Create codespell.yml (#1698)

* fixup! Format Python code with psf/black push

* Create codespell.yml

* fixup! Format Python code with psf/black push

.github/workflows/codespell.yml

@@ -0,0 +1,14 @@
+# GitHub Action to automate the identification of common misspellings in text files
+# https://github.com/codespell-project/codespell
+name: codespell
+on: [push, pull_request]
+jobs:
+  codespell:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: actions/setup-python@v1
+      - run: pip install codespell flake8
+      - run: |
+          SKIP="./.*,./other/dictionary.txt,./other/words,./project_euler/problem_22/p022_names.txt,*.bak,*.gif,*.jpeg,*.jpg,*.json,*.png,*.pyc"
+          codespell -L ans,fo,hist,iff,secant,tim --skip=$SKIP

DIRECTORY.md

@@ -177,14 +177,14 @@
   * [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)
-  * [Max Sum Contigous Subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_sum_contigous_subsequence.py)
+  * [Max Sum Contiguous Subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_sum_contiguous_subsequence.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)
+  * [Receive File](https://github.com/TheAlgorithms/Python/blob/master/file_transfer/receive_file.py)
   * [Send File](https://github.com/TheAlgorithms/Python/blob/master/file_transfer/send_file.py)
 
 ## Fuzzy Logic
@@ -219,7 +219,7 @@
   * [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)
+  * [Multi Heuristic Astar](https://github.com/TheAlgorithms/Python/blob/master/graphs/multi_heuristic_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)
@@ -319,6 +319,7 @@
   * [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)
+  * [Square Root](https://github.com/TheAlgorithms/Python/blob/master/maths/square_root.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)
@@ -469,6 +470,8 @@
     * [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 30
+    * [Soln](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_30/soln.py)
   * Problem 31
     * [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_31/sol1.py)
   * Problem 32
@@ -508,6 +511,7 @@
   * [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)
+  * [Simulated Annealing](https://github.com/TheAlgorithms/Python/blob/master/searches/simulated_annealing.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)
 
@@ -564,7 +568,7 @@
   * [Reverse Words](https://github.com/TheAlgorithms/Python/blob/master/strings/reverse_words.py)
   * [Split](https://github.com/TheAlgorithms/Python/blob/master/strings/split.py)
   * [Upper](https://github.com/TheAlgorithms/Python/blob/master/strings/upper.py)
-  * [Word Occurence](https://github.com/TheAlgorithms/Python/blob/master/strings/word_occurence.py)
+  * [Word Occurrence](https://github.com/TheAlgorithms/Python/blob/master/strings/word_occurrence.py)
 
 ## Traversals
   * [Binary Tree Traversals](https://github.com/TheAlgorithms/Python/blob/master/traversals/binary_tree_traversals.py)

backtracking/n_queens.py

@@ -40,8 +40,8 @@ def isSafe(board, row, column):
 
 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 
+    It creates a state space tree and calls the safe function until it receives a 
+    False Boolean and terminates that branch and backtracks to the next 
     poosible solution branch.
     """
     if row >= len(board):
@@ -58,7 +58,7 @@ def solve(board, row):
         """
         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 
+        If all the combinations for that particular branch are successful the board is 
         reinitialized for the next possible combination.
         """
         if isSafe(board, row, i):
@@ -70,7 +70,7 @@ def solve(board, row):
 
 def printboard(board):
     """
-    Prints the boards that have a successfull combination.
+    Prints the boards that have a successful combination.
     """
     for i in range(len(board)):
         for j in range(len(board)):

ciphers/hill_cipher.py

@@ -3,15 +3,15 @@
 Hill Cipher:
 The below defined class 'HillCipher' implements the Hill Cipher algorithm.
 The Hill Cipher is an algorithm that implements modern linear algebra techniques
-In this algortihm, you have an encryption key matrix. This is what will be used
+In this algorithm, you have an encryption key matrix. This is what will be used
 in encoding and decoding your text.
 
-Algortihm:
+Algorithm:
 Let the order of the encryption key be N (as it is a square matrix).
 Your text is divided into batches of length N and converted to numerical vectors
 by a simple mapping starting with A=0 and so on.
 
-The key is then mulitplied with the newly created batch vector to obtain the
+The key is then multiplied with the newly created batch vector to obtain the
 encoded vector. After each multiplication modular 36 calculations are performed
 on the vectors so as to bring the numbers between 0 and 36 and then mapped with
 their corresponding alphanumerics.

ciphers/rsa_cipher.py

@@ -6,7 +6,7 @@ BYTE_SIZE = 256
 
 def main():
     filename = "encrypted_file.txt"
-    response = input(r"Encrypte\Decrypt [e\d]: ")
+    response = input(r"Encrypt\Decrypt [e\d]: ")
 
     if response.lower().startswith("e"):
         mode = "encrypt"

ciphers/rsa_key_generator.py

@@ -42,12 +42,12 @@ def makeKeyFiles(name, keySize):
 
