Follow Flake8 pep3101 and remove modulo formatting

ciphers/elgamal_key_generator.py

@@ -41,22 +41,19 @@ def make_key_files(name: str, key_size: int) -> None:
     if os.path.exists(f"{name}_pubkey.txt") or os.path.exists(f"{name}_privkey.txt"):
         print("\nWARNING:")
         print(
-            '"%s_pubkey.txt" or "%s_privkey.txt" already exists. \n'
+            f'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
             "Use a different name or delete these files and re-run this program."
-            % (name, name)
         )
         sys.exit()
 
     public_key, private_key = generate_key(key_size)
     print(f"\nWriting public key to file {name}_pubkey.txt...")
     with open(f"{name}_pubkey.txt", "w") as fo:
-        fo.write(
-            "%d,%d,%d,%d" % (public_key[0], public_key[1], public_key[2], public_key[3])
-        )
+        fo.write(f"{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}")
 
     print(f"Writing private key to file {name}_privkey.txt...")
     with open(f"{name}_privkey.txt", "w") as fo:
-        fo.write("%d,%d" % (private_key[0], private_key[1]))
+        fo.write(f"{private_key[0]},{private_key[1]}")
 
 
 def main() -> None:

ciphers/rsa_key_generator.py

@@ -37,9 +37,8 @@ def make_key_files(name: str, key_size: int) -> None:
     if os.path.exists(f"{name}_pubkey.txt") or os.path.exists(f"{name}_privkey.txt"):
         print("\nWARNING:")
         print(
-            '"%s_pubkey.txt" or "%s_privkey.txt" already exists. \n'
+            f'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
             "Use a different name or delete these files and re-run this program."
-            % (name, name)
         )
         sys.exit()
 

dynamic_programming/edit_distance.py

@@ -99,7 +99,7 @@ def min_distance_bottom_up(word1: str, word2: str) -> int:
     S2 = input("Enter the second string: ").strip()
 
     print()
-    print("The minimum Edit Distance is: %d" % (solver.solve(S1, S2)))
-    print("The minimum Edit Distance is: %d" % (min_distance_bottom_up(S1, S2)))
+    print(f"The minimum Edit Distance is: {solver.solve(S1, S2)}")
+    print(f"The minimum Edit Distance is: {min_distance_bottom_up(S1, S2)}")
     print()
     print("*************** End of Testing Edit Distance DP Algorithm ***************")

genetic_algorithm/basic_string.py

@@ -172,7 +172,7 @@ def mutate(child: str) -> str:
         " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
         "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
     )
+    generation, population, target = basic(target_str, genes_list)
     print(
-        "\nGeneration: %s\nTotal Population: %s\nTarget: %s"
-        % basic(target_str, genes_list)
+        f"\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"
     )

graphs/minimum_spanning_tree_boruvka.py

@@ -63,7 +63,7 @@ def __str__(self):
         for tail in self.adjacency:
             for head in self.adjacency[tail]:
                 weight = self.adjacency[head][tail]
-                string += "%d -> %d == %d\n" % (head, tail, weight)
+                string += f"{head} -> {tail} == {weight}\n"
         return string.rstrip("\n")
 
     def get_edges(self):

machine_learning/linear_regression.py

@@ -82,7 +82,7 @@ def run_linear_regression(data_x, data_y):
     for i in range(0, iterations):
         theta = run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta)
         error = sum_of_square_error(data_x, data_y, len_data, theta)
-        print("At Iteration %d - Error is %.5f " % (i + 1, error))
+        print(f"At Iteration {i + 1} - Error is {error:.5f}")
 
     return theta
 

matrix/sherman_morrison.py

@@ -31,14 +31,14 @@ def __str__(self) -> str:
         """
 
         # Prefix
-        s = "Matrix consist of %d rows and %d columns\n" % (self.row, self.column)
+        s = f"Matrix consist of {self.row} rows and {self.column} columns\n"
 
         # Make string identifier
         max_element_length = 0
         for row_vector in self.array:
             for obj in row_vector:
                 max_element_length = max(max_element_length, len(str(obj)))
-        string_format_identifier = "%%%ds" % (max_element_length,)
+        string_format_identifier = f"%{max_element_length}s"
 
         # Make string and return
         def single_line(row_vector: list[float]) -> str:
@@ -252,7 +252,7 @@ def test1() -> None:
         v[0, 0], v[1, 0], v[2, 0] = 4, -2, 5
         print(f"u is {u}")
         print(f"v is {v}")
-        print("uv^T is %s" % (u * v.transpose()))
+        print(f"uv^T is {u * v.transpose()}")
         # Sherman Morrison
         print(f"(a + uv^T)^(-1) is {ainv.sherman_morrison(u, v)}")
 

neural_network/back_propagation_neural_network.py

@@ -117,7 +117,7 @@ def build(self):
 
     def summary(self):
         for i, layer in enumerate(self.layers[:]):
-            print("------- layer %d -------" % i)
+            print(f"------- layer {i} -------")
             print("weight.shape ", np.shape(layer.weight))
             print("bias.shape ", np.shape(layer.bias))
 

neural_network/convolution_neural_network.py

@@ -219,7 +219,7 @@ def train(
         mse = 10000
         while rp < n_repeat and mse >= error_accuracy:
             error_count = 0
-            print("-------------Learning Time %d--------------" % rp)
+            print(f"-------------Learning Time {rp}--------------")
             for p in range(len(datas_train)):
                 # print('------------Learning Image: %d--------------'%p)
                 data_train = np.asmatrix(datas_train[p])