pyupgrade --py37-plus **/*.py (TheAlgorithms#1654)

* pyupgrade --py37-plus **/*.py

* fixup! Format Python code with psf/black push

backtracking/all_combinations.py

@@ -1,5 +1,3 @@
-# -*- coding: utf-8 -*-
-
 """
         In this problem, we want to determine all possible combinations of k
         numbers out of 1 ... n. We use backtracking to solve this problem.

ciphers/brute_force_caesar_cipher.py

@@ -40,7 +40,7 @@ def decrypt(message):
                 translated = translated + LETTERS[num]
             else:
                 translated = translated + symbol
-        print("Decryption using Key #%s: %s" % (key, translated))
+        print(f"Decryption using Key #{key}: {translated}")
 
 
 def main():

ciphers/hill_cipher.py

@@ -78,7 +78,7 @@ def checkDeterminant(self):
         req_l = len(self.key_string)
         if gcd(det, len(self.key_string)) != 1:
             raise ValueError(
-                "discriminant modular {0} of encryption key({1}) is not co prime w.r.t {2}.\nTry another key.".format(
+                "discriminant modular {} of encryption key({}) is not co prime w.r.t {}.\nTry another key.".format(
                     req_l, det, req_l
                 )
             )

ciphers/rsa_cipher.py

@@ -101,7 +101,7 @@ def encryptAndWriteToFile(
     for i in range(len(encryptedBlocks)):
         encryptedBlocks[i] = str(encryptedBlocks[i])
     encryptedContent = ",".join(encryptedBlocks)
-    encryptedContent = "%s_%s_%s" % (len(message), blockSize, encryptedContent)
+    encryptedContent = "{}_{}_{}".format(len(message), blockSize, encryptedContent)
     with open(messageFilename, "w") as fo:
         fo.write(encryptedContent)
     return encryptedContent

ciphers/rsa_key_generator.py

@@ -43,11 +43,11 @@ 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("%s,%s,%s" % (keySize, publicKey[0], publicKey[1]))
+        fo.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("%s,%s,%s" % (keySize, privateKey[0], privateKey[1]))
+        fo.write("{},{},{}".format(keySize, privateKey[0], privateKey[1]))
 
 
 if __name__ == "__main__":

ciphers/shuffled_shift_cipher.py

@@ -2,7 +2,7 @@
 import string
 
 
-class ShuffledShiftCipher(object):
+class ShuffledShiftCipher:
     """
     This algorithm uses the Caesar Cipher algorithm but removes the option to
     use brute force to decrypt the message.

ciphers/simple_substitution_cipher.py

@@ -17,7 +17,7 @@ def main():
         mode = "decrypt"
         translated = decryptMessage(key, message)
 
-    print("\n%sion: \n%s" % (mode.title(), translated))
+    print("\n{}ion: \n{}".format(mode.title(), translated))
 
 
 def checkValidKey(key):

ciphers/xor_cipher.py

@@ -18,7 +18,7 @@
 """
 
 
-class XORCipher(object):
+class XORCipher:
     def __init__(self, key=0):
         """
                         simple constructor that receives a key or uses

compression/burrows_wheeler.py

@@ -135,16 +135,14 @@ def reverse_bwt(bwt_string: str, idx_original_string: int) -> str:
         idx_original_string = int(idx_original_string)
     except ValueError:
         raise TypeError(
-            (
-                "The parameter idx_original_string type must be int or passive"
-                " of cast to int."
-            )
+            "The parameter idx_original_string type must be int or passive"
+            " of cast to int."
         )
     if idx_original_string < 0:
         raise ValueError("The parameter idx_original_string must not be lower than 0.")
     if idx_original_string >= len(bwt_string):
         raise ValueError(
-            ("The parameter idx_original_string must be lower than" " len(bwt_string).")
+            "The parameter idx_original_string must be lower than" " len(bwt_string)."
         )
 
     ordered_rotations = [""] * len(bwt_string)

data_structures/binary_tree/avl_tree.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """
 An auto-balanced binary tree!
 """

data_structures/binary_tree/red_black_tree.py

@@ -467,7 +467,7 @@ def __repr__(self):
         from pprint import pformat
 
         if self.left is None and self.right is None:
-            return "'%s %s'" % (self.label, (self.color and "red") or "blk")
+            return "'{} {}'".format(self.label, (self.color and "red") or "blk")
         return pformat(
             {
                 "%s %s"

data_structures/binary_tree/treap.py

@@ -2,7 +2,7 @@
 from typing import Tuple
 
 
-class Node(object):
+class Node:
     """
     Treap's node
     Treap is a binary tree by value and heap by priority
@@ -18,11 +18,10 @@ def __repr__(self):
         from pprint import pformat
 
         if self.left is None and self.right is None:
-            return "'%s: %.5s'" % (self.value, self.prior)
+            return f"'{self.value}: {self.prior:.5}'"
         else:
             return pformat(
-                {"%s: %.5s" % (self.value, self.prior): (self.left, self.right)},
-                indent=1,
+                {f"{self.value}: {self.prior:.5}": (self.left, self.right)}, indent=1,
             )
 
     def __str__(self):

data_structures/data_structures/heap/heap_generic.py

@@ -1,4 +1,4 @@
-class Heap(object):
+class Heap:
     """A generic Heap class, can be used as min or max by passing the key function accordingly.
     """
 

data_structures/hashing/hash_table.py

@@ -28,7 +28,7 @@ def hash_function(self, key):
 
     def _step_by_step(self, step_ord):
 
