fixup! Format Python code with psf/black push

Author: github-actions

Diff for: machine_learning/linear_discriminant_analysis.py

@@ -102,9 +102,7 @@ def calculate_variance(items: list, means: list, total_count: int) -> float:
     :return: calculated variance for considered dataset
     """
 
-    squared_diff = (
-        []
-    )  # An empty list to store all squared differences
+    squared_diff = []  # An empty list to store all squared differences
     n_classes = len(means)  # Number of classes in dataSet
 
     # for loo iterates over number of elements in items
@@ -119,7 +117,9 @@ def calculate_variance(items: list, means: list, total_count: int) -> float:
 
 
 # Making predictions
-def predict_y_values(x_items: list, means: list, variance: float, probabilities: list) -> list:
+def predict_y_values(
+    x_items: list, means: list, variance: float, probabilities: list
+) -> list:
     """ This function predicts new indexes(groups for our data)
     :param x_items: a list containing all items(gaussian distribution of all classes)
     :param means: a list containing real mean values of each class
@@ -130,17 +130,20 @@ def predict_y_values(x_items: list, means: list, variance: float, probabilities:
 
     results = (
         []
-    )    # An empty list to store generated discriminant values of all items in dataset for each class
+    )  # 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)
 
@@ -156,7 +159,7 @@ def accuracy(actual_y: list, predicted_y: list) -> float:
     :param predicted_y: a list containing predicted Y values generated by 'predict_y_values' 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
@@ -176,19 +179,25 @@ 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(f"Your entered value is {user_input} , Number of classes should be positive!")
+                    print(
+                        f"Your entered value is {user_input} , Number of classes should be positive!"
+                    )
                     continue
             except ValueError:
                 print("Your entered value is not numerical!")
@@ -199,32 +208,41 @@ 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(f"Your entered value is {user_sd}, Standard deviation should not be negative!")
+                    print(
+                        f"Your entered value is {user_sd}, Standard deviation should not be negative!"
+                    )
                     continue
             except ValueError:
                 print("Your entered value is not numerical!")
 
         print("-" * 100)
 
         # Trying to get number of instances in classes and theirs means to generate dataset
-        counts = (
-            []
-        )  # An empty list to store instance counts of classes in dataset
+        counts = []  # An empty list to store instance counts of classes in dataset
         for i in range(n_classes):
             while True:
                 try:
-                    user_count = int(input(f"Enter The number of instances for class_{i+1}: "))
+                    user_count = int(
+                        input(f"Enter The number of instances for class_{i+1}: ")
+                    )
                     if user_count > 0:
                         counts.append(user_count)
                         break
                     else:
-                        print(f"Your entered value is {user_count}, Number of instances should be positive!")
+                        print(
+                            f"Your entered value is {user_count}, Number of instances should be positive!"
+                        )
                         continue
                 except ValueError:
                     print("Your entered value is not numerical!")
@@ -237,12 +255,16 @@ def main():
         for a in range(n_classes):
             while True:
                 try:
-                    user_mean = float(input(f"Enter the value of mean for class_{a+1}: "))
+                    user_mean = float(
+                        input(f"Enter the value of mean for class_{a+1}: ")
+                    )
                     if isinstance(user_mean, float):
                         user_means.append(user_mean)
                         break
                     else:
-                        print(f"Your entered value is {user_mean}, And this is not valid!")
+                        print(
+                            f"Your entered value is {user_mean}, And this is not valid!"
+                        )
 
                 except ValueError:
                     print("Your entered value is not numerical!")
@@ -264,9 +286,7 @@ def main():
         print("-" * 100)
 
         # Generating training dataset drawn from gaussian distribution
-        x = (
-            []
-        )  # An empty list to store generated values of gaussian distribution
+        x = []  # An empty list to store generated values of gaussian distribution
         # for loop iterates over number of classes
         for j in range(n_classes):
             # appending return values of 'gaussian_distribution' function to 'x' list
@@ -282,9 +302,7 @@ def main():
         print("-" * 100)
 
         # Calculating the value of actual mean for each class
-        actual_means = (
-            []
-        )  # An empty list to store value of actual means
+        actual_means = []  # An empty list to store value of actual means
         # for loop iterates over number of classes(data groupings)
         for k in range(n_classes):
             # appending return values of 'calculate_mean' function to 'actual_means' list
@@ -339,5 +357,5 @@ def main():
                 continue
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()