Merge pull request #477 from ParthS007/patch1

Fixes#476 : Remove Multiple Unused Imports and Variable

.gitignore

@@ -7,6 +7,7 @@ __pycache__/
 *.so
 
 # Distribution / packaging
+.vscode/
 .Python
 env/
 build/

.vscode/settings.json

@@ -1,3 +0,0 @@
-{
-    "python.pythonPath": "/usr/bin/python3"
-}

ArithmeticAnalysis/LUdecomposition.py

@@ -1,7 +1,7 @@
-import math
 import numpy
 
-def LUDecompose (table): #table that contains our data
+def LUDecompose (table):
+    #table that contains our data
     #table has to be a square array so we need to check first
     rows,columns=numpy.shape(table)
     L=numpy.zeros((rows,columns))
@@ -31,4 +31,4 @@ def LUDecompose (table): #table that contains our data
 matrix =numpy.array([[2,-2,1],[0,1,2],[5,3,1]])
 L,U = LUDecompose(matrix)
 print(L)
-print(U)
+print(U)

ArithmeticAnalysis/NewtonRaphsonMethod.py

@@ -3,16 +3,14 @@
 
 from sympy import diff
 from decimal import Decimal
-from math import sin, cos, exp
 
 def NewtonRaphson(func, a):
     ''' Finds root from the point 'a' onwards by Newton-Raphson method '''
     while True:
-        x = a
         c = Decimal(a) - ( Decimal(eval(func)) / Decimal(eval(str(diff(func)))) )
         
-        x = c
         a = c
+
         # This number dictates the accuracy of the answer
         if  abs(eval(func)) < 10**-15:
             return  c

Graphs/Multi_Hueristic_Astar.py

@@ -1,8 +1,6 @@
 from __future__ import print_function
 import heapq
 import numpy as np
-import math
-import copy
 
 try:
     xrange          # Python 2

Graphs/basic-graphs.py

@@ -140,7 +140,7 @@ from collections import deque
 
 
 def topo(G, ind=None, Q=[1]):
-    if ind == None:
+    if ind is None:
         ind = [0] * (len(G) + 1) 		# SInce oth Index is ignored
         for u in G:
             for v in G[u]:

Project Euler/Problem 09/sol2.py

@@ -3,7 +3,6 @@ a^2+b^2=c^2
 Given N, Check if there exists any Pythagorean triplet for which a+b+c=N
 Find maximum possible value of product of a,b,c among all such Pythagorean triplets, If there is no such Pythagorean triplet print -1."""
 #!/bin/python3
-import sys
 
 product=-1
 d=0

Project Euler/Problem 15/sol1.py

@@ -1,5 +1,5 @@
 from __future__ import print_function
-from math import factorial, ceil
+from math import factorial
 
 def lattice_paths(n):
 	n = 2*n #middle entry of odd rows starting at row 3 is the solution for n = 1, 2, 3,...

ciphers/affine_cipher.py

@@ -68,8 +68,8 @@ def getRandomKey():
     while True:
         keyA = random.randint(2, len(SYMBOLS))
         keyB = random.randint(2, len(SYMBOLS))
-    if cryptoMath.gcd(keyA, len(SYMBOLS)) == 1:
-        return keyA * len(SYMBOLS) + keyB
+        if cryptoMath.gcd(keyA, len(SYMBOLS)) == 1:
+            return keyA * len(SYMBOLS) + keyB
 
 if __name__ == '__main__':
     import doctest

data_structures/LinkedList/DoublyLinkedList.py

@@ -24,7 +24,7 @@ class LinkedList:
         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
-        if(self.head == None):
+        if(self.head is None):
             self.tail = None
         return temp
     
@@ -58,7 +58,7 @@ class LinkedList:
             current.next.previous = current.previous # 1 <--> 3
     
     def isEmpty(self): #Will return True if the list is empty
-        return(self.head == None)
+        return(self.head is None)
         
     def display(self): #Prints contents of the list
         current = self.head

data_structures/LinkedList/__init__.py

@@ -19,4 +19,4 @@ class LinkedList:
             return item
 
     def is_empty(self):
-        return self.head == None
+        return self.head is None

data_structures/LinkedList/singly_LinkedList.py

@@ -67,3 +67,4 @@ class Linked_List:
             current = next_node
         # Return prev in order to put the head at the end
         Head = prev
+        return Head

dynamic_programming/k_means_clustering_tensorflow.py

@@ -1,5 +1,5 @@
 import tensorflow as tf
-from random import choice, shuffle
+from random import shuffle
 from numpy import array
 
