[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

machine_learning/linear_regression.py

@@ -3,6 +3,7 @@
 from sklearn.linear_model import LinearRegression
 import matplotlib.pyplot as plt
 
+
 def collect_dataset():
     """Collect dataset of CSGO
     The dataset contains ADR vs Rating of a Player
@@ -21,6 +22,7 @@ def collect_dataset():
     dataset = np.array(data, dtype=float)
     return dataset
 
+
 def run_gradient_descent(X, y, learning_rate=0.0001550, iterations=100000):
     """Run gradient descent to find approximate coefficients
     :param X: feature matrix
@@ -31,18 +33,19 @@ def run_gradient_descent(X, y, learning_rate=0.0001550, iterations=100000):
     """
     m = X.shape[0]
     theta = np.zeros(X.shape[1])
-    
+
     for i in range(iterations):
         h = np.dot(X, theta)
         gradient = np.dot(X.T, (h - y)) / m
         theta -= learning_rate * gradient
-        
+
         if i % 10000 == 0:
             mse = np.mean((h - y) ** 2)
             print(f"Iteration {i}: MSE = {mse:.5f}")
-    
+
     return theta
 
+
 def calculate_ols_coefficients(X, y):
     """Calculate optimal coefficients using the normal equation
     :param X: feature matrix
@@ -51,47 +54,62 @@ def calculate_ols_coefficients(X, y):
     """
     return np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y)
 
+
 def main():
     """Driver function"""
     data = collect_dataset()
-    
+
     X = data[:, 0].reshape(-1, 1)
     y = data[:, 1]
-    
+
     # Add intercept term to X
     X_with_intercept = np.c_[np.ones(X.shape[0]), X]
-    
+
     # Gradient Descent
     gd_theta = run_gradient_descent(X_with_intercept, y)
-    print(f"Gradient Descent coefficients: intercept = {gd_theta[0]:.5f}, slope = {gd_theta[1]:.5f}")
-    
+    print(
+        f"Gradient Descent coefficients: intercept = {gd_theta[0]:.5f}, slope = {gd_theta[1]:.5f}"
+    )
+
     # Ordinary Least Squares (Normal Equation)
     ols_theta = calculate_ols_coefficients(X_with_intercept, y)
-    print(f"OLS coefficients: intercept = {ols_theta[0]:.5f}, slope = {ols_theta[1]:.5f}")
-    
+    print(
+        f"OLS coefficients: intercept = {ols_theta[0]:.5f}, slope = {ols_theta[1]:.5f}"
+    )
+
     # Sklearn for comparison
     reg = LinearRegression().fit(X, y)
-    print(f"Sklearn coefficients: intercept = {reg.intercept_:.5f}, slope = {reg.coef_[0]:.5f}")
-    
+    print(
+        f"Sklearn coefficients: intercept = {reg.intercept_:.5f}, slope = {reg.coef_[0]:.5f}"
+    )
+
     # Calculate and print MSE for each method
     gd_mse = np.mean((np.dot(X_with_intercept, gd_theta) - y) ** 2)
     ols_mse = np.mean((np.dot(X_with_intercept, ols_theta) - y) ** 2)
     sklearn_mse = np.mean((reg.predict(X) - y) ** 2)
-    
+
     print(f"Gradient Descent MSE: {gd_mse:.5f}")
     print(f"OLS MSE: {ols_mse:.5f}")
     print(f"Sklearn MSE: {sklearn_mse:.5f}")
-    
+
     # Plotting
     plt.scatter(X, y, color="lightgray", label="Data points")
-    plt.plot(X, np.dot(X_with_intercept, gd_theta), color="red", label="Gradient Descent")
-    plt.plot(X, np.dot(X_with_intercept, ols_theta), color="green", label="OLS (Normal Equation)")
+    plt.plot(
+        X, np.dot(X_with_intercept, gd_theta), color="red", label="Gradient Descent"
+    )
+    plt.plot(
+        X,
+        np.dot(X_with_intercept, ols_theta),
+        color="green",
+        label="OLS (Normal Equation)",
+    )
     plt.plot(X, reg.predict(X), color="blue", label="Sklearn")
     plt.legend()
     plt.xlabel("ADR")
     plt.ylabel("Rating")
     plt.title("Linear Regression: ADR vs Rating")
     plt.show()
 
+
 if __name__ == "__main__":
-    main()
+    main()