     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("{},{},{}".format(keySize, publicKey[0], publicKey[1]))
+    with open("%s_pubkey.txt" % name, "w") as out_file:
+        out_file.write("{},{},{}".format(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("{},{},{}".format(keySize, privateKey[0], privateKey[1]))
+    with open("%s_privkey.txt" % name, "w") as out_file:
+        out_file.write("{},{},{}".format(keySize, privateKey[0], privateKey[1]))
 
 
 if __name__ == "__main__":

compression/burrows_wheeler.py

@@ -157,11 +157,11 @@ 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 '{}'"
+    bwt_output_msg = "Burrows Wheeler transform 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"
+        "Reversing Burrows Wheeler transform for entry '{}' we get original"
         " string '{}'"
     )
     print(fmt.format(result["bwt_string"], original_string))

compression/huffman.py

@@ -21,7 +21,7 @@ class TreeNode:
 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.
+    frequencies, then convert the dict into a list of Letters.
     """
     chars = {}
     with open(file_path) as f:

compression/peak_signal_to_noise_ratio.py

@@ -1,6 +1,6 @@
 """
         Peak signal-to-noise ratio - PSNR - https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
-    Soruce: https://tutorials.techonical.com/how-to-calculate-psnr-value-of-two-images-using-python/
+    Source: https://tutorials.techonical.com/how-to-calculate-psnr-value-of-two-images-using-python/
 """
 
 import math

conversions/decimal_to_octal.py

@@ -24,7 +24,7 @@ def decimal_to_octal(num: int) -> str:
 
 
 def main():
-    """Print octal equivelents of decimal numbers."""
+    """Print octal equivalents of decimal numbers."""
     print("\n2 in octal is:")
     print(decimal_to_octal(2))  # = 2
     print("\n8 in octal is:")

data_structures/binary_tree/binary_search_tree.py

@@ -15,7 +15,7 @@ class Node:
 
         if self.left is None and self.right is None:
             return str(self.value)
-        return pformat({"%s" % (self.value): (self.left, self.right)}, indent=1,)
+        return pformat({"%s" % (self.value): (self.left, self.right)}, indent=1)
 
 
 class BinarySearchTree:

data_structures/binary_tree/lowest_common_ancestor.py

@@ -11,7 +11,7 @@ def swap(a, b):
     return a, b
 
 
-# creating sparse table which saves each nodes 2^ith parent
+# creating sparse table which saves each nodes 2^i-th parent
 def creatSparse(max_node, parent):
     j = 1
     while (1 << j) < max_node:

data_structures/binary_tree/number_of_possible_binary_trees.py

@@ -41,7 +41,7 @@ def binomial_coefficient(n: int, k: int) -> int:
 
 def catalan_number(node_count: int) -> int:
     """
-    We can find Catalan number many ways but here we use Binomial Coefficent because it
+    We can find Catalan number many ways but here we use Binomial Coefficient because it
     does the job in O(n)
 
     return the Catalan number of n using 2nCn/(n+1).

data_structures/binary_tree/red_black_tree.py

@@ -12,7 +12,7 @@ class RedBlackTree:
     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.
+    performance.
     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.
@@ -37,7 +37,7 @@ class RedBlackTree:
     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).
+        Performing one rotation can be done in O(1).
         """
         parent = self.parent
         right = self.right
@@ -656,7 +656,7 @@ def test_tree_traversal():
 
 
 def test_tree_chaining():
-    """Tests the three different tree chaning functions."""
+    """Tests the three different tree chaining 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]:

data_structures/binary_tree/treap.py

@@ -21,7 +21,7 @@ class Node:
             return f"'{self.value}: {self.prior:.5}'"
         else:
             return pformat(
-                {f"{self.value}: {self.prior:.5}": (self.left, self.right)}, indent=1,
+                {f"{self.value}: {self.prior:.5}": (self.left, self.right)}, indent=1
             )
 
     def __str__(self):
@@ -161,7 +161,7 @@ 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. "
+        "enter numbers to create a tree, + value to add value into treap, - value to erase all nodes with value. 'q' to quit. "
     )
 
     args = input()

data_structures/heap/binomial_heap.py

@@ -49,7 +49,7 @@ 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
+        - Insert element in a heap with n elements: 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)

data_structures/heap/heap.py

@@ -23,6 +23,7 @@ class Heap(object):
     [1, 5, 7, 9, 11, 15, 25, 100, 103, 107, 201]
     >>>
     """
+
     def __init__(self):
         self.h = []
         self.curr_size = 0
@@ -107,28 +108,28 @@ def main():
         [2, 5, 3, 0, 2, 3, 0, 3],
         [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
         [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
-        [-45, -2, -5]
+        [-45, -2, -5],
     ]:
-        print('source unsorted list: %s' % unsorted)
+        print("source unsorted list: %s" % unsorted)
 
         h = Heap()
         h.build_heap(unsorted)
-        print('after build heap: ', end=' ')
+        print("after build heap: ", end=" ")
         h.display()
 