 

machine_learning/linear_regression.py

@@ -59,7 +59,6 @@ def sum_of_square_error(data_x, data_y, len_data, theta):
     :param theta     : contains the feature vector
     :return          : sum of square error computed from given feature's
     """
-    error = 0.0
     prod = np.dot(theta, data_x.transpose())
     prod -= data_y.transpose()
     sum_elem = np.sum(np.square(prod))

other/game_of_life/game_o_life.py

@@ -28,9 +28,8 @@ Game-Of-Life Rules:
  comes a live cell, as if by reproduction.
  '''
 import numpy as np
-import random, time, sys
+import random, sys
 from matplotlib import pyplot as plt
-import matplotlib.animation as animation
 from matplotlib.colors import ListedColormap
 
 usage_doc='Usage of script: script_nama <size_of_canvas:int>'

other/primelib.py

@@ -290,7 +290,7 @@ def goldbach(number):
     
     while (i < lenPN and loop):
         
-        j = i+1;
+        j = i+1
         
         
         while (j < lenPN and loop):
@@ -300,9 +300,8 @@ def goldbach(number):
                 ans.append(primeNumbers[i])
                 ans.append(primeNumbers[j])
                 
-            j += 1;
-            
-            
+            j += 1
+
         i += 1
         
     # precondition

searches/interpolation_search.py

@@ -2,7 +2,6 @@
 This is pure python implementation of interpolation search algorithm
 """
 from __future__ import print_function
-import bisect
 
 try:
     raw_input          # Python 2

searches/quick_select.py

@@ -1,8 +1,5 @@
-import collections
-import sys
 import random
-import time
-import math
+
 """
 A python implementation of the quick select algorithm, which is efficient for calculating the value that would appear in the index of a list if it would be sorted, even if it is not already sorted
 https://en.wikipedia.org/wiki/Quickselect
@@ -25,23 +22,23 @@ def _partition(data, pivot):
             equal.append(element)
     return less, equal, greater
     
-    def quickSelect(list, k):
+def quickSelect(list, k):
     #k = len(list) // 2 when trying to find the median (index that value would be when list is sorted)
-      smaller = []
-      larger = []
-      pivot = random.randint(0, len(list) - 1)
-      pivot = list[pivot]
-      count = 0
-      smaller, equal, larger =_partition(list, pivot)
-      count = len(equal)
-      m = len(smaller)
+    smaller = []
+    larger = []
+    pivot = random.randint(0, len(list) - 1)
+    pivot = list[pivot]
+    count = 0
+    smaller, equal, larger =_partition(list, pivot)
+    count = len(equal)
+    m = len(smaller)
 
-      #k is the pivot
-      if m <= k < m + count:
+    #k is the pivot
+    if m <= k < m + count:
         return pivot
     # must be in smaller
-      elif m > k:
+    elif m > k:
         return quickSelect(smaller, k)
     #must be in larger
-      else:
+    else:
         return quickSelect(larger, k - (m + count))

sorts/external-sort.py

@@ -4,7 +4,6 @@
 # Sort large text files in a minimum amount of memory
 #
 import os
-import sys
 import argparse
 
 class FileSplitter(object):

sorts/random_normaldistribution_quicksort.py

@@ -2,7 +2,6 @@ from __future__ import print_function
 from random import randint
 from tempfile import TemporaryFile
 import numpy as np
-import math
 
 
 

sorts/tree_sort.py

@@ -11,12 +11,12 @@ class node():
     def insert(self,val):
         if self.val:
             if val < self.val:
-                if self.left == None:
+                if self.left is None:
                     self.left = node(val)
                 else:
                     self.left.insert(val)
             elif val > self.val:
-                if self.right == None:
+                if self.right is None:
                     self.right = node(val)
                 else:
                     self.right.insert(val)

strings/min-cost-string-conversion.py

@@ -68,7 +68,6 @@ def assemble_transformation(ops, i, j):
 			return seq
 
 if __name__ == '__main__':
-	from time import sleep
 	_, operations = compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)
 
 	m = len(operations)