Modernize Python 2 code to get ready for Python 3

ciphers/playfair_cipher.py

@@ -85,7 +85,7 @@ def decode(ciphertext, key):
     plaintext = ""
 
     # https://en.wikipedia.org/wiki/Playfair_cipher#Description
-    for char1, char2 in chunk(ciphertext, 2):
+    for char1, char2 in chunker(ciphertext, 2):
         row1, col1 = divmod(table.index(char1), 5)
         row2, col2 = divmod(table.index(char2), 5)
 

data_structures/AVL/AVL.py

@@ -1,6 +1,7 @@
 '''
 A AVL tree
 '''
+from __future__ import print_function
 
 
 class Node:

data_structures/Graph/BellmanFord.py

@@ -1,3 +1,5 @@
+from __future__ import print_function
+
 def printDist(dist, V):
 	print("\nVertex Distance")
 	for i in range(V):

data_structures/Graph/BreadthFirstSearch.py

@@ -1,4 +1,6 @@
 # Author: OMKAR PATHAK
+from __future__ import print_function
+
 
 class Graph():
     def __init__(self):

data_structures/Graph/DepthFirstSearch.py

@@ -1,4 +1,6 @@
 # Author: OMKAR PATHAK
+from __future__ import print_function
+
 
 class Graph():
     def __init__(self):

data_structures/Graph/Dijkstra.py

@@ -1,3 +1,4 @@
+from __future__ import print_function
 
 def printDist(dist, V):
 	print("\nVertex Distance")

data_structures/Graph/FloydWarshall.py

@@ -1,3 +1,4 @@
+from __future__ import print_function
 
 def printDist(dist, V):
 	print("\nThe shortest path matrix using Floyd Warshall algorithm\n")
@@ -7,7 +8,7 @@ def printDist(dist, V):
 				print(int(dist[i][j]),end = "\t")
 			else:
 				print("INF",end="\t")
-		print();
+		print()
 
 
 
@@ -29,19 +30,19 @@ def FloydWarshall(graph, V):
 
 
 #MAIN
-V = int(input("Enter number of vertices: "));
-E = int(input("Enter number of edges: "));
+V = int(input("Enter number of vertices: "))
+E = int(input("Enter number of edges: "))
 
 graph = [[float('inf') for i in range(V)] for j in range(V)]
 
 for i in range(V):
-	graph[i][i] = 0.0;
+	graph[i][i] = 0.0
 
 for i in range(E):
 	print("\nEdge ",i+1)
 	src = int(input("Enter source:"))
 	dst = int(input("Enter destination:"))
 	weight = float(input("Enter weight:"))
-	graph[src][dst] = weight;
+	graph[src][dst] = weight
 
 FloydWarshall(graph, V)

data_structures/Graph/Graph_list.py

@@ -1,3 +1,6 @@
+from __future__ import print_function
+
+
 class Graph:
     def __init__(self, vertex):
         self.vertex = vertex

data_structures/Graph/Graph_matrix.py

@@ -1,3 +1,6 @@
+from __future__ import print_function
+
+
 class Graph:
 
     def __init__(self, vertex):

data_structures/Graph/dijkstra_algorithm.py

@@ -2,6 +2,7 @@
 # Author: Shubham Malik
 # References: https://en.wikipedia.org/wiki/Dijkstra%27s_algorithm
 
+from __future__ import print_function
 import math
 import sys
 # For storing the vertex set to retreive node with the lowest distance

data_structures/LinkedList/DoublyLinkedList.py

@@ -3,6 +3,9 @@
 - This is an example of a double ended, doubly linked list.
 - Each link references the next link and the previous one.
 '''
+from __future__ import print_function
+
+
 class LinkedList:
     def __init__(self):
         self.head = None
@@ -70,4 +73,4 @@ class Link:
     def __init__(self, x):
         self.value = x
     def displayLink(self):
-        print("{}".format(self.value), end=" ")
+        print("{}".format(self.value), end=" ")

dynamic_programming/edit_distance.py

@@ -7,6 +7,8 @@
 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.
 """
+from __future__ import print_function
+
 
 class EditDistance:
     """

dynamic_programming/fibonacci.py

@@ -1,6 +1,7 @@
 """
 This is a pure Python implementation of Dynamic Programming solution to the fibonacci sequence problem.
 """
+from __future__ import print_function
 
 
 class Fibonacci:

machine_learning/k_means_clust.py

@@ -46,6 +46,7 @@
   5. Have fun..
   
 '''
+from __future__ import print_function
 from sklearn.metrics import pairwise_distances
 import numpy as np
 
@@ -169,4 +170,4 @@ def kmeans(data, k, initial_centroids, maxiter=500, record_heterogeneity=None, v
     initial_centroids = get_initial_centroids(dataset['data'], k, seed=0)
     centroids, cluster_assignment = kmeans(dataset['data'], k, initial_centroids, maxiter=400,
                                         record_heterogeneity=heterogeneity, verbose=True)
-    plot_heterogeneity(heterogeneity, k)
+    plot_heterogeneity(heterogeneity, k)

machine_learning/scoring_functions.py

@@ -41,7 +41,7 @@ def rmse(predict, actual):
     actual = np.array(actual)
 
     difference = predict - actual
-    square_diff = np.square(dfference)
+    square_diff = np.square(difference)
     mean_square_diff = square_diff.mean()
     score = np.sqrt(mean_square_diff)
     return score

other/FindingPrimes.py

@@ -2,6 +2,9 @@
 -The sieve of Eratosthenes is an algorithm used to find prime numbers, less than or equal to a given value.
 -Illustration: https://upload.wikimedia.org/wikipedia/commons/b/b9/Sieve_of_Eratosthenes_animation.gif
 '''
+from __future__ import print_function
+
+
 from math import sqrt
 def SOE(n):
     check = round(sqrt(n)) #Need not check for multiples past the square root of n

sorts/cyclesort.py

@@ -1,4 +1,7 @@
 # Code contributed by Honey Sharma
+from __future__ import print_function
+
+
 def cycle_sort(array):
     ans = 0