[pre-commit.ci] pre-commit autoupdate (TheAlgorithms#9543)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.0.291 → v0.0.292](astral-sh/ruff-pre-commit@v0.0.291...v0.0.292)
- [github.com/codespell-project/codespell: v2.2.5 → v2.2.6](codespell-project/codespell@v2.2.5...v2.2.6)
- [github.com/tox-dev/pyproject-fmt: 1.1.0 → 1.2.0](tox-dev/pyproject-fmt@1.1.0...1.2.0)

* updating DIRECTORY.md

* Fix typos in test_min_spanning_tree_prim.py

* Fix typos

* codespell --ignore-words-list=manuel

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
Co-authored-by: Tianyi Zheng <tianyizheng02@gmail.com>
Co-authored-by: Christian Clauss <cclauss@me.com>

Author: 4 people

.pre-commit-config.yaml

@@ -26,7 +26,7 @@ repos:
       - id: black
 
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.2.5
+    rev: v2.2.6
     hooks:
       - id: codespell
         additional_dependencies:

computer_vision/cnn_classification.py.DISABLED.txt

@@ -11,10 +11,10 @@ Download dataset from :
 https://lhncbc.nlm.nih.gov/LHC-publications/pubs/TuberculosisChestXrayImageDataSets.html
 
 1. Download the dataset folder and create two folder training set and test set
-in the parent dataste folder
+in the parent dataset folder
 2. Move 30-40 image from both TB positive and TB Negative folder
 in the test set folder
-3. The labels of the iamges will be extracted from the folder name
+3. The labels of the images will be extracted from the folder name
 the image is present in.
 
 """

computer_vision/mosaic_augmentation.py

@@ -8,7 +8,7 @@
 import cv2
 import numpy as np
 
-# Parrameters
+# Parameters
 OUTPUT_SIZE = (720, 1280)  # Height, Width
 SCALE_RANGE = (0.4, 0.6)  # if height or width lower than this scale, drop it.
 FILTER_TINY_SCALE = 1 / 100

dynamic_programming/min_distance_up_bottom.py

@@ -1,11 +1,8 @@
 """
 Author  : Alexander Pantyukhin
 Date    : October 14, 2022
-This is implementation Dynamic Programming up bottom approach
-to find edit distance.
-The aim is to demonstate up bottom approach for solving the task.
-The implementation was tested on the
-leetcode: https://leetcode.com/problems/edit-distance/
+This is an implementation of the up-bottom approach to find edit distance.
+The implementation was tested on Leetcode: https://leetcode.com/problems/edit-distance/
 
 Levinstein distance
 Dynamic Programming: up -> down.
@@ -30,10 +27,10 @@ def min_distance_up_bottom(word1: str, word2: str) -> int:
 
     @functools.cache
     def min_distance(index1: int, index2: int) -> int:
-        # if first word index is overflow - delete all from the second word
+        # if first word index overflows - delete all from the second word
         if index1 >= len_word1:
             return len_word2 - index2
-        # if second word index is overflow - delete all from the first word
+        # if second word index overflows - delete all from the first word
         if index2 >= len_word2:
             return len_word1 - index1
         diff = int(word1[index1] != word2[index2])  # current letters not identical

graphs/tests/test_min_spanning_tree_prim.py

@@ -22,12 +22,12 @@ def test_prim_successful_result():
         [1, 7, 11],
     ]
 
-    adjancency = defaultdict(list)
+    adjacency = defaultdict(list)
     for node1, node2, cost in edges:
-        adjancency[node1].append([node2, cost])
-        adjancency[node2].append([node1, cost])
+        adjacency[node1].append([node2, cost])
+        adjacency[node2].append([node1, cost])
 
-    result = mst(adjancency)
+    result = mst(adjacency)
 
     expected = [
         [7, 6, 1],

hashes/sha1.py

@@ -1,26 +1,28 @@
 """
-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.
+Implementation of the SHA1 hash function and gives utilities to find hash of string or
+hash of text from a file. Also contains a Test class to verify that the generated hash
+matches what is returned by the hashlib library
+
 Usage: python sha1.py --string "Hello World!!"
        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
 