-        print('max value: %s' % h.get_max())
-        print('delete max value: ', end=' ')
+        print("max value: %s" % h.get_max())
+        print("delete max value: ", end=" ")
         h.display()
 
         h.insert(100)
-        print('after insert new value 100: ', end=' ')
+        print("after insert new value 100: ", end=" ")
         h.display()
 
         h.heap_sort()
-        print('heap sort: ', end=' ')
+        print("heap sort: ", end=" ")
         h.display()
         print()
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()

data_structures/linked_list/deque_doubly.py

@@ -3,7 +3,7 @@ Implementing Deque using DoublyLinkedList ...
 Operations:
     1. insertion in the front -> O(1)
     2. insertion in the end -> O(1)
-    3. remove fron the front -> O(1)
+    3. remove from the front -> O(1)
     4. remove from the end -> O(1)
 """
 

data_structures/linked_list/doubly_linked_list.py

@@ -3,7 +3,7 @@
 - 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"""
+ - Advantages over SLL - IT can be traversed in both forward and backward direction.,Delete operation is more efficient"""
 
 
 class LinkedList:  # making main class named linked list

data_structures/linked_list/singly_linked_list.py

@@ -79,7 +79,7 @@ class LinkedList:
         # END represents end of the LinkedList
         return string_repr + "END"
 
-    # Indexing Support. Used to get a node at particaular position
+    # Indexing Support. Used to get a node at particular position
     def __getitem__(self, index):
         current = self.head
 

data_structures/linked_list/swap_nodes.py

@@ -14,7 +14,7 @@ class LinkedList:
     def print_list(self):
         temp = self.head
         while temp is not None:
-            print(temp.data, end=' ')
+            print(temp.data, end=" ")
             temp = temp.next
         print()
 

data_structures/stacks/postfix_evaluation.py

@@ -54,7 +54,7 @@ def Solve(Postfix):
 
             Stack.append(
                 str(Opr[x](int(A), int(B)))
-            )  # evaluate the 2 values poped from stack & push result to stack
+            )  # evaluate the 2 values popped from stack & push result to stack
             print(
                 x.rjust(8),
                 ("push(" + A + x + B + ")").ljust(12),

data_structures/stacks/stock_span_problem.py

@@ -21,7 +21,7 @@ def calculateSpan(price, S):
     # Calculate span values for rest of the elements
     for i in range(1, n):
 
-        # Pop elements from stack whlie stack is not
+        # Pop elements from stack while 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()

digital_image_processing/index_calculation.py

@@ -541,7 +541,7 @@ cl = IndexCalculation()
 # instantiating the class with the values
 #cl = indexCalculation(red=red, green=green, blue=blue, redEdge=redEdge, nir=nir)
 
-# how set the values after instantiate the class cl, (for update the data or when dont 
+# how set the values after instantiate the class cl, (for update the data or when don't
 # instantiating the class with the values)
 cl.setMatrices(red=red, green=green, blue=blue, redEdge=redEdge, nir=nir)
 
@@ -551,8 +551,8 @@ indexValue_form1    = cl.calculation("CCCI", red=red, green=green, blue=blue,
                                      redEdge=redEdge, nir=nir).astype(np.float64)
 indexValue_form2    = cl.CCCI()
 
-# calculating the index with the values directly -- you can set just the values preferred -- 
-# note: the *calculation* fuction performs the function *setMatrices*
+# calculating the index with the values directly -- you can set just the values
+# preferred  note: the *calculation* function performs the function *setMatrices*
 indexValue_form3    = cl.calculation("CCCI", red=red, green=green, blue=blue,
                                      redEdge=redEdge, nir=nir).astype(np.float64)
 

digital_image_processing/test_digital_image_processing.py

@@ -42,7 +42,7 @@ def test_gen_gaussian_kernel():
 # canny.py
 def test_canny():
     canny_img = imread("digital_image_processing/image_data/lena_small.jpg", 0)
-    # assert ambiguos array for all == True
+    # assert ambiguous array for all == True
     assert canny_img.all()
     canny_array = canny.canny(canny_img)
     # assert canny array for at least one True

dynamic_programming/bitmask.py

@@ -42,7 +42,7 @@ class AssignmentUsingBitmask:
         if self.dp[mask][taskno] != -1:
             return self.dp[mask][taskno]
 
-        # Number of ways when we dont this task in the arrangement
+        # Number of ways when we don't this task in the arrangement
         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.

dynamic_programming/knapsack.py

@@ -49,9 +49,9 @@ def knapsack_with_example_solution(W: int, wt: list, val: list):
 
     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.
+    of the i-th item.
     val: list, the vector of values for all items where val[i] is the value
-    of te ith item
+    of the i-th item
 
     Returns
     -------

dynamic_programming/longest_sub_array.py

@@ -1,5 +1,5 @@
 """
-Auther  : Yvonne
+Author  : Yvonne
 
 This is a pure Python implementation of Dynamic Programming solution to the longest_sub_array problem.
 

dynamic_programming/subset_generation.py

@@ -1,10 +1,10 @@
-# python program to print all subset combination of n element in given set of r element .
+# Python program to print all subset combinations 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):
+def combination_util(arr, n, r, index, data, i):
     # Current combination is ready to be printed,
     # print it
     if index == r:
@@ -15,29 +15,26 @@ def combinationUtil(arr, n, r, index, data, i):
     #  When no more elements are there to put in data[]
     if i >= n:
         return
-    # current is included, put next at next
-    # location
+    # current is included, put next at next location
     data[index] = arr[i]
-    combinationUtil(arr, n, r, index + 1, data, i + 1)
+    combination_util(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)
+    combination_util(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
+def print_combination(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)
+    # Print all combination using temporary array 'data[]'
+    combination_util(arr, n, r, 0, data, 0)
 