-        print("step {0}".format(step_ord))
+        print(f"step {step_ord}")
         print([i for i in range(len(self.values))])
         print(self.values)
 

data_structures/stacks/stack.py

@@ -1,7 +1,7 @@
 __author__ = "Omkar Pathak"
 
 
-class Stack(object):
+class Stack:
     """ A stack is an abstract data type that serves as a collection of
     elements with two principal operations: push() and pop(). push() adds an
     element to the top of the stack, and pop() removes an element from the top

digital_image_processing/histogram_equalization/histogram_stretch.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """
 Created on Fri Sep 28 15:22:29 2018
 

digital_image_processing/index_calculation.py

@@ -389,10 +389,7 @@ def Hue(self):
             :return: index
         """
         return np.arctan(
-            (
-                ((2 * self.red - self.green - self.blue) / 30.5)
-                * (self.green - self.blue)
-            )
+            ((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue)
         )
 
     def IVI(self, a=None, b=None):

dynamic_programming/fast_fibonacci.py

@@ -1,5 +1,4 @@
 #!/usr/bin/python
-# encoding=utf8
 
 """
 This program calculates the nth Fibonacci number in O(log(n)).

dynamic_programming/k_means_clustering_tensorflow.py

@@ -40,7 +40,7 @@ def TFKMeansCluster(vectors, noofclusters):
         ##First lets ensure we have a Variable vector for each centroid,
         ##initialized to one of the vectors from the available data points
         centroids = [
-            tf.Variable((vectors[vector_indices[i]])) for i in range(noofclusters)
+            tf.Variable(vectors[vector_indices[i]]) for i in range(noofclusters)
         ]
         ##These nodes will assign the centroid Variables the appropriate
         ##values

dynamic_programming/matrix_chain_order.py

@@ -46,7 +46,7 @@ def main():
     # 30*35 35*15 15*5 5*10 10*20 20*25
     Matrix, OptimalSolution = MatrixChainOrder(array)
 
-    print("No. of Operation required: " + str((Matrix[1][n - 1])))
+    print("No. of Operation required: " + str(Matrix[1][n - 1]))
     PrintOptimalSolution(OptimalSolution, 1, n - 1)
 
 

graphs/basic_graphs.py

@@ -48,7 +48,7 @@
 
 
 def dfs(G, s):
-    vis, S = set([s]), [s]
+    vis, S = {s}, [s]
     print(s)
     while S:
         flag = 0
@@ -76,7 +76,7 @@ def dfs(G, s):
 
 
 def bfs(G, s):
-    vis, Q = set([s]), deque([s])
+    vis, Q = {s}, deque([s])
     print(s)
     while Q:
         u = Q.popleft()
@@ -255,7 +255,7 @@ def krusk(E_and_n):
     # Sort edges on the basis of distance
     (E, n) = E_and_n
     E.sort(reverse=True, key=lambda x: x[2])
-    s = [set([i]) for i in range(1, n + 1)]
+    s = [{i} for i in range(1, n + 1)]
     while True:
         if len(s) == 1:
             break

graphs/breadth_first_search.py

@@ -1,5 +1,4 @@
 #!/usr/bin/python
-# encoding=utf8
 
 """ Author: OMKAR PATHAK """
 

graphs/depth_first_search.py

@@ -1,5 +1,4 @@
 #!/usr/bin/python
-# encoding=utf8
 
 """ Author: OMKAR PATHAK """
 

graphs/edmonds_karp_multiple_source_and_sink.py

@@ -57,7 +57,7 @@ def setMaximumFlowAlgorithm(self, Algorithm):
         self.maximumFlowAlgorithm = Algorithm(self)
 
 
-class FlowNetworkAlgorithmExecutor(object):
+class FlowNetworkAlgorithmExecutor:
     def __init__(self, flowNetwork):
         self.flowNetwork = flowNetwork
         self.verticesCount = flowNetwork.verticesCount
@@ -80,7 +80,7 @@ def _algorithm(self):
 
 class MaximumFlowAlgorithmExecutor(FlowNetworkAlgorithmExecutor):
     def __init__(self, flowNetwork):
-        super(MaximumFlowAlgorithmExecutor, self).__init__(flowNetwork)
+        super().__init__(flowNetwork)
         # use this to save your result
         self.maximumFlow = -1
 
