESSI", "JANESSA", "HANA", "HA", - "ELEASE", "CARLETTA", "BRITANY", "SHONA", "SALOME", "ROSAMOND", "REGENA", "RAINA", "NGOC", "NELIA", - "LOUVENIA", "LESIA", "LATRINA", "LATICIA", "LARHONDA", "JINA", "JACKI", "HOLLIS", "HOLLEY", "EMMY", - "DEEANN", "CORETTA", "ARNETTA", "VELVET", "THALIA", "SHANICE", "NETA", "MIKKI", "MICKI", "LONNA", - "LEANA", "LASHUNDA", "KILEY", "JOYE", "JACQULYN", "IGNACIA", "HYUN", "HIROKO", "HENRY", "HENRIETTE", - "ELAYNE", "DELINDA", "DARNELL", "DAHLIA", "COREEN", "CONSUELA", "CONCHITA", "CELINE", "BABETTE", "AYANNA", - "ANETTE", "ALBERTINA", "SKYE", "SHAWNEE", "SHANEKA", "QUIANA", "PAMELIA", "MIN", "MERRI", "MERLENE", - "MARGIT", "KIESHA", "KIERA", "KAYLENE", "JODEE", "JENISE", "ERLENE", "EMMIE", "ELSE", "DARYL", - "DALILA", "DAISEY", "CODY", "CASIE", "BELIA", "BABARA", "VERSIE", "VANESA", "SHELBA", "SHAWNDA", - "SAM", "NORMAN", "NIKIA", "NAOMA", "MARNA", "MARGERET", "MADALINE", "LAWANA", "KINDRA", "JUTTA", - "JAZMINE", "JANETT", "HANNELORE", "GLENDORA", "GERTRUD", "GARNETT", "FREEDA", "FREDERICA", "FLORANCE", "FLAVIA", - "DENNIS", "CARLINE", "BEVERLEE", "ANJANETTE", "VALDA", "TRINITY", "TAMALA", "STEVIE", "SHONNA", "SHA", - "SARINA", "ONEIDA", "MICAH", "MERILYN", "MARLEEN", "LURLINE", "LENNA", "KATHERIN", "JIN", "JENI", - "HAE", "GRACIA", "GLADY", "FARAH", "ERIC", "ENOLA", "EMA", "DOMINQUE", "DEVONA", "DELANA", - "CECILA", "CAPRICE", "ALYSHA", "ALI", "ALETHIA", "VENA", "THERESIA", "TAWNY", "SONG", "SHAKIRA", - "SAMARA", "SACHIKO", "RACHELE", "PAMELLA", "NICKY", "MARNI", "MARIEL", "MAREN", "MALISA", "LIGIA", - "LERA", "LATORIA", "LARAE", "KIMBER", "KATHERN", "KAREY", "JENNEFER", "JANETH", "HALINA", "FREDIA", - "DELISA", "DEBROAH", "CIERA", "CHIN", "ANGELIKA", "ANDREE", "ALTHA", "YEN", "VIVAN", "TERRESA", - "TANNA", "SUK", "SUDIE", "SOO", "SIGNE", "SALENA", "RONNI", "REBBECCA", "MYRTIE", "MCKENZIE", - "MALIKA", "MAIDA", "LOAN", "LEONARDA", "KAYLEIGH", "FRANCE", "ETHYL", "ELLYN", "DAYLE", "CAMMIE", - "BRITTNI", "BIRGIT", "AVELINA", "ASUNCION", "ARIANNA", "AKIKO", "VENICE", "TYESHA", "TONIE", "TIESHA", - "TAKISHA", "STEFFANIE", "SINDY", "SANTANA", "MEGHANN", "MANDA", "MACIE", "LADY", "KELLYE", "KELLEE", - "JOSLYN", "JASON", "INGER", "INDIRA", "GLINDA", "GLENNIS", "FERNANDA", "FAUSTINA", "ENEIDA", "ELICIA", - "DOT", "DIGNA", "DELL", "ARLETTA", "ANDRE", "WILLIA", "TAMMARA", "TABETHA", "SHERRELL", "SARI", - "REFUGIO", "REBBECA", "PAULETTA", "NIEVES", "NATOSHA", "NAKITA", "MAMMIE", "KENISHA", "KAZUKO", "KASSIE", - "GARY", "EARLEAN", "DAPHINE", "CORLISS", "CLOTILDE", "CAROLYNE", "BERNETTA", "AUGUSTINA", "AUDREA", "ANNIS", - "ANNABELL", "YAN", "TENNILLE", "TAMICA", "SELENE", "SEAN", "ROSANA", "REGENIA", "QIANA", "MARKITA", - "MACY", "LEEANNE", "LAURINE", "KYM", "JESSENIA", "JANITA", "GEORGINE", "GENIE", "EMIKO", "ELVIE", - "DEANDRA", "DAGMAR", "CORIE", "COLLEN", "CHERISH", "ROMAINE", "PORSHA", "PEARLENE", "MICHELINE", "MERNA", - "MARGORIE", "MARGARETTA", "LORE", "KENNETH", "JENINE", "HERMINA", "FREDERICKA", "ELKE", "DRUSILLA", "DORATHY", - "DIONE", "DESIRE", "CELENA", "BRIGIDA", "ANGELES", "ALLEGRA", "THEO", "TAMEKIA", "SYNTHIA", "STEPHEN", - "SOOK", "SLYVIA", "ROSANN", "REATHA", "RAYE", "MARQUETTA", "MARGART", "LING", "LAYLA", "KYMBERLY", - "KIANA", "KAYLEEN", "KATLYN", "KARMEN", "JOELLA", "IRINA", "EMELDA", "ELENI", "DETRA", "CLEMMIE", - "CHERYLL", "CHANTELL", "CATHEY", "ARNITA", "ARLA", "ANGLE", "ANGELIC", "ALYSE", "ZOFIA", "THOMASINE", - "TENNIE", "SON", "SHERLY", "SHERLEY", "SHARYL", "REMEDIOS", "PETRINA", "NICKOLE", "MYUNG", "MYRLE", - "MOZELLA", "LOUANNE", "LISHA", "LATIA", "LANE", "KRYSTA", "JULIENNE", "JOEL", "JEANENE", "JACQUALINE", - "ISAURA", "GWENDA", "EARLEEN", "DONALD", "CLEOPATRA", "CARLIE", "AUDIE", "ANTONIETTA", "ALISE", "ALEX", - "VERDELL", "VAL", "TYLER", "TOMOKO", "THAO", "TALISHA", "STEVEN", "SO", "SHEMIKA", "SHAUN", - "SCARLET", "SAVANNA", "SANTINA", "ROSIA", "RAEANN", "ODILIA", "NANA", "MINNA", "MAGAN", "LYNELLE", - "LE", "KARMA", "JOEANN", "IVANA", "INELL", "ILANA", "HYE", "HONEY", "HEE", "GUDRUN", - "FRANK", "DREAMA", "CRISSY", "CHANTE", "CARMELINA", "ARVILLA", "ARTHUR", "ANNAMAE", "ALVERA", "ALEIDA", - "AARON", "YEE", "YANIRA", "VANDA", "TIANNA", "TAM", "STEFANIA", "SHIRA", "PERRY", "NICOL", - "NANCIE", "MONSERRATE", "MINH", "MELYNDA", "MELANY", "MATTHEW", "LOVELLA", "LAURE", "KIRBY", "KACY", - "JACQUELYNN", "HYON", "GERTHA", "FRANCISCO", "ELIANA", "CHRISTENA", "CHRISTEEN", "CHARISE", "CATERINA", "CARLEY", - "CANDYCE", "ARLENA", "AMMIE", "YANG", "WILLETTE", "VANITA", "TUYET", "TINY", "SYREETA", "SILVA", - "SCOTT", "RONALD", "PENNEY", "NYLA", "MICHAL", "MAURICE", "MARYAM", "MARYA", "MAGEN", "LUDIE", - "LOMA", "LIVIA", "LANELL", "KIMBERLIE", "JULEE", "DONETTA", "DIEDRA", "DENISHA", "DEANE", "DAWNE", - "CLARINE", "CHERRYL", "BRONWYN", "BRANDON", "ALLA", "VALERY", "TONDA", "SUEANN", "SORAYA", "SHOSHANA", - "SHELA", "SHARLEEN", "SHANELLE", "NERISSA", "MICHEAL", "MERIDITH", "MELLIE", "MAYE", "MAPLE", "MAGARET", - "LUIS", "LILI", "LEONILA", "LEONIE", "LEEANNA", "LAVONIA", "LAVERA", "KRISTEL", "KATHEY", "KATHE", - "JUSTIN", "JULIAN", "JIMMY", "JANN", "ILDA", "HILDRED", "HILDEGARDE", "GENIA", "FUMIKO", "EVELIN", - "ERMELINDA", "ELLY", "DUNG", "DOLORIS", "DIONNA", "DANAE", "BERNEICE", "ANNICE", "ALIX", "VERENA", - "VERDIE", "TRISTAN", "SHAWNNA", "SHAWANA", "SHAUNNA", "ROZELLA", "RANDEE", "RANAE", "MILAGRO", "LYNELL", - "LUISE", "LOUIE", "LOIDA", "LISBETH", "KARLEEN", "JUNITA", "JONA", "ISIS", "HYACINTH", "HEDY", - "GWENN", "ETHELENE", "ERLINE", "EDWARD", "DONYA", "DOMONIQUE", "DELICIA", "DANNETTE", "CICELY", "BRANDA", - "BLYTHE", "BETHANN", "ASHLYN", "ANNALEE", "ALLINE", "YUKO", "VELLA", "TRANG", "TOWANDA", "TESHA", - "SHERLYN", "NARCISA", "MIGUELINA", "MERI", "MAYBELL", "MARLANA", "MARGUERITA", "MADLYN", "LUNA", "LORY", - "LORIANN", "LIBERTY", "LEONORE", "LEIGHANN", "LAURICE", "LATESHA", "LARONDA", "KATRICE", "KASIE", "KARL", - "KALEY", "JADWIGA", "GLENNIE", "GEARLDINE", "FRANCINA", "EPIFANIA", "DYAN", "DORIE", "DIEDRE", "DENESE", - "DEMETRICE", "DELENA", "DARBY", "CRISTIE", "CLEORA", "CATARINA", "CARISA", "BERNIE", "BARBERA", "ALMETA", - "TRULA", "TEREASA", "SOLANGE", "SHEILAH", "SHAVONNE", "SANORA", "ROCHELL", "MATHILDE", "MARGARETA", "MAIA", - "LYNSEY", "LAWANNA", "LAUNA", "KENA", "KEENA", "KATIA", "JAMEY", "GLYNDA", "GAYLENE", "ELVINA", - "ELANOR", "DANUTA", "DANIKA", "CRISTEN", "CORDIE", "COLETTA", "CLARITA", "CARMON", "BRYNN", "AZUCENA", - "AUNDREA", "ANGELE", "YI", "WALTER", "VERLIE", "VERLENE", "TAMESHA", "SILVANA", "SEBRINA", "SAMIRA", - "REDA", "RAYLENE", "PENNI", "PANDORA", "NORAH", "NOMA", "MIREILLE", "MELISSIA", "MARYALICE", "LARAINE", - "KIMBERY", "KARYL", "KARINE", "KAM", "JOLANDA", "JOHANA", "JESUSA", "JALEESA", "JAE", "JACQUELYNE", - "IRISH", "ILUMINADA", "HILARIA", "HANH", "GENNIE", "FRANCIE", "FLORETTA", "EXIE", "EDDA", "DREMA", - "DELPHA", "BEV", "BARBAR", "ASSUNTA", "ARDELL", "ANNALISA", "ALISIA", "YUKIKO", "YOLANDO", "WONDA", - "WEI", "WALTRAUD", "VETA", "TEQUILA", "TEMEKA", "TAMEIKA", "SHIRLEEN", "SHENITA", "PIEDAD", "OZELLA", - "MIRTHA", "MARILU", "KIMIKO", "JULIANE", "JENICE", "JEN", "JANAY", "JACQUILINE", "HILDE", "FE", - "FAE", "EVAN", "EUGENE", "ELOIS", "ECHO", "DEVORAH", "CHAU", "BRINDA", "BETSEY", "ARMINDA", - "ARACELIS", "APRYL", "ANNETT", "ALISHIA", "VEOLA", "USHA", "TOSHIKO", "THEOLA", "TASHIA", "TALITHA", - "SHERY", "RUDY", "RENETTA", "REIKO", "RASHEEDA", "OMEGA", "OBDULIA", "MIKA", "MELAINE", "MEGGAN", - "MARTIN", "MARLEN", "MARGET", "MARCELINE", "MANA", "MAGDALEN", "LIBRADA", "LEZLIE", "LEXIE", "LATASHIA", - "LASANDRA", "KELLE", "ISIDRA", "ISA", "INOCENCIA", "GWYN", "FRANCOISE", "ERMINIA", "ERINN", "DIMPLE", - "DEVORA", "CRISELDA", "ARMANDA", "ARIE", "ARIANE", "ANGELO", "ANGELENA", "ALLEN", "ALIZA", "ADRIENE", - "ADALINE", "XOCHITL", "TWANNA", "TRAN", "TOMIKO", "TAMISHA", "TAISHA", "SUSY", "SIU", "RUTHA", - "ROXY", "RHONA", "RAYMOND", "OTHA", "NORIKO", "NATASHIA", "MERRIE", "MELVIN", "MARINDA", "MARIKO", - "MARGERT", "LORIS", "LIZZETTE", "LEISHA", "KAILA", "KA", "JOANNIE", "JERRICA", "JENE", "JANNET", - "JANEE", "JACINDA", "HERTA", "ELENORE", "DORETTA", "DELAINE", "DANIELL", "CLAUDIE", "CHINA", "BRITTA", - "APOLONIA", "AMBERLY", "ALEASE", "YURI", "YUK", "WEN", "WANETA", "UTE", "TOMI", "SHARRI", - "SANDIE", "ROSELLE", "REYNALDA", "RAGUEL", "PHYLICIA", "PATRIA", "OLIMPIA", "ODELIA", "MITZIE", "MITCHELL", - "MISS", "MINDA", "MIGNON", "MICA", "MENDY", "MARIVEL", "MAILE", "LYNETTA", "LAVETTE", "LAURYN", - "LATRISHA", "LAKIESHA", "KIERSTEN", "KARY", "JOSPHINE", "JOLYN", "JETTA", "JANISE", "JACQUIE", "IVELISSE", - "GLYNIS", "GIANNA", "GAYNELLE", "EMERALD", "DEMETRIUS", "DANYELL", "DANILLE", "DACIA", "CORALEE", "CHER", - "CEOLA", "BRETT", "BELL", "ARIANNE", "ALESHIA", "YUNG", "WILLIEMAE", "TROY", "TRINH", "THORA", - "TAI", "SVETLANA", "SHERIKA", "SHEMEKA", "SHAUNDA", "ROSELINE", "RICKI", "MELDA", "MALLIE", "LAVONNA", - "LATINA", "LARRY", "LAQUANDA", "LALA", "LACHELLE", "KLARA", "KANDIS", "JOHNA", "JEANMARIE", "JAYE", - "HANG", "GRAYCE", "GERTUDE", "EMERITA", "EBONIE", "CLORINDA", "CHING", "CHERY", "CAROLA", "BREANN", - "BLOSSOM", "BERNARDINE", "BECKI", "ARLETHA", "ARGELIA", "ARA", "ALITA", "YULANDA", "YON", "YESSENIA", - "TOBI", "TASIA", "SYLVIE", "SHIRL", "SHIRELY", "SHERIDAN", "SHELLA", "SHANTELLE", "SACHA", "ROYCE", - "REBECKA", "REAGAN", "PROVIDENCIA", "PAULENE", "MISHA", "MIKI", "MARLINE", "MARICA", "LORITA", "LATOYIA", - "LASONYA", "KERSTIN", "KENDA", "KEITHA", "KATHRIN", "JAYMIE", "JACK", "GRICELDA", "GINETTE", "ERYN", - "ELINA", "ELFRIEDA", "DANYEL", "CHEREE", "CHANELLE", "BARRIE", "AVERY", "AURORE", "ANNAMARIA", "ALLEEN", - "AILENE", "AIDE", "YASMINE", "VASHTI", "VALENTINE", "TREASA", "TORY", "TIFFANEY", "SHERYLL", "SHARIE", - "SHANAE", "SAU", "RAISA", "PA", "NEDA", "MITSUKO", "MIRELLA", "MILDA", "MARYANNA", "MARAGRET", - "MABELLE", "LUETTA", "LORINA", "LETISHA", "LATARSHA", "LANELLE", "LAJUANA", "KRISSY", "KARLY", "KARENA", - "JON", "JESSIKA", "JERICA", "JEANELLE", "JANUARY", "JALISA", "JACELYN", "IZOLA", "IVEY", "GREGORY", - "EUNA", "ETHA", "DREW", "DOMITILA", "DOMINICA", "DAINA", "CREOLA", "CARLI", "CAMIE", "BUNNY", - "BRITTNY", "ASHANTI", "ANISHA", "ALEEN", "ADAH", "YASUKO", "WINTER", "VIKI", "VALRIE", "TONA", - "TINISHA", "THI", "TERISA", "TATUM", "TANEKA", "SIMONNE", "SHALANDA", "SERITA", "RESSIE", "REFUGIA", - "PAZ", "OLENE", "NA", "MERRILL", "MARGHERITA", "MANDIE", "MAN", "MAIRE", "LYNDIA", "LUCI", - "LORRIANE", "LORETA", "LEONIA", "LAVONA", "LASHAWNDA", "LAKIA", "KYOKO", "KRYSTINA", "KRYSTEN", "KENIA", - "KELSI", "JUDE", "JEANICE", "ISOBEL", "GEORGIANN", "GENNY", "FELICIDAD", "EILENE", "DEON", "DELOISE", - "DEEDEE", "DANNIE", "CONCEPTION", "CLORA", "CHERILYN", "CHANG", "CALANDRA", "BERRY", "ARMANDINA", "ANISA", - "ULA", "TIMOTHY", "TIERA", "THERESSA", "STEPHANIA", "SIMA", "SHYLA", "SHONTA", "SHERA", "SHAQUITA", - "SHALA", "SAMMY", "ROSSANA", "NOHEMI", "NERY", "MORIAH", "MELITA", "MELIDA", "MELANI", "MARYLYNN", - "MARISHA", "MARIETTE", "MALORIE", "MADELENE", "LUDIVINA", "LORIA", "LORETTE", "LORALEE", "LIANNE", "LEON", - "LAVENIA", "LAURINDA", "LASHON", "KIT", "KIMI", "KEILA", "KATELYNN", "KAI", "JONE", "JOANE", - "JI", "JAYNA", "JANELLA", "JA", "HUE", "HERTHA", "FRANCENE", "ELINORE", "DESPINA", "DELSIE", - "DEEDRA", "CLEMENCIA", "CARRY", "CAROLIN", "CARLOS", "BULAH", "BRITTANIE", "BOK", "BLONDELL", "BIBI", - "BEAULAH", "BEATA", "ANNITA", "AGRIPINA", "VIRGEN", "VALENE", "UN", "TWANDA", "TOMMYE", "TOI", - "TARRA", "TARI", "TAMMERA", "SHAKIA", "SADYE", "RUTHANNE", "ROCHEL", "RIVKA", "PURA", "NENITA", - "NATISHA", "MING", "MERRILEE", "MELODEE", "MARVIS", "LUCILLA", "LEENA", "LAVETA", "LARITA", "LANIE", - "KEREN", "ILEEN", "GEORGEANN", "GENNA", "GENESIS", "FRIDA", "EWA", "EUFEMIA", "EMELY", "ELA", - "EDYTH", "DEONNA", "DEADRA", "DARLENA", "CHANELL", "CHAN", "CATHERN", "CASSONDRA", "CASSAUNDRA", "BERNARDA", - "BERNA", "ARLINDA", "ANAMARIA", "ALBERT", "WESLEY", "VERTIE", "VALERI", "TORRI", "TATYANA", "STASIA", - "SHERISE", "SHERILL", "SEASON", "SCOTTIE", "SANDA", "RUTHE", "ROSY", "ROBERTO", "ROBBI", "RANEE", - "QUYEN", "PEARLY", "PALMIRA", "ONITA", "NISHA", "NIESHA", "NIDA", "NEVADA", "NAM", "MERLYN", - "MAYOLA", "MARYLOUISE", "MARYLAND", "MARX", "MARTH", "MARGENE", "MADELAINE", "LONDA", "LEONTINE", "LEOMA", - "LEIA", "LAWRENCE", "LAURALEE", "LANORA", "LAKITA", "KIYOKO", "KETURAH", "KATELIN", "KAREEN", "JONIE", - "JOHNETTE", "JENEE", "JEANETT", "IZETTA", "HIEDI", "HEIKE", "HASSIE", "HAROLD", "GIUSEPPINA", "GEORGANN", - "FIDELA", "FERNANDE", "ELWANDA", "ELLAMAE", "ELIZ", "DUSTI", "DOTTY", "CYNDY", "CORALIE", "CELESTA", - "ARGENTINA", "ALVERTA", "XENIA", "WAVA", "VANETTA", "TORRIE", "TASHINA", "TANDY", "TAMBRA", "TAMA", - "STEPANIE", "SHILA", "SHAUNTA", "SHARAN", "SHANIQUA", "SHAE", "SETSUKO", "SERAFINA", "SANDEE", "ROSAMARIA", - "PRISCILA", "OLINDA", "NADENE", "MUOI", "MICHELINA", "MERCEDEZ", "MARYROSE", "MARIN", "MARCENE", "MAO", - "MAGALI", "MAFALDA", "LOGAN", "LINN", "LANNIE", "KAYCE", "KAROLINE", "KAMILAH", "KAMALA", "JUSTA", - "JOLINE", "JENNINE", "JACQUETTA", "IRAIDA", "GERALD", "GEORGEANNA", "FRANCHESCA", "FAIRY", "EMELINE", "ELANE", - "EHTEL", "EARLIE", "DULCIE", "DALENE", "CRIS", "CLASSIE", "CHERE", "CHARIS", "CAROYLN", "CARMINA", - "CARITA", "BRIAN", "BETHANIE", "AYAKO", "ARICA", "AN", "ALYSA", "ALESSANDRA", "AKILAH", "ADRIEN", - "ZETTA", "YOULANDA", "YELENA", "YAHAIRA", "XUAN", "WENDOLYN", "VICTOR", "TIJUANA", "TERRELL", "TERINA", - "TERESIA", "SUZI", "SUNDAY", "SHERELL", "SHAVONDA", "SHAUNTE", "SHARDA", "SHAKITA", "SENA", "RYANN", - "RUBI", "RIVA", "REGINIA", "REA", "RACHAL", "PARTHENIA", "PAMULA", "MONNIE", "MONET", "MICHAELE", - "MELIA", "MARINE", "MALKA", "MAISHA", "LISANDRA", "LEO", "LEKISHA", "LEAN", "LAURENCE", "LAKENDRA", - "KRYSTIN", "KORTNEY", "KIZZIE", "KITTIE", "KERA", "KENDAL", "KEMBERLY", "KANISHA", "JULENE", "JULE", - "JOSHUA", "JOHANNE", "JEFFREY", "JAMEE", "HAN", "HALLEY", "GIDGET", "GALINA", "FREDRICKA", "FLETA", - "FATIMAH", "EUSEBIA", "ELZA", "ELEONORE", "DORTHEY", "DORIA", "DONELLA", "DINORAH", "DELORSE", "CLARETHA", - "CHRISTINIA", "CHARLYN", "BONG", "BELKIS", "AZZIE", "ANDERA", "AIKO", "ADENA", "YER", "YAJAIRA", - "WAN", "VANIA", "ULRIKE", "TOSHIA", "TIFANY", "STEFANY", "SHIZUE", "SHENIKA", "SHAWANNA", "SHAROLYN", - "SHARILYN", "SHAQUANA", "SHANTAY", "SEE", "ROZANNE", "ROSELEE", "RICKIE", "REMONA", "REANNA", "RAELENE", - "QUINN", "PHUNG", "PETRONILA", "NATACHA", "NANCEY", "MYRL", "MIYOKO", "MIESHA", "MERIDETH", "MARVELLA", - "MARQUITTA", "MARHTA", "MARCHELLE", "LIZETH", "LIBBIE", "LAHOMA", "LADAWN", "KINA", "KATHELEEN", "KATHARYN", - "KARISA", "KALEIGH", "JUNIE", "JULIEANN", "JOHNSIE", "JANEAN", "JAIMEE", "JACKQUELINE", "HISAKO", "HERMA", - "HELAINE", "GWYNETH", "GLENN", "GITA", "EUSTOLIA", "EMELINA", "ELIN", "EDRIS", "DONNETTE", "DONNETTA", - "DIERDRE", "DENAE", "DARCEL", "CLAUDE", "CLARISA", "CINDERELLA", "CHIA", "CHARLESETTA", "CHARITA", "CELSA", - "CASSY", "CASSI", "CARLEE", "BRUNA", "BRITTANEY", "BRANDE", "BILLI", "BAO", "ANTONETTA", "ANGLA", - "ANGELYN", "ANALISA", "ALANE", "WENONA", "WENDIE", "VERONIQUE", "VANNESA", "TOBIE", "TEMPIE", "SUMIKO", - "SULEMA", "SPARKLE", "SOMER", "SHEBA", "SHAYNE", "SHARICE", "SHANEL", "SHALON", "SAGE", "ROY", - "ROSIO", "ROSELIA", "RENAY", "REMA", "REENA", "PORSCHE", "PING", "PEG", "OZIE", "ORETHA", - "ORALEE", "ODA", "NU", "NGAN", "NAKESHA", "MILLY", "MARYBELLE", "MARLIN", "MARIS", "MARGRETT", - "MARAGARET", "MANIE", "LURLENE", "LILLIA", "LIESELOTTE", "LAVELLE", "LASHAUNDA", "LAKEESHA", "KEITH", "KAYCEE", - "KALYN", "JOYA", "JOETTE", "JENAE", "JANIECE", "ILLA", "GRISEL", "GLAYDS", "GENEVIE", "GALA", - "FREDDA", "FRED", "ELMER", "ELEONOR", "DEBERA", "DEANDREA", "DAN", "CORRINNE", "CORDIA", "CONTESSA", - "COLENE", "CLEOTILDE", "CHARLOTT", "CHANTAY", "CECILLE", "BEATRIS", "AZALEE", "ARLEAN", "ARDATH", "ANJELICA", - "ANJA", "ALFREDIA", "ALEISHA", "ADAM", "ZADA", "YUONNE", "XIAO", "WILLODEAN", "WHITLEY", "VENNIE", - "VANNA", "TYISHA", "TOVA", "TORIE", "TONISHA", "TILDA", "TIEN", "TEMPLE", "SIRENA", "SHERRIL", - "SHANTI", "SHAN", "SENAIDA", "SAMELLA", "ROBBYN", "RENDA", "REITA", "PHEBE", "PAULITA", "NOBUKO", - "NGUYET", "NEOMI", "MOON", "MIKAELA", "MELANIA", "MAXIMINA", "MARG", "MAISIE", "LYNNA", "LILLI", - "LAYNE", "LASHAUN", "LAKENYA", "LAEL", "KIRSTIE", "KATHLINE", "KASHA", "KARLYN", "KARIMA", "JOVAN", - "JOSEFINE", "JENNELL", "JACQUI", "JACKELYN", "HYO", "HIEN", "GRAZYNA", "FLORRIE", "FLORIA", "ELEONORA", - "DWANA", "DORLA", "DONG", "DELMY", "DEJA", "DEDE", "DANN", "CRYSTA", "CLELIA", "CLARIS", - "CLARENCE", "CHIEKO", "CHERLYN", "CHERELLE", "CHARMAIN", "CHARA", "CAMMY", "BEE", "ARNETTE", "ARDELLE", - "ANNIKA", "AMIEE", "AMEE", "ALLENA", "YVONE", "YUKI", "YOSHIE", "YEVETTE", "YAEL", "WILLETTA", - "VONCILE", "VENETTA", "TULA", "TONETTE", "TIMIKA", "TEMIKA", "TELMA", "TEISHA", "TAREN", "TA", - "STACEE", "SHIN", "SHAWNTA", "SATURNINA", "RICARDA", "POK", "PASTY", "ONIE", "NUBIA", "MORA", - "MIKE", "MARIELLE", "MARIELLA", "MARIANELA", "MARDELL", "MANY", "LUANNA", "LOISE", "LISABETH", "LINDSY", - "LILLIANA", "LILLIAM", "LELAH", "LEIGHA", "LEANORA", "LANG", "KRISTEEN", "KHALILAH", "KEELEY", "KANDRA", - "JUNKO", "JOAQUINA", "JERLENE", "JANI", "JAMIKA", "JAME", "HSIU", "HERMILA", "GOLDEN", "GENEVIVE", - "EVIA", "EUGENA", "EMMALINE", "ELFREDA", "ELENE", "DONETTE", "DELCIE", "DEEANNA", "DARCEY", "CUC", - "CLARINDA", "CIRA", "CHAE", "CELINDA", "CATHERYN", "CATHERIN", "CASIMIRA", "CARMELIA", "CAMELLIA", "BREANA", - "BOBETTE", "BERNARDINA", "BEBE", "BASILIA", "ARLYNE", "AMAL", "ALAYNA", "ZONIA", "ZENIA", "YURIKO", - "YAEKO", "WYNELL", "WILLOW", "WILLENA", "VERNIA", "TU", "TRAVIS", "TORA", "TERRILYN", "TERICA", - "TENESHA", "TAWNA", "TAJUANA", "TAINA", "STEPHNIE", "SONA", "SOL", "SINA", "SHONDRA", "SHIZUKO", - "SHERLENE", "SHERICE", "SHARIKA", "ROSSIE", "ROSENA", "RORY", "RIMA", "RIA", "RHEBA", "RENNA", - "PETER", "NATALYA", "NANCEE", "MELODI", "MEDA", "MAXIMA", "MATHA", "MARKETTA", "MARICRUZ", "MARCELENE", - "MALVINA", "LUBA", "LOUETTA", "LEIDA", "LECIA", "LAURAN", "LASHAWNA", "LAINE", "KHADIJAH", "KATERINE", - "KASI", "KALLIE", "JULIETTA", "JESUSITA", "JESTINE", "JESSIA", "JEREMY", "JEFFIE", "JANYCE", "ISADORA", - "GEORGIANNE", "FIDELIA", "EVITA", "EURA", "EULAH", "ESTEFANA", "ELSY", "ELIZABET", "ELADIA", "DODIE", - "DION", "DIA", "DENISSE", "DELORAS", "DELILA", "DAYSI", "DAKOTA", "CURTIS", "CRYSTLE", "CONCHA", - "COLBY", "CLARETTA", "CHU", "CHRISTIA", "CHARLSIE", "CHARLENA", "CARYLON", "BETTYANN", "ASLEY", "ASHLEA", - "AMIRA", "AI", "AGUEDA", "AGNUS", "YUETTE", "VINITA", "VICTORINA", "TYNISHA", "TREENA", "TOCCARA", - "TISH", "THOMASENA", "TEGAN", "SOILA", "SHILOH", "SHENNA", "SHARMAINE", "SHANTAE", "SHANDI", "SEPTEMBER", - "SARAN", "SARAI", "SANA", "SAMUEL", "SALLEY", "ROSETTE", "ROLANDE", "REGINE", "OTELIA", "OSCAR", - "OLEVIA", "NICHOLLE", "NECOLE", "NAIDA", "MYRTA", "MYESHA", "MITSUE", "MINTA", "MERTIE", "MARGY", - "MAHALIA", "MADALENE", "LOVE", "LOURA", "LOREAN", "LEWIS", "LESHA", "LEONIDA", "LENITA", "LAVONE", - "LASHELL", "LASHANDRA", "LAMONICA", "KIMBRA", "KATHERINA", "KARRY", "KANESHA", "JULIO", "JONG", "JENEVA", - "JAQUELYN", "HWA", "GILMA", "GHISLAINE", "GERTRUDIS", "FRANSISCA", "FERMINA", "ETTIE", "ETSUKO", "ELLIS", - "ELLAN", "ELIDIA", "EDRA", "DORETHEA", "DOREATHA", "DENYSE", "DENNY", "DEETTA", "DAINE", "CYRSTAL", - "CORRIN", "CAYLA", "CARLITA", "CAMILA", "BURMA", "BULA", "BUENA", "BLAKE", "BARABARA", "AVRIL", - "AUSTIN", "ALAINE", "ZANA", "WILHEMINA", "WANETTA", "VIRGIL", "VI", "VERONIKA", "VERNON", "VERLINE", - "VASILIKI", "TONITA", "TISA", "TEOFILA", "TAYNA", "TAUNYA", "TANDRA", "TAKAKO", "SUNNI", "SUANNE", - "SIXTA", "SHARELL", "SEEMA", "RUSSELL", "ROSENDA", "ROBENA", "RAYMONDE", "PEI", "PAMILA", "OZELL", - "NEIDA", "NEELY", "MISTIE", "MICHA", "MERISSA", "MAURITA", "MARYLN", "MARYETTA", "MARSHALL", "MARCELL", - "MALENA", "MAKEDA", "MADDIE", "LOVETTA", "LOURIE", "LORRINE", "LORILEE", "LESTER", "LAURENA", "LASHAY", - "LARRAINE", "LAREE", "LACRESHA", "KRISTLE", "KRISHNA", "KEVA", "KEIRA", "KAROLE", "JOIE", "JINNY", - "JEANNETTA", "JAMA", "HEIDY", "GILBERTE", "GEMA", "FAVIOLA", "EVELYNN", "ENDA", "ELLI", "ELLENA", - "DIVINA", "DAGNY", "COLLENE", "CODI", "CINDIE", "CHASSIDY", "CHASIDY", "CATRICE", "CATHERINA", "CASSEY", - "CAROLL", "CARLENA", "CANDRA", "CALISTA", "BRYANNA", "BRITTENY", "BEULA", "BARI", "AUDRIE", "AUDRIA", - "ARDELIA", "ANNELLE", "ANGILA", "ALONA", "ALLYN", "DOUGLAS", "ROGER", "JONATHAN", "RALPH", "NICHOLAS", - "BENJAMIN", "BRUCE", "HARRY", "WAYNE", "STEVE", "HOWARD", "ERNEST", "PHILLIP", "TODD", "CRAIG", - "ALAN", "PHILIP", "EARL", "DANNY", "BRYAN", "STANLEY", "LEONARD", "NATHAN", "MANUEL", "RODNEY", - "MARVIN", "VINCENT", "JEFFERY", "JEFF", "CHAD", "JACOB", "ALFRED", "BRADLEY", "HERBERT", "FREDERICK", - "EDWIN", "DON", "RICKY", "RANDALL", "BARRY", "BERNARD", "LEROY", "MARCUS", "THEODORE", "CLIFFORD", - "MIGUEL", "JIM", "TOM", "CALVIN", "BILL", "LLOYD", "DEREK", "WARREN", "DARRELL", "JEROME", - "FLOYD", "ALVIN", "TIM", "GORDON", "GREG", "JORGE", "DUSTIN", "PEDRO", "DERRICK", "ZACHARY", - "HERMAN", "GLEN", "HECTOR", "RICARDO", "RICK", "BRENT", "RAMON", "GILBERT", "MARC", "REGINALD", - "RUBEN", "NATHANIEL", "RAFAEL", "EDGAR", "MILTON", "RAUL", "BEN", "CHESTER", "DUANE", "FRANKLIN", - "BRAD", "RON", "ROLAND", "ARNOLD", "HARVEY", "JARED", "ERIK", "DARRYL", "NEIL", "JAVIER", - "FERNANDO", "CLINTON", "TED", "MATHEW", "TYRONE", "DARREN", "LANCE", "KURT", "ALLAN", "NELSON", - "GUY", "CLAYTON", "HUGH", "MAX", "DWAYNE", "DWIGHT", "ARMANDO", "FELIX", "EVERETT", "IAN", - "WALLACE", "KEN", "BOB", "ALFREDO", "ALBERTO", "DAVE", "IVAN", "BYRON", "ISAAC", "MORRIS", - "CLIFTON", "WILLARD", "ROSS", "ANDY", "SALVADOR", "KIRK", "SERGIO", "SETH", "KENT", "TERRANCE", - "EDUARDO", "TERRENCE", "ENRIQUE", "WADE", "STUART", "FREDRICK", "ARTURO", "ALEJANDRO", "NICK", "LUTHER", - "WENDELL", "JEREMIAH", "JULIUS", "OTIS", "TREVOR", "OLIVER", "LUKE", "HOMER", "GERARD", "DOUG", - "KENNY", "HUBERT", "LYLE", "MATT", "ALFONSO", "ORLANDO", "REX", "CARLTON", "ERNESTO", "NEAL", - "PABLO", "LORENZO", "OMAR", "WILBUR", "GRANT", "HORACE", "RODERICK", "ABRAHAM", "WILLIS", "RICKEY", - "ANDRES", "CESAR", "JOHNATHAN", "MALCOLM", "RUDOLPH", "DAMON", "KELVIN", "PRESTON", "ALTON", "ARCHIE", - "MARCO", "WM", "PETE", "RANDOLPH", "GARRY", "GEOFFREY", "JONATHON", "FELIPE", "GERARDO", "ED", - "DOMINIC", "DELBERT", "COLIN", "GUILLERMO", "EARNEST", "LUCAS", "BENNY", "SPENCER", "RODOLFO", "MYRON", - "EDMUND", "GARRETT", "SALVATORE", "CEDRIC", "LOWELL", "GREGG", "SHERMAN", "WILSON", "SYLVESTER", "ROOSEVELT", - "ISRAEL", "JERMAINE", "FORREST", "WILBERT", "LELAND", "SIMON", "CLARK", "IRVING", "BRYANT", "OWEN", - "RUFUS", "WOODROW", "KRISTOPHER", "MACK", "LEVI", "MARCOS", "GUSTAVO", "JAKE", "LIONEL", "GILBERTO", - "CLINT", "NICOLAS", "ISMAEL", "ORVILLE", "ERVIN", "DEWEY", "AL", "WILFRED", "JOSH", "HUGO", - "IGNACIO", "CALEB", "TOMAS", "SHELDON", "ERICK", "STEWART", "DOYLE", "DARREL", "ROGELIO", "TERENCE", - "SANTIAGO", "ALONZO", "ELIAS", "BERT", "ELBERT", "RAMIRO", "CONRAD", "NOAH", "GRADY", "PHIL", - "CORNELIUS", "LAMAR", "ROLANDO", "CLAY", "PERCY", "DEXTER", "BRADFORD", "DARIN", "AMOS", "MOSES", - "IRVIN", "SAUL", "ROMAN", "RANDAL", "TIMMY", "DARRIN", "WINSTON", "BRENDAN", "ABEL", "DOMINICK", - "BOYD", "EMILIO", "ELIJAH", "DOMINGO", "EMMETT", "MARLON", "EMANUEL", "JERALD", "EDMOND", "EMIL", - "DEWAYNE", "WILL", "OTTO", "TEDDY", "REYNALDO", "BRET", "JESS", "TRENT", "HUMBERTO", "EMMANUEL", - "STEPHAN", "VICENTE", "LAMONT", "GARLAND", "MILES", "EFRAIN", "HEATH", "RODGER", "HARLEY", "ETHAN", - "ELDON", "ROCKY", "PIERRE", "JUNIOR", "FREDDY", "ELI", "BRYCE", "ANTOINE", "STERLING", "CHASE", - "GROVER", "ELTON", "CLEVELAND", "DYLAN", "CHUCK", "DAMIAN", "REUBEN", "STAN", "AUGUST", "LEONARDO", - "JASPER", "RUSSEL", "ERWIN", "BENITO", "HANS", "MONTE", "