Improved readability (#1615)

* improved readability

* further readability improvements

* removed csv file and added f

arithmetic_analysis/newton_forward_interpolation.py

@@ -19,7 +19,7 @@ def ucal(u, p):
 
 
 def main():
-    n = int(input("enter the numbers of values"))
+    n = int(input("enter the numbers of values: "))
     y = []
     for i in range(n):
         y.append([])
@@ -28,14 +28,14 @@ def main():
             y[i].append(j)
             y[i][j] = 0
 
-    print("enter the values of parameters in a list")
+    print("enter the values of parameters in a list: ")
     x = list(map(int, input().split()))
 
-    print("enter the values of corresponding parameters")
+    print("enter the values of corresponding parameters: ")
     for i in range(n):
         y[i][0] = float(input())
 
-    value = int(input("enter the value to interpolate"))
+    value = int(input("enter the value to interpolate: "))
     u = (value - x[0]) / (x[1] - x[0])
 
     # for calculating forward difference table
@@ -48,7 +48,7 @@ def main():
     for i in range(1, n):
         summ += (ucal(u, i) * y[0][i]) / math.factorial(i)
 
-    print("the value at {} is {}".format(value, summ))
+    print(f"the value at {value} is {summ}")
 
 
 if __name__ == "__main__":

ciphers/trafid_cipher.py

@@ -117,4 +117,4 @@ if __name__ == "__main__":
     msg = "DEFEND THE EAST WALL OF THE CASTLE."
     encrypted = encryptMessage(msg, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
     decrypted = decryptMessage(encrypted, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
-    print("Encrypted: {}\nDecrypted: {}".format(encrypted, decrypted))
+    print(f"Encrypted: {encrypted}\nDecrypted: {decrypted}")

data_structures/linked_list/doubly_linked_list.py

@@ -76,4 +76,4 @@ class Link:
         self.value = x
 
     def displayLink(self):
-        print("{}".format(self.value), end=" ")
+        print(f"{self.value}", end=" ")

divide_and_conquer/convex_hull.py

@@ -50,7 +50,7 @@ class Point:
             except ValueError as e:
                 e.args = (
                     "x and y must be both numeric types "
-                    "but got {}, {} instead".format(type(x), type(y)),
+                    f"but got {type(x)}, {type(y)} instead"
                 )
                 raise
 
@@ -88,7 +88,7 @@ class Point:
         return False
 
     def __repr__(self):
-        return "({}, {})".format(self.x, self.y)
+        return f"({self.x}, {self.y})"
 
     def __hash__(self):
         return hash(self.x)
@@ -136,8 +136,8 @@ def _construct_points(list_of_tuples):
                 points.append(Point(p[0], p[1]))
             except (IndexError, TypeError):
                 print(
-                    "Ignoring deformed point {}. All points"
+                    f"Ignoring deformed point {p}. All points"
-                    " must have at least 2 coordinates.".format(p)
+                    " must have at least 2 coordinates."
                 )
     return points
 
@@ -184,7 +184,7 @@ def _validate_input(points):
     """
 
     if not points:
-        raise ValueError("Expecting a list of points but got {}".format(points))
+        raise ValueError(f"Expecting a list of points but got {points}")
 
     if isinstance(points, set):
         points = list(points)
@@ -196,12 +196,12 @@ def _validate_input(points):
             else:
                 raise ValueError(
                     "Expecting an iterable of type Point, list or tuple. "
-                    "Found objects of type {} instead".format(type(points[0]))
+                    f"Found objects of type {type(points[0])} instead"
                 )
         elif not hasattr(points, "__iter__"):
             raise ValueError(
                 "Expecting an iterable object "
-                "but got an non-iterable type {}".format(points)
+                f"but got an non-iterable type {points}"
             )
     except TypeError as e:
         print("Expecting an iterable of type Point, list or tuple.")

