[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -26,7 +26,7 @@ repos:
           - --profile=black
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.0.0
+    rev: v3.1.0
     hooks:
       - id: pyupgrade
         args:
@@ -55,14 +55,14 @@ repos:
         additional_dependencies: [types-requests]
 
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.2.1
+    rev: v2.2.2
     hooks:
       - id: codespell
         args:
           - --ignore-words-list=ans,crate,damon,fo,followings,hist,iff,mater,secant,som,sur,tim,zar
-          - --skip="./.*,./strings/dictionary.txt,./strings/words.txt,./project_euler/problem_022/p022_names.txt"
         exclude: |
           (?x)^(
+              ciphers/prehistoric_men.txt |
               strings/dictionary.txt |
               strings/words.txt |
               project_euler/problem_022/p022_names.txt

DIRECTORY.md

@@ -642,6 +642,7 @@
   * [Tower Of Hanoi](other/tower_of_hanoi.py)
 
 ## Physics
+  * [Casimir Effect](physics/casimir_effect.py)
   * [Horizontal Projectile Motion](physics/horizontal_projectile_motion.py)
   * [Lorentz Transformation Four Vector](physics/lorentz_transformation_four_vector.py)
   * [N Body Simulation](physics/n_body_simulation.py)
@@ -928,6 +929,7 @@
   * [Deutsch Jozsa](quantum/deutsch_jozsa.py)
   * [Half Adder](quantum/half_adder.py)
   * [Not Gate](quantum/not_gate.py)
+  * [Q Full Adder](quantum/q_full_adder.py)
   * [Quantum Entanglement](quantum/quantum_entanglement.py)
   * [Ripple Adder Classic](quantum/ripple_adder_classic.py)
   * [Single Qubit Measure](quantum/single_qubit_measure.py)

machine_learning/local_weighted_learning/local_weighted_learning.md

@@ -29,7 +29,7 @@ This training phase is possible when data points are linear, but there again com
 So, here comes the role of non-parametric algorithm which doesn't compute predictions based on fixed set of params. Rather parameters $\theta$ are computed individually for each query point/data point x.
 <br />
 <br />
-While Computing $\theta$ , a higher "preferance" is given to points in the vicinity of x than points farther from x.
+While Computing $\theta$ , a higher preference is given to points in the vicinity of x than points farther from x.
 
 Cost Function J($\theta$) = $\sum_{i=1}^m$ $w^i$ (($\theta$)$^T$ $x^i$ - $y^i$)$^2$
 

maths/is_square_free.py

@@ -15,7 +15,7 @@ def is_square_free(factors: list[int]) -> bool:
     False
 
     These are wrong but should return some value
-    it simply checks for repition in the numbers.
+    it simply checks for repetition in the numbers.
     >>> is_square_free([1, 3, 4, 'sd', 0.0])
     True