Remove some print statements within algorithmic functions

DIRECTORY.md

@@ -360,6 +360,7 @@
   * [Dijkstra](graphs/dijkstra.py)
   * [Dijkstra 2](graphs/dijkstra_2.py)
   * [Dijkstra Algorithm](graphs/dijkstra_algorithm.py)
+  * [Dijkstra Alternate](graphs/dijkstra_alternate.py)
   * [Dinic](graphs/dinic.py)
   * [Directed And Undirected (Weighted) Graph](graphs/directed_and_undirected_(weighted)_graph.py)
   * [Edmonds Karp Multiple Source And Sink](graphs/edmonds_karp_multiple_source_and_sink.py)
@@ -460,6 +461,7 @@
   * [Similarity Search](machine_learning/similarity_search.py)
   * [Support Vector Machines](machine_learning/support_vector_machines.py)
   * [Word Frequency Functions](machine_learning/word_frequency_functions.py)
+  * [Xgboostclassifier](machine_learning/xgboostclassifier.py)
 
 ## Maths
   * [3N Plus 1](maths/3n_plus_1.py)
@@ -534,6 +536,7 @@
   * [Line Length](maths/line_length.py)
   * [Lucas Lehmer Primality Test](maths/lucas_lehmer_primality_test.py)
   * [Lucas Series](maths/lucas_series.py)
+  * [Maclaurin Sin](maths/maclaurin_sin.py)
   * [Matrix Exponentiation](maths/matrix_exponentiation.py)
   * [Max Sum Sliding Window](maths/max_sum_sliding_window.py)
   * [Median Of Two Arrays](maths/median_of_two_arrays.py)
@@ -936,6 +939,7 @@
   * [Not Gate](quantum/not_gate.py)
   * [Q Full Adder](quantum/q_full_adder.py)
   * [Quantum Entanglement](quantum/quantum_entanglement.py)
+  * [Quantum Random](quantum/quantum_random.py)
   * [Ripple Adder Classic](quantum/ripple_adder_classic.py)
   * [Single Qubit Measure](quantum/single_qubit_measure.py)
 

cellular_automata/game_of_life.py

@@ -66,7 +66,6 @@ def run(canvas: list[list[bool]]) -> list[list[bool]]:
     next_gen_canvas = np.array(create_canvas(current_canvas.shape[0]))
     for r, row in enumerate(current_canvas):
         for c, pt in enumerate(row):
-            # print(r-1,r+2,c-1,c+2)
             next_gen_canvas[r][c] = __judge_point(
                 pt, current_canvas[r - 1 : r + 2, c - 1 : c + 2]
             )

digital_image_processing/index_calculation.py

@@ -105,7 +105,6 @@ class IndexCalculation:
     """
 
     def __init__(self, red=None, green=None, blue=None, red_edge=None, nir=None):
-        # print("Numpy version: " + np.__version__)
         self.set_matricies(red=red, green=green, blue=blue, red_edge=red_edge, nir=nir)
 
     def set_matricies(self, red=None, green=None, blue=None, red_edge=None, nir=None):

divide_and_conquer/max_subarray_sum.py

@@ -69,8 +69,10 @@ def max_subarray_sum(array, left, 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)
-)
+if __name__ == "__main__":
+    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),
+    )

divide_and_conquer/strassen_matrix_multiplication.py

@@ -67,11 +67,6 @@ def matrix_dimensions(matrix: list) -> tuple[int, int]:
     return len(matrix), len(matrix[0])
 
 
-def print_matrix(matrix: list) -> None:
-    for i in range(len(matrix)):
-        print(matrix[i])
-
-
 def actual_strassen(matrix_a: list, matrix_b: list) -> list:
     """
     Recursive function to calculate the product of two matrices, using the Strassen

dynamic_programming/longest_sub_array.py

@@ -14,7 +14,6 @@ class SubArray:
     def __init__(self, arr):
         # we need a list not a string, so do something to change the type
         self.array = arr.split(",")
-        print(("the input array is:", self.array))
 
     def solve_sub_array(self):
         rear = [int(self.array[0])] * len(self.array)

dynamic_programming/max_non_adjacent_sum.py

@@ -7,7 +7,7 @@ def maximum_non_adjacent_sum(nums: list[int]) -> int:
     """
     Find the maximum non-adjacent sum of the integers in the nums input list
 
-    >>> print(maximum_non_adjacent_sum([1, 2, 3]))
+    >>> maximum_non_adjacent_sum([1, 2, 3])
     4
     >>> maximum_non_adjacent_sum([1, 5, 3, 7, 2, 2, 6])
     18

dynamic_programming/subset_generation.py

@@ -37,7 +37,8 @@ def print_combination(arr, n, r):
     combination_util(arr, n, r, 0, data, 0)
 
 
-# Driver function to check for above function
-arr = [10, 20, 30, 40, 50]
-print_combination(arr, len(arr), 3)
-# This code is contributed by Ambuj sahu
+if __name__ == "__main__":
+    # Driver function to check for above function
+    arr = [10, 20, 30, 40, 50]
+    print_combination(arr, len(arr), 3)
+    # This code is contributed by Ambuj sahu

dynamic_programming/sum_of_subset.py

@@ -1,13 +1,14 @@
-def is_sum_subset(arr, arr_len, required_sum):
+def is_sum_subset(arr: list[int], required_sum: int) -> bool:
     """
-    >>> is_sum_subset([2, 4, 6, 8], 4, 5)
+    >>> is_sum_subset([2, 4, 6, 8], 5)
     False
-    >>> is_sum_subset([2, 4, 6, 8], 4, 14)
+    >>> is_sum_subset([2, 4, 6, 8], 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(required_sum + 1)] for i in range(arr_len + 1)]
+    arr_len = len(arr)
+    subset = [[False for _ in range(required_sum + 1)] for _ in range(arr_len + 1)]
 