BLAINE", "ERNIE", "CURT", "QUENTIN", - "AGUSTIN", "MURRAY", "JAMAL", "ADOLFO", "HARRISON", "TYSON", "BURTON", "BRADY", "ELLIOTT", "WILFREDO", - "BART", "JARROD", "VANCE", "DENIS", "DAMIEN", "JOAQUIN", "HARLAN", "DESMOND", "ELLIOT", "DARWIN", - "GREGORIO", "BUDDY", "XAVIER", "KERMIT", "ROSCOE", "ESTEBAN", "ANTON", "SOLOMON", "SCOTTY", "NORBERT", - "ELVIN", "WILLIAMS", "NOLAN", "ROD", "QUINTON", "HAL", "BRAIN", "ROB", "ELWOOD", "KENDRICK", - "DARIUS", "MOISES", "FIDEL", "THADDEUS", "CLIFF", "MARCEL", "JACKSON", "RAPHAEL", "BRYON", "ARMAND", - "ALVARO", "JEFFRY", "DANE", "JOESPH", "THURMAN", "NED", "RUSTY", "MONTY", "FABIAN", "REGGIE", - "MASON", "GRAHAM", "ISAIAH", "VAUGHN", "GUS", "LOYD", "DIEGO", "ADOLPH", "NORRIS", "MILLARD", - "ROCCO", "GONZALO", "DERICK", "RODRIGO", "WILEY", "RIGOBERTO", "ALPHONSO", "TY", "NOE", "VERN", - "REED", "JEFFERSON", "ELVIS", "BERNARDO", "MAURICIO", "HIRAM", "DONOVAN", "BASIL", "RILEY", "NICKOLAS", - "MAYNARD", "SCOT", "VINCE", "QUINCY", "EDDY", "SEBASTIAN", "FEDERICO", "ULYSSES", "HERIBERTO", "DONNELL", - "COLE", "DAVIS", "GAVIN", "EMERY", "WARD", "ROMEO", "JAYSON", "DANTE", "CLEMENT", "COY", - "MAXWELL", "JARVIS", "BRUNO", "ISSAC", "DUDLEY", "BROCK", "SANFORD", "CARMELO", "BARNEY", "NESTOR", - "STEFAN", "DONNY", "ART", "LINWOOD", "BEAU", "WELDON", "GALEN", "ISIDRO", "TRUMAN", "DELMAR", - "JOHNATHON", "SILAS", "FREDERIC", "DICK", "IRWIN", "MERLIN", "CHARLEY", "MARCELINO", "HARRIS", "CARLO", - "TRENTON", "KURTIS", "HUNTER", "AURELIO", "WINFRED", "VITO", "COLLIN", "DENVER", "CARTER", "LEONEL", - "EMORY", "PASQUALE", "MOHAMMAD", "MARIANO", "DANIAL", "LANDON", "DIRK", "BRANDEN", "ADAN", "BUFORD", - "GERMAN", "WILMER", "EMERSON", "ZACHERY", "FLETCHER", "JACQUES", "ERROL", "DALTON", "MONROE", "JOSUE", - "EDWARDO", "BOOKER", "WILFORD", "SONNY", "SHELTON", "CARSON", "THERON", "RAYMUNDO", "DAREN", "HOUSTON", - "ROBBY", "LINCOLN", "GENARO", "BENNETT", "OCTAVIO", "CORNELL", "HUNG", "ARRON", "ANTONY", "HERSCHEL", - "GIOVANNI", "GARTH", "CYRUS", "CYRIL", "RONNY", "LON", "FREEMAN", "DUNCAN", "KENNITH", "CARMINE", - "ERICH", "CHADWICK", "WILBURN", "RUSS", "REID", "MYLES", "ANDERSON", "MORTON", "JONAS", "FOREST", - "MITCHEL", "MERVIN", "ZANE", "RICH", "JAMEL", "LAZARO", "ALPHONSE", "RANDELL", "MAJOR", "JARRETT", - "BROOKS", "ABDUL", "LUCIANO", "SEYMOUR", "EUGENIO", "MOHAMMED", "VALENTIN", "CHANCE", "ARNULFO", "LUCIEN", - "FERDINAND", "THAD", "EZRA", "ALDO", "RUBIN", "ROYAL", "MITCH", "EARLE", "ABE", "WYATT", - "MARQUIS", "LANNY", "KAREEM", "JAMAR", "BORIS", "ISIAH", "EMILE", "ELMO", "ARON", "LEOPOLDO", - "EVERETTE", "JOSEF", "ELOY", "RODRICK", "REINALDO", "LUCIO", "JERROD", "WESTON", "HERSHEL", "BARTON", - "PARKER", "LEMUEL", "BURT", "JULES", "GIL", "ELISEO", "AHMAD", "NIGEL", "EFREN", "ANTWAN", - "ALDEN", "MARGARITO", "COLEMAN", "DINO", "OSVALDO", "LES", "DEANDRE", "NORMAND", "KIETH", "TREY", - "NORBERTO", "NAPOLEON", "JEROLD", "FRITZ", "ROSENDO", "MILFORD", "CHRISTOPER", "ALFONZO", "LYMAN", "JOSIAH", - "BRANT", "WILTON", "RICO", "JAMAAL", "DEWITT", "BRENTON", "OLIN", "FOSTER", "FAUSTINO", "CLAUDIO", - "JUDSON", "GINO", "EDGARDO", "ALEC", "TANNER", "JARRED", "DONN", "TAD", "PRINCE", "PORFIRIO", - "ODIS", "LENARD", "CHAUNCEY", "TOD", "MEL", "MARCELO", "KORY", "AUGUSTUS", "KEVEN", "HILARIO", - "BUD", "SAL", "ORVAL", "MAURO", "ZACHARIAH", "OLEN", "ANIBAL", "MILO", "JED", "DILLON", - "AMADO", "NEWTON", "LENNY", "RICHIE", "HORACIO", "BRICE", "MOHAMED", "DELMER", "DARIO", "REYES", - "MAC", "JONAH", "JERROLD", "ROBT", "HANK", "RUPERT", "ROLLAND", "KENTON", "DAMION", "ANTONE", - "WALDO", "FREDRIC", "BRADLY", "KIP", "BURL", "WALKER", "TYREE", "JEFFEREY", "AHMED", "WILLY", - "STANFORD", "OREN", "NOBLE", "MOSHE", "MIKEL", "ENOCH", "BRENDON", "QUINTIN", "JAMISON", "FLORENCIO", - "DARRICK", "TOBIAS", "HASSAN", "GIUSEPPE", "DEMARCUS", "CLETUS", "TYRELL", "LYNDON", "KEENAN", "WERNER", - "GERALDO", "COLUMBUS", "CHET", "BERTRAM", "MARKUS", "HUEY", "HILTON", "DWAIN", "DONTE", "TYRON", - "OMER", "ISAIAS", "HIPOLITO", "FERMIN", "ADALBERTO", "BO", "BARRETT", "TEODORO", "MCKINLEY", "MAXIMO", - "GARFIELD", "RALEIGH", "LAWERENCE", "ABRAM", "RASHAD", "KING", "EMMITT", "DARON", "SAMUAL", "MIQUEL", - "EUSEBIO", "DOMENIC", "DARRON", "BUSTER", "WILBER", "RENATO", "JC", "HOYT", "HAYWOOD", "EZEKIEL", - "CHAS", "FLORENTINO", "ELROY", "CLEMENTE", "ARDEN", "NEVILLE", "EDISON", "DESHAWN", "NATHANIAL", "JORDON", - "DANILO", "CLAUD", "SHERWOOD", "RAYMON", "RAYFORD", "CRISTOBAL", "AMBROSE", "TITUS", "HYMAN", "FELTON", - "EZEQUIEL", "ERASMO", "STANTON", "LONNY", "LEN", "IKE", "MILAN", "LINO", "JAROD", "HERB", - "ANDREAS", "WALTON", "RHETT", "PALMER", "DOUGLASS", "CORDELL", "OSWALDO", "ELLSWORTH", "VIRGILIO", "TONEY", - "NATHANAEL", "DEL", "BENEDICT", "MOSE", "JOHNSON", "ISREAL", "GARRET", "FAUSTO", "ASA", "ARLEN", - "ZACK", "WARNER", "MODESTO", "FRANCESCO", "MANUAL", "GAYLORD", "GASTON", "FILIBERTO", "DEANGELO", "MICHALE", - "GRANVILLE", "WES", "MALIK", "ZACKARY", "TUAN", "ELDRIDGE", "CRISTOPHER", "CORTEZ", "ANTIONE", "MALCOM", - "LONG", "KOREY", "JOSPEH", "COLTON", "WAYLON", "VON", "HOSEA", "SHAD", "SANTO", "RUDOLF", - "ROLF", "REY", "RENALDO", "MARCELLUS", "LUCIUS", "KRISTOFER", "BOYCE", "BENTON", "HAYDEN", "HARLAND", - "ARNOLDO", "RUEBEN", "LEANDRO", "KRAIG", "JERRELL", "JEROMY", "HOBERT", "CEDRICK", "ARLIE", "WINFORD", - "WALLY", "LUIGI", "KENETH", "JACINTO", "GRAIG", "FRANKLYN", "EDMUNDO", "SID", "PORTER", "LEIF", - "JERAMY", "BUCK", "WILLIAN", "VINCENZO", "SHON", "LYNWOOD", "JERE", "HAI", "ELDEN", "DORSEY", - "DARELL", "BRODERICK", "ALONSO" - ] - total_sum = 0 - temp_sum = 0 - name.sort() - for i in range(len(name)): - for j in name[i]: - temp_sum += ord(j) - ord('A') + 1 - total_sum += (i + 1) * temp_sum - temp_sum = 0 - print(total_sum) +# -*- coding: latin-1 -*- +""" +Name scores +Problem 22 +Using names.txt (right click and 'Save Link/Target As...'), a 46K text file +containing over five-thousand first names, begin by sorting it into +alphabetical order. Then working out the alphabetical value for each name, +multiply this value by its alphabetical position in the list to obtain a name +score. -if __name__ == '__main__': - main() +For example, when the list is sorted into alphabetical order, COLIN, which is +worth 3 + 15 + 12 + 9 + 14 = 53, is the 938th name in the list. So, COLIN would +obtain a score of 938 × 53 = 49714. + +What is the total of all the name scores in the file? +""" +import os + + +def solution(): + """Returns the total of all the name scores in the file. + + >>> solution() + 871198282 + """ + total_sum = 0 + temp_sum = 0 + with open(os.path.dirname(__file__) + "/p022_names.txt") as file: + name = str(file.readlines()[0]) + name = name.replace('"', "").split(",") + + name.sort() + for i in range(len(name)): + for j in name[i]: + temp_sum += ord(j) - ord("A") + 1 + total_sum += (i + 1) * temp_sum + temp_sum = 0 + return total_sum + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_23/sol1.py b/project_euler/problem_23/sol1.py new file mode 100644 index 000000000000..a72b6123e3ee --- /dev/null +++ b/project_euler/problem_23/sol1.py @@ -0,0 +1,52 @@ +""" +A perfect number is a number for which the sum of its proper divisors is exactly +equal to the number. For example, the sum of the proper divisors of 28 would be +1 + 2 + 4 + 7 + 14 = 28, which means that 28 is a perfect number. + +A number n is called deficient if the sum of its proper divisors is less than n +and it is called abundant if this sum exceeds n. + +As 12 is the smallest abundant number, 1 + 2 + 3 + 4 + 6 = 16, the smallest +number that can be written as the sum of two abundant numbers is 24. By +mathematical analysis, it can be shown that all integers greater than 28123 +can be written as the sum of two abundant numbers. However, this upper limit +cannot be reduced any further by analysis even though it is known that the +greatest number that cannot be expressed as the sum of two abundant numbers +is less than this limit. + +Find the sum of all the positive integers which cannot be written as the sum +of two abundant numbers. +""" + + +def solution(limit=28123): + """ + Finds the sum of all the positive integers which cannot be written as + the sum of two abundant numbers + as described by the statement above. + + >>> solution() + 4179871 + """ + sumDivs = [1] * (limit + 1) + + for i in range(2, int(limit ** 0.5) + 1): + sumDivs[i * i] += i + for k in range(i + 1, limit // i + 1): + sumDivs[k * i] += k + i + + abundants = set() + res = 0 + + for n in range(1, limit + 1): + if sumDivs[n] > n: + abundants.add(n) + + if not any((n - a in abundants) for a in abundants): + res += n + + return res + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_234/__init__.py b/project_euler/problem_234/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_234/sol1.py b/project_euler/problem_234/sol1.py new file mode 100644 index 000000000000..28d82b550c85 --- /dev/null +++ b/project_euler/problem_234/sol1.py @@ -0,0 +1,56 @@ +""" +https://projecteuler.net/problem=234 + +For an integer n ≥ 4, we define the lower prime square root of n, denoted by +lps(n), as the largest prime ≤ √n and the upper prime square root of n, ups(n), +as the smallest prime ≥ √n. + +So, for example, lps(4) = 2 = ups(4), lps(1000) = 31, ups(1000) = 37. Let us +call an integer n ≥ 4 semidivisible, if one of lps(n) and ups(n) divides n, +but not both. + +The sum of the semidivisible numbers not exceeding 15 is 30, the numbers are 8, +10 and 12. 15 is not semidivisible because it is a multiple of both lps(15) = 3 +and ups(15) = 5. As a further example, the sum of the 92 semidivisible numbers +up to 1000 is 34825. + +What is the sum of all semidivisible numbers not exceeding 999966663333 ? +""" + + +def fib(a, b, n): + + if n == 1: + return a + elif n == 2: + return b + elif n == 3: + return str(a) + str(b) + + temp = 0 + for x in range(2, n): + c = str(a) + str(b) + temp = b + b = c + a = temp + return c + + +def solution(n): + """Returns the sum of all semidivisible numbers not exceeding n.""" + semidivisible = [] + for x in range(n): + l = [i for i in input().split()] + c2 = 1 + while 1: + if len(fib(l[0], l[1], c2)) < int(l[2]): + c2 += 1 + else: + break + semidivisible.append(fib(l[0], l[1], c2 + 1)[int(l[2]) - 1]) + return semidivisible + + +if __name__ == "__main__": + for i in solution(int(str(input()).strip())): + print(i) diff --git a/project_euler/problem_24/__init__.py b/project_euler/problem_24/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_24/sol1.py b/project_euler/problem_24/sol1.py index b20493cb03af..1c6378b38260 100644 --- a/project_euler/problem_24/sol1.py +++ b/project_euler/problem_24/sol1.py @@ -1,7 +1,27 @@ +""" +A permutation is an ordered arrangement of objects. For example, 3124 is one +possible permutation of the digits 1, 2, 3 and 4. If all of the permutations +are listed numerically or alphabetically, we call it lexicographic order. The +lexicographic permutations of 0, 1 and 2 are: + + 012 021 102 120 201 210 + +What is the millionth lexicographic permutation of the digits 0, 1, 2, 3, 4, 5, +6, 7, 8 and 9? +""" from itertools import permutations -def main(): - result=list(map("".join, permutations('0123456789'))) - print(result[999999]) -if __name__ == '__main__': - main() \ No newline at end of file + +def solution(): + """Returns the millionth lexicographic permutation of the digits 0, 1, 2, + 3, 4, 5, 6, 7, 8 and 9. + + >>> solution() + '2783915460' + """ + result = list(map("".join, permutations("0123456789"))) + return result[999999] + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_25/__init__.py b/project_euler/problem_25/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_25/sol1.py b/project_euler/problem_25/sol1.py index f8cea3093dcf..8fce32285976 100644 --- a/project_euler/problem_25/sol1.py +++ b/project_euler/problem_25/sol1.py @@ -1,31 +1,70 @@ -from __future__ import print_function +# -*- coding: utf-8 -*- +""" +The Fibonacci sequence is defined by the recurrence relation: + + Fn = Fn−1 + Fn−2, where F1 = 1 and F2 = 1. + +Hence the first 12 terms will be: + + F1 = 1 + F2 = 1 + F3 = 2 + F4 = 3 + F5 = 5 + F6 = 8 + F7 = 13 + F8 = 21 + F9 = 34 + F10 = 55 + F11 = 89 + F12 = 144 + +The 12th term, F12, is the first term to contain three digits. + +What is the index of the first term in the Fibonacci sequence to contain 1000 +digits? +""" -try: - xrange #Python 2 -except NameError: - xrange = range #Python 3 def fibonacci(n): - if n == 1 or type(n) is not int: - return 0 - elif n == 2: - return 1 - else: - sequence = [0, 1] - for i in xrange(2, n+1): - sequence.append(sequence[i-1] + sequence[i-2]) + if n == 1 or type(n) is not int: + return 0 + elif n == 2: + return 1 + else: + sequence = [0, 1] + for i in range(2, n + 1): + sequence.append(sequence[i - 1] + sequence[i - 2]) + + return sequence[n] - return sequence[n] def fibonacci_digits_index(n): - digits = 0 - index = 2 + digits = 0 + index = 2 + + while digits < n: + index += 1 + digits = len(str(fibonacci(index))) + + return index + + +def solution(n): + """Returns the index of the first term in the Fibonacci sequence to contain + n digits. - while digits < n: - index += 1 - digits = len(str(fibonacci(index))) + >>> solution(1000) + 4782 + >>> solution(100) + 476 + >>> solution(50) + 237 + >>> solution(3) + 12 + """ + return fibonacci_digits_index(n) - return index -if __name__ == '__main__': - print(fibonacci_digits_index(1000)) \ No newline at end of file +if __name__ == "__main__": + print(solution(int(str(input()).strip()))) diff --git a/project_euler/problem_25/sol2.py b/project_euler/problem_25/sol2.py index 35147a9bfb14..d754e2ddd722 100644 --- a/project_euler/problem_25/sol2.py +++ b/project_euler/problem_25/sol2.py @@ -1,10 +1,57 @@ -def fibonacci_genrator(): - a, b = 0,1 - while True: - a,b = b,a+b - yield b -answer = 1 -gen = fibonacci_genrator() -while len(str(next(gen))) < 1000: - answer += 1 -assert answer+1 == 4782 +# -*- coding: utf-8 -*- +""" +The Fibonacci sequence is defined by the recurrence relation: + + Fn = Fn−1 + Fn−2, where F1 = 1 and F2 = 1. + +Hence the first 12 terms will be: + + F1 = 1 + F2 = 1 + F3 = 2 + F4 = 3 + F5 = 5 + F6 = 8 + F7 = 13 + F8 = 21 + F9 = 34 + F10 = 55 + F11 = 89 + F12 = 144 + +The 12th term, F12, is the first term to contain three digits. + +What is the index of the first term in the Fibonacci sequence to contain 1000 +digits? +""" + + +def fibonacci_generator(): + a, b = 0, 1 + while True: + a, b = b, a + b + yield b + + +def solution(n): + """Returns the index of the first term in the Fibonacci sequence to contain + n digits. + + >>> solution(1000) + 4782 + >>> solution(100) + 476 + >>> solution(50) + 237 + >>> solution(3) + 12 + """ + answer = 1 + gen = fibonacci_generator() + while len(str(next(gen))) < n: + answer += 1 + return answer + 1 + + +if __name__ == "__main__": + print(solution(int(str(input()).strip()))) diff --git a/project_euler/problem_25/sol3.py b/project_euler/problem_25/sol3.py new file mode 100644 index 000000000000..4e3084ce5456 --- /dev/null +++ b/project_euler/problem_25/sol3.py @@ -0,0 +1,57 @@ +# -*- coding: utf-8 -*- +""" +The Fibonacci sequence is defined by the recurrence relation: + + Fn = Fn−1 + Fn−2, where F1 = 1 and F2 = 1. + +Hence the first 12 terms will be: + + F1 = 1 + F2 = 1 + F3 = 2 + F4 = 3 + F5 = 5 + F6 = 8 + F7 = 13 + F8 = 21 + F9 = 34 + F10 = 55 + F11 = 89 + F12 = 144 + +The 12th term, F12, is the first term to contain three digits. + +What is the index of the first term in the Fibonacci sequence to contain 1000 +digits? +""" + + +def solution(n): + """Returns the index of the first term in the Fibonacci sequence to contain + n digits. + + >>> solution(1000) + 4782 + >>> solution(100) + 476 + >>> solution(50) + 237 + >>> solution(3) + 12 + """ + f1, f2 = 1, 1 + index = 2 + while True: + i = 0 + f = f1 + f2 + f1, f2 = f2, f + index += 1 + for j in str(f): + i += 1 + if i == n: + break + return index + + +if __name__ == "__main__": + print(solution(int(str(input()).strip()))) diff --git a/project_euler/problem_27/problem_27_sol1.py b/project_euler/problem_27/problem_27_sol1.py new file mode 100644 index 000000000000..dbd07f81b713 --- /dev/null +++ b/project_euler/problem_27/problem_27_sol1.py @@ -0,0 +1,69 @@ +""" +Euler discovered the remarkable quadratic formula: +n2 + n + 41 +It turns out that the formula will produce 40 primes for the consecutive values +n = 0 to 39. However, when n = 40, 402 + 40 + 41 = 40(40 + 1) + 41 is divisible +by 41, and certainly when n = 41, 412 + 41 + 41 is clearly divisible by 41. +The incredible formula n2 − 79n + 1601 was discovered, which produces 80 primes +for the consecutive values n = 0 to 79. The product of the coefficients, −79 and +1601, is −126479. +Considering quadratics of the form: +n² + an + b, where |a| < 1000 and |b| < 1000 +where |n| is the modulus/absolute value of ne.g. |11| = 11 and |−4| = 4 +Find the product of the coefficients, a and b, for the quadratic expression that +produces the maximum number of primes for consecutive values of n, starting with +n = 0. +""" + +import math + + +def is_prime(k: int) -> bool: + """ + Determine if a number is prime + >>> is_prime(10) + False + >>> is_prime(11) + True + """ + if k < 2 or k % 2 == 0: + return False + elif k == 2: + return True + else: + for x in range(3, int(math.sqrt(k) + 1), 2): + if k % x == 0: + return False + return True + + +def solution(a_limit: int, b_limit: int) -> int: + """ + >>> solution(1000, 1000) + -59231 + >>> solution(200, 1000) + -59231 + >>> solution(200, 200) + -4925 + >>> solution(-1000, 1000) + 0 + >>> solution(-1000, -1000) + 0 + """ + longest = [0, 0, 0] # length, a, b + for a in range((a_limit * -1) + 1, a_limit): + for b in range(2, b_limit): + if is_prime(b): + count = 0 + n = 0 + while is_prime((n ** 2) + (a * n) + b): + count += 1 + n += 1 + if count > longest[0]: + longest = [count, a, b] + ans = longest[1] * longest[2] + return ans + + +if __name__ == "__main__": + print(solution(1000, 1000)) diff --git a/project_euler/problem_28/__init__.py b/project_euler/problem_28/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_28/sol1.py b/project_euler/problem_28/sol1.py index 4942115ce537..11b48fea9adf 100644 --- a/project_euler/problem_28/sol1.py +++ b/project_euler/problem_28/sol1.py @@ -1,29 +1,55 @@ -from __future__ import print_function +""" +Starting with the number 1 and moving to the right in a clockwise direction a 5 +by 5 spiral is formed as follows: + + 21 22 23 24 25 + 20 7 8 9 10 + 19 6 1 2 11 + 18 5 4 3 12 + 17 16 15 14 13 + +It can be verified that the sum of the numbers on the diagonals is 101. + +What is the sum of the numbers on the diagonals in a 1001 by 1001 spiral formed +in the same way? +""" + from math import ceil -try: - xrange #Python 2 -except NameError: - xrange = range #Python 3 def diagonal_sum(n): - total = 1 - - for i in xrange(1, int(ceil(n/2.0))): - odd = 2*i+1 - even = 2*i - total = total + 4*odd**2 - 6*even - - return total - -if __name__ == '__main__': - import sys - - if len(sys.argv) == 1: - print(diagonal_sum(1001)) - else: - try: - n = int(sys.argv[1]) - diagonal_sum(n) - except ValueError: - print('Invalid entry - please enter a number') \ No newline at end of file + """Returns the sum of the numbers on the diagonals in a n by n spiral + formed in the same way. + + >>> diagonal_sum(1001) + 669171001 + >>> diagonal_sum(500) + 82959497 + >>> diagonal_sum(100) + 651897 + >>> diagonal_sum(50) + 79697 + >>> diagonal_sum(10) + 537 + """ + total = 1 + + for i in range(1, int(ceil(n / 2.0))): + odd = 2 * i + 1 + even = 2 * i + total = total + 4 * odd ** 2 - 6 * even + + return total + + +if __name__ == "__main__": + import sys + + if len(sys.argv) == 1: + print(diagonal_sum(1001)) + else: + try: + n = int(sys.argv[1]) + print(diagonal_sum(n)) + except ValueError: + print("Invalid entry - please enter a number") diff --git a/project_euler/problem_29/__init__.py b/project_euler/problem_29/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_29/solution.py b/project_euler/problem_29/solution.py index 64d35c84d9ca..4313a7b06392 100644 --- a/project_euler/problem_29/solution.py +++ b/project_euler/problem_29/solution.py @@ -1,33 +1,50 @@ -def main(): +""" +Consider all integer combinations of ab for 2 <= a <= 5 and 2 <= b <= 5: + +2^2=4, 2^3=8, 2^4=16, 2^5=32 +3^2=9, 3^3=27, 3^4=81, 3^5=243 +4^2=16, 4^3=64, 4^4=256, 4^5=1024 +5^2=25, 5^3=125, 5^4=625, 5^5=3125 + +If they are then placed in numerical order, with any repeats removed, we get +the following sequence of 15 distinct terms: + +4, 8, 9, 16, 25, 27, 32, 64, 81, 125, 243, 256, 625, 1024, 3125 + +How many distinct terms are in the sequence generated by ab +for 2 <= a <= 100 and 2 <= b <= 100? +""" + + +def solution(n): + """Returns the number of distinct terms in the sequence generated by a^b + for 2 <= a <= 100 and 2 <= b <= 100. + + >>> solution(100) + 9183 + >>> solution(50) + 2184 + >>> solution(20) + 324 + >>> solution(5) + 15 + >>> solution(2) + 1 + >>> solution(1) + 0 """ - Consider all integer combinations of ab for 2 <= a <= 5 and 2 <= b <= 5: - - 22=4, 23=8, 24=16, 25=32 - 32=9, 33=27, 34=81, 35=243 - 42=16, 43=64, 44=256, 45=1024 - 52=25, 53=125, 54=625, 55=3125 - If they are then placed in numerical order, with any repeats removed, - we get the following sequence of 15 distinct terms: - - 4, 8, 9, 16, 25, 27, 32, 64, 81, 125, 243, 256, 625, 1024, 3125 - - How many distinct terms are in the sequence generated by ab - for 2 <= a <= 100 and 2 <= b <= 100? - """ - collectPowers = set() currentPow = 0 - N = 101 # maximum limit + N = n + 1 # maximum limit for a in range(2, N): for b in range(2, N): - currentPow = a**b # calculates the current power - collectPowers.add(currentPow) # adds the result to the set - - print("Number of terms ", len(collectPowers)) + currentPow = a ** b # calculates the current power + collectPowers.add(currentPow) # adds the result to the set + return len(collectPowers) -if __name__ == '__main__': - main() +if __name__ == "__main__": + print("Number of terms ", solution(int(str(input()).strip()))) diff --git a/project_euler/problem_31/__init__.py b/project_euler/problem_31/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_31/sol1.py b/project_euler/problem_31/sol1.py index 33653722f890..187fb9167a13 100644 --- a/project_euler/problem_31/sol1.py +++ b/project_euler/problem_31/sol1.py @@ -1,10 +1,5 @@ # -*- coding: utf-8 -*- -from __future__ import print_function -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 -''' +""" Coin sums Problem 31 In England the currency is made up of pound, £, and pence, p, and there are @@ -15,7 +10,7 @@ 1×£1 + 1×50p + 2×20p + 1×5p + 1×2p + 3×1p How many different ways can £2 be made using any number of coins? -''' +""" def one_pence(): @@ -50,4 +45,21 @@ def two_pound(x): return 0 if x < 0 else two_pound(x - 200) + one_pound(x) -print(two_pound(200)) +def solution(n): + """Returns the number of different ways can £n be made using any number of + coins? + + >>> solution(500) + 6295434 + >>> solution(200) + 73682 + >>> solution(50) + 451 + >>> solution(10) + 11 + """ + return two_pound(n) + + +if __name__ == "__main__": + print(solution(int(str(input()).strip()))) diff --git a/project_euler/problem_32/sol32.py b/project_euler/problem_32/sol32.py new file mode 100644 index 000000000000..0abc04829a9a --- /dev/null +++ b/project_euler/problem_32/sol32.py @@ -0,0 +1,60 @@ +""" +We shall say that an n-digit number is pandigital if it makes use of all the +digits 1 to n exactly once; for example, the 5-digit number, 15234, is 1 through +5 pandigital. + +The product 7254 is unusual, as the identity, 39 × 186 = 7254, containing +multiplicand, multiplier, and