-SHA1 hash or SHA1 sum of a string is a cryptographic function which means it is easy
+SHA1 hash or SHA1 sum of a string is a cryptographic function, which means it is easy
 to calculate forwards but extremely difficult to calculate backwards. What this means
-is, you can easily calculate the hash of  a string, but it is extremely difficult to
-know the original string if you have its hash. This property is useful to communicate
-securely, send encrypted messages and is very useful in payment systems, blockchain
-and cryptocurrency etc.
-The Algorithm as described in the reference:
+is you can easily calculate the hash of a string, but it is extremely difficult to know
+the original string if you have its hash. This property is useful for communicating
+securely, send encrypted messages and is very useful in payment systems, blockchain and
+cryptocurrency etc.
+
+The algorithm as described in the reference:
 First we start with a message. The message is padded and the length of the message
 is added to the end. It is then split into blocks of 512 bits or 64 bytes. The blocks
 are then processed one at a time. Each block must be expanded and compressed.
-The value after each compression is added to a 160bit buffer called the current hash
-state. After the last block is processed the current hash state is returned as
+The value after each compression is added to a 160-bit buffer called the current hash
+state. After the last block is processed, the current hash state is returned as
 the final hash.
+
 Reference: https://deadhacker.com/2006/02/21/sha-1-illustrated/
 """
 import argparse
@@ -30,18 +32,18 @@
 
 class SHA1Hash:
     """
-    Class to contain the entire pipeline for SHA1 Hashing Algorithm
+    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
+        Initiates the variables data and h. h is a list of 5 8-digit hexadecimal
         numbers corresponding to
         (1732584193, 4023233417, 2562383102, 271733878, 3285377520)
         respectively. We will start with this as a message digest. 0x is how you write
-        Hexadecimal numbers in Python
+        hexadecimal numbers in Python
         """
         self.data = data
         self.h = [0x67452301, 0xEFCDAB89, 0x98BADCFE, 0x10325476, 0xC3D2E1F0]
@@ -90,7 +92,7 @@ def final_hash(self):
         For each block, the variable h that was initialized is copied to a,b,c,d,e
         and these 5 variables a,b,c,d,e undergo several changes. After all the blocks
         are processed, these 5 variables are pairwise added to h ie a to h[0], b to h[1]
-        and so on.  This h becomes our final hash which is returned.
+        and so on. This h becomes our final hash which is returned.
         """
         self.padded_data = self.padding()
         self.blocks = self.split_blocks()
@@ -135,7 +137,7 @@ def test_sha1_hash():
 def main():
     """
     Provides option 'string' or 'file' to take input and prints the calculated SHA1
-    hash.  unittest.main() has been commented because we probably don't want to run
+    hash. unittest.main() has been commented out because we probably don't want to run
     the test each time.
     """
     # unittest.main()

maths/pi_generator.py

@@ -3,60 +3,53 @@ def calculate_pi(limit: int) -> str:
     https://en.wikipedia.org/wiki/Leibniz_formula_for_%CF%80
     Leibniz Formula for Pi
 
-    The Leibniz formula is the special case arctan 1 = 1/4 Pi .
+    The Leibniz formula is the special case arctan(1) = pi / 4.
     Leibniz's formula converges extremely slowly: it exhibits sublinear convergence.
 
     Convergence (https://en.wikipedia.org/wiki/Leibniz_formula_for_%CF%80#Convergence)
 
     We cannot try to prove against an interrupted, uncompleted generation.
     https://en.wikipedia.org/wiki/Leibniz_formula_for_%CF%80#Unusual_behaviour
-    The errors can in fact be predicted;
-    but those calculations also approach infinity for accuracy.
+    The errors can in fact be predicted, but those calculations also approach infinity
+    for accuracy.
 
-    Our output will always be a string since we can defintely store all digits in there.
-    For simplicity' sake, let's just compare against known values and since our outpit
-    is a string, we need to convert to float.
+    Our output will be a string so that we can definitely store all digits.
 