     # for each arr value, a sum of zero(0) can be formed by not taking any element
     # hence True/1
@@ -25,10 +26,7 @@ def is_sum_subset(arr, arr_len, required_sum):
             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[arr_len][required_sum])
+    return subset[arr_len][required_sum]
 
 
 if __name__ == "__main__":

machine_learning/forecasting/run.py

@@ -1,7 +1,7 @@
 """
 this is code for forecasting
 but i modified it and used it for safety checker of data
-for ex: you have a online shop and for some reason some data are
+for ex: you have an online shop and for some reason some data are
 missing (the amount of data that u expected are not supposed to be)
         then we can use it
 *ps : 1. ofc we can use normal statistic method but in this case
@@ -91,14 +91,14 @@ def interquartile_range_checker(train_user: list) -> float:
     return low_lim
 
 
-def data_safety_checker(list_vote: list, actual_result: float) -> None:
+def data_safety_checker(list_vote: list, actual_result: float) -> bool:
     """
     Used to review all the votes (list result prediction)
     and compare it to the actual result.
     input : list of predictions
     output : print whether it's safe or not
-    >>> data_safety_checker([2,3,4],5.0)
-    Today's data is not safe.
+    >>> data_safety_checker([2, 3, 4], 5.0)
+    False
     """
     safe = 0
     not_safe = 0
@@ -107,50 +107,55 @@ def data_safety_checker(list_vote: list, actual_result: float) -> None:
             safe = not_safe + 1
         else:
             if abs(abs(i) - abs(actual_result)) <= 0.1:
-                safe = safe + 1
+                safe += 1
             else:
-                not_safe = not_safe + 1
-    print(f"Today's data is {'not ' if safe <= not_safe else ''}safe.")
+                not_safe += 1
+    return safe > not_safe
 
 
-# data_input_df = pd.read_csv("ex_data.csv", header=None)
-data_input = [[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
-data_input_df = pd.DataFrame(data_input, columns=["total_user", "total_even", "days"])
+if __name__ == "__main__":
+    # data_input_df = pd.read_csv("ex_data.csv", header=None)
+    data_input = [[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
+    data_input_df = pd.DataFrame(
+        data_input, columns=["total_user", "total_even", "days"]
+    )
 
-"""
-data column = total user in a day, how much online event held in one day,
-what day is that(sunday-saturday)
-"""
+    """
+    data column = total user in a day, how much online event held in one day,
+    what day is that(sunday-saturday)
+    """
 
-# start normalization
-normalize_df = Normalizer().fit_transform(data_input_df.values)
-# split data
-total_date = normalize_df[:, 2].tolist()
-total_user = normalize_df[:, 0].tolist()
-total_match = normalize_df[:, 1].tolist()
-
-# for svr (input variable = total date and total match)
-x = normalize_df[:, [1, 2]].tolist()
-x_train = x[: len(x) - 1]
-x_test = x[len(x) - 1 :]
-
-# for linear reression & sarimax
-trn_date = total_date[: len(total_date) - 1]
-trn_user = total_user[: len(total_user) - 1]
-trn_match = total_match[: len(total_match) - 1]
-
-tst_date = total_date[len(total_date) - 1 :]
-tst_user = total_user[len(total_user) - 1 :]
-tst_match = total_match[len(total_match) - 1 :]
-
-
-# voting system with forecasting
-res_vote = []
-res_vote.append(
-    linear_regression_prediction(trn_date, trn_user, trn_match, tst_date, tst_match)
-)
-res_vote.append(sarimax_predictor(trn_user, trn_match, tst_match))
-res_vote.append(support_vector_regressor(x_train, x_test, trn_user))
-
-# check the safety of todays'data^^
-data_safety_checker(res_vote, tst_user)
+    # start normalization
+    normalize_df = Normalizer().fit_transform(data_input_df.values)
+    # split data
+    total_date = normalize_df[:, 2].tolist()
+    total_user = normalize_df[:, 0].tolist()
+    total_match = normalize_df[:, 1].tolist()
+
+    # for svr (input variable = total date and total match)
+    x = normalize_df[:, [1, 2]].tolist()
+    x_train = x[: len(x) - 1]
+    x_test = x[len(x) - 1 :]
+
+    # for linear regression & sarimax
+    trn_date = total_date[: len(total_date) - 1]
+    trn_user = total_user[: len(total_user) - 1]
+    trn_match = total_match[: len(total_match) - 1]
+
+    tst_date = total_date[len(total_date) - 1 :]
+    tst_user = total_user[len(total_user) - 1 :]
+    tst_match = total_match[len(total_match) - 1 :]
+
+    # voting system with forecasting
+    res_vote = []
+    res_vote.append(
+        linear_regression_prediction(trn_date, trn_user, trn_match, tst_date, tst_match)
+    )
+    res_vote.append(sarimax_predictor(trn_user, trn_match, tst_match))
+    res_vote.append(support_vector_regressor(x_train, x_test, trn_user))
+
+    # check the safety of today's data
+    if data_safety_checker(res_vote, tst_user):
+        print("Today's data is safe.")
+    else:
+        print("Today's data is not safe.")