Wrap lines that go beyond GiHub Editor

Author: cclauss

ciphers/rsa_cipher.py

@@ -1,4 +1,7 @@
-import sys, rsa_key_generator as rkg, os
+import os
+import sys
+
+import rsa_key_generator as rkg
 
 DEFAULT_BLOCK_SIZE = 128
 BYTE_SIZE = 256
@@ -92,7 +95,9 @@ def encryptAndWriteToFile(
     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."
+            "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)
         )
 
@@ -117,7 +122,9 @@ def readFromFileAndDecrypt(messageFilename, 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. Did you specify the correct key file and encrypted file?"
+            "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)
         )
 

ciphers/rsa_key_generator.py

@@ -1,5 +1,9 @@
-import random, sys, os
-import rabin_miller as rabinMiller, cryptomath_module as cryptoMath
+import os
+import random
+import sys
+
+import cryptomath_module as cryptoMath
+import rabin_miller as rabinMiller
 
 
 def main():
@@ -35,7 +39,8 @@ def makeKeyFiles(name, keySize):
     ):
         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.'
+            '"%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()

data_structures/linked_list/doubly_linked_list.py

@@ -1,9 +1,12 @@
 """
-- A linked list is similar to an array, it holds values. However, links in a linked list do not have indexes.
+- 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 efficient"""
+- 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 efficient"""
 
 
 class LinkedList:  # making main class named linked list
@@ -13,7 +16,7 @@ def __init__(self):
 
     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
+        if self.isEmpty():  # Set the first element added to be the tail
             self.tail = newLink
         else:
             self.head.previous = newLink  # newLink <-- currenthead(head)
@@ -23,7 +26,9 @@ def insertHead(self, x):
     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
+        # oldHead --> 2ndElement(head) nothing pointing at it so the old head will be
+        # removed
+        self.head.previous = None
         if self.head is None:
             self.tail = None  # if empty linked list
         return temp

divide_and_conquer/strassen_matrix_multiplication.py

@@ -31,12 +31,19 @@ def matrix_subtraction(matrix_a: List, matrix_b: List):
 
 def split_matrix(a: List,) -> Tuple[List, List, List, List]:
     """
-    Given an even length matrix, returns the top_left, top_right, bot_left, bot_right quadrant.
+    Given an even length matrix, returns the top_left, top_right, bot_left, bot_right
+    quadrant.
 
     >>> split_matrix([[4,3,2,4],[2,3,1,1],[6,5,4,3],[8,4,1,6]])
     ([[4, 3], [2, 3]], [[2, 4], [1, 1]], [[6, 5], [8, 4]], [[4, 3], [1, 6]])
-    >>> split_matrix([[4,3,2,4,4,3,2,4],[2,3,1,1,2,3,1,1],[6,5,4,3,6,5,4,3],[8,4,1,6,8,4,1,6],[4,3,2,4,4,3,2,4],[2,3,1,1,2,3,1,1],[6,5,4,3,6,5,4,3],[8,4,1,6,8,4,1,6]])
-    ([[4, 3, 2, 4], [2, 3, 1, 1], [6, 5, 4, 3], [8, 4, 1, 6]], [[4, 3, 2, 4], [2, 3, 1, 1], [6, 5, 4, 3], [8, 4, 1, 6]], [[4, 3, 2, 4], [2, 3, 1, 1], [6, 5, 4, 3], [8, 4, 1, 6]], [[4, 3, 2, 4], [2, 3, 1, 1], [6, 5, 4, 3], [8, 4, 1, 6]])
+    >>> split_matrix([
+    ...     [4,3,2,4,4,3,2,4],[2,3,1,1,2,3,1,1],[6,5,4,3,6,5,4,3],[8,4,1,6,8,4,1,6],
+    ...     [4,3,2,4,4,3,2,4],[2,3,1,1,2,3,1,1],[6,5,4,3,6,5,4,3],[8,4,1,6,8,4,1,6]
+    ... ])  doctest: +NORMALIZE_WHITESPACE
+    ([[4, 3, 2, 4], [2, 3, 1, 1], [6, 5, 4, 3], [8, 4, 1, 6]], [[4, 3, 2, 4],
+      [2, 3, 1, 1], [6, 5, 4, 3], [8, 4, 1, 6]], [[4, 3, 2, 4], [2, 3, 1, 1],
+      [6, 5, 4, 3], [8, 4, 1, 6]], [[4, 3, 2, 4], [2, 3, 1, 1], [6, 5, 4, 3],
+      [8, 4, 1, 6]])
     """
     if len(a) % 2 != 0 or len(a[0]) % 2 != 0:
         raise Exception("Odd matrices are not supported!")
@@ -66,8 +73,8 @@ def print_matrix(matrix: List) -> None:
 