     >>> import math
     >>> float(calculate_pi(15)) == math.pi
     True
 
-    Since we cannot predict errors or interrupt any infinite alternating
-    series generation since they approach infinity,
-    or interrupt any alternating series, we are going to need math.isclose()
+    Since we cannot predict errors or interrupt any infinite alternating series
+    generation since they approach infinity, or interrupt any alternating series, we'll
+    need math.isclose()
 
     >>> math.isclose(float(calculate_pi(50)), math.pi)
     True
-
     >>> math.isclose(float(calculate_pi(100)), math.pi)
     True
 
-    Since math.pi-constant contains only 16 digits, here some test with preknown values:
+    Since math.pi contains only 16 digits, here are some tests with known values:
 
     >>> calculate_pi(50)
     '3.14159265358979323846264338327950288419716939937510'
     >>> calculate_pi(80)
     '3.14159265358979323846264338327950288419716939937510582097494459230781640628620899'
-
-    To apply the Leibniz formula for calculating pi,
-    the variables q, r, t, k, n, and l are used for the iteration process.
     """
+    # Variables used for the iteration process
     q = 1
     r = 0
     t = 1
     k = 1
     n = 3
     l = 3
+
     decimal = limit
     counter = 0
 
     result = ""
 
-    """
-    We will avoid using yield since we otherwise get a Generator-Object,
-    which we can't just compare against anything. We would have to make a list out of it
-    after the generation, so we will just stick to plain return logic:
-    """
+    # We can't compare against anything if we make a generator,
+    # so we'll stick with plain return logic
     while counter != decimal + 1:
         if 4 * q + r - t < n * t:
             result += str(n)

maths/radians.py

@@ -3,7 +3,7 @@
 
 def radians(degree: float) -> float:
     """
-    Coverts the given angle from degrees to radians
+    Converts the given angle from degrees to radians
     https://en.wikipedia.org/wiki/Radian
 
     >>> radians(180)
@@ -16,7 +16,7 @@ def radians(degree: float) -> float:
     1.9167205845401725
 
     >>> from math import radians as math_radians
-    >>> all(abs(radians(i)-math_radians(i)) <= 0.00000001  for i in range(-2, 361))
+    >>> all(abs(radians(i) - math_radians(i)) <= 1e-8 for i in range(-2, 361))
     True
     """
 

maths/square_root.py

@@ -19,14 +19,13 @@ def get_initial_point(a: float) -> float:
 
 
 def square_root_iterative(
-    a: float, max_iter: int = 9999, tolerance: float = 0.00000000000001
+    a: float, max_iter: int = 9999, tolerance: float = 1e-14
 ) -> float:
     """
-    Square root is aproximated using Newtons method.
+    Square root approximated using Newton's method.
     https://en.wikipedia.org/wiki/Newton%27s_method
 
-    >>> all(abs(square_root_iterative(i)-math.sqrt(i)) <= .00000000000001
-    ...     for i in range(500))
+    >>> all(abs(square_root_iterative(i) - math.sqrt(i)) <= 1e-14 for i in range(500))
     True
 
     >>> square_root_iterative(-1)

neural_network/convolution_neural_network.py

@@ -2,7 +2,7 @@
      - - - - - -- - - - - - - - - - - - - - - - - - - - - - -
     Name - - CNN - Convolution Neural Network For Photo Recognizing
     Goal - - Recognize Handing Writing Word Photo
-    Detail：Total 5 layers neural network
+    Detail: Total 5 layers neural network
             * Convolution layer
             * Pooling layer
             * Input layer layer of BP
@@ -24,7 +24,7 @@ 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 conv1_get: [a,c,d], size, number, step of convolution kernel
         :param size_p1: pooling size
         :param bp_num1: units number of flatten layer
         :param bp_num2: units number of hidden layer
@@ -71,7 +71,7 @@ def save_model(self, save_path):
         with open(save_path, "wb") as f:
             pickle.dump(model_dic, f)
 
-        print(f"Model saved： {save_path}")
+        print(f"Model saved: {save_path}")
 
     @classmethod
     def read_model(cls, model_path):
@@ -210,7 +210,7 @@ def _calculate_gradient_from_pool(
     def train(
         self, patterns, datas_train, datas_teach, n_repeat, error_accuracy, draw_e=bool
     ):
-        # model traning
+        # model training
         print("----------------------Start Training-------------------------")
         print((" - - Shape: Train_Data  ", np.shape(datas_train)))
         print((" - - Shape: Teach_Data  ", np.shape(datas_teach)))