@@ -93,7 +93,7 @@ def getMaximumFlow(self):
 
 class PushRelabelExecutor(MaximumFlowAlgorithmExecutor):
     def __init__(self, flowNetwork):
-        super(PushRelabelExecutor, self).__init__(flowNetwork)
+        super().__init__(flowNetwork)
 
         self.preflow = [[0] * self.verticesCount for i in range(self.verticesCount)]
 

graphs/graph_list.py

@@ -1,12 +1,11 @@
 #!/usr/bin/python
-# encoding=utf8
 
 # Author: OMKAR PATHAK
 
 # We can use Python's dictionary for constructing the graph.
 
 
-class AdjacencyList(object):
+class AdjacencyList:
     def __init__(self):
         self.List = {}
 

hashes/hamming_code.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 # Author: João Gustavo A. Amorim & Gabriel Kunz
 # Author email: [email protected] and [email protected]
 # Coding date:  apr 2019
@@ -100,7 +99,7 @@ def emitterConverter(sizePar, data):
         # Performs a template of bit positions - who should be given,
         # and who should be parity
         if qtdBP < sizePar:
-            if ((np.log(x) / np.log(2))).is_integer():
+            if (np.log(x) / np.log(2)).is_integer():
                 dataOutGab.append("P")
                 qtdBP = qtdBP + 1
             else:
@@ -170,7 +169,7 @@ def receptorConverter(sizePar, data):
         # Performs a template of bit positions - who should be given,
         #  and who should be parity
         if qtdBP < sizePar:
-            if ((np.log(x) / np.log(2))).is_integer():
+            if (np.log(x) / np.log(2)).is_integer():
                 dataOutGab.append("P")
                 qtdBP = qtdBP + 1
             else:
@@ -204,7 +203,7 @@ def receptorConverter(sizePar, data):
         # Performs a template position of bits - who should be given,
         # and who should be parity
         if qtdBP < sizePar:
-            if ((np.log(x) / np.log(2))).is_integer():
+            if (np.log(x) / np.log(2)).is_integer():
                 dataOutGab.append("P")
                 qtdBP = qtdBP + 1
             else:

linear_algebra/src/lib.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """
 Created on Mon Feb 26 14:29:11 2018
 
@@ -25,7 +24,7 @@
 import random
 
 
-class Vector(object):
+class Vector:
     """
         This class represents a vector of arbitrary size.
         You need to give the vector components. 
@@ -205,7 +204,7 @@ def randomVector(N, a, b):
     return Vector(ans)
 
 
-class Matrix(object):
+class Matrix:
     """
     class: Matrix
     This class represents a arbitrary matrix.

linear_algebra/src/test_linear_algebra.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """
 Created on Mon Feb 26 15:40:07 2018
 

machine_learning/k_means_clust.py

@@ -126,7 +126,7 @@ def plot_heterogeneity(heterogeneity, k):
     plt.plot(heterogeneity, linewidth=4)
     plt.xlabel("# Iterations")
     plt.ylabel("Heterogeneity")
-    plt.title("Heterogeneity of clustering over time, K={0:d}".format(k))
+    plt.title(f"Heterogeneity of clustering over time, K={k:d}")
     plt.rcParams.update({"font.size": 16})
     plt.show()
 
@@ -164,7 +164,7 @@ def kmeans(
             num_changed = np.sum(prev_cluster_assignment != cluster_assignment)
             if verbose:
                 print(
-                    "    {0:5d} elements changed their cluster assignment.".format(
+                    "    {:5d} elements changed their cluster assignment.".format(
                         num_changed
                     )
                 )

machine_learning/logistic_regression.py

@@ -1,5 +1,4 @@
 #!/usr/bin/python
-# -*- coding: utf-8 -*-
 
 ## Logistic Regression from scratch
 

machine_learning/sequential_minimum_optimization.py

@@ -1,4 +1,3 @@
-# coding: utf-8
 """
     Implementation of sequential minimal optimization(SMO) for support vector machines(SVM).
 
@@ -29,7 +28,6 @@
     http://web.cs.iastate.edu/~honavar/smo-svm.pdf
 """
 
-from __future__ import division
 
 import os
 import sys
@@ -44,7 +42,7 @@
 CANCER_DATASET_URL = "http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data"
 
 
-class SmoSVM(object):
+class SmoSVM:
     def __init__(
         self,
         train,
@@ -405,7 +403,7 @@ def length(self):
         return self.samples.shape[0]
 
 
-class Kernel(object):
+class Kernel:
     def __init__(self, kernel, degree=1.0, coef0=0.0, gamma=1.0):
         self.degree = np.float64(degree)
         self.coef0 = np.float64(coef0)