product is 1 through 9 pandigital. + +Find the sum of all products whose multiplicand/multiplier/product identity can +be written as a 1 through 9 pandigital. + +HINT: Some products can be obtained in more than one way so be sure to only +include it once in your sum. +""" +import itertools + + +def isCombinationValid(combination): + """ + Checks if a combination (a tuple of 9 digits) + is a valid product equation. + + >>> isCombinationValid(('3', '9', '1', '8', '6', '7', '2', '5', '4')) + True + + >>> isCombinationValid(('1', '2', '3', '4', '5', '6', '7', '8', '9')) + False + + """ + return ( + int("".join(combination[0:2])) * int("".join(combination[2:5])) + == int("".join(combination[5:9])) + ) or ( + int("".join(combination[0])) * int("".join(combination[1:5])) + == int("".join(combination[5:9])) + ) + + +def solution(): + """ + Finds the sum of all products whose multiplicand/multiplier/product identity + can be written as a 1 through 9 pandigital + + >>> solution() + 45228 + """ + + return sum( + set( + [ + int("".join(pandigital[5:9])) + for pandigital in itertools.permutations("123456789") + if isCombinationValid(pandigital) + ] + ) + ) + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_33/__init__.py b/project_euler/problem_33/__init__.py new file mode 100644 index 000000000000..8b137891791f --- /dev/null +++ b/project_euler/problem_33/__init__.py @@ -0,0 +1 @@ + diff --git a/project_euler/problem_33/sol1.py b/project_euler/problem_33/sol1.py new file mode 100644 index 000000000000..0992c96935f5 --- /dev/null +++ b/project_euler/problem_33/sol1.py @@ -0,0 +1,55 @@ +""" +Problem: + +The fraction 49/98 is a curious fraction, as an inexperienced +mathematician in attempting to simplify it may incorrectly believe +that 49/98 = 4/8, which is correct, is obtained by cancelling the 9s. + +We shall consider fractions like, 30/50 = 3/5, to be trivial examples. + +There are exactly four non-trivial examples of this type of fraction, +less than one in value, and containing two digits in the numerator +and denominator. + +If the product of these four fractions is given in its lowest common +terms, find the value of the denominator. +""" + + +def isDigitCancelling(num, den): + if num != den: + if num % 10 == den // 10: + if (num // 10) / (den % 10) == num / den: + return True + + +def solve(digit_len: int) -> str: + """ + >>> solve(2) + '16/64 , 19/95 , 26/65 , 49/98' + >>> solve(3) + '16/64 , 19/95 , 26/65 , 49/98' + >>> solve(4) + '16/64 , 19/95 , 26/65 , 49/98' + >>> solve(0) + '' + >>> solve(5) + '16/64 , 19/95 , 26/65 , 49/98' + """ + solutions = [] + den = 11 + last_digit = int("1" + "0" * digit_len) + for num in range(den, last_digit): + while den <= 99: + if (num != den) and (num % 10 == den // 10) and (den % 10 != 0): + if isDigitCancelling(num, den): + solutions.append("{}/{}".format(num, den)) + den += 1 + num += 1 + den = 10 + solutions = " , ".join(solutions) + return solutions + + +if __name__ == "__main__": + print(solve(2)) diff --git a/project_euler/problem_36/__init__.py b/project_euler/problem_36/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_36/sol1.py b/project_euler/problem_36/sol1.py index d78e7e59f210..39088cf25dd4 100644 --- a/project_euler/problem_36/sol1.py +++ b/project_euler/problem_36/sol1.py @@ -1,30 +1,53 @@ -from __future__ import print_function -''' +""" Double-base palindromes Problem 36 The decimal number, 585 = 10010010012 (binary), is palindromic in both bases. -Find the sum of all numbers, less than one million, which are palindromic in base 10 and base 2. +Find the sum of all numbers, less than one million, which are palindromic in +base 10 and base 2. + +(Please note that the palindromic number, in either base, may not include +leading zeros.) +""" -(Please note that the palindromic number, in either base, may not include leading zeros.) -''' -try: - xrange #Python 2 -except NameError: - xrange = range #Python 3 def is_palindrome(n): - n = str(n) + n = str(n) + + if n == n[::-1]: + return True + else: + return False + + +def solution(n): + """Return the sum of all numbers, less than n , which are palindromic in + base 10 and base 2. - if n == n[::-1]: - return True - else: - return False + >>> solution(1000000) + 872187 + >>> solution(500000) + 286602 + >>> solution(100000) + 286602 + >>> solution(1000) + 1772 + >>> solution(100) + 157 + >>> solution(10) + 25 + >>> solution(2) + 1 + >>> solution(1) + 0 + """ + total = 0 -total = 0 + for i in range(1, n): + if is_palindrome(i) and is_palindrome(bin(i).split("b")[1]): + total += i + return total -for i in xrange(1, 1000000): - if is_palindrome(i) and is_palindrome(bin(i).split('b')[1]): - total += i -print(total) \ No newline at end of file +if __name__ == "__main__": + print(solution(int(str(input().strip())))) diff --git a/project_euler/problem_40/__init__.py b/project_euler/problem_40/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_40/sol1.py b/project_euler/problem_40/sol1.py index ab4017512a1a..786725e274b1 100644 --- a/project_euler/problem_40/sol1.py +++ b/project_euler/problem_40/sol1.py @@ -1,26 +1,46 @@ -#-.- coding: latin-1 -.- -from __future__ import print_function -''' +# -.- coding: latin-1 -.- +""" Champernowne's constant Problem 40 -An irrational decimal fraction is created by concatenating the positive integers: +An irrational decimal fraction is created by concatenating the positive +integers: 0.123456789101112131415161718192021... It can be seen that the 12th digit of the fractional part is 1. -If dn represents the nth digit of the fractional part, find the value of the following expression. +If dn represents the nth digit of the fractional part, find the value of the +following expression. d1 × d10 × d100 × d1000 × d10000 × d100000 × d1000000 -''' +""" -constant = [] -i = 1 -while len(constant) < 1e6: - constant.append(str(i)) - i += 1 +def solution(): + """Returns -constant = ''.join(constant) + >>> solution() + 210 + """ + constant = [] + i = 1 -print(int(constant[0])*int(constant[9])*int(constant[99])*int(constant[999])*int(constant[9999])*int(constant[99999])*int(constant[999999])) \ No newline at end of file + while len(constant) < 1e6: + constant.append(str(i)) + i += 1 + + constant = "".join(constant) + + return ( + int(constant[0]) + * int(constant[9]) + * int(constant[99]) + * int(constant[999]) + * int(constant[9999]) + * int(constant[99999]) + * int(constant[999999]) + ) + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_42/solution42.py b/project_euler/problem_42/solution42.py new file mode 100644 index 000000000000..2380472153c6 --- /dev/null +++ b/project_euler/problem_42/solution42.py @@ -0,0 +1,48 @@ +""" +The nth term of the sequence of triangle numbers is given by, tn = ½n(n+1); so +the first ten triangle numbers are: + +1, 3, 6, 10, 15, 21, 28, 36, 45, 55, ... + +By converting each letter in a word to a number corresponding to its +alphabetical position and adding these values we form a word value. For example, +the word value for SKY is 19 + 11 + 25 = 55 = t10. If the word value is a +triangle number then we shall call the word a triangle word. + +Using words.txt (right click and 'Save Link/Target As...'), a 16K text file +containing nearly two-thousand common English words, how many are triangle +words? +""" +import os + + +# Precomputes a list of the 100 first triangular numbers +TRIANGULAR_NUMBERS = [int(0.5 * n * (n + 1)) for n in range(1, 101)] + + +def solution(): + """ + Finds the amount of triangular words in the words file. + + >>> solution() + 162 + """ + script_dir = os.path.dirname(os.path.realpath(__file__)) + wordsFilePath = os.path.join(script_dir, "words.txt") + + words = "" + with open(wordsFilePath, "r") as f: + words = f.readline() + + words = list(map(lambda word: word.strip('"'), words.strip("\r\n").split(","))) + words = list( + filter( + lambda word: word in TRIANGULAR_NUMBERS, + map(lambda word: sum(map(lambda x: ord(x) - 64, word)), words), + ) + ) + return len(words) + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_42/words.txt b/project_euler/problem_42/words.txt new file mode 100644 index 000000000000..af3aeb42f151 --- /dev/null +++ b/project_euler/problem_42/words.txt @@ -0,0 +1 @@ +"A","ABILITY","ABLE","ABOUT","ABOVE","ABSENCE","ABSOLUTELY","ACADEMIC","ACCEPT","ACCESS","ACCIDENT","ACCOMPANY","ACCORDING","ACCOUNT","ACHIEVE","ACHIEVEMENT","ACID","ACQUIRE","ACROSS","ACT","ACTION","ACTIVE","ACTIVITY","ACTUAL","ACTUALLY","ADD","ADDITION","ADDITIONAL","ADDRESS","ADMINISTRATION","ADMIT","ADOPT","ADULT","ADVANCE","ADVANTAGE","ADVICE","ADVISE","AFFAIR","AFFECT","AFFORD","AFRAID","AFTER","AFTERNOON","AFTERWARDS","AGAIN","AGAINST","AGE","AGENCY","AGENT","AGO","AGREE","AGREEMENT","AHEAD","AID","AIM","AIR","AIRCRAFT","ALL","ALLOW","ALMOST","ALONE","ALONG","ALREADY","ALRIGHT","ALSO","ALTERNATIVE","ALTHOUGH","ALWAYS","AMONG","AMONGST","AMOUNT","AN","ANALYSIS","ANCIENT","AND","ANIMAL","ANNOUNCE","ANNUAL","ANOTHER","ANSWER","ANY","ANYBODY","ANYONE","ANYTHING","ANYWAY","APART","APPARENT","APPARENTLY","APPEAL","APPEAR","APPEARANCE","APPLICATION","APPLY","APPOINT","APPOINTMENT","APPROACH","APPROPRIATE","APPROVE","AREA","ARGUE","ARGUMENT","ARISE","ARM","ARMY","AROUND","ARRANGE","ARRANGEMENT","ARRIVE","ART","ARTICLE","ARTIST","AS","ASK","ASPECT","ASSEMBLY","ASSESS","ASSESSMENT","ASSET","ASSOCIATE","ASSOCIATION","ASSUME","ASSUMPTION","AT","ATMOSPHERE","ATTACH","ATTACK","ATTEMPT","ATTEND","ATTENTION","ATTITUDE","ATTRACT","ATTRACTIVE","AUDIENCE","AUTHOR","AUTHORITY","AVAILABLE","AVERAGE","AVOID","AWARD","AWARE","AWAY","AYE","BABY","BACK","BACKGROUND","BAD","BAG","BALANCE","BALL","BAND","BANK","BAR","BASE","BASIC","BASIS","BATTLE","BE","BEAR","BEAT","BEAUTIFUL","BECAUSE","BECOME","BED","BEDROOM","BEFORE","BEGIN","BEGINNING","BEHAVIOUR","BEHIND","BELIEF","BELIEVE","BELONG","BELOW","BENEATH","BENEFIT","BESIDE","BEST","BETTER","BETWEEN","BEYOND","BIG","BILL","BIND","BIRD","BIRTH","BIT","BLACK","BLOCK","BLOOD","BLOODY","BLOW","BLUE","BOARD","BOAT","BODY","BONE","BOOK","BORDER","BOTH","BOTTLE","BOTTOM","BOX","BOY","BRAIN","BRANCH","BREAK","BREATH","BRIDGE","BRIEF","BRIGHT","BRING","BROAD","BROTHER","BUDGET","BUILD","BUILDING","BURN","BUS","BUSINESS","BUSY","BUT","BUY","BY","CABINET","CALL","CAMPAIGN","CAN","CANDIDATE","CAPABLE","CAPACITY","CAPITAL","CAR","CARD","CARE","CAREER","CAREFUL","CAREFULLY","CARRY","CASE","CASH","CAT","CATCH","CATEGORY","CAUSE","CELL","CENTRAL","CENTRE","CENTURY","CERTAIN","CERTAINLY","CHAIN","CHAIR","CHAIRMAN","CHALLENGE","CHANCE","CHANGE","CHANNEL","CHAPTER","CHARACTER","CHARACTERISTIC","CHARGE","CHEAP","CHECK","CHEMICAL","CHIEF","CHILD","CHOICE","CHOOSE","CHURCH","CIRCLE","CIRCUMSTANCE","CITIZEN","CITY","CIVIL","CLAIM","CLASS","CLEAN","CLEAR","CLEARLY","CLIENT","CLIMB","CLOSE","CLOSELY","CLOTHES","CLUB","COAL","CODE","COFFEE","COLD","COLLEAGUE","COLLECT","COLLECTION","COLLEGE","COLOUR","COMBINATION","COMBINE","COME","COMMENT","COMMERCIAL","COMMISSION","COMMIT","COMMITMENT","COMMITTEE","COMMON","COMMUNICATION","COMMUNITY","COMPANY","COMPARE","COMPARISON","COMPETITION","COMPLETE","COMPLETELY","COMPLEX","COMPONENT","COMPUTER","CONCENTRATE","CONCENTRATION","CONCEPT","CONCERN","CONCERNED","CONCLUDE","CONCLUSION","CONDITION","CONDUCT","CONFERENCE","CONFIDENCE","CONFIRM","CONFLICT","CONGRESS","CONNECT","CONNECTION","CONSEQUENCE","CONSERVATIVE","CONSIDER","CONSIDERABLE","CONSIDERATION","CONSIST","CONSTANT","CONSTRUCTION","CONSUMER","CONTACT","CONTAIN","CONTENT","CONTEXT","CONTINUE","CONTRACT","CONTRAST","CONTRIBUTE","CONTRIBUTION","CONTROL","CONVENTION","CONVERSATION","COPY","CORNER","CORPORATE","CORRECT","COS","COST","COULD","COUNCIL","COUNT","COUNTRY","COUNTY","COUPLE","COURSE","COURT","COVER","CREATE","CREATION","CREDIT","CRIME","CRIMINAL","CRISIS","CRITERION","CRITICAL","CRITICISM","CROSS","CROWD","CRY","CULTURAL","CULTURE","CUP","CURRENT","CURRENTLY","CURRICULUM","CUSTOMER","CUT","DAMAGE","DANGER","DANGEROUS","DARK","DATA","DATE","DAUGHTER","DAY","DEAD","DEAL","DEATH","DEBATE","DEBT","DECADE","DECIDE","DECISION","DECLARE","DEEP","DEFENCE","DEFENDANT","DEFINE","DEFINITION","DEGREE","DELIVER","DEMAND","DEMOCRATIC","DEMONSTRATE","DENY","DEPARTMENT","DEPEND","DEPUTY","DERIVE","DESCRIBE","DESCRIPTION","DESIGN","DESIRE","DESK","DESPITE","DESTROY","DETAIL","DETAILED","DETERMINE","DEVELOP","DEVELOPMENT","DEVICE","DIE","DIFFERENCE","DIFFERENT","DIFFICULT","DIFFICULTY","DINNER","DIRECT","DIRECTION","DIRECTLY","DIRECTOR","DISAPPEAR","DISCIPLINE","DISCOVER","DISCUSS","DISCUSSION","DISEASE","DISPLAY","DISTANCE","DISTINCTION","DISTRIBUTION","DISTRICT","DIVIDE","DIVISION","DO","DOCTOR","DOCUMENT","DOG","DOMESTIC","DOOR","DOUBLE","DOUBT","DOWN","DRAW","DRAWING","DREAM","DRESS","DRINK","DRIVE","DRIVER","DROP","DRUG","DRY","DUE","DURING","DUTY","EACH","EAR","EARLY","EARN","EARTH","EASILY","EAST","EASY","EAT","ECONOMIC","ECONOMY","EDGE","EDITOR","EDUCATION","EDUCATIONAL","EFFECT","EFFECTIVE","EFFECTIVELY","EFFORT","EGG","EITHER","ELDERLY","ELECTION","ELEMENT","ELSE","ELSEWHERE","EMERGE","EMPHASIS","EMPLOY","EMPLOYEE","EMPLOYER","EMPLOYMENT","EMPTY","ENABLE","ENCOURAGE","END","ENEMY","ENERGY","ENGINE","ENGINEERING","ENJOY","ENOUGH","ENSURE","ENTER","ENTERPRISE","ENTIRE","ENTIRELY","ENTITLE","ENTRY","ENVIRONME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E","TRADITION","TRADITIONAL","TRAFFIC","TRAIN","TRAINING","TRANSFER","TRANSPORT","TRAVEL","TREAT","TREATMENT","TREATY","TREE","TREND","TRIAL","TRIP","TROOP","TROUBLE","TRUE","TRUST","TRUTH","TRY","TURN","TWICE","TYPE","TYPICAL","UNABLE","UNDER","UNDERSTAND","UNDERSTANDING","UNDERTAKE","UNEMPLOYMENT","UNFORTUNATELY","UNION","UNIT","UNITED","UNIVERSITY","UNLESS","UNLIKELY","UNTIL","UP","UPON","UPPER","URBAN","US","USE","USED","USEFUL","USER","USUAL","USUALLY","VALUE","VARIATION","VARIETY","VARIOUS","VARY","VAST","VEHICLE","VERSION","VERY","VIA","VICTIM","VICTORY","VIDEO","VIEW","VILLAGE","VIOLENCE","VISION","VISIT","VISITOR","VITAL","VOICE","VOLUME","VOTE","WAGE","WAIT","WALK","WALL","WANT","WAR","WARM","WARN","WASH","WATCH","WATER","WAVE","WAY","WE","WEAK","WEAPON","WEAR","WEATHER","WEEK","WEEKEND","WEIGHT","WELCOME","WELFARE","WELL","WEST","WESTERN","WHAT","WHATEVER","WHEN","WHERE","WHEREAS","WHETHER","WHICH","WHILE","WHILST","WHITE","WHO","WHOLE","WHOM","WHOSE","WHY","WIDE","WIDELY","WIFE","WILD","WILL","WIN","WIND","WINDOW","WINE","WING","WINNER","WINTER","WISH","WITH","WITHDRAW","WITHIN","WITHOUT","WOMAN","WONDER","WONDERFUL","WOOD","WORD","WORK","WORKER","WORKING","WORKS","WORLD","WORRY","WORTH","WOULD","WRITE","WRITER","WRITING","WRONG","YARD","YEAH","YEAR","YES","YESTERDAY","YET","YOU","YOUNG","YOUR","YOURSELF","YOUTH" diff --git a/project_euler/problem_48/__init__.py b/project_euler/problem_48/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_48/sol1.py b/project_euler/problem_48/sol1.py index 5c4bdb0f6384..06ad1408dcef 100644 --- a/project_euler/problem_48/sol1.py +++ b/project_euler/problem_48/sol1.py @@ -1,21 +1,24 @@ -from __future__ import print_function -''' +""" Self Powers Problem 48 The series, 11 + 22 + 33 + ... + 1010 = 10405071317. Find the last ten digits of the series, 11 + 22 + 33 + ... + 10001000. -''' +""" -try: - xrange -except NameError: - xrange = range -total = 0 -for i in xrange(1, 1001): - total += i**i +def solution(): + """Returns the last 10 digits of the series, 11 + 22 + 33 + ... + 10001000. + >>> solution() + '9110846700' + """ + total = 0 + for i in range(1, 1001): + total += i ** i + return str(total)[-10:] -print(str(total)[-10:]) \ No newline at end of file + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_52/__init__.py b/project_euler/problem_52/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_52/sol1.py b/project_euler/problem_52/sol1.py index 376b4cfa1d63..df5c46ae05d1 100644 --- a/project_euler/problem_52/sol1.py +++ b/project_euler/problem_52/sol1.py @@ -1,23 +1,37 @@ -from __future__ import print_function -''' +""" Permuted multiples Problem 52 -It can be seen that the number, 125874, and its double, 251748, contain exactly the same digits, but in a different order. +It can be seen that the number, 125874, and its double, 251748, contain exactly +the same digits, but in a different order. -Find the smallest positive integer, x, such that 2x, 3x, 4x, 5x, and 6x, contain the same digits. -''' -i = 1 +Find the smallest positive integer, x, such that 2x, 3x, 4x, 5x, and 6x, +contain the same digits. +""" -while True: - if sorted(list(str(i))) == \ - sorted(list(str(2*i))) == \ - sorted(list(str(3*i))) == \ - sorted(list(str(4*i))) == \ - sorted(list(str(5*i))) == \ - sorted(list(str(6*i))): - break - i += 1 +def solution(): + """Returns the smallest positive integer, x, such that 2x, 3x, 4x, 5x, and + 6x, contain the same digits. -print(i) \ No newline at end of file + >>> solution() + 142857 + """ + i = 1 + + while True: + if ( + sorted(list(str(i))) + == sorted(list(str(2 * i))) + == sorted(list(str(3 * i))) + == sorted(list(str(4 * i))) + == sorted(list(str(5 * i))) + == sorted(list(str(6 * i))) + ): + return i + + i += 1 + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_53/__init__.py b/project_euler/problem_53/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_53/sol1.py b/project_euler/problem_53/sol1.py index ed6d5329eb4e..f17508b005d1 100644 --- a/project_euler/problem_53/sol1.py +++ b/project_euler/problem_53/sol1.py @@ -1,13 +1,11 @@ -#-.- coding: latin-1 -.- -from __future__ import print_function -from math import factorial -''' +# -.- coding: latin-1 -.- +""" Combinatoric selections Problem 53 There are exactly ten ways of selecting three from five, 12345: -123, 124, 125, 134, 135, 145, 234, 235, 245, and 345 + 123, 124, 125, 134, 135, 145, 234, 235, 245, and 345 In combinatorics, we use the notation, 5C3 = 10. @@ -16,21 +14,31 @@ nCr = n!/(r!(n−r)!),where r ≤ n, n! = n×(n−1)×...×3×2×1, and 0! = 1. It is not until n = 23, that a value exceeds one-million: 23C10 = 1144066. -How many, not necessarily distinct, values of nCr, for 1 ≤ n ≤ 100, are greater than one-million? -''' -try: - xrange #Python 2 -except NameError: - xrange = range #Python 3 +How many, not necessarily distinct, values of nCr, for 1 ≤ n ≤ 100, are greater +than one-million? +""" +from math import factorial + def combinations(n, r): - return factorial(n)/(factorial(r)*factorial(n-r)) + return factorial(n) / (factorial(r) * factorial(n - r)) + + +def solution(): + """Returns the number of values of nCr, for 1 ≤ n ≤ 100, are greater than + one-million + + >>> solution() + 4075 + """ + total = 0 -total = 0 + for i in range(1, 101): + for j in range(1, i + 1): + if combinations(i, j) > 1e6: + total += 1 + return total -for i in xrange(1, 101): - for j in xrange(1, i+1): - if combinations(i, j) > 1e6: - total += 1 -print(total) \ No newline at end of file +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_551/__init__.py b/project_euler/problem_551/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_551/sol1.py b/project_euler/problem_551/sol1.py new file mode 100644 index 000000000000..307d644fbfc9 --- /dev/null +++ b/project_euler/problem_551/sol1.py @@ -0,0 +1,204 @@ +""" +Sum of digits sequence +Problem 551 + +Let a(0), a(1),... be an interger sequence defined by: + a(0) = 1 + for n >= 1, a(n) is the sum of the digits of all preceding terms + +The sequence starts with 1, 1, 2, 4, 8, ... +You are given a(10^6) = 31054319. + +Find a(10^15) +""" + +ks = [k for k in range(2, 20 + 1)] +base = [10 ** k for k in range(ks[-1] + 1)] +memo = {} + + +def next_term(a_i, k, i, n): + """ + Calculates and updates a_i in-place to either the n-th term or the + smallest term for which c > 10^k when the terms are written in the form: + a(i) = b * 10^k + c + + For any a(i), if digitsum(b) and c have the same value, the difference + between subsequent terms will be the same until c >= 10^k. This difference + is cached to greatly speed up the computation. + + Arguments: + a_i -- array of digits starting from the one's place that represent + the i-th term in the sequence + k -- k when terms are written in the from a(i) = b*10^k + c. + Term are calulcated until c > 10^k or the n-th term is reached. + i -- position along the sequence + n -- term to caluclate up to if k is large enough + + Return: a tuple of difference between ending term and starting term, and + the number of terms calculated. ex. if starting term is a_0=1, and + ending term is a_10=62, then (61, 9) is returned. + """ + # ds_b - digitsum(b) + ds_b = 0 + for j in range(k, len(a_i)): + ds_b += a_i[j] + c = 0 + for j in range(min(len(a_i), k)): + c += a_i[j] * base[j] + + diff, dn = 0, 0 + max_dn = n - i + + sub_memo = memo.get(ds_b) + + if sub_memo != None: + jumps = sub_memo.get(c) + + if jumps != None and len(jumps) > 0: + # find and make the largest jump without going over + max_jump = -1 + for _k in range(len(jumps) - 1, -1, -1): + if jumps[_k][2] <= k and jumps[_k][1] <= max_dn: + max_jump = _k + break + + if max_jump >= 0: + diff, dn, _kk = jumps[max_jump] + # since the difference between jumps is cached, add c + new_c = diff + c + for j in range(min(k, len(a_i))): + new_c, a_i[j] = divmod(new_c, 10) + if new_c > 0: + add(a_i, k, new_c) + + else: + sub_memo[c] = [] + else: + sub_memo = {c: []} + memo[ds_b] = sub_memo + + if dn >= max_dn or c + diff >= base[k]: + return diff, dn + + if k > ks[0]: + while True: + # keep doing smaller jumps + _diff, terms_jumped = next_term(a_i, k - 1, i + dn, n) + diff += _diff + dn += terms_jumped + + if dn >= max_dn or c + diff >= base[k]: + break + else: + # would be too small a jump, just compute sequential terms instead + _diff, terms_jumped = compute(a_i, k, i + dn, n) + diff += _diff + dn += terms_jumped + + jumps = sub_memo[c] + + # keep jumps sorted by # of terms skipped + j = 0 + while j < len(jumps): + if jumps[j][1] > dn: + break + j += 1 + + # cache the jump for this value digitsum(b) and c + sub_memo[c].insert(j, (diff, dn, k)) + return (diff, dn) + + +def compute(a_i, k, i, n): + """ + same as next_term(a_i, k, i, n) but computes terms without memoizing results. + """ + if i >= n: + return 0, i + if k > len(a_i): + a_i.extend([0 for _ in range(k - len(a_i))]) + + # note: a_i -> b * 10^k + c + # ds_b -> digitsum(b) + # ds_c -> digitsum(c) + start_i = i + ds_b, ds_c, diff = 0, 0, 0 + for j in range(len(a_i)): + if j >= k: + ds_b += a_i[j] + else: + ds_c += a_i[j] + + while i < n: + i += 1 + addend = ds_c + ds_b + diff += addend + ds_c = 0 + for j in range(k): + s = a_i[j] + addend + addend, a_i[j] = divmod(s, 10) + + ds_c += a_i[j] + + if addend > 0: + break + + if addend > 0: + add(a_i, k, addend) + return diff, i - start_i + + +def add(digits, k, addend): + """ + adds addend to digit array given in digits + starting at index k + """ + for j in range(k, len(digits)): + s = digits[j] + addend + if s >= 10: + quotient, digits[j] = divmod(s, 10) + addend = addend // 10 + quotient + else: + digits[j] = s + addend = addend // 10 + + if addend == 0: + break + + while addend > 0: + addend, digit = divmod(addend, 10) + digits.append(digit) + + +def solution(n): + """ + returns n-th term of sequence + + >>> solution(10) + 62 + + >>> solution(10**6) + 31054319 + + >>> solution(10**15) + 73597483551591773 + """ + + digits = [1] + i = 1 + dn = 0 + while True: + diff, terms_jumped = next_term(digits, 20, i + dn, n) + dn += terms_jumped + if dn == n - i: + break + + a_n = 0 + for j in range(len(digits)): + a_n += digits[j] * 10 ** j + return a_n + + +if __name__ == "__main__": + print(solution(10 ** 15)) diff --git a/project_euler/problem_56/__init__.py b/project_euler/problem_56/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_56/sol1.py b/project_euler/problem_56/sol1.py new file mode 