 
 # Driver function to check for above function
 arr = [10, 20, 30, 40, 50]
-printcombination(arr, len(arr), 3)
+print_combination(arr, len(arr), 3)
 # This code is contributed by Ambuj sahu

graphs/a_star.py

@@ -35,7 +35,7 @@ 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
+    ]  # the reference grid
     closed[init[0]][init[1]] = 1
     action = [
         [0 for col in range(len(grid[0]))] for row in range(len(grid))

graphs/basic_graphs.py

@@ -69,7 +69,7 @@ def dfs(G, s):
         Args :  G - Dictionary of edges
                 s - Starting Node
         Vars :  vis - Set of visited nodes
-                Q - Traveral Stack
+                Q - Traversal Stack
 --------------------------------------------------------------------------------
 """
 from collections import deque

graphs/breadth_first_search.py

@@ -7,12 +7,12 @@ class Graph:
     def __init__(self):
         self.vertex = {}
 
-    # for printing the Graph vertexes
+    # for printing the Graph vertices
     def printGraph(self):
         for i in self.vertex.keys():
             print(i, " -> ", " -> ".join([str(j) for j in self.vertex[i]]))
 
-    # for adding the edge beween two vertexes
+    # for adding the edge between two vertices
     def addEdge(self, fromVertex, toVertex):
         # check if vertex is already present,
         if fromVertex in self.vertex.keys():
@@ -22,10 +22,10 @@ class Graph:
             self.vertex[fromVertex] = [toVertex]
 
     def BFS(self, startVertex):
-        # Take a list for stoting already visited vertexes
+        # Take a list for stoting already visited vertices
         visited = [False] * len(self.vertex)
 
-        # create a list to store all the vertexes for BFS
+        # create a list to store all the vertices for BFS
         queue = []
 
         # mark the source node as visited and enqueue it

graphs/depth_first_search.py

@@ -7,13 +7,13 @@ class Graph:
     def __init__(self):
         self.vertex = {}
 
-    # for printing the Graph vertexes
+    # for printing the Graph vertices
     def printGraph(self):
         print(self.vertex)
         for i in self.vertex.keys():
             print(i, " -> ", " -> ".join([str(j) for j in self.vertex[i]]))
 
-    # for adding the edge beween two vertexes
+    # for adding the edge between two vertices
     def addEdge(self, fromVertex, toVertex):
         # check if vertex is already present,
         if fromVertex in self.vertex.keys():
@@ -37,7 +37,7 @@ class Graph:
 
         print(startVertex, end=" ")
 
-        # Recur for all the vertexes that are adjacent to this node
+        # Recur for all the vertices that are adjacent to this node
         for i in self.vertex.keys():
             if visited[i] == False:
                 self.DFSRec(i, visited)

graphs/dijkstra.py

@@ -22,7 +22,7 @@ DIJKSTRA(graph G, start vertex s, destination vertex d):
 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
+between vertices 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
@@ -35,7 +35,7 @@ import heapq
 
 
 def dijkstra(graph, start, end):
-    """Return the cost of the shortest path between vertexes start and end.
+    """Return the cost of the shortest path between vertices start and end.
 
     >>> dijkstra(G, "E", "C")
     6

graphs/dijkstra_algorithm.py

@@ -5,7 +5,7 @@
 import math
 import sys
 
-# For storing the vertex set to retreive node with the lowest distance
+# For storing the vertex set to retrieve node with the lowest distance
 
 
 class PriorityQueue:
@@ -103,9 +103,7 @@ class Graph:
     def show_graph(self):
         # u -> v(w)
         for u in self.adjList:
-            print(
-                u, "->", " -> ".join(str(f"{v}({w})") for v, w in self.adjList[u]),
-            )
+            print(u, "->", " -> ".join(str(f"{v}({w})") for v, w in self.adjList[u]))
 
     def dijkstra(self, src):
         # Flush old junk values in par[]

graphs/directed_and_undirected_(weighted)_graph.py

@@ -3,7 +3,7 @@ import random as rand
 import math as math
 import time
 
-# the dfault weight is 1 if not assigend but all the implementation is weighted
+# the dfault weight is 1 if not assigned but all the implementation is weighted
 
 
 class DirectedGraph:
@@ -12,7 +12,7 @@ class DirectedGraph:
 
     # adding vertices and edges
     # adding the weight is optional
-    # handels repetition
+    # handles repetition
     def add_pair(self, u, v, w=1):
         if self.graph.get(u):
             if self.graph[u].count([w, v]) == 0:
@@ -25,14 +25,14 @@ class DirectedGraph:
     def all_nodes(self):
         return list(self.graph)
 
-    # handels if the input does not exist
+    # handles 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
+    # if no destination is meant the default value is -1
     def dfs(self, s=-2, d=-1):
         if s == d:
             return []
@@ -71,7 +71,7 @@ class DirectedGraph:
             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
+    # c is the count of nodes you want and if you leave it or pass -1 to the function the count
     # will be random from 10 to 10000
     def fill_graph_randomly(self, c=-1):
         if c == -1:
@@ -271,7 +271,7 @@ class Graph:
 
     # adding vertices and edges
     # adding the weight is optional
-    # handels repetition
+    # handles repetition
     def add_pair(self, u, v, w=1):
         # check if the u exists
         if self.graph.get(u):
@@ -290,7 +290,7 @@ class Graph:
             # if u does not exist
             self.graph[v] = [[w, u]]
 
