fixup! Format Python code with psf/black push

Author: github-actions

Diff for: machine_learning/linear_discriminant_analysis.py

@@ -42,7 +42,7 @@
 # importing modules
 from random import gauss
 from math import log
-from os import system, name     # to use < clear > or < cls > commands in terminal or cmd
+from os import system, name  # to use < clear > or < cls > commands in terminal or cmd
 
 
 # Making training dataset drawn from a gaussian distribution
@@ -128,23 +128,28 @@ def predict(x_items: list, means: list, variance: float, probabilities: list) ->
     :return: a list containing predicted Y values
     """
 
-    results = []    # An empty list to store generated discriminant values of all items in dataset for each class
+    results = (
+        []
+    )  # An empty list to store generated discriminant values of all items in dataset for each class
     # for loop iterates over number of elements in list
     for i in range(len(x_items)):
         # for loop iterates over number of inner items of each element
         for j in range(len(x_items[i])):
-            temp = []   # to store all discriminant values of each item as a list
+            temp = []  # to store all discriminant values of each item as a list
             # for loop iterates over number of classes we have in our dataset
             for k in range(len(x_items)):
                 # appending values of discriminants for each class to 'temp' list
-                temp.append(x_items[i][j] * (means[k] / variance) - (means[k] ** 2 / (2 * variance)) +
-                            log(probabilities[k]))
+                temp.append(
+                    x_items[i][j] * (means[k] / variance)
+                    - (means[k] ** 2 / (2 * variance))
+                    + log(probabilities[k])
+                )
             # appending discriminant values of each item to 'results' list
             results.append(temp)
 
     print("Generated Discriminants: \n", results)
 
-    predicted_index = []    # An empty list to store predicted indexes
+    predicted_index = []  # An empty list to store predicted indexes
     # for loop iterates over elements in 'results'
     for l in results:
         # after calculating the discriminant value for each class , the class with the largest
@@ -160,7 +165,7 @@ def accuracy(actual_y: list, predicted_y: list) -> float:
     :param predicted_y: a list containing predicted Y values generated by 'predict' function
     :return: percentage of accuracy
     """
-    correct = 0     # initial value for number of correct predictions
+    correct = 0  # initial value for number of correct predictions
     # for loop iterates over one element of each list at a time (zip mode)
     for i, j in zip(actual_y, predicted_y):
         # if actual Y value equals to predicted Y value
@@ -181,19 +186,27 @@ def main():
 
         print(" Linear Discriminant Analysis ".center(100, "*"))
         print("*" * 100, "\n")
-        print("First of all we should specify the number of classes that \n"
-              "we want to generate as training dataset")
+        print(
+            "First of all we should specify the number of classes that \n"
+            "we want to generate as training dataset"
+        )
 
         # Trying to get number of classes
         n_classes = 0
         while True:
             try:
-                user_input = int(input("Enter the number of classes (Data Groupings): "))
+                user_input = int(
+                    input("Enter the number of classes (Data Groupings): ")
+                )
                 if user_input > 0:
                     n_classes = user_input
                     break
                 else:
-                    print("Your entered value is {} , Number of classes should be positive!".format(user_input))
+                    print(
+                        "Your entered value is {} , Number of classes should be positive!".format(
+                            user_input
+                        )
+                    )
                     continue
             except ValueError:
                 print("Your entered value is not numerical!")
@@ -204,13 +217,22 @@ def main():
         # Trying to get the value of standard deviation
         while True:
             try:
-                user_sd = float(input("Enter the value of standard deviation"
-                                      "(Default value is 1.0 for all classes): ") or "1.0")
+                user_sd = float(
+                    input(
+                        "Enter the value of standard deviation"
+                        "(Default value is 1.0 for all classes): "
+                    )
+                    or "1.0"
+                )
                 if user_sd >= 0.0:
                     std_dev = user_sd
                     break
                 else:
-                    print("Your entered value is {}, Standard deviation should not be negative!".format(user_sd))
+                    print(
+                        "Your entered value is {}, Standard deviation should not be negative!".format(
+                            user_sd
+                        )
+                    )
                     continue
             except ValueError:
                 print("Your entered value is not numerical!")
@@ -222,28 +244,44 @@ def main():
         for i in range(n_classes):
             while True:
                 try:
-                    user_count = int(input("Enter The number of instances for class_{}: ".format(i + 1)))
+                    user_count = int(
+                        input(
+                            "Enter The number of instances for class_{}: ".format(i + 1)
+                        )
+                    )
                     if user_count > 0:
                         counts.append(user_count)
                         break
                     else:
-                        print("Your entered value is {}, Number of instances should be positive!".format(user_count))
+                        print(
+                            "Your entered value is {}, Number of instances should be positive!".format(
+                                user_count
+                            )
+                        )
                         continue
                 except ValueError:
                     print("Your entered value is not numerical!")
 
         print("-" * 100)
 
-        user_means = []  # An empty list to store values of user-entered means of classes
+        user_means = (
+            []
+        )  # An empty list to store values of user-entered means of classes
         for a in range(n_classes):
             while True:
                 try:
-                    user_mean = float(input("Enter the value of mean for class_{}: ".format(a + 1)))
+                    user_mean = float(
+                        input("Enter the value of mean for class_{}: ".format(a + 1))
+                    )
                     if isinstance(user_mean, float):
                         user_means.append(user_mean)
                         break
                     else:
-                        print("Your entered value is {}, And this is not valid!".format(user_mean))
+                        print(
+                            "Your entered value is {}, And this is not valid!".format(
+                                user_mean
+                            )
+                        )
 
                 except ValueError:
                     print("Your entered value is not numerical!")
@@ -293,7 +331,9 @@ def main():
         print("-" * 100)
 
         # Calculating the value of probabilities for each class
-        probabilities = []  # An empty list to store values of probabilities for each class
+        probabilities = (
+            []
+        )  # An empty list to store values of probabilities for each class
         # # for loop iterates over number of classes(data groupings)
         for l in range(n_classes):
             # appending return values of 'prob_calc' function to 'probabilities' list
@@ -334,5 +374,5 @@ def main():
                 continue
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()