100644 index 000000000000..d5225efcb9e5 --- /dev/null +++ b/project_euler/problem_56/sol1.py @@ -0,0 +1,32 @@ +def maximum_digital_sum(a: int, b: int) -> int: + """ + Considering natural numbers of the form, a**b, where a, b < 100, + what is the maximum digital sum? + :param a: + :param b: + :return: + >>> maximum_digital_sum(10,10) + 45 + + >>> maximum_digital_sum(100,100) + 972 + + >>> maximum_digital_sum(100,200) + 1872 + """ + + # RETURN the MAXIMUM from the list of SUMs of the list of INT converted from STR of BASE raised to the POWER + return max( + [ + sum([int(x) for x in str(base ** power)]) + for base in range(a) + for power in range(b) + ] + ) + + +# Tests +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/project_euler/problem_67/__init__.py b/project_euler/problem_67/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_67/sol1.py b/project_euler/problem_67/sol1.py new file mode 100644 index 000000000000..9494ff7bbabd --- /dev/null +++ b/project_euler/problem_67/sol1.py @@ -0,0 +1,49 @@ +""" +Problem Statement: +By starting at the top of the triangle below and moving to adjacent numbers on +the row below, the maximum total from top to bottom is 23. +3 +7 4 +2 4 6 +8 5 9 3 +That is, 3 + 7 + 4 + 9 = 23. +Find the maximum total from top to bottom in triangle.txt (right click and +'Save Link/Target As...'), a 15K text file containing a triangle with +one-hundred rows. +""" +import os + + +def solution(): + """ + Finds the maximum total in a triangle as described by the problem statement + above. + + >>> solution() + 7273 + """ + script_dir = os.path.dirname(os.path.realpath(__file__)) + triangle = os.path.join(script_dir, "triangle.txt") + + with open(triangle, "r") as f: + triangle = f.readlines() + + a = map(lambda x: x.rstrip("\r\n").split(" "), triangle) + a = list(map(lambda x: list(map(lambda y: int(y), x)), a)) + + for i in range(1, len(a)): + for j in range(len(a[i])): + if j != len(a[i - 1]): + number1 = a[i - 1][j] + else: + number1 = 0 + if j > 0: + number2 = a[i - 1][j - 1] + else: + number2 = 0 + a[i][j] += max(number1, number2) + return max(a[-1]) + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_67/triangle.txt b/project_euler/problem_67/triangle.txt new file mode 100644 index 000000000000..00aa2bc6382d --- /dev/null +++ b/project_euler/problem_67/triangle.txt @@ -0,0 +1,100 @@ +59 +73 41 +52 40 09 +26 53 06 34 +10 51 87 86 81 +61 95 66 57 25 68 +90 81 80 38 92 67 73 +30 28 51 76 81 18 75 44 +84 14 95 87 62 81 17 78 58 +21 46 71 58 02 79 62 39 31 09 +56 34 35 53 78 31 81 18 90 93 15 +78 53 04 21 84 93 32 13 97 11 37 51 +45 03 81 79 05 18 78 86 13 30 63 99 95 +39 87 96 28 03 38 42 17 82 87 58 07 22 57 +06 17 51 17 07 93 09 07 75 97 95 78 87 08 53 +67 66 59 60 88 99 94 65 55 77 55 34 27 53 78 28 +76 40 41 04 87 16 09 42 75 69 23 97 30 60 10 79 87 +12 10 44 26 21 36 32 84 98 60 13 12 36 16 63 31 91 35 +70 39 06 05 55 27 38 48 28 22 34 35 62 62 15 14 94 89 86 +66 56 68 84 96 21 34 34 34 81 62 40 65 54 62 05 98 03 02 60 +38 89 46 37 99 54 34 53 36 14 70 26 02 90 45 13 31 61 83 73 47 +36 10 63 96 60 49 41 05 37 42 14 58 84 93 96 17 09 43 05 43 06 59 +66 57 87 57 61 28 37 51 84 73 79 15 39 95 88 87 43 39 11 86 77 74 18 +54 42 05 79 30 49 99 73 46 37 50 02 45 09 54 52 27 95 27 65 19 45 26 45 +71 39 17 78 76 29 52 90 18 99 78 19 35 62 71 19 23 65 93 85 49 33 75 09 02 +33 24 47 61 60 55 32 88 57 55 91 54 46 57 07 77 98 52 80 99 24 25 46 78 79 05 +92 09 13 55 10 67 26 78 76 82 63 49 51 31 24 68 05 57 07 54 69 21 67 43 17 63 12 +24 59 06 08 98 74 66 26 61 60 13 03 09 09 24 30 71 08 88 70 72 70 29 90 11 82 41 34 +66 82 67 04 36 60 92 77 91 85 62 49 59 61 30 90 29 94 26 41 89 04 53 22 83 41 09 74 90 +48 28 26 37 28 52 77 26 51 32 18 98 79 36 62 13 17 08 19 54 89 29 73 68 42 14 08 16 70 37 +37 60 69 70 72 71 09 59 13 60 38 13 57 36 09 30 43 89 30 39 15 02 44 73 05 73 26 63 56 86 12 +55 55 85 50 62 99 84 77 28 85 03 21 27 22 19 26 82 69 54 04 13 07 85 14 01 15 70 59 89 95 10 19 +04 09 31 92 91 38 92 86 98 75 21 05 64 42 62 84 36 20 73 42 21 23 22 51 51 79 25 45 85 53 03 43 22 +75 63 02 49 14 12 89 14 60 78 92 16 44 82 38 30 72 11 46 52 90 27 08 65 78 03 85 41 57 79 39 52 33 48 +78 27 56 56 39 13 19 43 86 72 58 95 39 07 04 34 21 98 39 15 39 84 89 69 84 46 37 57 59 35 59 50 26 15 93 +42 89 36 27 78 91 24 11 17 41 05 94 07 69 51 96 03 96 47 90 90 45 91 20 50 56 10 32 36 49 04 53 85 92 25 65 +52 09 61 30 61 97 66 21 96 92 98 90 06 34 96 60 32 69 68 33 75 84 18 31 71 50 84 63 03 03 19 11 28 42 75 45 45 +61 31 61 68 96 34 49 39 05 71 76 59 62 67 06 47 96 99 34 21 32 47 52 07 71 60 42 72 94 56 82 83 84 40 94 87 82 46 +01 20 60 14 17 38 26 78 66 81 45 95 18 51 98 81 48 16 53 88 37 52 69 95 72 93 22 34 98 20 54 27 73 61 56 63 60 34 63 +93 42 94 83 47 61 27 51 79 79 45 01 44 73 31 70 83 42 88 25 53 51 30 15 65 94 80 44 61 84 12 77 02 62 02 65 94 42 14 94 +32 73 09 67 68 29 74 98 10 19 85 48 38 31 85 67 53 93 93 77 47 67 39 72 94 53 18 43 77 40 78 32 29 59 24 06 02 83 50 60 66 +32 01 44 30 16 51 15 81 98 15 10 62 86 79 50 62 45 60 70 38 31 85 65 61 64 06 69 84 14 22 56 43 09 48 66 69 83 91 60 40 36 61 +92 48 22 99 15 95 64 43 01 16 94 02 99 19 17 69 11 58 97 56 89 31 77 45 67 96 12 73 08 20 36 47 81 44 50 64 68 85 40 81 85 52 09 +91 35 92 45 32 84 62 15 19 64 21 66 06 01 52 80 62 59 12 25 88 28 91 50 40 16 22 99 92 79 87 51 21 77 74 77 07 42 38 42 74 83 02 05 +46 19 77 66 24 18 05 32 02 84 31 99 92 58 96 72 91 36 62 99 55 29 53 42 12 37 26 58 89 50 66 19 82 75 12 48 24 87 91 85 02 07 03 76 86 +99 98 84 93 07 17 33 61 92 20 66 60 24 66 40 30 67 05 37 29 24 96 03 27 70 62 13 04 45 47 59 88 43 20 66 15 46 92 30 04 71 66 78 70 53 99 +67 60 38 06 88 04 17 72 10 99 71 07 42 25 54 05 26 64 91 50 45 71 06 30 67 48 69 82 08 56 80 67 18 46 66 63 01 20 08 80 47 07 91 16 03 79 87 +18 54 78 49 80 48 77 40 68 23 60 88 58 80 33 57 11 69 55 53 64 02 94 49 60 92 16 35 81 21 82 96 25 24 96 18 02 05 49 03 50 77 06 32 84 27 18 38 +68 01 50 04 03 21 42 94 53 24 89 05 92 26 52 36 68 11 85 01 04 42 02 45 15 06 50 04 53 73 25 74 81 88 98 21 67 84 79 97 99 20 95 04 40 46 02 58 87 +94 10 02 78 88 52 21 03 88 60 06 53 49 71 20 91 12 65 07 49 21 22 11 41 58 99 36 16 09 48 17 24 52 36 23 15 72 16 84 56 02 99 43 76 81 71 29 39 49 17 +64 39 59 84 86 16 17 66 03 09 43 06 64 18 63 29 68 06 23 07 87 14 26 35 17 12 98 41 53 64 78 18 98 27 28 84 80 67 75 62 10 11 76 90 54 10 05 54 41 39 66 +43 83 18 37 32 31 52 29 95 47 08 76 35 11 04 53 35 43 34 10 52 57 12 36 20 39 40 55 78 44 07 31 38 26 08 15 56 88 86 01 52 62 10 24 32 05 60 65 53 28 57 99 +03 50 03 52 07 73 49 92 66 80 01 46 08 67 25 36 73 93 07 42 25 53 13 96 76 83 87 90 54 89 78 22 78 91 73 51 69 09 79 94 83 53 09 40 69 62 10 79 49 47 03 81 30 +71 54 73 33 51 76 59 54 79 37 56 45 84 17 62 21 98 69 41 95 65 24 39 37 62 03 24 48 54 64 46 82 71 78 33 67 09 16 96 68 52 74 79 68 32 21 13 78 96 60 09 69 20 36 +73 26 21 44 46 38 17 83 65 98 07 23 52 46 61 97 33 13 60 31 70 15 36 77 31 58 56 93 75 68 21 36 69 53 90 75 25 82 39 50 65 94 29 30 11 33 11 13 96 02 56 47 07 49 02 +76 46 73 30 10 20 60 70 14 56 34 26 37 39 48 24 55 76 84 91 39 86 95 61 50 14 53 93 64 67 37 31 10 84 42 70 48 20 10 72 60 61 84 79 69 65 99 73 89 25 85 48 92 56 97 16 +03 14 80 27 22 30 44 27 67 75 79 32 51 54 81 29 65 14 19 04 13 82 04 91 43 40 12 52 29 99 07 76 60 25 01 07 61 71 37 92 40 47 99 66 57 01 43 44 22 40 53 53 09 69 26 81 07 +49 80 56 90 93 87 47 13 75 28 87 23 72 79 32 18 27 20 28 10 37 59 21 18 70 04 79 96 03 31 45 71 81 06 14 18 17 05 31 50 92 79 23 47 09 39 47 91 43 54 69 47 42 95 62 46 32 85 +37 18 62 85 87 28 64 05 77 51 47 26 30 65 05 70 65 75 59 80 42 52 25 20 44 10 92 17 71 95 52 14 77 13 24 55 11 65 26 91 01 30 63 15 49 48 41 17 67 47 03 68 20 90 98 32 04 40 68 +90 51 58 60 06 55 23 68 05 19 76 94 82 36 96 43 38 90 87 28 33 83 05 17 70 83 96 93 06 04 78 47 80 06 23 84 75 23 87 72 99 14 50 98 92 38 90 64 61 58 76 94 36 66 87 80 51 35 61 38 +57 95 64 06 53 36 82 51 40 33 47 14 07 98 78 65 39 58 53 06 50 53 04 69 40 68 36 69 75 78 75 60 03 32 39 24 74 47 26 90 13 40 44 71 90 76 51 24 36 50 25 45 70 80 61 80 61 43 90 64 11 +18 29 86 56 68 42 79 10 42 44 30 12 96 18 23 18 52 59 02 99 67 46 60 86 43 38 55 17 44 93 42 21 55 14 47 34 55 16 49 24 23 29 96 51 55 10 46 53 27 92 27 46 63 57 30 65 43 27 21 20 24 83 +81 72 93 19 69 52 48 01 13 83 92 69 20 48 69 59 20 62 05 42 28 89 90 99 32 72 84 17 08 87 36 03 60 31 36 36 81 26 97 36 48 54 56 56 27 16 91 08 23 11 87 99 33 47 02 14 44 73 70 99 43 35 33 +90 56 61 86 56 12 70 59 63 32 01 15 81 47 71 76 95 32 65 80 54 70 34 51 40 45 33 04 64 55 78 68 88 47 31 47 68 87 03 84 23 44 89 72 35 08 31 76 63 26 90 85 96 67 65 91 19 14 17 86 04 71 32 95 +37 13 04 22 64 37 37 28 56 62 86 33 07 37 10 44 52 82 52 06 19 52 57 75 90 26 91 24 06 21 14 67 76 30 46 14 35 89 89 41 03 64 56 97 87 63 22 34 03 79 17 45 11 53 25 56 96 61 23 18 63 31 37 37 47 +77 23 26 70 72 76 77 04 28 64 71 69 14 85 96 54 95 48 06 62 99 83 86 77 97 75 71 66 30 19 57 90 33 01 60 61 14 12 90 99 32 77 56 41 18 14 87 49 10 14 90 64 18 50 21 74 14 16 88 05 45 73 82 47 74 44 +22 97 41 13 34 31 54 61 56 94 03 24 59 27 98 77 04 09 37 40 12 26 87 09 71 70 07 18 64 57 80 21 12 71 83 94 60 39 73 79 73 19 97 32 64 29 41 07 48 84 85 67 12 74 95 20 24 52 41 67 56 61 29 93 35 72 69 +72 23 63 66 01 11 07 30 52 56 95 16 65 26 83 90 50 74 60 18 16 48 43 77 37 11 99 98 30 94 91 26 62 73 45 12 87 73 47 27 01 88 66 99 21 41 95 80 02 53 23 32 61 48 32 43 43 83 14 66 95 91 19 81 80 67 25 88 +08 62 32 18 92 14 83 71 37 96 11 83 39 99 05 16 23 27 10 67 02 25 44 11 55 31 46 64 41 56 44 74 26 81 51 31 45 85 87 09 81 95 22 28 76 69 46 48 64 87 67 76 27 89 31 11 74 16 62 03 60 94 42 47 09 34 94 93 72 +56 18 90 18 42 17 42 32 14 86 06 53 33 95 99 35 29 15 44 20 49 59 25 54 34 59 84 21 23 54 35 90 78 16 93 13 37 88 54 19 86 67 68 55 66 84 65 42 98 37 87 56 33 28 58 38 28 38 66 27 52 21 81 15 08 22 97 32 85 27 +91 53 40 28 13 34 91 25 01 63 50 37 22 49 71 58 32 28 30 18 68 94 23 83 63 62 94 76 80 41 90 22 82 52 29 12 18 56 10 08 35 14 37 57 23 65 67 40 72 39 93 39 70 89 40 34 07 46 94 22 20 05 53 64 56 30 05 56 61 88 27 +23 95 11 12 37 69 68 24 66 10 87 70 43 50 75 07 62 41 83 58 95 93 89 79 45 39 02 22 05 22 95 43 62 11 68 29 17 40 26 44 25 71 87 16 70 85 19 25 59 94 90 41 41 80 61 70 55 60 84 33 95 76 42 63 15 09 03 40 38 12 03 32 +09 84 56 80 61 55 85 97 16 94 82 94 98 57 84 30 84 48 93 90 71 05 95 90 73 17 30 98 40 64 65 89 07 79 09 19 56 36 42 30 23 69 73 72 07 05 27 61 24 31 43 48 71 84 21 28 26 65 65 59 65 74 77 20 10 81 61 84 95 08 52 23 70 +47 81 28 09 98 51 67 64 35 51 59 36 92 82 77 65 80 24 72 53 22 07 27 10 21 28 30 22 48 82 80 48 56 20 14 43 18 25 50 95 90 31 77 08 09 48 44 80 90 22 93 45 82 17 13 96 25 26 08 73 34 99 06 49 24 06 83 51 40 14 15 10 25 01 +54 25 10 81 30 64 24 74 75 80 36 75 82 60 22 69 72 91 45 67 03 62 79 54 89 74 44 83 64 96 66 73 44 30 74 50 37 05 09 97 70 01 60 46 37 91 39 75 75 18 58 52 72 78 51 81 86 52 08 97 01 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71 64 65 66 21 78 62 81 74 42 20 83 70 73 95 78 45 92 27 34 53 71 15 +30 11 85 31 34 71 13 48 05 14 44 03 19 67 23 73 19 57 06 90 94 72 57 69 81 62 59 68 88 57 55 69 49 13 07 87 97 80 89 05 71 05 05 26 38 40 16 62 45 99 18 38 98 24 21 26 62 74 69 04 85 57 77 35 58 67 91 79 79 57 86 28 66 34 72 51 76 78 36 95 63 90 08 78 47 63 45 31 22 70 52 48 79 94 15 77 61 67 68 +23 33 44 81 80 92 93 75 94 88 23 61 39 76 22 03 28 94 32 06 49 65 41 34 18 23 08 47 62 60 03 63 33 13 80 52 31 54 73 43 70 26 16 69 57 87 83 31 03 93 70 81 47 95 77 44 29 68 39 51 56 59 63 07 25 70 07 77 43 53 64 03 94 42 95 39 18 01 66 21 16 97 20 50 90 16 70 10 95 69 29 06 25 61 41 26 15 59 63 35 diff --git a/project_euler/problem_76/__init__.py b/project_euler/problem_76/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_76/sol1.py b/project_euler/problem_76/sol1.py index 2832f6d7afb6..ed0ee6b507e9 100644 --- a/project_euler/problem_76/sol1.py +++ b/project_euler/problem_76/sol1.py @@ -1,5 +1,4 @@ -from __future__ import print_function -''' +""" Counting Summations Problem 76 @@ -12,24 +11,44 @@ 2 + 1 + 1 + 1 1 + 1 + 1 + 1 + 1 -How many different ways can one hundred be written as a sum of at least two positive integers? -''' -try: - xrange #Python 2 -except NameError: - xrange = range #Python 3 +How many different ways can one hundred be written as a sum of at least two +positive integers? +""" + def partition(m): - memo = [[0 for _ in xrange(m)] for _ in xrange(m+1)] - for i in xrange(m+1): - memo[i][0] = 1 + """Returns the number of different ways one hundred can be written as a sum + of at least two positive integers. + + >>> partition(100) + 190569291 + >>> partition(50) + 204225 + >>> partition(30) + 5603 + >>> partition(10) + 41 + >>> partition(5) + 6 + >>> partition(3) + 2 + >>> partition(2) + 1 + >>> partition(1) + 0 + """ + memo = [[0 for _ in range(m)] for _ in range(m + 1)] + for i in range(m + 1): + memo[i][0] = 1 + + for n in range(m + 1): + for k in range(1, m): + memo[n][k] += memo[n][k - 1] + if n > k: + memo[n][k] += memo[n - k - 1][k] - for n in xrange(m+1): - for k in xrange(1, m): - memo[n][k] += memo[n][k-1] - if n > k: - memo[n][k] += memo[n-k-1][k] + return memo[m][m - 1] - 1 - return (memo[m][m-1] - 1) -print(partition(100)) \ No newline at end of file +if __name__ == "__main__": + print(partition(int(str(input()).strip()))) diff --git a/project_euler/problem_99/__init__.py b/project_euler/problem_99/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_99/base_exp.txt b/project_euler/problem_99/base_exp.txt new file mode 100644 index 000000000000..abe95aa86036 --- /dev/null +++ b/project_euler/problem_99/base_exp.txt @@ -0,0 +1,1000 @@ +519432,525806 +632382,518061 +78864,613712 +466580,530130 +780495,510032 +525895,525320 +15991,714883 +960290,502358 +760018,511029 +166800,575487 +210884,564478 +555151,523163 +681146,515199 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+Comparing two numbers written in index form like 2'11 and 3'7 is not difficult, as any calculator would confirm that 2^11 = 2048 < 3^7 = 2187. + +However, confirming that 632382^518061 > 519432^525806 would be much more difficult, as both numbers contain over three million digits. + +Using base_exp.txt, a 22K text file containing one thousand lines with a base/exponent pair on each line, determine which line number has the greatest numerical value. + +NOTE: The first two lines in the file represent the numbers in the example given above. +""" + +import os +from math import log10 + + +def find_largest(data_file: str="base_exp.txt") -> int: + """ + >>> find_largest() + 709 + """ + largest = [0, 0] + for i, line in enumerate( + open(os.path.join(os.path.dirname(__file__), data_file)) + ): + a, x = list(map(int, line.split(","))) + if x * log10(a) > largest[0]: + largest = [x * log10(a), i + 1] + return largest[1] + + +if __name__ == "__main__": + print(find_largest()) diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 000000000000..824f534a245f --- /dev/null +++ b/requirements.txt @@ -0,0 +1,16 @@ +beautifulsoup4 +black +fake_useragent +flake8 +matplotlib +mypy +numpy +opencv-python +pandas +pillow +pytest +requests +scikit-fuzzy +sklearn +sympy +tensorflow diff --git a/scripts/build_directory_md.py b/scripts/build_directory_md.py new file mode 100755 index 000000000000..1043e6ccb696 --- /dev/null +++ b/scripts/build_directory_md.py @@ -0,0 +1,45 @@ +#!/usr/bin/env python3 + +import os +from typing import Iterator + +URL_BASE = "https://github.com/TheAlgorithms/Python/blob/master" + + +def good_filepaths(top_dir: str = ".") -> Iterator[str]: + for dirpath, dirnames, filenames in os.walk(top_dir): + dirnames[:] = [d for d in dirnames if d != "scripts" and d[0] not in "._"] + for filename in filenames: + if filename == "__init__.py": + continue + if os.path.splitext(filename)[1] in (".py", ".ipynb"): + yield os.path.join(dirpath, filename).lstrip("./") + + +def md_prefix(i): + return f"{i * ' '}*" if i else "\n##" + + +def print_path(old_path: str, new_path: str) -> str: + old_parts = old_path.split(os.sep) + for i, new_part in enumerate(new_path.split(os.sep)): + if i + 1 > len(old_parts) or old_parts[i] != new_part: + if new_part: + print(f"{md_prefix(i)} {new_part.replace('_', ' ').title()}") + return new_path + + +def print_directory_md(top_dir: str = ".") -> None: + old_path = "" + for filepath in sorted(good_filepaths()): + filepath, filename = os.path.split(filepath) + if filepath != old_path: + old_path = print_path(old_path, filepath) + indent = (filepath.count(os.sep) + 1) if filepath else 0 + url = "/".join((URL_BASE, filepath, filename)).replace(" ", "%20") + filename = os.path.splitext(filename.replace("_", " ").title())[0] + print(f"{md_prefix(indent)} [{filename}]({url})") + + +if __name__ == "__main__": + print_directory_md(".") diff --git a/scripts/validate_filenames.py b/scripts/validate_filenames.py new file mode 100755 index 000000000000..51dd6a40cb41 --- /dev/null +++ b/scripts/validate_filenames.py @@ -0,0 +1,29 @@ +#!/usr/bin/env python3 + +import os +from build_directory_md import good_filepaths + +filepaths = list(good_filepaths()) +assert filepaths, "good_filepaths() failed!" + + +upper_files = [file for file in filepaths if file != file.lower()] +if upper_files: + print(f"{len(upper_files)} files contain uppercase characters:") + print("\n".join(upper_files) + "\n") + +space_files = [file for file in filepaths if " " in file] +if space_files: + print(f"{len(space_files)} files contain space characters:") + print("\n".join(space_files) + "\n") + +nodir_files = [file for file in filepaths if os.sep not in file] +if nodir_files: + print(f"{len(nodir_files)} files are not in a directory:") + print("\n".join(nodir_files) + "\n") + +bad_files = len(upper_files + space_files + nodir_files) +if bad_files: + import sys + + sys.exit(bad_files) diff --git a/searches/binary_search.py b/searches/binary_search.py index 7df45883c09a..76a50560e943 100644 --- a/searches/binary_search.py +++ b/searches/binary_search.py @@ -9,22 +9,16 @@ For manual testing run: python binary_search.py """ -from __future__ import print_function import bisect -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - def binary_search(sorted_collection, item): """Pure implementation of binary search algorithm in Python - Be careful collection must be sorted, otherwise result will be + Be careful collection must be ascending sorted, otherwise result will be unpredictable - :param sorted_collection: some sorted collection with comparable items + :param sorted_collection: some ascending sorted collection with comparable items :param item: item value to search :return: index of found item or None if item is not found @@ -45,25 +39,24 @@ def binary_search(sorted_collection, item): right = len(sorted_collection) - 1 while left <= right: - midpoint = (left + right) // 2 + midpoint = left + (right - left) // 2 current_item = sorted_collection[midpoint] if current_item == item: return midpoint + elif item < current_item: + right = midpoint - 1 else: - if item < current_item: - right = midpoint - 1 - else: - left = midpoint + 1 + left = midpoint + 1 return None def binary_search_std_lib(sorted_collection, item): """Pure implementation of binary search algorithm in Python using stdlib - Be careful collection must be sorted, otherwise result will be + Be careful collection must be ascending sorted, otherwise result will be unpredictable - :param sorted_collection: some sorted collection with comparable items + :param sorted_collection: some ascending sorted collection with comparable items :param item: item value to search :return: index of found item or None if item is not found @@ -85,15 +78,16 @@ def binary_search_std_lib(sorted_collection, item): return index return None + def binary_search_by_recursion(sorted_collection, item, left, right): """Pure implementation of binary search algorithm in Python by recursion - Be careful collection must be sorted, otherwise result will be + Be careful collection must be ascending sorted, otherwise result will be unpredictable First recursion should be started with left=0 and right=(len(sorted_collection)-1) - :param sorted_collection: some sorted collection with comparable items + :param sorted_collection: some ascending sorted collection with comparable items :param item: item value to search :return: index of found item or None if item is not found @@ -110,24 +104,25 @@ def binary_search_by_recursion(sorted_collection, item, left, right): >>> binary_search_std_lib([0, 5, 7, 10, 15], 6) """ - if (right < left): + if right < left: return None - + midpoint = left + (right - left) // 2 if sorted_collection[midpoint] == item: return midpoint elif sorted_collection[midpoint] > item: - return binary_search_by_recursion(sorted_collection, item, left, midpoint-1) + return binary_search_by_recursion(sorted_collection, item, left, midpoint - 1) else: - return binary_search_by_recursion(sorted_collection, item, midpoint+1, right) - + return binary_search_by_recursion(sorted_collection, item, midpoint + 1, right) + + def __assert_sorted(collection): - """Check if collection is sorted, if not - raises :py:class:`ValueError` + """Check if collection is ascending sorted, if not - raises :py:class:`ValueError` :param collection: collection - :return: True if collection is sorted - :raise: :py:class:`ValueError` if collection is not sorted + :return: True if collection is ascending sorted + :raise: :py:class:`ValueError` if collection is not ascending sorted Examples: >>> __assert_sorted([0, 1, 2, 4]) @@ -136,26 +131,27 @@ def __assert_sorted(collection): >>> __assert_sorted([10, -1, 5]) Traceback (most recent call last): ... - ValueError: Collection must be sorted + ValueError: Collection must be ascending sorted """ if collection != sorted(collection): - raise ValueError('Collection must be sorted') + raise ValueError("Collection must be ascending sorted") return True -if __name__ == '__main__': +if __name__ == "__main__": import sys - user_input = raw_input('Enter numbers separated by comma:\n').strip() - collection = [int(item) for item in user_input.split(',')] + + user_input = input("Enter numbers separated by comma:\n").strip() + collection = [int(item) for item in user_input.split(",")] try: __assert_sorted(collection) except ValueError: - sys.exit('Sequence must be sorted to apply binary search') + sys.exit("Sequence must be ascending sorted to apply binary search") - target_input = raw_input('Enter a single number to be found in the list:\n') + target_input = input("Enter a single number to be found in the list:\n") target = int(target_input) result = binary_search(collection, target) if result is not None: - print('{} found at positions: {}'.format(target, result)) + print("{} found at positions: {}".format(target, result)) else: - print('Not found') + print("Not found") diff --git a/searches/fibonacci_search.py b/searches/fibonacci_search.py new file mode 100644 index 000000000000..67f2df505d4e --- /dev/null +++ b/searches/fibonacci_search.py @@ -0,0 +1,54 @@ +# run using python fibonacci_search.py -v + +""" +@params +arr: input array +val: the value to be searched +output: the index of element in the array or -1 if not found +return 0 if input array is empty +""" + + +def fibonacci_search(arr, val): + + """ + >>> fibonacci_search([1,6,7,0,0,0], 6) + 1 + >>> fibonacci_search([1,-1, 5, 2, 9], 10) + -1 + >>> fibonacci_search([], 9) + 0 + """ + fib_N_2 = 0 + fib_N_1 = 1 + fibNext = fib_N_1 + fib_N_2 + length = len(arr) + if length == 0: + return 0 + while fibNext < len(arr): + fib_N_2 = fib_N_1 + fib_N_1 = fibNext + fibNext = fib_N_1 + fib_N_2 + index = -1 + while fibNext > 1: + i = min(index + fib_N_2, (length - 1)) + if arr[i] < val: + fibNext = fib_N_1 + fib_N_1 = fib_N_2 + fib_N_2 = fibNext - fib_N_1 + index = i + elif arr[i] > val: + fibNext = fib_N_2 + fib_N_1 = fib_N_1 - fib_N_2 + fib_N_2 = fibNext - fib_N_1 + else: + return i + if (fib_N_1 and index < length - 1) and (arr[index + 1] == val): + return index + 1 + return -1 + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/searches/interpolation_search.py b/searches/interpolation_search.py index db9893bdb5d4..d1873083bf8a 100644 --- a/searches/interpolation_search.py +++ b/searches/interpolation_search.py @@ -1,19 +1,13 @@ """ This is pure python implementation of interpolation search algorithm """ -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 def interpolation_search(sorted_collection, item): """Pure implementation of interpolation search algorithm in Python - Be careful collection must be sorted, otherwise result will be + Be careful collection must be ascending sorted, otherwise result will be unpredictable - :param sorted_collection: some sorted collection with comparable items + :param sorted_collection: some ascending sorted collection with comparable items :param item: item value to search :return: index of found item or None if item is not found """ @@ -21,78 +15,126 @@ def interpolation_search(sorted_collection, item): right = len(sorted_collection) - 1 while left <= right: - point = left + ((item - sorted_collection[left]) * (right - left)) // (sorted_collection[right] - sorted_collection[left]) - - #out of range check - if point<0 or point>=len(sorted_collection): + # avoid devided by 0 during interpolation + if sorted_collection[left] == sorted_collection[right]: + if sorted_collection[left] == item: + return left + else: + return None + + point = left + ((item - sorted_collection[left]) * (right - left)) // ( + sorted_collection[right] - sorted_collection[left] + ) + + # out of range check + if point < 0 or point >= len(sorted_collection): return None current_item = sorted_collection[point] if current_item == item: return point else: - if item < current_item: - right = point - 1 + if point < left: + right = left + left = point + elif point > right: + left = right + right = point else: - left = point + 1 + if item < current_item: + right = point - 1 + else: + left = point + 1 return None def interpolation_search_by_recursion(sorted_collection, item, left, right): """Pure implementation of interpolation search algorithm in Python by recursion - Be careful collection must be sorted, otherwise result will be + Be careful collection must be ascending sorted, otherwise result will be unpredictable First recursion should be started with left=0 and right=(len(sorted_collection)-1) - :param sorted_collection: some sorted collection with comparable items + :param sorted_collection: some ascending sorted collection with comparable items :param item: item value to search :return: index of found item or None if item is not found """ - point = left + ((item - sorted_collection[left]) * (right - left)) // (sorted_collection[right] - sorted_collection[left]) - #out of range check - if point<0 or point>=len(sorted_collection): + # avoid devided by 0 during interpolation + if sorted_collection[left] == sorted_collection[right]: + if sorted_collection[left] == item: + return left + else: + return None + + point = left + ((item - sorted_collection[left]) * (right - left)) // ( + sorted_collection[right] - sorted_collection[left] + ) + + # out of range check + if point < 0 or point >= len(sorted_collection): return None if sorted_collection[point] == item: return point - elif sorted_collection[point] > item: - return interpolation_search_by_recursion(sorted_collection, item, left, point-1) + elif point < left: + return interpolation_search_by_recursion(sorted_collection, item, point, left) + elif point > right: + return interpolation_search_by_recursion(sorted_collection, item, right, left) else: - return interpolation_search_by_recursion(sorted_collection, item, point+1, right) - + if sorted_collection[point] > item: + return interpolation_search_by_recursion( + sorted_collection, item, left, point - 1 + ) + else: + return interpolation_search_by_recursion( + sorted_collection, item, point + 1, right + ) + + def __assert_sorted(collection): - """Check if collection is sorted, if not - raises :py:class:`ValueError` + """Check if collection is ascending sorted, if not - raises :py:class:`ValueError` :param collection: collection - :return: True if collection is sorted - :raise: :py:class:`ValueError` if collection is not sorted + :return: True if collection is ascending sorted + :raise: :py:class:`ValueError` if collection is not ascending sorted Examples: >>> __assert_sorted([0, 1, 2, 4]) True >>> __assert_sorted([10, -1, 5]) Traceback (most recent call last): ... - ValueError: Collection must be sorted + ValueError: Collection must be ascending sorted """ if collection != sorted(collection): - raise ValueError('Collection must be sorted') + raise ValueError("Collection must be ascending sorted") return True -if __name__ == '__main__': +if __name__ == "__main__": import sys - user_input = raw_input('Enter numbers separated by comma:\n').strip() + """ + user_input = input('Enter numbers separated by comma:\n').strip() collection = [int(item) for item in user_input.split(',')] try: __assert_sorted(collection) except ValueError: - sys.exit('Sequence must be sorted to apply interpolation search') + sys.exit('Sequence must be ascending sorted to apply interpolation search') - target_input = raw_input('Enter a single number to be found in the list:\n') + target_input = input('Enter a single number to be found in the list:\n') target = int(target_input) + """ + + debug = 0 + if debug == 1: + collection = [10, 30, 40, 45, 50, 66, 77, 93] + try: + __assert_sorted(collection) + except ValueError: + sys.exit("Sequence must be ascending sorted to apply interpolation search") + target = 67 + result = interpolation_search(collection, target) if result is not None: - print('{} found at positions: {}'.format(target, result)) + print("{} found at positions: {}".format(target, result)) else: - print('Not found') + print("Not found") diff --git a/searches/jump_search.py b/searches/jump_search.py index 10cb933f2f35..e191cf2d4b27 100644 --- a/searches/jump_search.py +++ b/searches/jump_search.py @@ -1,10 +1,11 @@ -from __future__ import print_function import math + + def jump_search(arr, x): n = len(arr) step = int(math.floor(math.sqrt(n))) prev = 0 - while arr[min(step, n)-1] < x: + while arr[min(step, n) - 1] < x: prev = step step += int(math.floor(math.sqrt(n))) if prev >= n: @@ -19,8 +20,7 @@ def jump_search(arr, x): return -1 - -arr = [ 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610] +arr = [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610] x = 55 index = jump_search(arr, x) -print("\nNumber " + str(x) +" is at index " + str(index)); +print("\nNumber " + str(x) + " is at index " + str(index)) diff --git a/searches/linear_search.py b/searches/linear_search.py index 058322f21d09..ab20f3527bb3 100644 --- a/searches/linear_search.py +++ b/searches/linear_search.py @@ -9,12 +9,7 @@ For manual testing run: python linear_search.py """ -from __future__ import print_function -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 def linear_search(sequence, target): """Pure implementation of linear search algorithm in Python @@ -42,14 +37,14 @@ def linear_search(sequence, target): return None -if __name__ == '__main__': - user_input = raw_input('Enter numbers separated by comma:\n').strip() - sequence = [int(item) for item in user_input.split(',')] +if __name__ == "__main__": + user_input = input("Enter numbers separated by comma:\n").strip() + sequence = [int(item) for item in user_input.split(",")] - target_input = raw_input('Enter a single number to be found in the list:\n') + target_input = input("Enter a single number to be found in the list:\n") target = int(target_input) result = linear_search(sequence, target) if result is not None: - print('{} found at positions: {}'.format(target, result)) + print("{} found at positions: {}".format(target, result)) else: - print('Not found') + print("Not found") diff --git a/searches/quick_select.py b/searches/quick_select.py index 1596cf040e0c..17dca395f73c 100644 --- a/searches/quick_select.py +++ b/searches/quick_select.py @@ -1,10 +1,13 @@ -import random - """ -A python implementation of the quick select algorithm, which is efficient for calculating the value that would appear in the index of a list if it would be sorted, even if it is not already sorted +A Python implementation of the quick select algorithm, which is efficient for +calculating the value that would appear in the index of a list if it would be +sorted, even if it is not already sorted https://en.wikipedia.org/wiki/Quickselect """ -def _partition(data, pivot): +import random + + +def _partition(data: list, pivot) -> tuple: """ Three way partition the data into smaller, equal and greater lists, in relationship to the pivot @@ -14,31 +17,46 @@ def _partition(data, pivot): """ less, equal, greater = [], [], [] for element in data: - if element.address < pivot.address: + if element < pivot: less.append(element) - elif element.address > pivot.address: + elif element > pivot: greater.append(element) else: equal.append(element) return less, equal, greater - -def quickSelect(list, k): - #k = len(list) // 2 when trying to find the median (index that value would be when list is sorted) - smaller = [] - larger = [] - pivot = random.randint(0, len(list) - 1) - pivot = list[pivot] + + +def quick_select(items: list, index: int): + """ + >>> quick_select([2, 4, 5, 7, 899, 54, 32], 5) + 54 + >>> quick_select([2, 4, 5, 7, 899, 54, 32], 1) + 4 + >>> quick_select([5, 4, 3, 2], 2) + 4 + >>> quick_select([3, 5, 7, 10, 2, 12], 3) + 7 + """ + # index = len(items) // 2 when trying to find the median + # (value of index when items is sorted) + + # invalid input + if index >= len(items) or index < 0: + return None + + pivot = random.randint(0, len(items) - 1) + pivot = items[pivot] count = 0 - smaller, equal, larger =_partition(list, pivot) + smaller, equal, larger = _partition(items, pivot) count = len(equal) m = len(smaller) - #k is the pivot - if m <= k < m + count: + # index is the pivot + if m <= index < m + count: return pivot # must be in smaller - elif m > k: - return quickSelect(smaller, k) - #must be in larger + elif m > index: + return quick_select(smaller, index) + # must be in larger else: - return quickSelect(larger, k - (m + count)) + return quick_select(larger, index - (m + count)) diff --git a/searches/sentinel_linear_search.py b/searches/sentinel_linear_search.py index 336cc5ab3b74..6c4da9b21189 100644 --- a/searches/sentinel_linear_search.py +++ b/searches/sentinel_linear_search.py @@ -10,6 +10,7 @@ python sentinel_linear_search.py """ + def sentinel_linear_search(sequence, target): """Pure implementation of sentinel linear search algorithm in Python @@ -44,19 +45,14 @@ def sentinel_linear_search(sequence, target): return index -if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - - user_input = raw_input('Enter numbers separated by comma:\n').strip() - sequence = [int(item) for item in user_input.split(',')] +if __name__ == "__main__": + user_input = input("Enter numbers separated by comma:\n").strip() + sequence = [int(item) for item in user_input.split(",")] - target_input = raw_input('Enter a single number to be found in the list:\n') + target_input = input("Enter a single number to be found in the list:\n") target = int(target_input) result = sentinel_linear_search(sequence, target) if result is not None: - print('{} found at positions: {}'.format(target, result)) + print("{} found at positions: {}".format(target, result)) else: - print('Not found') \ No newline at end of file + print("Not found") diff --git a/searches/simple-binary-search.py b/searches/simple-binary-search.py new file mode 100644 index 000000000000..80e43ea346b2 --- /dev/null +++ b/searches/simple-binary-search.py @@ -0,0 +1,26 @@ +# A binary search implementation to test if a number is in a list of elements + + +def binary_search(a_list, item): + """ + >>> test_list = [0, 1, 2, 8, 13, 17, 19, 32, 42] + >>> print(binary_search(test_list, 3)) + False + >>> print(binary_search(test_list, 13)) + True + """ + if len(a_list) == 0: + return False + midpoint = len(a_list) // 2 + if a_list[midpoint] == item: + return True + if item < a_list[midpoint]: + return binary_search(a_list[:midpoint], item) + else: + return binary_search(a_list[midpoint + 1 :], item) + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/searches/tabu_search.py b/searches/tabu_search.py index e21ddd53cc78..9a1478244503 100644 --- a/searches/tabu_search.py +++ b/searches/tabu_search.py @@ -38,7 +38,7 @@ def generate_neighbours(path): and the cost (distance) for each neighbor. Example of dict_of_neighbours: - >>> dict_of_neighbours[a] + >>) dict_of_neighbours[a] [[b,20],[c,18],[d,22],[e,26]] This indicates the neighbors of node (city) 'a', which has neighbor the node 'b' with distance 20, @@ -55,13 +55,17 @@ def generate_neighbours(path): _list.append([line.split()[1], line.split()[2]]) dict_of_neighbours[line.split()[0]] = _list else: - dict_of_neighbours[line.split()[0]].append([line.split()[1], line.split()[2]]) + dict_of_neighbours[line.split()[0]].append( + [line.split()[1], line.split()[2]] + ) if line.split()[1] not in dict_of_neighbours: _list = list() _list.append([line.split()[0], line.split()[2]]) dict_of_neighbours[line.split()[1]] = _list else: - dict_of_neighbours[line.split()[1]].append([line.split()[0], line.split()[2]]) + dict_of_neighbours[line.split()[1]].append( + [line.split()[0], line.split()[2]] + ) return dict_of_neighbours @@ -111,8 +115,11 @@ def generate_first_solution(path, dict_of_neighbours): break position += 1 - distance_of_first_solution = distance_of_first_solution + int( - dict_of_neighbours[first_solution[-2]][position][1]) - 10000 + distance_of_first_solution = ( + distance_of_first_solution + + int(dict_of_neighbours[first_solution[-2]][position][1]) + - 10000 + ) return first_solution, distance_of_first_solution @@ -130,7 +137,7 @@ def find_neighborhood(solution, dict_of_neighbours): Example: - >>> find_neighborhood(['a','c','b','d','e','a']) + >>) find_neighborhood(['a','c','b','d','e','a']) [['a','e','b','d','c','a',90], [['a','c','d','b','e','a',90],['a','d','b','c','e','a',93], ['a','c','b','e','d','a',102], ['a','c','e','d','b','a',113], ['a','b','c','d','e','a',93]] @@ -167,7 +174,9 @@ def find_neighborhood(solution, dict_of_neighbours): return neighborhood_of_solution -def tabu_search(first_solution, distance_of_first_solution, dict_of_neighbours, iters, size): +def tabu_search( + first_solution, distance_of_first_solution, dict_of_neighbours, iters, size +): """ Pure implementation of Tabu search algorithm for a Travelling Salesman Problem in Python. @@ -207,8 +216,10 @@ def tabu_search(first_solution, distance_of_first_solution, dict_of_neighbours, break i = i + 1 - if [first_exchange_node, second_exchange_node] not in tabu_list and [second_exchange_node, - first_exchange_node] not in tabu_list: + if [first_exchange_node, second_exchange_node] not in tabu_list and [ + second_exchange_node, + first_exchange_node, + ] not in tabu_list: tabu_list.append([first_exchange_node, second_exchange_node]) found = True solution = best_solution[:-1] @@ -231,10 +242,17 @@ def tabu_search(first_solution, distance_of_first_solution, dict_of_neighbours, def main(args=None): dict_of_neighbours = generate_neighbours(args.File) - first_solution, distance_of_first_solution = generate_first_solution(args.File, dict_of_neighbours) + first_solution, distance_of_first_solution = generate_first_solution( + args.File, dict_of_neighbours + ) - best_sol, best_cost = tabu_search(first_solution, distance_of_first_solution, dict_of_neighbours, args.Iterations, - args.Size) + best_sol, best_cost = tabu_search( + first_solution, + distance_of_first_solution, + dict_of_neighbours, + args.Iterations, + args.Size, + ) print("Best solution: {0}, with total distance: {1}.".format(best_sol, best_cost)) @@ -242,11 +260,22 @@ def main(args=None): if __name__ == "__main__": parser = argparse.ArgumentParser(description="Tabu Search") parser.add_argument( - "-f", "--File", type=str, help="Path to the file containing the data", required=True) + "-f", + "--File", + type=str, + help="Path to the file containing the data", + required=True, + ) parser.add_argument( - "-i", "--Iterations", type=int, help="How many iterations the algorithm should perform", required=True) + "-i", + "--Iterations", + type=int, + help="How many iterations the algorithm should perform", + required=True, + ) parser.add_argument( - "-s", "--Size", type=int, help="Size of the tabu list", required=True) + "-s", "--Size", type=int, help="Size of the tabu list", required=True + ) # Pass the arguments to main method sys.exit(main(parser.parse_args())) diff --git a/searches/ternary_search.py b/searches/ternary_search.py index c610f9b3c6da..43407b7e5538 100644 --- a/searches/ternary_search.py +++ b/searches/ternary_search.py @@ -1,107 +1,103 @@ -''' +""" This is a type of divide and conquer algorithm which divides the search space into -3 parts and finds the target value based on the property of the array or list +3 parts and finds the target value based on the property of the array or list (usually monotonic property). Time Complexity : O(log3 N) Space Complexity : O(1) -''' -from __future__ import print_function - +""" import sys -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - # This is the precision for this function which can be altered. # It is recommended for users to keep this number greater than or equal to 10. precision = 10 # This is the linear search that will occur after the search space has become smaller. def lin_search(left, right, A, target): - for i in range(left, right+1): - if(A[i] == target): + for i in range(left, right + 1): + if A[i] == target: return i + # This is the iterative method of the ternary search algorithm. def ite_ternary_search(A, target): left = 0 - right = len(A) - 1; - while(True): - if(left a[j]) agrees with the direction, -# then a[i] and a[j] are interchanged.*/ +# The parameter dir indicates the sorting direction, ASCENDING +# or DESCENDING; if (a[i] > a[j]) agrees with the direction, +# then a[i] and a[j] are interchanged.*/ def compAndSwap(a, i, j, dire): if (dire == 1 and a[i] > a[j]) or (dire == 0 and a[i] < a[j]): a[i], a[j] = a[j], a[i] @@ -12,8 +12,8 @@ def compAndSwap(a, i, j, dire): # if dir = 1, and in descending order otherwise (means dir=0). -# The sequence to be sorted starts at index position low, -# the parameter cnt is the number of elements to be sorted. +# The sequence to be sorted starts at index position low, +# the parameter cnt is the number of elements to be sorted. def bitonicMerge(a, low, cnt, dire): if cnt > 1: k = int(cnt / 2) @@ -26,7 +26,7 @@ def bitonicMerge(a, low, cnt, dire): # sorting its two halves in opposite sorting orders, and then -# calls bitonicMerge to make them in the same order +# calls bitonicMerge to make them in the same order def bitonicSort(a, low, cnt, dire): if cnt > 1: k = int(cnt / 2) @@ -42,15 +42,16 @@ def sort(a, N, up): bitonicSort(a, 0, N, up) -# Driver code to test above -a = [] +if __name__ == "__main__": + # Driver code to test above + a = [] -n = int(input()) -for i in range(n): - a.append(int(input())) -up = 1 + n = int(input().strip()) + for i in range(n): + a.append(int(input().strip())) + up = 1 -sort(a, n, up) -print("\n\nSorted array is") -for i in range(n): - print("%d" % a[i]) + sort(a, n, up) + print("\n\nSorted array is") + for i in range(n): + print("%d" % a[i]) diff --git a/sorts/bogosort.py b/sorts/bogo_sort.py similarity index 58% rename from sorts/bogosort.py rename to sorts/bogo_sort.py index 33eac66bf21c..0afa444e5b8e 100644 --- a/sorts/bogosort.py +++ b/sorts/bogo_sort.py @@ -1,28 +1,27 @@ """ This is a pure python implementation of the bogosort algorithm For doctests run following command: -python -m doctest -v bogosort.py +python -m doctest -v bogo_sort.py or -python3 -m doctest -v bogosort.py +python3 -m doctest -v bogo_sort.py For manual testing run: -python bogosort.py +python bogo_sort.py """ -from __future__ import print_function import random -def bogosort(collection): +def bogo_sort(collection): """Pure implementation of the bogosort algorithm in Python :param collection: some mutable ordered collection with heterogeneous comparable items inside :return: the same collection ordered by ascending Examples: - >>> bogosort([0, 5, 3, 2, 2]) + >>> bogo_sort([0, 5, 3, 2, 2]) [0, 2, 2, 3, 5] - >>> bogosort([]) + >>> bogo_sort([]) [] - >>> bogosort([-2, -5, -45]) + >>> bogo_sort([-2, -5, -45]) [-45, -5, -2] """ @@ -38,12 +37,8 @@ def isSorted(collection): random.shuffle(collection) return collection -if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - user_input = raw_input('Enter numbers separated by a comma:\n').strip() - unsorted = [int(item) for item in user_input.split(',')] - print(bogosort(unsorted)) +if __name__ == "__main__": + user_input = input("Enter numbers separated by a comma:\n").strip() + unsorted = [int(item) for item in user_input.split(",")] + print(bogo_sort(unsorted)) diff --git a/sorts/bubble_sort.py b/sorts/bubble_sort.py index e17fc3358d53..eb356bc7dcad 100644 --- a/sorts/bubble_sort.py +++ b/sorts/bubble_sort.py @@ -1,6 +1,3 @@ -from __future__ import print_function - - def bubble_sort(collection): """Pure implementation of bubble sort algorithm in Python @@ -9,34 +6,38 @@ def bubble_sort(collection): :return: the same collection ordered by ascending Examples: - >>> bubble_sort([0, 5, 3, 2, 2]) + >>> bubble_sort([0, 5, 2, 3, 2]) [0, 2, 2, 3, 5] >>> bubble_sort([]) [] - >>> bubble_sort([-2, -5, -45]) + >>> bubble_sort([-2, -45, -5]) [-45, -5, -2] - - >>> bubble_sort([-23,0,6,-4,34]) - [-23,-4,0,6,34] + + >>> bubble_sort([-23, 0, 6, -4, 34]) + [-23, -4, 0, 6, 34] + + >>> bubble_sort([-23, 0, 6, -4, 34]) == sorted([-23, 0, 6, -4, 34]) + True """ length = len(collection) - for i in range(length-1): + for i in range(length - 1): swapped = False - for j in range(length-1-i): - if collection[j] > collection[j+1]: + for j in range(length - 1 - i): + if collection[j] > collection[j + 1]: swapped = True - collection[j], collection[j+1] = collection[j+1], collection[j] - if not swapped: break # Stop iteration if the collection is sorted. + collection[j], collection[j + 1] = collection[j + 1], collection[j] + if not swapped: + break # Stop iteration if the collection is sorted. return collection -if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - user_input = raw_input('Enter numbers separated by a comma:').strip() - unsorted = [int(item) for item in user_input.split(',')] - print(*bubble_sort(unsorted), sep=',') +if __name__ == "__main__": + import time + + user_input = input("Enter numbers separated by a comma:").strip() + unsorted = [int(item) for item in user_input.split(",")] + start = time.process_time() + print(*bubble_sort(unsorted), sep=",") + print(f"Processing time: {time.process_time() - start}") diff --git a/sorts/bucket_sort.py b/sorts/bucket_sort.py index bd4281e463eb..217ee5893c4b 100644 --- a/sorts/bucket_sort.py +++ b/sorts/bucket_sort.py @@ -1,57 +1,49 @@ #!