 def actual_strassen(matrix_a: List, matrix_b: List) -> List:
     """
-    Recursive function to calculate the product of two matrices, using the Strassen Algorithm.
-    It only supports even length matrices.
+    Recursive function to calculate the product of two matrices, using the Strassen
+    Algorithm.  It only supports even length matrices.
     """
     if matrix_dimensions(matrix_a) == (2, 2):
         return default_matrix_multiplication(matrix_a, matrix_b)
@@ -106,7 +113,8 @@ def strassen(matrix1: List, matrix2: List) -> List:
     """
     if matrix_dimensions(matrix1)[1] != matrix_dimensions(matrix2)[0]:
         raise Exception(
-            f"Unable to multiply these matrices, please check the dimensions. \nMatrix A:{matrix1} \nMatrix B:{matrix2}"
+            f"Unable to multiply these matrices, please check the dimensions. \n"
+            f"Matrix A:{matrix1} \nMatrix B:{matrix2}"
         )
     dimension1 = matrix_dimensions(matrix1)
     dimension2 = matrix_dimensions(matrix2)
@@ -119,7 +127,8 @@ def strassen(matrix1: List, matrix2: List) -> List:
     new_matrix1 = matrix1
     new_matrix2 = matrix2
 
-    # Adding zeros to the matrices so that the arrays dimensions are the same and also power of 2
+    # Adding zeros to the matrices so that the arrays dimensions are the same and also
+    # power of 2
     for i in range(0, maxim):
         if i < dimension1[0]:
             for j in range(dimension1[1], maxim):

dynamic_programming/bitmask.py

@@ -4,10 +4,9 @@
 Here Bitmasking and DP are used for solving this.
 
 Question :-
-We have N tasks and M people. Each person in M can do only certain of these tasks. Also a person can do only one task and a task is performed only by one person.
+We have N tasks and M people. Each person in M can do only certain of these tasks. Also
+a person can do only one task and a task is performed only by one person.
 Find the total no of ways in which the tasks can be distributed.
-
-
 """
 from collections import defaultdict
 
@@ -25,7 +24,8 @@ def __init__(self, task_performed, total):
 
         self.task = defaultdict(list)  # stores the list of persons for each task
 
-        # final_mask is used to check if all persons are included by setting all bits to 1
+        # final_mask is used to check if all persons are included by setting all bits
+        # to 1
         self.final_mask = (1 << len(task_performed)) - 1
 
     def CountWaysUtil(self, mask, task_no):
@@ -45,15 +45,17 @@ def CountWaysUtil(self, mask, task_no):
         # Number of ways when we don't this task in the arrangement
         total_ways_util = self.CountWaysUtil(mask, task_no + 1)
 
-        # now assign the tasks one by one to all possible persons and recursively assign for the remaining tasks.
+        # now assign the tasks one by one to all possible persons and recursively
+        # assign for the remaining tasks.
         if task_no in self.task:
             for p in self.task[task_no]:
 
                 # if p is already given a task
                 if mask & (1 << p):
                     continue
 
-                # assign this task to p and change the mask value. And recursively assign tasks with the new mask value.
+                # assign this task to p and change the mask value. And recursively
+                # assign tasks with the new mask value.
                 total_ways_util += self.CountWaysUtil(mask | (1 << p), task_no + 1)
 
         # save the value.
@@ -85,6 +87,7 @@ def countNoOfWays(self, task_performed):
     )
     """
     For the particular example the tasks can be distributed as
-    (1,2,3), (1,2,4), (1,5,3), (1,5,4), (3,1,4), (3,2,4), (3,5,4), (4,1,3), (4,2,3), (4,5,3)
+    (1,2,3), (1,2,4), (1,5,3), (1,5,4), (3,1,4),
+    (3,2,4), (3,5,4), (4,1,3), (4,2,3), (4,5,3)
     total 10
     """

dynamic_programming/edit_distance.py

@@ -2,10 +2,12 @@
 Author  : Turfa Auliarachman
 Date    : October 12, 2016
 
-This is a pure Python implementation of Dynamic Programming solution to the edit distance problem.
+This is a pure Python implementation of Dynamic Programming solution to the edit
+distance problem.
 