-    # handels if the input does not exist
+    # handles if the input does not exist
     def remove_pair(self, u, v):
         if self.graph.get(u):
             for _ in self.graph[u]:
@@ -302,7 +302,7 @@ class Graph:
                 if _[1] == u:
                     self.graph[v].remove(_)
 
-    # if no destination is meant the defaut value is -1
+    # if no destination is meant the default value is -1
     def dfs(self, s=-2, d=-1):
         if s == d:
             return []
@@ -341,7 +341,7 @@ class Graph:
             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
+    # c is the count of nodes you want and if you leave it or pass -1 to the function the count
     # will be random from 10 to 10000
     def fill_graph_randomly(self, c=-1):
         if c == -1:

hashes/sha1.py

@@ -8,7 +8,7 @@ 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 crytpographic function which means it is easy
-to calculate forwards but extemely difficult to calculate backwards. What this means
+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
@@ -139,7 +139,7 @@ class SHA1HashTest(unittest.TestCase):
 def main():
     """
     Provides option 'string' or 'file' to take input and prints the calculated SHA1 hash.
-    unittest.main() has been commented because we probably dont want to run
+    unittest.main() has been commented because we probably don't want to run
     the test each time.
     """
     # unittest.main()

linear_algebra/README.md

@@ -8,7 +8,7 @@ This module contains classes and functions for doing linear algebra.
 
 ### class Vector  
 -
-    - This class represents a vector of arbitray size and related operations.  
+    - This class represents a vector of arbitrary size and related operations.  
 
     **Overview about the methods:**    
         

machine_learning/k_nearest_neighbours.py

@@ -47,7 +47,7 @@ def classifier(train_data, train_target, classes, point, k=5):
         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
+    # Most commonly occurring class among them
     # is the class into which the point is classified
     result = Counter(votes).most_common(1)[0][0]
     return classes[result]

machine_learning/scoring_functions.py

@@ -10,7 +10,7 @@ import numpy as np
     even log is used.
 
     Using log and roots can be perceived as tools for penalizing big
-    erors. However, using appropriate metrics depends on the situations,
+    errors. However, using appropriate metrics depends on the situations,
     and types of data
 """
 

machine_learning/sequential_minimum_optimization.py

@@ -1,8 +1,10 @@
 """
-    Implementation of sequential minimal optimization(SMO) for support vector machines(SVM).
+    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.
+    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:
@@ -18,7 +20,8 @@ Usage:
 
         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 = 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)
 
@@ -72,7 +75,7 @@ class SmoSVM:
 
         self.choose_alpha = self._choose_alphas()
 
-    # Calculate alphas using SMO algorithsm
+    # Calculate alphas using SMO algorithm
     def fit(self):
         K = self._k
         state = None
@@ -227,7 +230,7 @@ class SmoSVM:
     def _choose_a1(self):
         """
         Choose first alpha ;steps:
-           1:Fisrt loop over all sample
+           1:First 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.
         """
@@ -261,9 +264,11 @@ class SmoSVM:
     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.
+           1: Choose alpha2 which gets 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)]
 
@@ -316,7 +321,7 @@ class SmoSVM:
         # 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
+            # a2_new has a boundary
             if a2_new_unc >= H:
                 a2_new = H
             elif a2_new_unc <= L:
@@ -357,7 +362,7 @@ class SmoSVM:
             else:
                 a2_new = a2
 
-        # a1_new has a boundry too
+        # a1_new has a boundary too
         a1_new = a1 + s * (a2 - a2_new)
         if a1_new < 0:
             a2_new += s * a1_new
@@ -471,7 +476,7 @@ def test_cancel_data():
     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
+    # 2: dividing 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:]
 
@@ -568,7 +573,7 @@ def plot_partition_boundary(
 ):
     """
     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
+    For this reason, we generate randomly distributed 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.
 

machine_learning/support_vector_machines.py

@@ -18,7 +18,7 @@ def Linearsvc(train_x, train_y):
 
 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.
+    # various parameters like "kernel","gamma","C" can effectively tuned for a given machine learning model.
     SVC = svm.SVC(gamma="auto")
     SVC.fit(train_x, train_y)
     return SVC

maths/matrix_exponentiation.py

@@ -12,7 +12,7 @@ https://www.hackerearth.com/practice/notes/matrix-exponentiation-1/
 
 class Matrix:
     def __init__(self, arg):
-        if isinstance(arg, list):  # Initialzes a matrix identical to the one provided.
+        if isinstance(arg, list):  # Initializes 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.
@@ -50,7 +50,7 @@ def fibonacci_with_matrix_exponentiation(n, f1, f2):
 
 
 def simple_fibonacci(n, f1, f2):
-    # Trival Cases
+    # Trivial Cases
     if n == 1:
         return f1
     elif n == 2:

maths/mobius_function.py

@@ -1,5 +1,5 @@
 """
-Refrences: https://en.wikipedia.org/wiki/M%C3%B6bius_function
+References: https://en.wikipedia.org/wiki/M%C3%B6bius_function
 References: wikipedia:square free number
 python/black : True
 flake8 : True

maths/prime_sieve_eratosthenes.py

@@ -26,7 +26,7 @@ def prime_sieve_eratosthenes(num):
     p = 2
 
     while p * p <= num:
-        if primes[p] == True:
+        if primes[p]:
             for i in range(p * p, num + 1, p):
                 primes[i] = False
         p += 1

maths/simpson_rule.py

@@ -1,7 +1,7 @@
 """
 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 approach of suming 'Equally Spaced Abscissas'
 
 method 2:
 "Simpson Rule"

maths/square_root.py

@@ -22,7 +22,7 @@ def square_root_iterative(
     a: float, max_iter: int = 9999, tolerance: float = 0.00000000000001
 ) -> float:
     """
-    Sqaure root is aproximated using Newtons method.
+    Square root is aproximated using Newtons method.
     https://en.wikipedia.org/wiki/Newton%27s_method
     