/usr/bin/env python + +"""Illustrate how to implement bucket sort algorithm.""" + # Author: OMKAR PATHAK # This program will illustrate how to implement bucket sort algorithm -# Wikipedia says: Bucket sort, or bin sort, is a sorting algorithm that works by distributing the -# elements of an array into a number of buckets. Each bucket is then sorted individually, either using -# a different sorting algorithm, or by recursively applying the bucket sorting algorithm. It is a -# distribution sort, and is a cousin of radix sort in the most to least significant digit flavour. -# Bucket sort is a generalization of pigeonhole sort. Bucket sort can be implemented with comparisons -# and therefore can also be considered a comparison sort algorithm. The computational complexity estimates -# involve the number of buckets. +# Wikipedia says: Bucket sort, or bin sort, is a sorting algorithm that works +# by distributing the elements of an array into a number of buckets. +# Each bucket is then sorted individually, either using a different sorting +# algorithm, or by recursively applying the bucket sorting algorithm. It is a +# distribution sort, and is a cousin of radix sort in the most to least +# significant digit flavour. +# Bucket sort is a generalization of pigeonhole sort. Bucket sort can be +# implemented with comparisons and therefore can also be considered a +# comparison sort algorithm. The computational complexity estimates involve the +# number of buckets. # Time Complexity of Solution: -# Best Case O(n); Average Case O(n); Worst Case O(n) - -from __future__ import print_function -from insertion_sort import insertion_sort -import math +# Worst case scenario occurs when all the elements are placed in a single bucket. The overall performance +# would then be dominated by the algorithm used to sort each bucket. In this case, O(n log n), because of TimSort +# +# Average Case O(n + (n^2)/k + k), where k is the number of buckets +# +# If k = O(n), time complexity is O(n) DEFAULT_BUCKET_SIZE = 5 -def bucketSort(myList, bucketSize=DEFAULT_BUCKET_SIZE): - if(len(myList) == 0): - print('You don\'t have any elements in array!') - - minValue = myList[0] - maxValue = myList[0] - - # For finding minimum and maximum values - for i in range(0, len(myList)): - if myList[i] < minValue: - minValue = myList[i] - elif myList[i] > maxValue: - maxValue = myList[i] - - # Initialize buckets - bucketCount = math.floor((maxValue - minValue) / bucketSize) + 1 - buckets = [] - for i in range(0, bucketCount): - buckets.append([]) - - # For putting values in buckets - for i in range(0, len(myList)): - buckets[math.floor((myList[i] - minValue) / bucketSize)].append(myList[i]) - - # Sort buckets and place back into input array - sortedArray = [] - for i in range(0, len(buckets)): - insertion_sort(buckets[i]) - for j in range(0, len(buckets[i])): - sortedArray.append(buckets[i][j]) - - return sortedArray - -if __name__ == '__main__': - sortedArray = bucketSort([12, 23, 4, 5, 3, 2, 12, 81, 56, 95]) - print(sortedArray) + +def bucket_sort(my_list, bucket_size=DEFAULT_BUCKET_SIZE): + if len(my_list) == 0: + raise Exception("Please add some elements in the array.") + + min_value, max_value = (min(my_list), max(my_list)) + bucket_count = (max_value - min_value) // bucket_size + 1 + buckets = [[] for _ in range(int(bucket_count))] + + for i in range(len(my_list)): + buckets[int((my_list[i] - min_value) // bucket_size)].append(my_list[i]) + + return sorted( + [buckets[i][j] for i in range(len(buckets)) for j in range(len(buckets[i]))] + ) + + +if __name__ == "__main__": + user_input = input("Enter numbers separated by a comma:").strip() + unsorted = [float(n) for n in user_input.split(",") if len(user_input) > 0] + print(bucket_sort(unsorted)) diff --git a/sorts/cocktail_shaker_sort.py b/sorts/cocktail_shaker_sort.py index 8ad3383bbe9f..ab624421a3d6 100644 --- a/sorts/cocktail_shaker_sort.py +++ b/sorts/cocktail_shaker_sort.py @@ -1,32 +1,26 @@ -from __future__ import print_function - def cocktail_shaker_sort(unsorted): """ Pure implementation of the cocktail shaker sort algorithm in Python. """ - for i in range(len(unsorted)-1, 0, -1): + for i in range(len(unsorted) - 1, 0, -1): swapped = False - + for j in range(i, 0, -1): - if unsorted[j] < unsorted[j-1]: - unsorted[j], unsorted[j-1] = unsorted[j-1], unsorted[j] + if unsorted[j] < unsorted[j - 1]: + unsorted[j], unsorted[j - 1] = unsorted[j - 1], unsorted[j] swapped = True for j in range(i): - if unsorted[j] > unsorted[j+1]: - unsorted[j], unsorted[j+1] = unsorted[j+1], unsorted[j] + if unsorted[j] > unsorted[j + 1]: + unsorted[j], unsorted[j + 1] = unsorted[j + 1], unsorted[j] swapped = True - + if not swapped: return unsorted - -if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - - user_input = raw_input('Enter numbers separated by a comma:\n').strip() - unsorted = [int(item) for item in user_input.split(',')] + + +if __name__ == "__main__": + user_input = input("Enter numbers separated by a comma:\n").strip() + unsorted = [int(item) for item in user_input.split(",")] cocktail_shaker_sort(unsorted) print(unsorted) diff --git a/sorts/comb_sort.py b/sorts/comb_sort.py index 22b6f66f04cc..3c4c57483e3f 100644 --- a/sorts/comb_sort.py +++ b/sorts/comb_sort.py @@ -12,6 +12,7 @@ python comb_sort.py """ + def comb_sort(data): """Pure implementation of comb sort algorithm in Python :param collection: some mutable ordered collection with heterogeneous @@ -38,21 +39,16 @@ def comb_sort(data): i = 0 while gap + i < len(data): - if data[i] > data[i+gap]: + if data[i] > data[i + gap]: # Swap values - data[i], data[i+gap] = data[i+gap], data[i] + data[i], data[i + gap] = data[i + gap], data[i] swapped = True i += 1 return data -if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - - user_input = raw_input('Enter numbers separated by a comma:\n').strip() - unsorted = [int(item) for item in user_input.split(',')] +if __name__ == "__main__": + user_input = input("Enter numbers separated by a comma:\n").strip() + unsorted = [int(item) for item in user_input.split(",")] print(comb_sort(unsorted)) diff --git a/sorts/counting_sort.py b/sorts/counting_sort.py index ad98f1a0da4c..b672d4af47cb 100644 --- a/sorts/counting_sort.py +++ b/sorts/counting_sort.py @@ -8,8 +8,6 @@ python counting_sort.py """ -from __future__ import print_function - def counting_sort(collection): """Pure implementation of counting sort algorithm in Python @@ -44,7 +42,7 @@ def counting_sort(collection): # sum each position with it's predecessors. now, counting_arr[i] tells # us how many elements <= i has in the collection for i in range(1, counting_arr_length): - counting_arr[i] = counting_arr[i] + counting_arr[i-1] + counting_arr[i] = counting_arr[i] + counting_arr[i - 1] # create the output collection ordered = [0] * coll_len @@ -52,24 +50,24 @@ def counting_sort(collection): # place the elements in the output, respecting the original order (stable # sort) from end to begin, updating counting_arr for i in reversed(range(0, coll_len)): - ordered[counting_arr[collection[i] - coll_min]-1] = collection[i] + ordered[counting_arr[collection[i] - coll_min] - 1] = collection[i] counting_arr[collection[i] - coll_min] -= 1 return ordered + def counting_sort_string(string): - return ''.join([chr(i) for i in counting_sort([ord(c) for c in string])]) + """ + >>> counting_sort_string("thisisthestring") + 'eghhiiinrsssttt' + """ + return "".join([chr(i) for i in counting_sort([ord(c) for c in string])]) -if __name__ == '__main__': +if __name__ == "__main__": # Test string sort assert "eghhiiinrsssttt" == counting_sort_string("thisisthestring") - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - - user_input = raw_input('Enter numbers separated by a comma:\n').strip() - unsorted = [int(item) for item in user_input.split(',')] + user_input = input("Enter numbers separated by a comma:\n").strip() + unsorted = [int(item) for item in user_input.split(",")] print(counting_sort(unsorted)) diff --git a/sorts/cyclesort.py b/sorts/cycle_sort.py similarity index 74% rename from sorts/cyclesort.py rename to sorts/cycle_sort.py index ee19a1ade360..4ce6a2a0e757 100644 --- a/sorts/cyclesort.py +++ b/sorts/cycle_sort.py @@ -1,7 +1,4 @@ # Code contributed by Honey Sharma -from __future__ import print_function - - def cycle_sort(array): ans = 0 @@ -44,17 +41,12 @@ def cycle_sort(array): # Main Code starts here -if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - -user_input = raw_input('Enter numbers separated by a comma:\n') -unsorted = [int(item) for item in user_input.split(',')] -n = len(unsorted) -cycle_sort(unsorted) - -print("After sort : ") -for i in range(0, n): - print(unsorted[i], end=' ') +if __name__ == "__main__": + user_input = input("Enter numbers separated by a comma:\n") + unsorted = [int(item) for item in user_input.split(",")] + n = len(unsorted) + cycle_sort(unsorted) + + print("After sort : ") + for i in range(0, n): + print(unsorted[i], end=" ") diff --git a/sorts/double_sort.py b/sorts/double_sort.py new file mode 100644 index 000000000000..aca4b97ca775 --- /dev/null +++ b/sorts/double_sort.py @@ -0,0 +1,42 @@ +def double_sort(lst): + """this sorting algorithm sorts an array using the principle of bubble sort , + but does it both from left to right and right to left , + hence i decided to call it "double sort" + :param collection: mutable ordered sequence of elements + :return: the same collection in ascending order + Examples: + >>> double_sort([-1 ,-2 ,-3 ,-4 ,-5 ,-6 ,-7]) + [-7, -6, -5, -4, -3, -2, -1] + >>> double_sort([]) + [] + >>> double_sort([-1 ,-2 ,-3 ,-4 ,-5 ,-6]) + [-6, -5, -4, -3, -2, -1] + >>> double_sort([-3, 10, 16, -42, 29]) == sorted([-3, 10, 16, -42, 29]) + True + """ + no_of_elements = len(lst) + for i in range( + 0, int(((no_of_elements - 1) / 2) + 1) + ): # we dont need to traverse to end of list as + for j in range(0, no_of_elements - 1): + if ( + lst[j + 1] < lst[j] + ): # applying bubble sort algorithm from left to right (or forwards) + temp = lst[j + 1] + lst[j + 1] = lst[j] + lst[j] = temp + if ( + lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j] + ): # applying bubble sort algorithm from right to left (or backwards) + temp = lst[no_of_elements - 1 - j] + lst[no_of_elements - 1 - j] = lst[no_of_elements - 2 - j] + lst[no_of_elements - 2 - j] = temp + return lst + + +if __name__ == "__main__": + print("enter the list to be sorted") + lst = [int(x) for x in input().split()] # inputing elements of the list in one line + sorted_lst = double_sort(lst) + print("the sorted list is") + print(sorted_lst) diff --git a/sorts/external-sort.py b/sorts/external_sort.py similarity index 79% rename from sorts/external-sort.py rename to sorts/external_sort.py index 1638e9efafee..abdcb29f95b2 100644 --- a/sorts/external-sort.py +++ b/sorts/external_sort.py @@ -6,8 +6,9 @@ import os import argparse + class FileSplitter(object): - BLOCK_FILENAME_FORMAT = 'block_{0}.dat' + BLOCK_FILENAME_FORMAT = "block_{0}.dat" def __init__(self, filename): self.filename = filename @@ -15,7 +16,7 @@ def __init__(self, filename): def write_block(self, data, block_number): filename = self.BLOCK_FILENAME_FORMAT.format(block_number) - with open(filename, 'w') as file: + with open(filename, "w") as file: file.write(data) self.block_filenames.append(filename) @@ -36,7 +37,7 @@ def split(self, block_size, sort_key=None): else: lines.sort(key=sort_key) - self.write_block(''.join(lines), i) + self.write_block("".join(lines), i) i += 1 def cleanup(self): @@ -63,14 +64,16 @@ def __init__(self, files): self.buffers = {i: None for i in range(self.num_buffers)} def get_dict(self): - return {i: self.buffers[i] for i in range(self.num_buffers) if i not in self.empty} + return { + i: self.buffers[i] for i in range(self.num_buffers) if i not in self.empty + } def refresh(self): for i in range(self.num_buffers): if self.buffers[i] is None and i not in self.empty: self.buffers[i] = self.files[i].readline() - if self.buffers[i] == '': + if self.buffers[i] == "": self.empty.add(i) self.files[i].close() @@ -92,7 +95,7 @@ def __init__(self, merge_strategy): def merge(self, filenames, outfilename, buffer_size): buffers = FilesArray(self.get_file_handles(filenames, buffer_size)) - with open(outfilename, 'w', buffer_size) as outfile: + with open(outfilename, "w", buffer_size) as outfile: while buffers.refresh(): min_index = self.merge_strategy.select(buffers.get_dict()) outfile.write(buffers.unshift(min_index)) @@ -101,12 +104,11 @@ def get_file_handles(self, filenames, buffer_size): files = {} for i in range(len(filenames)): - files[i] = open(filenames[i], 'r', buffer_size) + files[i] = open(filenames[i], "r", buffer_size) return files - class ExternalSort(object): def __init__(self, block_size): self.block_size = block_size @@ -118,7 +120,7 @@ def sort(self, filename, sort_key=None): merger = FileMerger(NWayMerge()) buffer_size = self.block_size / (num_blocks + 1) - merger.merge(splitter.get_block_filenames(), filename + '.out', buffer_size) + merger.merge(splitter.get_block_filenames(), filename + ".out", buffer_size) splitter.cleanup() @@ -127,32 +129,29 @@ def get_number_blocks(self, filename, block_size): def parse_memory(string): - if string[-1].lower() == 'k': + if string[-1].lower() == "k": return int(string[:-1]) * 1024 - elif string[-1].lower() == 'm': + elif string[-1].lower() == "m": return int(string[:-1]) * 1024 * 1024 - elif string[-1].lower() == 'g': + elif string[-1].lower() == "g": return int(string[:-1]) * 1024 * 1024 * 1024 else: return int(string) - def main(): parser = argparse.ArgumentParser() - parser.add_argument('-m', - '--mem', - help='amount of memory to use for sorting', - default='100M') - parser.add_argument('filename', - metavar='', - nargs=1, - help='name of file to sort') + parser.add_argument( + "-m", "--mem", help="amount of memory to use for sorting", default="100M" + ) + parser.add_argument( + "filename", metavar="", nargs=1, help="name of file to sort" + ) args = parser.parse_args() sorter = ExternalSort(parse_memory(args.mem)) sorter.sort(args.filename[0]) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/sorts/gnome_sort.py b/sorts/gnome_sort.py index 2927b097f11d..58a44c94da43 100644 --- a/sorts/gnome_sort.py +++ b/sorts/gnome_sort.py @@ -1,30 +1,25 @@ -from __future__ import print_function +"""Gnome Sort Algorithm.""" + def gnome_sort(unsorted): - """ - Pure implementation of the gnome sort algorithm in Python. - """ + """Pure implementation of the gnome sort algorithm in Python.""" if len(unsorted) <= 1: return unsorted - + i = 1 - + while i < len(unsorted): - if unsorted[i-1] <= unsorted[i]: + if unsorted[i - 1] <= unsorted[i]: i += 1 else: - unsorted[i-1], unsorted[i] = unsorted[i], unsorted[i-1] + unsorted[i - 1], unsorted[i] = unsorted[i], unsorted[i - 1] i -= 1 - if (i == 0): + if i == 0: i = 1 - -if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - - user_input = raw_input('Enter numbers separated by a comma:\n').strip() - unsorted = [int(item) for item in user_input.split(',')] + + +if __name__ == "__main__": + user_input = input("Enter numbers separated by a comma:\n").strip() + unsorted = [int(item) for item in user_input.split(",")] gnome_sort(unsorted) print(unsorted) diff --git a/sorts/heap_sort.py b/sorts/heap_sort.py index 3c72abca8059..a39ae2b88da2 100644 --- a/sorts/heap_sort.py +++ b/sorts/heap_sort.py @@ -1,4 +1,4 @@ -''' +""" This is a pure python implementation of the heap sort algorithm. For doctests run following command: @@ -8,9 +8,7 @@ For manual testing run: python heap_sort.py -''' - -from __future__ import print_function +""" def heapify(unsorted, index, heap_size): @@ -29,7 +27,7 @@ def heapify(unsorted, index, heap_size): def heap_sort(unsorted): - ''' + """ Pure implementation of the heap sort algorithm in Python :param collection: some mutable ordered collection with heterogeneous comparable items inside @@ -44,7 +42,7 @@ def heap_sort(unsorted): >>> heap_sort([-2, -5, -45]) [-45, -5, -2] - ''' + """ n = len(unsorted) for i in range(n // 2 - 1, -1, -1): heapify(unsorted, i, n) @@ -53,12 +51,8 @@ def heap_sort(unsorted): heapify(unsorted, 0, i) return unsorted -if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - user_input = raw_input('Enter numbers separated by a comma:\n').strip() - unsorted = [int(item) for item in user_input.split(',')] +if __name__ == "__main__": + user_input = input("Enter numbers separated by a comma:\n").strip() + unsorted = [int(item) for item in user_input.split(",")] print(heap_sort(unsorted)) diff --git a/sorts/i_sort.py b/sorts/i_sort.py new file mode 100644 index 000000000000..f6100a8d0819 --- /dev/null +++ b/sorts/i_sort.py @@ -0,0 +1,21 @@ +def insertionSort(arr): + """ + >>> a = arr[:] + >>> insertionSort(a) + >>> a == sorted(a) + True + """ + for i in range(1, len(arr)): + key = arr[i] + j = i - 1 + while j >= 0 and key < arr[j]: + arr[j + 1] = arr[j] + j -= 1 + arr[j + 1] = key + + +arr = [12, 11, 13, 5, 6] +insertionSort(arr) +print("Sorted array is:") +for i in range(len(arr)): + print("%d" % arr[i]) diff --git a/sorts/insertion_sort.py b/sorts/insertion_sort.py index 59917ac059a7..b767018c3d57 100644 --- a/sorts/insertion_sort.py +++ b/sorts/insertion_sort.py @@ -9,7 +9,6 @@ For manual testing run: python insertion_sort.py """ -from __future__ import print_function def insertion_sort(collection): @@ -29,20 +28,23 @@ def insertion_sort(collection): >>> insertion_sort([-2, -5, -45]) [-45, -5, -2] """ - for index in range(1, len(collection)): - while index > 0 and collection[index - 1] > collection[index]: - collection[index], collection[index - 1] = collection[index - 1], collection[index] - index -= 1 - return collection + for loop_index in range(1, len(collection)): + insertion_index = loop_index + while ( + insertion_index > 0 + and collection[insertion_index - 1] > collection[insertion_index] + ): + collection[insertion_index], collection[insertion_index - 1] = ( + collection[insertion_index - 1], + collection[insertion_index], + ) + insertion_index -= 1 + return collection -if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - user_input = raw_input('Enter numbers separated by a comma:\n').strip() - unsorted = [int(item) for item in user_input.split(',')] +if __name__ == "__main__": + user_input = input("Enter numbers separated by a comma:\n").strip() + unsorted = [int(item) for item in user_input.split(",")] print(insertion_sort(unsorted)) diff --git a/sorts/merge_sort.py b/sorts/merge_sort.py index ca4d319fa7f1..13f1144d4ad3 100644 --- a/sorts/merge_sort.py +++ b/sorts/merge_sort.py @@ -9,7 +9,6 @@ For manual testing run: python merge_sort.py """ -from __future__ import print_function def merge_sort(collection): @@ -29,44 +28,25 @@ def merge_sort(collection): >>> merge_sort([-2, -5, -45]) [-45, -5, -2] """ - length = len(collection) - if length > 1: - midpoint = length // 2 - left_half = merge_sort(collection[:midpoint]) - right_half = merge_sort(collection[midpoint:]) - i = 0 - j = 0 - k = 0 - left_length = len(left_half) - right_length = len(right_half) - while i < left_length and j < right_length: - if left_half[i] < right_half[j]: - collection[k] = left_half[i] - i += 1 - else: - collection[k] = right_half[j] - j += 1 - k += 1 - while i < left_length: - collection[k] = left_half[i] - i += 1 - k += 1 - - while j < right_length: - collection[k] = right_half[j] - j += 1 - k += 1 - - return collection - - -if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - - user_input = raw_input('Enter numbers separated by a comma:\n').strip() - unsorted = [int(item) for item in user_input.split(',')] - print(merge_sort(unsorted)) + def merge(left, right): + """merge left and right + :param left: left collection + :param right: right collection + :return: merge result + """ + result = [] + while left and right: + result.append((left if left[0] <= right[0] else right).pop(0)) + return result + left + right + + if len(collection) <= 1: + return collection + mid = len(collection) // 2 + return merge(merge_sort(collection[:mid]), merge_sort(collection[mid:])) + + +if __name__ == "__main__": + user_input = input("Enter numbers separated by a comma:\n").strip() + unsorted = [int(item) for item in user_input.split(",")] + print(*merge_sort(unsorted), sep=",") diff --git a/sorts/merge_sort_fastest.py b/sorts/merge_sort_fastest.py index 9fc9275aacba..f3c067795dd5 100644 --- a/sorts/merge_sort_fastest.py +++ b/sorts/merge_sort_fastest.py @@ -1,19 +1,40 @@ -''' -Python implementation of merge sort algorithm. +""" +Python implementation of the fastest merge sort algorithm. Takes an average of 0.6 microseconds to sort a list of length 1000 items. Best Case Scenario : O(n) -Worst Case Scenario : O(n) -''' -def merge_sort(LIST): - start = [] - end = [] - while len(LIST) > 1: - a = min(LIST) - b = max(LIST) - start.append(a) - end.append(b) - LIST.remove(a) - LIST.remove(b) - if LIST: start.append(LIST[0]) +Worst Case Scenario : O(n^2) because native python functions:min, max and remove are already O(n) +""" + + +def merge_sort(collection): + """Pure implementation of the fastest merge sort algorithm in Python + + :param collection: some mutable ordered collection with heterogeneous + comparable items inside + :return: a collection ordered by ascending + + Examples: + >>> merge_sort([0, 5, 3, 2, 2]) + [0, 2, 2, 3, 5] + + >>> merge_sort([]) + [] + + >>> merge_sort([-2, -5, -45]) + [-45, -5, -2] + """ + start, end = [], [] + while len(collection) > 1: + min_one, max_one = min(collection), max(collection) + start.append(min_one) + end.append(max_one) + collection.remove(min_one) + collection.remove(max_one) end.reverse() - return (start + end) + return start + collection + end + + +if __name__ == "__main__": + user_input = input("Enter numbers separated by a comma:\n").strip() + unsorted = [int(item) for item in user_input.split(",")] + print(*merge_sort(unsorted), sep=",") diff --git a/sorts/odd_even_transposition_parallel.py b/sorts/odd_even_transposition_parallel.py new file mode 100644 index 000000000000..4d2f377024d2 --- /dev/null +++ b/sorts/odd_even_transposition_parallel.py @@ -0,0 +1,154 @@ +""" +This is an implementation of odd-even transposition sort. + +It works by performing a series of parallel swaps between odd and even pairs of +variables in the list. + +This implementation represents each variable in the list with a process and +each process communicates with its neighboring processes in the list to perform +comparisons. +They are synchronized with locks and message passing but other forms of +synchronization could be used. +""" +from multiprocessing import Process, Pipe, Lock + +# lock used to ensure that two processes do not access a pipe at the same time +processLock = Lock() + +""" +The function run by the processes that sorts the list + +position = the position in the list the prcoess represents, used to know which + neighbor we pass our value to +value = the initial value at list[position] +LSend, RSend = the pipes we use to send to our left and right neighbors +LRcv, RRcv = the pipes we use to receive from our left and right neighbors +resultPipe = the pipe used to send results back to main +""" + + +def oeProcess(position, value, LSend, RSend, LRcv, RRcv, resultPipe): + global processLock + + # we perform n swaps since after n swaps we know we are sorted + # we *could* stop early if we are sorted already, but it takes as long to + # find out we are sorted as it does to sort the list with this algorithm + for i in range(0, 10): + + if (i + position) % 2 == 0 and RSend != None: + # send your value to your right neighbor + processLock.acquire() + RSend[1].send(value) + processLock.release() + + # receive your right neighbor's value + processLock.acquire() + temp = RRcv[0].recv() + processLock.release() + + # take the lower value since you are on the left + value = min(value, temp) + elif (i + position) % 