dynamic_programming/knapsack.py

@@ -81,13 +81,13 @@ def knapsack_with_example_solution(W: int, wt: list, val: list):
         raise ValueError(
             "The number of weights must be the "
             "same as the number of values.\nBut "
-            "got {} weights and {} values".format(num_items, len(val))
+            f"got {num_items} weights and {len(val)} values"
         )
     for i in range(num_items):
         if not isinstance(wt[i], int):
             raise TypeError(
                 "All weights must be integers but "
-                "got weight of type {} at index {}".format(type(wt[i]), i)
+                f"got weight of type {type(wt[i])} at index {i}"
             )
 
     optimal_val, dp_table = knapsack(W, wt, val, num_items)

graphs/dijkstra_algorithm.py

@@ -106,7 +106,7 @@ class Graph:
             print(
                 u,
                 "->",
-                " -> ".join(str("{}({})".format(v, w)) for v, w in self.adjList[u]),
+                " -> ".join(str(f"{v}({w})") for v, w in self.adjList[u]),
             )
 
     def dijkstra(self, src):
@@ -139,9 +139,9 @@ class Graph:
         self.show_distances(src)
 
     def show_distances(self, src):
-        print("Distance from node: {}".format(src))
+        print(f"Distance from node: {src}")
         for u in range(self.num_nodes):
-            print("Node {} has distance: {}".format(u, self.dist[u]))
+            print(f"Node {u} has distance: {self.dist[u]}")
 
     def show_path(self, src, dest):
         # To show the shortest path from src to dest
@@ -161,9 +161,9 @@ class Graph:
         path.append(src)
         path.reverse()
 
-        print("----Path to reach {} from {}----".format(dest, src))
+        print(f"----Path to reach {dest} from {src}----")
         for u in path:
-            print("{}".format(u), end=" ")
+            print(f"{u}", end=" ")
             if u != dest:
                 print("-> ", end="")
 

graphs/edmonds_karp_multiple_source_and_sink.py

@@ -190,4 +190,4 @@ if __name__ == "__main__":
     # and calculate
     maximumFlow = flowNetwork.findMaximumFlow()
 
-    print("maximum flow is {}".format(maximumFlow))
+    print(f"maximum flow is {maximumFlow}")

graphs/page_rank.py

@@ -27,9 +27,7 @@ class Node:
         self.outbound.append(node)
 
     def __repr__(self):
-        return "Node {}: Inbound: {} ; Outbound: {}".format(
+        return f"Node {self.name}: Inbound: {self.inbound} ; Outbound: {self.outbound}"
-            self.name, self.inbound, self.outbound
-        )
 
 
 def page_rank(nodes, limit=3, d=0.85):
@@ -42,7 +40,7 @@ def page_rank(nodes, limit=3, d=0.85):
         outbounds[node.name] = len(node.outbound)
 
     for i in range(limit):
-        print("======= Iteration {} =======".format(i + 1))
+        print(f"======= Iteration {i + 1} =======")
         for j, node in enumerate(nodes):
             ranks[node.name] = (1 - d) + d * sum(
                 [ranks[ib] / outbounds[ib] for ib in node.inbound]

machine_learning/knn_sklearn.py

@@ -7,8 +7,8 @@ iris = load_iris()
 iris.keys()
 
 
-print("Target names: \n {} ".format(iris.target_names))
+print(f"Target names: \n {iris.target_names} ")
-print("\n Features: \n {}".format(iris.feature_names))
+print(f"\n Features: \n {iris.feature_names}")
 
 # Train set e Test set
 X_train, X_test, y_train, y_test = train_test_split(

machine_learning/linear_discriminant_analysis.py

@@ -174,8 +174,8 @@ def accuracy(actual_y: list, predicted_y: list) -> float:
 def main():
     """ This function starts execution phase """
     while True:
-        print(" Linear Discriminant Analysis ".center(100, "*"))
+        print(" Linear Discriminant Analysis ".center(50, "*"))
-        print("*" * 100, "\n")
+        print("*" * 50, "\n")
         print("First of all we should specify the number of classes that")
         print("we want to generate as training dataset")
         # Trying to get number of classes
@@ -239,7 +239,7 @@ def main():
                     else:
                         print(
                             f"Your entered value is {user_count}, Number of "
-                            f"instances should be positive!"
+                            "instances should be positive!"
                         )
                         continue
                 except ValueError:
@@ -302,7 +302,7 @@ def main():
         # for loop iterates over number of elements in 'probabilities' list and print
         # out them in separated line
         for i, probability in enumerate(probabilities, 1):
-            print("Probability of class_{} is: {}".format(i, probability))
+            print(f"Probability of class_{i} is: {probability}")
         print("-" * 100)
 