     >>> all(abs(square_root_iterative(i)-math.sqrt(i)) <= .00000000000001  for i in range(0, 500))

maths/trapezoidal_rule.py

@@ -1,7 +1,7 @@
 """
 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 approach of suming 'Equally Spaced Abscissas'
 
 method 1:
 "extended trapezoidal rule"

maths/zellers_congruence.py

@@ -31,17 +31,17 @@ def zeller(date_input: str) -> str:
         ...
     ValueError: invalid literal for int() with base 10: '.4'
 
-    Validate second seperator:
+    Validate second separator:
     >>> zeller('01-31*2010')
     Traceback (most recent call last):
         ...
-    ValueError: Date seperator must be '-' or '/'
+    ValueError: Date separator must be '-' or '/'
 
-    Validate first seperator:
+    Validate first separator:
     >>> zeller('01^31-2010')
     Traceback (most recent call last):
         ...
-    ValueError: Date seperator must be '-' or '/'
+    ValueError: Date separator must be '-' or '/'
 
     Validate out of range year:
     >>> zeller('01-31-8999')
@@ -55,7 +55,7 @@ def zeller(date_input: str) -> str:
         ...
     TypeError: zeller() missing 1 required positional argument: 'date_input'
 
-    Test length fo date_input:
+    Test length of date_input:
     >>> zeller('')
     Traceback (most recent call last):
         ...
@@ -92,7 +92,7 @@ def zeller(date_input: str) -> str:
     sep_1: str = date_input[2]
     # Validate
     if sep_1 not in ["-", "/"]:
-        raise ValueError("Date seperator must be '-' or '/'")
+        raise ValueError("Date separator must be '-' or '/'")
 
     # Get day
     d: int = int(date_input[3] + date_input[4])
@@ -100,11 +100,11 @@ def zeller(date_input: str) -> str:
     if not 0 < d < 32:
         raise ValueError("Date must be between 1 - 31")
 
-    # Get second seperator
+    # Get second separator
     sep_2: str = date_input[5]
     # Validate
     if sep_2 not in ["-", "/"]:
-        raise ValueError("Date seperator must be '-' or '/'")
+        raise ValueError("Date separator must be '-' or '/'")
 
     # Get year
     y: int = int(date_input[6] + date_input[7] + date_input[8] + date_input[9])

matrix/matrix_class.py

@@ -1,4 +1,4 @@
-# An OOP aproach to representing and manipulating matrices
+# An OOP approach to representing and manipulating matrices
 
 
 class Matrix:

matrix/spiral_print.py

@@ -1,5 +1,5 @@
 """
-This program print the matix in spiral form.
+This program print the matrix in spiral form.
 This problem has been solved through recursive way.
 
       Matrix must satisfy below conditions

networking_flow/minimum_cut.py

@@ -35,7 +35,7 @@ def mincut(graph, source, sink):
     parent = [-1] * (len(graph))
     max_flow = 0
     res = []
-    temp = [i[:] for i in graph]  # Record orignial cut, copy.
+    temp = [i[:] for i in graph]  # Record original cut, copy.
     while BFS(graph, source, sink, parent):
         path_flow = float("Inf")
         s = sink

neural_network/convolution_neural_network.py

@@ -1,12 +1,12 @@
 """
      - - - - - -- - - - - - - - - - - - - - - - - - - - - - -
     Name - - CNN - Convolution Neural Network For Photo Recognizing
-    Goal - - Recognize Handing Writting Word Photo
+    Goal - - Recognize Handing Writing Word Photo
     Detail：Total 5 layers neural network
             * Convolution layer
             * Pooling layer
             * Input layer layer of BP
-            * Hiden layer of BP
+            * Hidden layer of BP
             * Output layer of BP
     Author: Stephen Lee
     Github: 245885195@qq.com
@@ -116,7 +116,7 @@ class CNN:
                     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
+        # calculate 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):
@@ -163,12 +163,12 @@ class CNN:
             featuremap_pooled.append(map_pooled)
         return featuremap_pooled
 
-    def _expand(self, datas):
+    def _expand(self, data):
         # 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])
+        for i in range(len(data)):
+            shapes = np.shape(data[i])
+            data_listed = data[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)
@@ -185,7 +185,7 @@ class CNN:
         self, out_map, pd_pool, num_map, size_map, size_pooling
     ):
         """
-        calcluate the gradient from the data slice of pool layer
+        calculate 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]
@@ -217,7 +217,7 @@ class CNN:
         all_mse = []
         mse = 10000
         while rp < n_repeat and mse >= error_accuracy:
-            alle = 0
+            error_count = 0
             print("-------------Learning Time %d--------------" % rp)
             for p in range(len(datas_train)):
                 # print('------------Learning Image: %d--------------'%p)
@@ -246,7 +246,7 @@ class CNN:
                 bp_out3 = self.sig(bp_net_k)
 