2 != 0 and LSend != None: + # send your value to your left neighbor + processLock.acquire() + LSend[1].send(value) + processLock.release() + + # receive your left neighbor's value + processLock.acquire() + temp = LRcv[0].recv() + processLock.release() + + # take the higher value since you are on the right + value = max(value, temp) + # after all swaps are performed, send the values back to main + resultPipe[1].send(value) + + +""" +the function which creates the processes that perform the parallel swaps + +arr = the list to be sorted +""" + + +def OddEvenTransposition(arr): + + processArray = [] + + resultPipe = [] + + # initialize the list of pipes where the values will be retrieved + for _ in arr: + resultPipe.append(Pipe()) + + # creates the processes + # the first and last process only have one neighbor so they are made outside + # of the loop + tempRs = Pipe() + tempRr = Pipe() + processArray.append( + Process( + target=oeProcess, + args=(0, arr[0], None, tempRs, None, tempRr, resultPipe[0]), + ) + ) + tempLr = tempRs + tempLs = tempRr + + for i in range(1, len(arr) - 1): + tempRs = Pipe() + tempRr = Pipe() + processArray.append( + Process( + target=oeProcess, + args=(i, arr[i], tempLs, tempRs, tempLr, tempRr, resultPipe[i]), + ) + ) + tempLr = tempRs + tempLs = tempRr + + processArray.append( + Process( + target=oeProcess, + args=( + len(arr) - 1, + arr[len(arr) - 1], + tempLs, + None, + tempLr, + None, + resultPipe[len(arr) - 1], + ), + ) + ) + + # start the processes + for p in processArray: + p.start() + + # wait for the processes to end and write their values to the list + for p in range(0, len(resultPipe)): + arr[p] = resultPipe[p][0].recv() + processArray[p].join() + + return arr + + +# creates a reverse sorted list and sorts it +def main(): + arr = [] + + for i in range(10, 0, -1): + arr.append(i) + print("Initial List") + print(*arr) + + list = OddEvenTransposition(arr) + + print("Sorted List\n") + print(*arr) + + +if __name__ == "__main__": + main() diff --git a/sorts/odd_even_transposition_single_threaded.py b/sorts/odd_even_transposition_single_threaded.py new file mode 100644 index 000000000000..ec045d9dd08d --- /dev/null +++ b/sorts/odd_even_transposition_single_threaded.py @@ -0,0 +1,35 @@ +""" +This is a non-parallelized implementation of odd-even transpostiion sort. + +Normally the swaps in each set happen simultaneously, without that the algorithm +is no better than bubble sort. +""" + + +def OddEvenTransposition(arr): + for i in range(0, len(arr)): + for i in range(i % 2, len(arr) - 1, 2): + if arr[i + 1] < arr[i]: + arr[i], arr[i + 1] = arr[i + 1], arr[i] + print(*arr) + + return arr + + +# creates a list and sorts it +def main(): + list = [] + + for i in range(10, 0, -1): + list.append(i) + print("Initial List") + print(*list) + + list = OddEvenTransposition(list) + + print("Sorted List\n") + print(*list) + + +if __name__ == "__main__": + main() diff --git a/sorts/pancake_sort.py b/sorts/pancake_sort.py index 26fd40b7f67c..ee54e57f9e0f 100644 --- a/sorts/pancake_sort.py +++ b/sorts/pancake_sort.py @@ -1,16 +1,39 @@ -# Pancake sort algorithm -# Only can reverse array from 0 to i +""" +This is a pure python implementation of the pancake sort algorithm +For doctests run following command: +python3 -m doctest -v pancake_sort.py +or +python -m doctest -v pancake_sort.py +For manual testing run: +python pancake_sort.py +""" -def pancakesort(arr): + +def pancake_sort(arr): + """Sort Array with Pancake Sort. + :param arr: Collection containing comparable items + :return: Collection ordered in ascending order of items + Examples: + >>> pancake_sort([0, 5, 3, 2, 2]) + [0, 2, 2, 3, 5] + >>> pancake_sort([]) + [] + >>> pancake_sort([-2, -5, -45]) + [-45, -5, -2] + """ cur = len(arr) while cur > 1: # Find the maximum number in arr mi = arr.index(max(arr[0:cur])) - # Reverse from 0 to mi - arr = arr[mi::-1] + arr[mi+1:len(arr)] - # Reverse whole list - arr = arr[cur-1::-1] + arr[cur:len(arr)] + # Reverse from 0 to mi + arr = arr[mi::-1] + arr[mi + 1 : len(arr)] + # Reverse whole list + arr = arr[cur - 1 :: -1] + arr[cur : len(arr)] cur -= 1 return arr -print(pancakesort([0,10,15,3,2,9,14,13])) + +if __name__ == "__main__": + user_input = input("Enter numbers separated by a comma:\n").strip() + unsorted = [int(item) for item in user_input.split(",")] + print(pancake_sort(unsorted)) diff --git a/sorts/pigeon_sort.py b/sorts/pigeon_sort.py new file mode 100644 index 000000000000..cf900699bc8d --- /dev/null +++ b/sorts/pigeon_sort.py @@ -0,0 +1,68 @@ +""" + This is an implementation of Pigeon Hole Sort. + For doctests run following command: + + python3 -m doctest -v pigeon_sort.py + or + python -m doctest -v pigeon_sort.py + + For manual testing run: + python pigeon_sort.py +""" + + +def pigeon_sort(array): + """ + Implementation of pigeon hole sort algorithm + :param array: Collection of comparable items + :return: Collection sorted in ascending order + >>> pigeon_sort([0, 5, 3, 2, 2]) + [0, 2, 2, 3, 5] + >>> pigeon_sort([]) + [] + >>> pigeon_sort([-2, -5, -45]) + [-45, -5, -2] + """ + if len(array) == 0: + return array + + # Manually finds the minimum and maximum of the array. + min = array[0] + max = array[0] + + for i in range(len(array)): + if array[i] < min: + min = array[i] + elif array[i] > max: + max = array[i] + + # Compute the variables + holes_range = max - min + 1 + holes = [0 for _ in range(holes_range)] + holes_repeat = [0 for _ in range(holes_range)] + + # Make the sorting. + for i in range(len(array)): + index = array[i] - min + if holes[index] != array[i]: + holes[index] = array[i] + holes_repeat[index] += 1 + else: + holes_repeat[index] += 1 + + # Makes the array back by replacing the numbers. + index = 0 + for i in range(holes_range): + while holes_repeat[i] > 0: + array[index] = holes[i] + index += 1 + holes_repeat[i] -= 1 + + # Returns the sorted array. + return array + + +if __name__ == "__main__": + user_input = input("Enter numbers separated by comma:\n") + unsorted = [int(x) for x in user_input.split(",")] + print(pigeon_sort(unsorted)) diff --git a/sorts/quick_sort.py b/sorts/quick_sort.py index e01d319a4b29..29e10206f720 100644 --- a/sorts/quick_sort.py +++ b/sorts/quick_sort.py @@ -9,7 +9,6 @@ For manual testing run: python quick_sort.py """ -from __future__ import print_function def quick_sort(collection): @@ -33,18 +32,20 @@ def quick_sort(collection): if length <= 1: return collection else: - pivot = collection[0] - greater = [element for element in collection[1:] if element > pivot] - lesser = [element for element in collection[1:] if element <= pivot] + # Use the last element as the first pivot + pivot = collection.pop() + # Put elements greater than pivot in greater list + # Put elements lesser than pivot in lesser list + greater, lesser = [], [] + for element in collection: + if element > pivot: + greater.append(element) + else: + lesser.append(element) return quick_sort(lesser) + [pivot] + quick_sort(greater) -if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - - user_input = raw_input('Enter numbers separated by a comma:\n').strip() - unsorted = [ int(item) for item in user_input.split(',') ] - print( quick_sort(unsorted) ) +if __name__ == "__main__": + user_input = input("Enter numbers separated by a comma:\n").strip() + unsorted = [int(item) for item in user_input.split(",")] + print(quick_sort(unsorted)) diff --git a/sorts/quick_sort_3_partition.py b/sorts/quick_sort_3_partition.py index def646cdbc50..a25ac7def802 100644 --- a/sorts/quick_sort_3_partition.py +++ b/sorts/quick_sort_3_partition.py @@ -1,5 +1,3 @@ -from __future__ import print_function - def quick_sort_3partition(sorting, left, right): if right <= left: return @@ -19,13 +17,9 @@ def quick_sort_3partition(sorting, left, right): quick_sort_3partition(sorting, left, a - 1) quick_sort_3partition(sorting, b + 1, right) -if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - user_input = raw_input('Enter numbers separated by a comma:\n').strip() - unsorted = [ int(item) for item in user_input.split(',') ] - quick_sort_3partition(unsorted,0,len(unsorted)-1) +if __name__ == "__main__": + user_input = input("Enter numbers separated by a comma:\n").strip() + unsorted = [int(item) for item in user_input.split(",")] + quick_sort_3partition(unsorted, 0, len(unsorted) - 1) print(unsorted) diff --git a/sorts/radix_sort.py b/sorts/radix_sort.py index e4cee61f35e3..2990247a0ac0 100644 --- a/sorts/radix_sort.py +++ b/sorts/radix_sort.py @@ -1,4 +1,4 @@ -def radixsort(lst): +def radix_sort(lst): RADIX = 10 placement = 1 @@ -6,21 +6,21 @@ def radixsort(lst): max_digit = max(lst) while placement < max_digit: - # declare and initialize buckets - buckets = [list() for _ in range( RADIX )] + # declare and initialize buckets + buckets = [list() for _ in range(RADIX)] - # split lst between lists - for i in lst: - tmp = int((i / placement) % RADIX) - buckets[tmp].append(i) + # split lst between lists + for i in lst: + tmp = int((i / placement) % RADIX) + buckets[tmp].append(i) - # empty lists into lst array - a = 0 - for b in range( RADIX ): - buck = buckets[b] - for i in buck: - lst[a] = i - a += 1 + # empty lists into lst array + a = 0 + for b in range(RADIX): + buck = buckets[b] + for i in buck: + lst[a] = i + a += 1 - # move to next - placement *= RADIX + # move to next + placement *= RADIX diff --git a/sorts/random_normal_distribution_quicksort.py b/sorts/random_normal_distribution_quicksort.py index dfa37da61e26..be3b90190407 100644 --- a/sorts/random_normal_distribution_quicksort.py +++ b/sorts/random_normal_distribution_quicksort.py @@ -1,66 +1,62 @@ -from __future__ import print_function from random import randint from tempfile import TemporaryFile import numpy as np - -def _inPlaceQuickSort(A,start,end): +def _inPlaceQuickSort(A, start, end): count = 0 - if start list: + """ + >>> for data in ([2, 1, 0], [2.2, 1.1, 0], "quick_sort"): + ... quick_sort(data) == sorted(data) + True + True + True + """ + if len(data) <= 1: + return data + else: + return ( + quick_sort([e for e in data[1:] if e <= data[0]]) + + [data[0]] + + quick_sort([e for e in data[1:] if e > data[0]]) + ) + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/sorts/selection_sort.py b/sorts/selection_sort.py index c1d66c5a8b38..6a9c063d3364 100644 --- a/sorts/selection_sort.py +++ b/sorts/selection_sort.py @@ -9,7 +9,6 @@ For manual testing run: python selection_sort.py """ -from __future__ import print_function def selection_sort(collection): @@ -36,18 +35,12 @@ def selection_sort(collection): for k in range(i + 1, length): if collection[k] < collection[least]: least = k - collection[least], collection[i] = ( - collection[i], collection[least] - ) + if least != i: + collection[least], collection[i] = (collection[i], collection[least]) return collection -if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - - user_input = raw_input('Enter numbers separated by a comma:\n').strip() - unsorted = [int(item) for item in user_input.split(',')] +if __name__ == "__main__": + user_input = input("Enter numbers separated by a comma:\n").strip() + unsorted = [int(item) for item in user_input.split(",")] print(selection_sort(unsorted)) diff --git a/sorts/shell_sort.py b/sorts/shell_sort.py index dc1846758243..ff9c2785b218 100644 --- a/sorts/shell_sort.py +++ b/sorts/shell_sort.py @@ -9,7 +9,6 @@ For manual testing run: python shell_sort.py """ -from __future__ import print_function def shell_sort(collection): @@ -43,12 +42,8 @@ def shell_sort(collection): return collection -if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - user_input = raw_input('Enter numbers separated by a comma:\n').strip() - unsorted = [int(item) for item in user_input.split(',')] +if __name__ == "__main__": + user_input = input("Enter numbers separated by a comma:\n").strip() + unsorted = [int(item) for item in user_input.split(",")] print(shell_sort(unsorted)) diff --git a/sorts/sorting_graphs.png b/sorts/sorting_graphs.png deleted file mode 100644 index 628245f3eb39..000000000000 Binary files a/sorts/sorting_graphs.png and /dev/null differ diff --git a/sorts/stooge_sort.py b/sorts/stooge_sort.py new file mode 100644 index 000000000000..de997a85df12 --- /dev/null +++ b/sorts/stooge_sort.py @@ -0,0 +1,40 @@ +def stooge_sort(arr): + """ + Examples: + >>> stooge_sort([18.1, 0, -7.1, -1, 2, 2]) + [-7.1, -1, 0, 2, 2, 18.1] + + >>> stooge_sort([]) + [] + """ + stooge(arr, 0, len(arr) - 1) + return arr + + +def stooge(arr, i, h): + + if i >= h: + return + + # If first element is smaller than the last then swap them + if arr[i] > arr[h]: + arr[i], arr[h] = arr[h], arr[i] + + # If there are more than 2 elements in the array + if h - i + 1 > 2: + t = (int)((h - i + 1) / 3) + + # Recursively sort first 2/3 elements + stooge(arr, i, (h - t)) + + # Recursively sort last 2/3 elements + stooge(arr, i + t, (h)) + + # Recursively sort first 2/3 elements + stooge(arr, i, (h - t)) + + +if __name__ == "__main__": + user_input = input("Enter numbers separated by a comma:\n").strip() + unsorted = [int(item) for item in user_input.split(",")] + print(stooge_sort(unsorted)) diff --git a/sorts/timsort.py b/sorts/tim_sort.py similarity index 62% rename from sorts/timsort.py rename to sorts/tim_sort.py index 80c5cd1e8d3f..b95ff34cf384 100644 --- a/sorts/timsort.py +++ b/sorts/tim_sort.py @@ -1,10 +1,6 @@ -from __future__ import print_function def binary_search(lst, item, start, end): if start == end: - if lst[start] > item: - return start - else: - return start + 1 + return start if lst[start] > item else start + 1 if start > end: return start @@ -23,7 +19,7 @@ def insertion_sort(lst): for index in range(1, length): value = lst[index] pos = binary_search(lst, value, 0, index - 1) - lst = lst[:pos] + [value] + lst[pos:index] + lst[index+1:] + lst = lst[:pos] + [value] + lst[pos:index] + lst[index + 1 :] return lst @@ -41,31 +37,35 @@ def merge(left, right): return [right[0]] + merge(left, right[1:]) -def timsort(lst): - runs, sorted_runs = [], [] +def tim_sort(lst): + """ + >>> tim_sort("Python") + ['P', 'h', 'n', 'o', 't', 'y'] + >>> tim_sort((1.1, 1, 0, -1, -1.1)) + [-1.1, -1, 0, 1, 1.1] + >>> tim_sort(list(reversed(list(range(7))))) + [0, 1, 2, 3, 4, 5, 6] + >>> tim_sort([3, 2, 1]) == insertion_sort([3, 2, 1]) + True + >>> tim_sort([3, 2, 1]) == sorted([3, 2, 1]) + True + """ length = len(lst) + runs, sorted_runs = [], [] new_run = [lst[0]] sorted_array = [] - - for i in range(1, length): - if i == length - 1: - new_run.append(lst[i]) - runs.append(new_run) - break - + i = 1 + while i < length: if lst[i] < lst[i - 1]: - if not new_run: - runs.append([lst[i - 1]]) - new_run.append(lst[i]) - else: - runs.append(new_run) - new_run = [] + runs.append(new_run) + new_run = [lst[i]] else: new_run.append(lst[i]) + i += 1 + runs.append(new_run) for run in runs: sorted_runs.append(insertion_sort(run)) - for run in sorted_runs: sorted_array = merge(sorted_array, run) @@ -74,9 +74,10 @@ def timsort(lst): def main(): - lst = [5,9,10,3,-4,5,178,92,46,-18,0,7] - sorted_lst = timsort(lst) + lst = [5, 9, 10, 3, -4, 5, 178, 92, 46, -18, 0, 7] + sorted_lst = tim_sort(lst) print(sorted_lst) -if __name__ == '__main__': + +if __name__ == "__main__": main() diff --git a/sorts/topological_sort.py b/sorts/topological_sort.py index 52dc34f4f733..e7a52f7c7714 100644 --- a/sorts/topological_sort.py +++ b/sorts/topological_sort.py @@ -1,11 +1,12 @@ -from __future__ import print_function +"""Topological Sort.""" + # a # / \ # b c # / \ # d e -edges = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []} -vertices = ['a', 'b', 'c', 'd', 'e'] +edges = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []} +vertices = ["a", "b", "c", "d", "e"] def topological_sort(start, visited, sort): @@ -29,5 +30,6 @@ def topological_sort(start, visited, sort): return sort -sort = topological_sort('a', [], []) -print(sort) +if __name__ == "__main__": + sort = topological_sort("a", [], []) + print(sort) diff --git a/sorts/tree_sort.py b/sorts/tree_sort.py index f8ecf84c6ff8..716170a94fd1 100644 --- a/sorts/tree_sort.py +++ b/sorts/tree_sort.py @@ -1,14 +1,18 @@ -# Tree_sort algorithm -# Build a BST and in order traverse. +""" +Tree_sort algorithm. -class node(): +Build a BST and in order traverse. +""" + + +class node: # BST data structure def __init__(self, val): self.val = val - self.left = None - self.right = None - - def insert(self,val): + self.left = None + self.right = None + + def insert(self, val): if self.val: if val < self.val: if self.left is None: @@ -23,23 +27,27 @@ def insert(self,val): else: self.val = val + def inorder(root, res): - # Recursive travesal + # Recursive travesal if root: - inorder(root.left,res) + inorder(root.left, res) res.append(root.val) - inorder(root.right,res) + inorder(root.right, res) + -def treesort(arr): +def tree_sort(arr): # Build BST if len(arr) == 0: return arr root = node(arr[0]) - for i in range(1,len(arr)): + for i in range(1, len(arr)): root.insert(arr[i]) - # Traverse BST in order. + # Traverse BST in order. res = [] - inorder(root,res) + inorder(root, res) return res -print(treesort([10,1,3,2,9,14,13])) \ No newline at end of file + +if __name__ == "__main__": + print(tree_sort([10, 1, 3, 2, 9, 14, 13])) diff --git a/sorts/wiggle_sort.py b/sorts/wiggle_sort.py index cc83487bdeb1..5e5220ffbf05 100644 --- a/sorts/wiggle_sort.py +++ b/sorts/wiggle_sort.py @@ -1,21 +1,26 @@ """ -Given an unsorted array nums, reorder it such that nums[0] < nums[1] > nums[2] < nums[3].... +Wiggle Sort. + +Given an unsorted array nums, reorder it such +that nums[0] < nums[1] > nums[2] < nums[3].... For example: -if input numbers = [3, 5, 2, 1, 6, 4] +if input numbers = [3, 5, 2, 1, 6, 4] one possible Wiggle Sorted answer is [3, 5, 1, 6, 2, 4]. """ -def wiggle_sort(nums): - for i in range(len(nums)): - if (i % 2 == 1) == (nums[i-1] > nums[i]): - nums[i-1], nums[i] = nums[i], nums[i-1] -print("Enter the array elements:\n") -array=list(map(int,input().split())) -print("The unsorted array is:\n") -print(array) -wiggle_sort(array) -print("Array after Wiggle sort:\n") -print(array) +def wiggle_sort(nums): + """Perform Wiggle Sort.""" + for i in range(len(nums)): + if (i % 2 == 1) == (nums[i - 1] > nums[i]): + nums[i - 1], nums[i] = nums[i], nums[i - 1] +if __name__ == "__main__": + print("Enter the array elements:\n") + array = list(map(int, input().split())) + print("The unsorted array is:\n") + print(array) + wiggle_sort(array) + print("Array after Wiggle sort:\n") + print(array) diff --git a/strings/aho-corasick.py b/strings/aho-corasick.py new file mode 100644 index 000000000000..b2f89450ee7a --- /dev/null +++ b/strings/aho-corasick.py @@ -0,0 +1,92 @@ +from collections import deque + + +class Automaton: + def __init__(self, keywords): + self.adlist = list() + self.adlist.append( + {"value": "", "next_states": [], "fail_state": 0, "output": []} + ) + + for keyword in keywords: + self.add_keyword(keyword) + self.set_fail_transitions() + + def find_next_state(self, current_state, char): + for state in self.adlist[current_state]["next_states"]: + if char == self.adlist[state]["value"]: + return state + return None + + def add_keyword(self, keyword): + current_state = 0 + for character in keyword: + if self.find_next_state(current_state, character): + current_state = self.find_next_state(current_state, character) + else: + self.adlist.append( + { + "value": character, + "next_states": [], + "fail_state": 0, + "output": [], + } + ) + self.adlist[current_state]["next_states"].append(len(self.adlist) - 1) + current_state = len(self.adlist) - 1 + self.adlist[current_state]["output"].append(keyword) + + def set_fail_transitions(self): + q = deque() + for node in self.adlist[0]["next_states"]: + q.append(node) + self.adlist[node]["fail_state"] = 0 + while q: + r = q.popleft() + for child in self.adlist[r]["next_states"]: + q.append(child) + state = self.adlist[r]["fail_state"] + while ( + self.find_next_state(state, self.adlist[child]["value"]) == None + and state != 0 + ): + state = self.adlist[state]["fail_state"] + self.adlist[child]["fail_state"] = self.find_next_state( + state, self.adlist[child]["value"] + ) + if self.adlist[child]["fail_state"] is None: + self.adlist[child]["fail_state"] = 0 + self.adlist[child]["output"] = ( + self.adlist[child]["output"] + + self.adlist[self.adlist[child]["fail_state"]]["output"] + ) + + def search_in(self, string): + """ + >>> A = Automaton(["what", "hat", "ver", "er"]) + >>> A.search_in("whatever, err ... , wherever") + {'what': [0], 'hat': [1], 'ver': [5, 25], 'er': [6, 10, 22, 26]} + """ + result = dict() # returns a dict with keywords and list of its occurences + current_state = 0 + for i in range(len(string)): + while ( + self.find_next_state(current_state, string[i]) is None + and current_state != 0 + ): + current_state = self.adlist[current_state]["fail_state"] + current_state = self.find_next_state(current_state, string[i]) + if current_state is None: + current_state = 0 + else: + for key in self.adlist[current_state]["output"]: + if not (key in result): + result[key] = [] + result[key].append((i - len(key) + 1)) + return result + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/strings/boyer_moore_search.py b/strings/boyer_moore_search.py new file mode 100644 index 000000000000..59ee76b860d3 --- /dev/null +++ b/strings/boyer_moore_search.py @@ -0,0 +1,83 @@ +""" +The algorithm finds the pattern in given text using following rule. + +The bad-character rule considers the mismatched character in Text. +The next occurrence of that character to the left in Pattern is found, + +If the mismatched character occurs to the left in Pattern, +a shift is proposed that aligns text block and pattern. + +If the mismatched character does not occur to the left in Pattern, +a shift is proposed that moves the entirety of Pattern past +the point of mismatch in the text. + +If there no mismatch then the pattern matches with text block. + +Time Complexity : O(n/m) + n=length of main string + m=length of pattern string +""" + + +class BoyerMooreSearch: + def __init__(self, text, pattern): + self.text, self.pattern = text, pattern + self.textLen, self.patLen = len(text), len(pattern) + + def match_in_pattern(self, char): + """ finds the index of char in pattern in reverse order + + Paremeters : + char (chr): character to be searched + + Returns : + i (int): index of char from last in pattern + -1 (int): if char is not found in pattern + """ + + for i in range(self.patLen - 1, -1, -1): + if char == self.pattern[i]: + return i + return -1 + + def mismatch_in_text(self, currentPos): + """ finds the index of mis-matched character in text when compared with pattern from last + + Paremeters : + currentPos (int): current index position of text + + Returns : + i (int): index of mismatched char from last in text + -1 (int): if there is no mis-match between pattern and text block + """ + + for i in range(self.patLen - 1, -1, -1): + if self.pattern[i] != self.text[currentPos + i]: + return currentPos + i + return -1 + + def bad_character_heuristic(self): + # searches pattern in text and returns index positions + positions = [] + for i in range(self.textLen - self.patLen + 1): + mismatch_index = self.mismatch_in_text(i) + if mismatch_index == -1: + positions.append(i) + else: + match_index = self.match_in_pattern(self.text[mismatch_index]) + i = ( + mismatch_index - match_index + ) # shifting index lgtm [py/multiple-definition] + return positions + + +text = "ABAABA" +pattern = "AB" +bms = BoyerMooreSearch(text, pattern) +positions = bms.bad_character_heuristic() + +if len(positions) == 0: + print("No match found") +else: + print("Pattern found in following positions: ") + print(positions) diff --git a/strings/knuth_morris_pratt.py b/strings/knuth_morris_pratt.py index 4553944284be..c7e96887c387 100644 --- a/strings/knuth_morris_pratt.py +++ b/strings/knuth_morris_pratt.py @@ -46,14 +46,14 @@ def get_failure_array(pattern): if pattern[i] == pattern[j]: i += 1 elif i > 0: - i = failure[i-1] + i = failure[i - 1] continue j += 1 failure.append(i) return failure -if __name__ == '__main__': +if __name__ == "__main__": # Test 1) pattern = "abc1abc12" text1 = "alskfjaldsabc1abc1abc12k23adsfabcabc" diff --git a/strings/levenshtein_distance.py b/strings/levenshtein_distance.py index 274dfd7ccf9b..9b8793544a99 100644 --- a/strings/levenshtein_distance.py +++ b/strings/levenshtein_distance.py @@ -64,15 +64,13 @@ def levenshtein_distance(first_word, second_word): return previous_row[-1] -if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - - first_word = raw_input('Enter the first word:\n').strip() - second_word = raw_input('Enter the second word:\n').strip() +if __name__ == "__main__": + first_word = input("Enter the first word:\n").strip() + second_word = input("Enter the second word:\n").strip() result = levenshtein_distance(first_word, second_word) - print('Levenshtein distance between {} and {} is {}'.format( - first_word, second_word, result)) + print( + "Levenshtein distance between {} and {} is {}".format( + first_word, second_word, result + ) + ) diff --git a/strings/manacher.py b/strings/manacher.py index 9a44b19ba77a..ef8a724d027d 100644 --- a/strings/manacher.py +++ b/strings/manacher.py @@ -1,10 +1,15 @@ -# calculate palindromic length from center with incresmenting difference -def palindromic_length( center, diff, string): - if center-diff == -1 or center+diff == len(string) or string[center-diff] != string[center+diff] : +# calculate palindromic length from center with incrementing difference +def palindromic_length(center, diff, string): + if ( + center - diff == -1 + or center + diff == len(string) + or string[center - diff] != string[center + diff] + ): return 0 - return 1 + palindromic_length(center, diff+1, string) + return 1 + palindromic_length(center, diff + 1, string) -def palindromic_string( input_string ): + +def palindromic_string(input_string): """ Manacher’s algorithm which finds Longest Palindromic Substring in linear time. @@ -16,37 +21,36 @@ def palindromic_string( input_string ): 3. return output_string from center - max_length to center + max_length and remove all "|" """ max_length = 0 - + # if input_string is "aba" than new_input_string become "a|b|a" new_input_string = "" output_string = "" # append each character + "|" in new_string for range(0, length-1) - for i in input_string[:len(input_string)-1] : + for i in input_string[: len(input_string) - 1]: new_input_string += i + "|" - #append last character + # append last character new_input_string += input_string[-1] - # for each character in new_string find corresponding palindromic string - for i in range(len(new_input_string)) : + for i in range(len(new_input_string)): # get palindromic length from ith position length = palindromic_length(i, 1, new_input_string) # update max_length and start position - if max_length < length : + if max_length < length: max_length = length start = i - - #create that string - for i in new_input_string[start-max_length:start+max_length+1] : + + # create that string + for i in new_input_string[start - max_length : start + max_length + 1]: if i != "|": output_string += i - + return output_string -if __name__ == '__main__': +if __name__ == "__main__": n = input() print(palindromic_string(n)) diff --git a/strings/min_cost_string_conversion.py b/strings/min_cost_string_conversion.py index de7f9f727283..abc9d2c65158 100644 --- a/strings/min_cost_string_conversion.py +++ b/strings/min_cost_string_conversion.py @@ -1,121 +1,118 @@ -from __future__ import print_function - -try: - xrange #Python 2 -except NameError: - xrange = range #Python 3 - -''' +""" Algorithm for calculating the most cost-efficient sequence for converting one string into another. The only allowed operations are ---Copy character with cost cC ---Replace character with cost cR ---Delete character with cost cD ---Insert character with cost cI -''' +""" + + def compute_transform_tables(X, Y, cC, cR, cD, cI): - X = list(X) - Y = list(Y) - m = len(X) - n = len(Y) + X = list(X) + Y = list(Y) + m = len(X) + n = len(Y) + + costs = [[0 for _ in range(n + 1)] for _ in range(m + 1)] + ops = [[0 for _ in range(n + 1)] for _ in range(m + 1)] - costs = [[0 for _ in xrange(n+1)] for _ in xrange(m+1)] - ops = [[0 for _ in xrange(n+1)] for _ in xrange(m+1)] + for i in range(1, m + 1): + costs[i][0] = i * cD + ops[i][0] = "D%c" % X[i - 1] - for i in xrange(1, m+1): - costs[i][0] = i*cD - ops[i][0] = 'D%c' % X[i-1] + for i in range(1, n + 1): + costs[0][i] = i * cI + ops[0][i] = "I%c" % Y[i - 1] - for i in xrange(1, n+1): - costs[0][i] = i*cI - ops[0][i] = 'I%c' % Y[i-1] + for i in range(1, m + 1): + for j in range(1, n + 1): + if X[i - 1] == Y[j - 1]: + costs[i][j] = costs[i - 1][j - 1] + cC + ops[i][j] = "C%c" % X[i - 1] + else: + costs[i][j] = costs[i - 1][j - 1] + cR + ops[i][j] = "R%c" % X[i - 1] + str(Y[j - 1]) - for i in xrange(1, m+1): - for j in xrange(1, n+1): - if X[i-1] == Y[j-1]: - costs[i][j] = costs[i-1][j-1] + cC - ops[i][j] = 'C%c' % X[i-1] - else: - costs[i][j] = costs[i-1][j-1] + cR - ops[i][j] = 'R%c' % X[i-1] + str(Y[j-1]) + if costs[i - 1][j] + cD < costs[i][j]: + costs[i][j] = costs[i - 1][j] + cD + ops[i][j] = "D%c" % X[i - 1] - if costs[i-1][j] + cD < costs[i][j]: - costs[i][j] = costs[i-1][j] + cD - ops[i][j] = 'D%c' % X[i-1] + if costs[i][j - 1] + cI < costs[i][j]: + costs[i][j] = costs[i][j - 1] + cI + ops[i][j] = "I%c" % Y[j - 1] - if costs[i][j-1] + cI < costs[i][j]: - costs[i][j] = costs[i][j-1] + cI - ops[i][j] = 'I%c' % Y[j-1] + return costs, ops - return costs, ops def assemble_transformation(ops, i, j): - if i == 0 and j == 0: - seq = [] - return seq - else: - if ops[i][j][0] == 'C' or ops[i][j][0] == 'R': - seq = assemble_transformation(ops, i-1, j-1) - seq.append(ops[i][j]) - return seq - elif ops[i][j][0] == 'D': - seq = assemble_transformation(ops, i-1, j) - seq.append(ops[i][j]) - return seq - else: - seq = assemble_transformation(ops, i, j-1) - seq.append(ops[i][j]) - return seq - -if __name__ == '__main__': - _, operations = compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2) - - m = len(operations) - n = len(operations[0]) - sequence = assemble_transformation(operations, m-1, n-1) - - string = list('Python') - i = 0 - cost = 0 - - with open('min_cost.txt', 'w') as file: - for op in sequence: - print(''.join(string)) - - if op[0] == 'C': - file.write('%-16s' % 'Copy %c' % op[1]) - file.write('\t\t\t' + ''.join(string)) - file.write('\r\n') - - cost -= 1 - elif op[0] == 'R': - string[i] = op[2] - - file.write('%-16s' % ('Replace %c' % op[1] + ' with ' + str(op[2]))) - file.write('\t\t' + ''.join(string)) - file.write('\r\n') - - cost += 1 - elif op[0] == 'D': - string.pop(i) - - file.write('%-16s' % 'Delete %c' % op[1]) - file.write('\t\t\t' + ''.join(string)) - file.write('\r\n') - - cost += 2 - else: - string.insert(i, op[1]) - - file.write('%-16s' % 'Insert %c' % op[1]) - file.write('\t\t\t' + ''.join(string)) - file.write('\r\n') - - cost += 2 - - i += 1 - - print(''.join(string)) - print('Cost: ', cost) - - file.write('\r\nMinimum cost: ' + str(cost)) + if i == 0 and j == 0: + seq = [] + return seq + else: + if ops[i][j][0] == "C" or ops[i][j][0] == "R": + seq = assemble_transformation(ops, i - 1, j - 1) + seq.append(ops[i][j]) + return seq + elif ops[i][j][0] == "D": + seq = assemble_transformation(ops, i - 1, j) + seq.append(ops[i][j]) + return seq + else: + seq = assemble_transformation(ops, i, j - 1) + seq.append(ops[i][j]) + return seq + + +if __name__ == "__main__": + _, operations = compute_transform_tables("Python", "Algorithms", -1, 1, 2, 2) + + m = len(operations) + n = len(operations[0]) + sequence = assemble_transformation(operations, m - 1, n - 1) + + string = list("Python") + i = 0 + cost = 0 + + with open("min_cost.txt", "w") as file: + for op in sequence: + print("".join(string)) + + if op[0] == "C": + file.write("%-16s" % "Copy %c" % op[1]) + file.write("\t\t\t" + "".join(string)) + file.write("\r\n") + + cost -= 1 + elif op[0] == "R": + string[i] = op[2] + + file.write("%-16s" % ("Replace %c" % op[1] + " with " + str(op[2]))) + file.write("\t\t" + "".join(string)) + file.write("\r\n") + + cost += 1 + elif op[0] == "D": + string.pop(i) + + file.write("%-16s" % "Delete %c" % op[1]) + file.write("\t\t\t" + "".join(string)) + file.write("\r\n") + + cost += 2 + else: + string.insert(i, op[1]) + + file.write("%-16s" % "Insert %c" % op[1]) + file.write("\t\t\t" + "".join(string)) + file.write("\r\n") + + cost += 2 + + i += 1 + + print("".join(string)) + print("Cost: ", cost) + + file.write("\r\nMinimum cost: " + str(cost)) diff --git a/strings/naiveStringSearch.py b/strings/naive_string_search.py similarity index 53% rename from strings/naiveStringSearch.py rename to strings/naive_string_search.py index 04c0d8157b24..a8c2ea584399 100644 --- a/strings/naiveStringSearch.py +++ b/strings/naive_string_search.py @@ -7,23 +7,26 @@ n=length of main string m=length of pattern string """ -def naivePatternSearch(mainString,pattern): - patLen=len(pattern) - strLen=len(mainString) - position=[] - for i in range(strLen-patLen+1): - match_found=True + + +def naivePatternSearch(mainString, pattern): + patLen = len(pattern) + strLen = len(mainString) + position = [] + for i in range(strLen - patLen + 1): + match_found = True for j in range(patLen): - if mainString[i+j]!=pattern[j]: - match_found=False + if mainString[i + j] != pattern[j]: + match_found = False break if match_found: position.append(i) return position -mainString="ABAAABCDBBABCDDEBCABC" -pattern="ABC" -position=naivePatternSearch(mainString,pattern) + +mainString = "ABAAABCDBBABCDDEBCABC" +pattern = "ABC" +position = naivePatternSearch(mainString, pattern) print("Pattern found in position ") for x in position: - print(x) \ No newline at end of file + print(x) diff --git a/strings/rabin_karp.py b/strings/rabin_karp.py index 04a849266ead..1fb145ec97fa 100644 --- a/strings/rabin_karp.py +++ b/strings/rabin_karp.py @@ -1,6 +1,11 @@ +# Numbers of alphabet which we call base +alphabet_size = 256 +# Modulus to hash a string +modulus = 1000003 + + def rabin_karp(pattern, text): """ - The Rabin-Karp Algorithm for finding a pattern within a piece of text with complexity O(nm), most efficient when it is used with multiple patterns as it is able to check if any of a set of patterns match a section of text in o(1) given the precomputed hashes. @@ -12,22 +17,42 @@ def rabin_karp(pattern, text): 2) Step through the text one character at a time passing a window with the same length as the pattern calculating the hash of the text within the window compare it with the hash of the pattern. Only testing equality if the hashes match - """ p_len = len(pattern) - p_hash = hash(pattern) + t_len = len(text) + if p_len > t_len: + return False + + p_hash = 0 + text_hash = 0 + modulus_power = 1 - for i in range(0, len(text) - (p_len - 1)): + # Calculating the hash of pattern and substring of text + for i in range(p_len): + p_hash = (ord(pattern[i]) + p_hash * alphabet_size) % modulus + text_hash = (ord(text[i]) + text_hash * alphabet_size) % modulus + if i == p_len - 1: + continue + modulus_power = (modulus_power * alphabet_size) % modulus - # written like this t - text_hash = hash(text[i:i + p_len]) - if text_hash == p_hash and \ - text[i:i + p_len] == pattern: + for i in range(0, t_len - p_len + 1): + if text_hash == p_hash and text[i : i + p_len] == pattern: return True + if i == t_len - p_len: + continue + # Calculating the ruling hash + text_hash = ( + (text_hash - ord(text[i]) * modulus_power) * alphabet_size + + ord(text[i + p_len]) + ) % modulus return False -if __name__ == '__main__': +def test_rabin_karp(): + """ + >>> test_rabin_karp() + Success. + """ # Test 1) pattern = "abc1abc12" text1 = "alskfjaldsabc1abc1abc12k23adsfabcabc" @@ -48,3 +73,15 @@ def rabin_karp(pattern, text): pattern = "abcdabcy" text = "abcxabcdabxabcdabcdabcy" assert rabin_karp(pattern, text) + + # Test 5) + pattern = "Lü" + text = "Lüsai" + assert rabin_karp(pattern, text) + pattern = "Lue" + assert not rabin_karp(pattern, text) + print("Success.") + + +if __name__ == "__main__": + test_rabin_karp() diff --git a/traversals/binary_tree_traversals.py b/traversals/binary_tree_traversals.py index 393664579146..31a73ae0c6a4 100644 --- a/traversals/binary_tree_traversals.py +++ b/traversals/binary_tree_traversals.py @@ -1,14 +1,8 @@ """ This is pure python implementation of tree traversal algorithms """ -from __future__ import print_function - import queue - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 +from typing import List class TreeNode: @@ -20,35 +14,45 @@ def __init__(self, data): def build_tree(): print("\n********Press N to stop entering at any point of time********\n") - print("Enter the value of the root node: ", end="") - check = raw_input().strip().lower() - if check == 'n': + check = input("Enter the value of the root node: ").strip().lower() or "n" + if check == "n": return None - data = int(check) - q = queue.Queue() - tree_node = TreeNode(data) + q: queue.Queue = queue.Queue() + tree_node = TreeNode(int(check)) q.put(tree_node) while not q.empty(): node_found = q.get() - print("Enter the left node of %s: " % node_found.data, end="") - check = raw_input().strip().lower() - if check == 'n': + msg = "Enter the left node of %s: " % node_found.data + check = input(msg).strip().lower() or "n" + if check == "n": return tree_node - left_data = int(check) - left_node = TreeNode(left_data) + left_node = TreeNode(int(check)) node_found.left = left_node q.put(left_node) - print("Enter the right node of %s: " % node_found.data, end="") - check = raw_input().strip().lower() - if check == 'n': + msg = "Enter the right node of %s: " % node_found.data + check = input(msg).strip().lower() or "n" + if check == "n": return tree_node - right_data = int(check) - right_node = TreeNode(right_data) + right_node = TreeNode(int(check)) node_found.right = right_node q.put(right_node) -def pre_order(node): +def pre_order(node: TreeNode) -> None: + """ + >>> root = TreeNode(1) + >>> tree_node2 = TreeNode(2) + >>> tree_node3 = TreeNode(3) + >>> tree_node4 = TreeNode(4) + >>> tree_node5 = TreeNode(5) + >>> tree_node6 = TreeNode(6) + >>> tree_node7 = TreeNode(7) + >>> root.left, root.right = tree_node2, tree_node3 + >>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5 + >>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7 + >>> pre_order(root) + 1 2 4 5 3 6 7 + """ if not isinstance(node, TreeNode) or not node: return print(node.data, end=" ") @@ -56,7 +60,21 @@ def pre_order(node): pre_order(node.right) -def in_order(node): +def in_order(node: TreeNode) -> None: + """ + >>> root = TreeNode(1) + >>> tree_node2 = TreeNode(2) + >>> tree_node3 = TreeNode(3) + >>> tree_node4 = TreeNode(4) + >>> tree_node5 = TreeNode(5) + >>> tree_node6 = TreeNode(6) + >>> tree_node7 = TreeNode(7) + >>> root.left, root.right = tree_node2, tree_node3 + >>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5 + >>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7 + >>> in_order(root) + 4 2 5 1 6 3 7 + """ if not isinstance(node, TreeNode) or not node: return in_order(node.left) @@ -64,7 +82,21 @@ def in_order(node): in_order(node.right) -def post_order(node): +def post_order(node: TreeNode) -> None: + """ + >>> root = TreeNode(1) + >>> tree_node2 = TreeNode(2) + >>> tree_node3 = TreeNode(3) + >>> tree_node4 = TreeNode(4) + >>> tree_node5 = TreeNode(5) + >>> tree_node6 = TreeNode(6) + >>> tree_node7 = TreeNode(7) + >>> root.left, root.right = tree_node2, tree_node3 + >>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5 + >>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7 + >>> post_order(root) + 4 5 2 6 7 3 1 + """ if not isinstance(node, TreeNode) or not node: return post_order(node.left) @@ -72,10 +104,24 @@ def post_order(node): print(node.data, end=" ") -def level_order(node): +def level_order(node: TreeNode) -> None: + """ + >>> root = TreeNode(1) + >>> tree_node2 = TreeNode(2) + >>> tree_node3 = TreeNode(3) + >>> tree_node4 = TreeNode(4) + >>> tree_node5 = TreeNode(5) + >>> tree_node6 = TreeNode(6) + >>> tree_node7 = TreeNode(7) + >>> root.left, root.right = tree_node2, tree_node3 + >>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5 + >>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7 + >>> level_order(root) + 1 2 3 4 5 6 7 + """ if not isinstance(node, TreeNode) or not node: return - q = queue.Queue() + q: queue.Queue = queue.Queue() q.put(node) while not q.empty(): node_dequeued = q.get() @@ -86,10 +132,26 @@ def level_order(node): q.put(node_dequeued.right) -def level_order_actual(node): +def level_order_actual(node: TreeNode) -> None: + """ + >>> root = TreeNode(1) + >>> tree_node2 = TreeNode(2) + >>> tree_node3 = TreeNode(3) + >>> tree_node4 = TreeNode(4) + >>> tree_node5 = TreeNode(5) + >>> tree_node6 = TreeNode(6) + >>> tree_node7 = TreeNode(7) + >>> root.left, root.right = tree_node2, tree_node3 + >>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5 + >>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7 + >>> level_order_actual(root) + 1 + 2 3 + 4 5 6 7 + """ if not isinstance(node, TreeNode) or not node: return - q = queue.Queue() + q: queue.Queue = queue.Queue() q.put(node) while not q.empty(): list = [] @@ -106,10 +168,24 @@ def level_order_actual(node): # iteration version -def pre_order_iter(node): +def pre_order_iter(node: TreeNode) -> None: + """ + >>> root = TreeNode(1) + >>> tree_node2 = TreeNode(2) + >>> tree_node3 = TreeNode(3) + >>> tree_node4 = TreeNode(4) + >>> tree_node5 = TreeNode(5) + >>> tree_node6 = TreeNode(6) + >>> tree_node7 = TreeNode(7) + >>> root.left, root.right = tree_node2, tree_node3 + >>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5 + >>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7 + >>> pre_order_iter(root) + 1 2 4 5 3 6 7 + """ if not isinstance(node, TreeNode) or not node: return - stack = [] + stack: List[TreeNode] = [] n = node while n or stack: while n: # start from root node, find its left child @@ -122,10 +198,24 @@ def pre_order_iter(node): n = n.right -def in_order_iter(node): +def in_order_iter(node: TreeNode) -> None: + """ + >>> root = TreeNode(1) + >>> tree_node2 = TreeNode(2) + >>> tree_node3 = TreeNode(3) + >>> tree_node4 = TreeNode(4) + >>> tree_node5 = TreeNode(5) + >>> tree_node6 = TreeNode(6) + >>> tree_node7 = TreeNode(7) + >>> root.left, root.right = tree_node2, tree_node3 + >>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5 + >>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7 + >>> in_order_iter(root) + 4 2 5 1 6 3 7 + """ if not isinstance(node, TreeNode) or not node: return - stack = [] + stack: List[TreeNode] = [] n = node while n or stack: while n: @@ -136,7 +226,21 @@ def in_order_iter(node): n = n.right -def post_order_iter(node): +def post_order_iter(node: TreeNode) -> None: + """ + >>> root = TreeNode(1) + >>> tree_node2 = TreeNode(2) + >>> tree_node3 = TreeNode(3) + >>> tree_node4 = TreeNode(4) + >>> tree_node5 = TreeNode(5) + >>> tree_node6 = TreeNode(6) + >>> tree_node7 = TreeNode(7) + >>> root.left, root.right = tree_node2, tree_node3 + >>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5 + >>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7 + >>> post_order_iter(root) + 4 5 2 6 7 3 1 + """ if not isinstance(node, TreeNode) or not node: return stack1, stack2 = [], [] @@ -153,38 +257,48 @@ def post_order_iter(node): print(stack2.pop().data, end=" ") -if __name__ == '__main__': - print("\n********* Binary Tree Traversals ************\n") +def prompt(s: str = "", width=50, char="*") -> str: + if not s: + return "\n" + width * char + left, extra = divmod(width - len(s) - 2, 2) + return f"{left * char} {s} {(left + extra) * char}" + + +if __name__ == "__main__": + import doctest + + doctest.testmod() + print(prompt("Binary Tree Traversals")) node = build_tree() - print("\n********* Pre Order Traversal ************") + print(prompt("Pre Order Traversal")) pre_order(node) - print("\n******************************************\n") + print(prompt() + "\n") - print("\n********* In Order Traversal ************") + print(prompt("In Order Traversal")) in_order(node) - print("\n******************************************\n") + print(prompt() + "\n") - print("\n********* Post Order Traversal ************") + print(prompt("Post Order Traversal")) post_order(node) - print("\n******************************************\n") + print(prompt() + "\n") - print("\n********* Level Order Traversal ************") + print(prompt("Level Order Traversal")) level_order(node) - print("\n******************************************\n") + print(prompt() + "\n") - print("\n********* Actual Level Order Traversal ************") + print(prompt("Actual Level Order Traversal")) level_order_actual(node) - print("\n******************************************\n") + print("*" * 50 + "\n") - print("\n********* Pre Order Traversal - Iteration Version ************") + print(prompt("Pre Order Traversal - Iteration Version")) pre_order_iter(node) - print("\n******************************************\n") + print(prompt() + "\n") - print("\n********* In Order Traversal - Iteration Version ************") + print(prompt("In Order Traversal - Iteration Version")) in_order_iter(node) - print("\n******************************************\n") + print(prompt() + "\n") - print("\n********* Post Order Traversal - Iteration Version ************") + print(prompt("Post Order Traversal - Iteration Version")) post_order_iter(node) - print("\n******************************************\n") + print(prompt()) diff --git a/web_programming/crawl_google_results.py b/web_programming/crawl_google_results.py new file mode 100644 index 000000000000..c31ec1526d3e --- /dev/null +++ b/web_programming/crawl_google_results.py @@ -0,0 +1,20 @@ +import sys +import webbrowser + +from bs4 import BeautifulSoup +from fake_useragent import UserAgent +import requests + +print("Googling.....") +url = "https://www.google.com/search?q=" + " ".join(sys.argv[1:]) +res = requests.get(url, headers={"UserAgent": UserAgent().random}) +# res.raise_for_status() +with open("project1a.html", "wb") as out_file: # only for knowing the class + for data in res.iter_content(10000): + out_file.write(data) +soup = BeautifulSoup(res.text, "html.parser") +links = list(soup.select(".eZt8xd"))[:5] + +print(len(links)) +for link in links: + webbrowser.open(f"http://google.com{link.get('href')}") diff --git a/web_programming/get_imdbtop.py b/web_programming/get_imdbtop.py new file mode 100644 index 000000000000..95fbeba7a772 --- /dev/null +++ b/web_programming/get_imdbtop.py @@ -0,0 +1,18 @@ +from bs4 import BeautifulSoup +import requests + + +def imdb_top(imdb_top_n): + base_url = (f"https://www.imdb.com/search/title?title_type=" + f"feature&sort=num_votes,desc&count={imdb_top_n}") + source = BeautifulSoup(requests.get(base_url).content, "html.parser") + for m in source.findAll("div", class_="lister-item mode-advanced"): + print("\n" + m.h3.a.text) # movie's name + print(m.find("span", attrs={"class": "genre"}).text) # genre + print(m.strong.text) # movie's rating + print(f"https://www.imdb.com{m.a.get('href')}") # movie's page link + print("*" * 40) + + +if __name__ == "__main__": + imdb_top(input("How many movies would you like to see? "))