 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.
+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.
 """
 
 

machine_learning/decision_tree.py

@@ -20,15 +20,19 @@ def mean_squared_error(self, labels, prediction):
         mean_squared_error:
         @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
+        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)
+        >>> 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)
-
+        >>> 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")
@@ -46,7 +50,8 @@ def train(self, X, y):
         """
 
         """
-        this section is to check that the inputs conform to our dimensionality constraints
+        this section is to check that the inputs conform to our dimensionality
+        constraints
         """
         if X.ndim != 1:
             print("Error: Input data set must be one dimensional")
@@ -72,7 +77,8 @@ def train(self, X, y):
         """
         loop over all possible splits for the decision tree. find the best split.
         if no split exists that is less than 2 * error for the entire array
-        then the data set is not split and the average for the entire array is used as the predictor
+        then the data set is not split and the average for the entire array is used as
+        the predictor
         """
         for i in range(len(X)):
             if len(X[:i]) < self.min_leaf_size:
@@ -136,7 +142,7 @@ 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 
+        return value: helper_mean_squared_error_test calculates the mean squared error
         """
         squared_error_sum = np.float(0)
         for label in labels:
@@ -147,9 +153,10 @@ def helper_mean_squared_error_test(labels, prediction):
 
 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.
+    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(-1.0, 1.0, 0.005)
     y = np.sin(X)

machine_learning/logistic_regression.py

@@ -1,15 +1,19 @@
 #!/usr/bin/python
 
-## Logistic Regression from scratch
+# Logistic Regression from scratch
 
 # In[62]:
 
 # In[63]:
 
 # 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:
+Coursera ML course
+https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac
+"""
 
 import numpy as np
 import matplotlib.pyplot as plt
@@ -21,7 +25,8 @@
 
 # In[67]:
 
-# sigmoid function or logistic function is used as a hypothesis function in classification problems
+# sigmoid function or logistic function is used as a hypothesis function in
+# classification problems
 
 
 def sigmoid_function(z):

machine_learning/support_vector_machines.py

@@ -3,6 +3,7 @@
 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()
@@ -17,8 +18,11 @@ 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 "kernel","gamma","C" can effectively tuned for a given machine learning model.
+    # 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 "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
@@ -27,8 +31,8 @@ def SVC(train_x, train_y):
 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
+    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])

maths/pi_monte_carlo_estimation.py

@@ -23,20 +23,24 @@ def random_unit_square(cls):
 
 def estimate_pi(number_of_simulations: int) -> float:
     """
-    Generates an estimate of the mathematical constant PI (see https://en.wikipedia.org/wiki/Monte_Carlo_method#Overview).
+    Generates an estimate of the mathematical constant PI.
+    See https://en.wikipedia.org/wiki/Monte_Carlo_method#Overview
 
-    The estimate is generated by Monte Carlo simulations. Let U be uniformly drawn from the unit square [0, 1) x [0, 1). The probability that U lies in the unit circle is:
+    The estimate is generated by Monte Carlo simulations. Let U be uniformly drawn from
+    the unit square [0, 1) x [0, 1). The probability that U lies in the unit circle is:
 
         P[U in unit circle] = 1/4 PI
 
     and therefore
 
         PI = 4 * P[U in unit circle]
 
-    We can get an estimate of the probability P[U in unit circle] (see https://en.wikipedia.org/wiki/Empirical_probability) by:
+    We can get an estimate of the probability P[U in unit circle].
+    See https://en.wikipedia.org/wiki/Empirical_probability by:
 
         1. Draw a point uniformly from the unit square.
-        2. Repeat the first step n times and count the number of points in the unit circle, which is called m.
+        2. Repeat the first step n times and count the number of points in the unit
+            circle, which is called m.
         3. An estimate of P[U in unit circle] is m/n
     """
     if number_of_simulations < 1:

maths/zellers_congruence.py

@@ -147,7 +147,8 @@ def zeller(date_input: str) -> str:
 
     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"
+        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)"