                 # --------------Model Leaning ------------------------
-                # calcluate error and gradient---------------
+                # calculate error and gradient---------------
                 pd_k_all = np.multiply(
                     (data_teach - bp_out3), np.multiply(bp_out3, (1 - bp_out3))
                 )
@@ -285,11 +285,11 @@ class CNN:
                 self.thre_bp2 = self.thre_bp2 - pd_j_all * self.rate_thre
                 # calculate the sum error of all single image
                 errors = np.sum(abs(data_teach - bp_out3))
-                alle = alle + errors
+                error_count += errors
                 # print('   ----Teach      ',data_teach)
                 # print('   ----BP_output  ',bp_out3)
             rp = rp + 1
-            mse = alle / patterns
+            mse = error_count / patterns
             all_mse.append(mse)
 
         def draw_error():

neural_network/perceptron.py

@@ -76,11 +76,11 @@ class Perceptron:
                     has_misclassified = True
             # print('Epoch: \n',epoch_count)
             epoch_count = epoch_count + 1
-            # if you want controle the epoch or just by erro
+            # if you want control the epoch or just by error
             if not has_misclassified:
                 print(("\nEpoch:\n", epoch_count))
                 print("------------------------\n")
-                # if epoch_count > self.epoch_number or not erro:
+                # if epoch_count > self.epoch_number or not error:
                 break
 
     def sort(self, sample) -> None:

other/linear_congruential_generator.py

@@ -13,7 +13,7 @@ class LinearCongruentialGenerator:
         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.
+        efficient values are powers of 2. 2^32 is a common value.
         """
         self.multiplier = multiplier
         self.increment = increment

other/primelib.py

@@ -88,7 +88,7 @@ def sieveEr(N):
     # precondition
     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: contains all natural numbers from 2 up to N
     beginList = [x for x in range(2, N + 1)]
 
     ans = []  # this list will be returns.
@@ -480,8 +480,8 @@ 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)
+        returns a list of all prime numbers between 'pNumber1' (exclusive)
+                and 'pNumber2' (exclusive)
     """
 
     # precondition

project_euler/problem_04/sol1.py

@@ -19,7 +19,7 @@ def solution(n):
     >>> solution(40000)
     39893
     """
-    # fetchs the next number
+    # fetches the next number
     for number in range(n - 1, 10000, -1):
 
         # converts number into string.

project_euler/problem_551/sol1.py

@@ -2,7 +2,7 @@
 Sum of digits sequence
 Problem 551
 
-Let a(0), a(1),... be an interger sequence defined by:
+Let a(0), a(1),... be an integer sequence defined by:
      a(0) = 1
      for n >= 1, a(n) is the sum of the digits of all preceding terms
 
@@ -33,7 +33,7 @@ def next_term(a_i, k, i, n):
     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
+    n -- term to calculate 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

searches/hill_climbing.py

@@ -87,7 +87,7 @@ def hill_climbing(
     """
     implementation of the hill climbling algorithm. We start with a given state, find
         all its neighbors, move towards the neighbor which provides the maximum (or
-        minimum) change. We keep doing this untill we are at a state where we do not
+        minimum) change. We keep doing this until we are at a state where we do not
         have any neighbors which can improve the solution.
         Args:
             search_prob: The search state at the start.

searches/interpolation_search.py

@@ -15,7 +15,7 @@ def interpolation_search(sorted_collection, item):
     right = len(sorted_collection) - 1
 
     while left <= right:
-        # avoid devided by 0 during interpolation
+        # avoid divided by 0 during interpolation
         if sorted_collection[left] == sorted_collection[right]:
             if sorted_collection[left] == item:
                 return left
@@ -59,7 +59,7 @@ def interpolation_search_by_recursion(sorted_collection, item, left, right):
     :return: index of found item or None if item is not found
     """
 
-    # avoid devided by 0 during interpolation
+    # avoid divided by 0 during interpolation
     if sorted_collection[left] == sorted_collection[right]:
         if sorted_collection[left] == item:
             return left

searches/jump_search.py

@@ -20,7 +20,7 @@ def jump_search(arr, x):
     return -1
 
 
+if __name__ == "__main__":
     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(f"Number {x} is at index {jump_search(arr, x)}")

searches/simulated_annealing.py

@@ -73,10 +73,8 @@ def simulated_annealing(
                     next_state = picked_neighbor
         current_temp = current_temp - (current_temp * rate_of_decrease)
 
-        if (
-            current_temp < threshold_temp or next_state is None
-        ):  # temperature below threshold, or
-            # couldnt find a suitaable neighbor
+        if current_temp < threshold_temp or next_state is None:
+            # temperature below threshold, or could not find a suitaable neighbor
             search_end = True
         else:
             current_state = next_state

searches/tabu_search.py

@@ -188,7 +188,7 @@ def tabu_search(
     and the cost (distance) for each neighbor.
     :param iters: The number of iterations that Tabu search will execute.
     :param size: The size of Tabu List.
-    :return best_solution_ever: The solution with the lowest distance that occured during the execution of Tabu search.
+    :return best_solution_ever: The solution with the lowest distance that occurred during the execution of Tabu search.
     :return best_cost: The total distance that Travelling Salesman will travel, if he follows the path in best_solution
     ever.
 