         # Calculating the values of variance for each class

machine_learning/sequential_minimum_optimization.py

@@ -446,7 +446,7 @@ def count_time(func):
         start_time = time.time()
         func(*args, **kwargs)
         end_time = time.time()
-        print("smo algorithm cost {} seconds".format(end_time - start_time))
+        print(f"smo algorithm cost {end_time - start_time} seconds")
 
     return call_func
 
@@ -500,11 +500,9 @@ def test_cancel_data():
         if test_tags[i] == predict[i]:
             score += 1
     print(
-        "\r\nall: {}\r\nright: {}\r\nfalse: {}".format(
+        f"\r\nall: {test_num}\r\nright: {score}\r\nfalse: {test_num - score}"
-            test_num, score, test_num - score
     )
-    )
+    print(f"Rough Accuracy: {score / test_tags.shape[0]}")
-    print("Rough Accuracy: {}".format(score / test_tags.shape[0]))
 
 
 def test_demonstration():

maths/binary_exponentiation.py

@@ -25,4 +25,4 @@ if __name__ == "__main__":
         print("Invalid literal for integer")
 
     RESULT = binary_exponentiation(BASE, POWER)
-    print("{}^({}) : {}".format(BASE, POWER, RESULT))
+    print(f"{BASE}^({POWER}) : {RESULT}")

maths/simpson_rule.py

@@ -44,7 +44,7 @@ def main():
     steps = 10.0  # define number of steps or resolution
     boundary = [a, b]  # define boundary of integration
     y = method_2(boundary, steps)
-    print("y = {0}".format(y))
+    print(f"y = {y}")
 
 
 if __name__ == "__main__":

maths/trapezoidal_rule.py

@@ -43,7 +43,7 @@ def main():
     steps = 10.0  # define number of steps or resolution
     boundary = [a, b]  # define boundary of integration
     y = method_1(boundary, steps)
-    print("y = {0}".format(y))
+    print(f"y = {y}")
 
 
 if __name__ == "__main__":

neural_network/input_data.py

@@ -331,9 +331,7 @@ def read_data_sets(
 
     if not 0 <= validation_size <= len(train_images):
         raise ValueError(
-            "Validation size should be between 0 and {}. Received: {}.".format(
+            f"Validation size should be between 0 and {len(train_images)}. Received: {validation_size}."
-                len(train_images), validation_size
-            )
         )
 
     validation_images = train_images[:validation_size]

searches/binary_search.py

@@ -152,6 +152,6 @@ if __name__ == "__main__":
     target = int(target_input)
     result = binary_search(collection, target)
     if result is not None:
-        print("{} found at positions: {}".format(target, result))
+        print(f"{target} found at positions: {result}")
     else:
         print("Not found")

searches/interpolation_search.py

@@ -135,6 +135,6 @@ if __name__ == "__main__":
 
     result = interpolation_search(collection, target)
     if result is not None:
-        print("{} found at positions: {}".format(target, result))
+        print(f"{target} found at positions: {result}")
     else:
         print("Not found")

searches/linear_search.py

@@ -45,6 +45,6 @@ if __name__ == "__main__":
     target = int(target_input)
     result = linear_search(sequence, target)
     if result is not None:
-        print("{} found at positions: {}".format(target, result))
+        print(f"{target} found at positions: {result}")
     else:
         print("Not found")

searches/sentinel_linear_search.py

@@ -53,6 +53,6 @@ if __name__ == "__main__":
     target = int(target_input)
     result = sentinel_linear_search(sequence, target)
     if result is not None:
-        print("{} found at positions: {}".format(target, result))
+        print(f"{target} found at positions: {result}")
     else:
         print("Not found")

searches/tabu_search.py

@@ -254,7 +254,7 @@ def main(args=None):
         args.Size,
     )
 
-    print("Best solution: {0}, with total distance: {1}.".format(best_sol, best_cost))
+    print(f"Best solution: {best_sol}, with total distance: {best_cost}.")
 
 
 if __name__ == "__main__":

searches/ternary_search.py

@@ -97,7 +97,7 @@ if __name__ == "__main__":
     result2 = rec_ternary_search(0, len(collection) - 1, collection, target)
 
     if result2 is not None:
-        print("Iterative search: {} found at positions: {}".format(target, result1))
+        print(f"Iterative search: {target} found at positions: {result1}")
-        print("Recursive search: {} found at positions: {}".format(target, result2))
+        print(f"Recursive search: {target} found at positions: {result2}")
     else:
         print("Not found")