sorts/bitonic_sort.py

@@ -22,7 +22,7 @@ def bitonicMerge(a, low, cnt, dire):
         bitonicMerge(a, low, k, dire)
         bitonicMerge(a, low + k, k, dire)
 
-        # This funcion first produces a bitonic sequence by recursively
+        # This function first produces a bitonic sequence by recursively
 
 
 # sorting its two halves in opposite sorting orders, and then

sorts/double_sort.py

@@ -17,7 +17,7 @@ def double_sort(lst):
     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
+    ):  # we don't need to traverse to end of list as
         for j in range(0, no_of_elements - 1):
             if (
                 lst[j + 1] < lst[j]

sorts/pigeonhole_sort.py

@@ -7,7 +7,7 @@ def pigeonhole_sort(a):
     """
     >>> a = [8, 3, 2, 7, 4, 6, 8]
     >>> b = sorted(a)  # a nondestructive sort
-    >>> pigeonhole_sort(a)  # a distructive sort
+    >>> pigeonhole_sort(a)  # a destructive sort
     >>> a == b
     True
     """

sorts/recursive_insertion_sort.py

@@ -69,7 +69,7 @@ def insert_next(collection: List, index: int):
 
 
 if __name__ == "__main__":
-    numbers = input("Enter integers seperated by spaces: ")
+    numbers = input("Enter integers separated by spaces: ")
     numbers = [int(num) for num in numbers.split()]
     rec_insertion_sort(numbers, len(numbers))
     print(numbers)

strings/aho-corasick.py

@@ -67,7 +67,7 @@ class Automaton:
         >>> 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
+        result = dict()  # returns a dict with keywords and list of its occurrences
         current_state = 0
         for i in range(len(string)):
             while (

strings/boyer_moore_search.py

@@ -27,7 +27,7 @@ class BoyerMooreSearch:
     def match_in_pattern(self, char):
         """ finds the index of char in pattern in reverse order
 
-        Paremeters : 
+        Parameters : 
             char (chr): character to be searched
         
         Returns :
@@ -43,12 +43,12 @@ class BoyerMooreSearch:
     def mismatch_in_text(self, currentPos):
         """ finds the index of mis-matched character in text when compared with pattern from last
 
-        Paremeters : 
+        Parameters : 
             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
+            -1 (int): if there is no mismatch between pattern and text block
         """
 
         for i in range(self.patLen - 1, -1, -1):

strings/manacher.py

@@ -13,12 +13,13 @@ def palindromic_string(input_string):
     """
     Manacher’s algorithm which finds Longest Palindromic Substring in linear time.
 
-    1. first this conver input_string("xyx") into new_string("x|y|x") where odd positions are actual input
-        characters.
+    1. first this convert input_string("xyx") into new_string("x|y|x") where odd
+        positions are actual input characters.
     2. for each character in new_string it find corresponding length and store,
         a. max_length
         b. max_length's center
-    3. return output_string from center - max_length to center + max_length and remove all "|"   
+    3. return output_string from center - max_length to center + max_length and remove
+        all "|"
     """
     max_length = 0
 
@@ -35,7 +36,7 @@ def palindromic_string(input_string):
     # for each character in new_string find corresponding palindromic string
     for i in range(len(new_input_string)):
 
-        # get palindromic length from ith position
+        # get palindromic length from i-th position
         length = palindromic_length(i, 1, new_input_string)
 
         # update max_length and start position

strings/split.py

@@ -1,17 +1,17 @@
-def split(string: str, seperator: str = " ") -> list:
+def split(string: str, separator: str = " ") -> list:
     """
-    Will split the string up into all the values seperated by the seperator (defaults to spaces)
+    Will split the string up into all the values separated by the separator (defaults to spaces)
     
-    >>> split("apple#banana#cherry#orange",seperator='#')
+    >>> split("apple#banana#cherry#orange",separator='#')
     ['apple', 'banana', 'cherry', 'orange']
     
     >>> split("Hello there")
     ['Hello', 'there']
     
-    >>> split("11/22/63",seperator = '/')
+    >>> split("11/22/63",separator = '/')
     ['11', '22', '63']
     
-    >>> split("12:43:39",seperator = ":")
+    >>> split("12:43:39",separator = ":")
     ['12', '43', '39']
     """
 
@@ -19,7 +19,7 @@ def split(string: str, seperator: str = " ") -> list:
 
     last_index = 0
     for index, char in enumerate(string):
-        if char == seperator:
+        if char == separator:
             split_words.append(string[last_index:index])
             last_index = index + 1
         elif index + 1 == len(string):

strings/word_occurrence.py

@@ -11,11 +11,11 @@ def word_occurence(sentence: str) -> dict:
     ...     in Counter(SENTENCE.split()).items())
     True
     """
-    occurence = defaultdict(int)
+    occurrence = defaultdict(int)
     # Creating a dictionary containing count of each word
     for word in sentence.split(" "):
-        occurence[word] += 1
-    return occurence
+        occurrence[word] += 1
+    return occurrence
 
 
 if __name__ == "__main__":