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

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

machine_learning/ridge_regression/model.py

@@ -3,18 +3,19 @@
 
 
 class RidgeRegression:
-    def __init__(self,
-            alpha: float = 0.001,
-            regularization_param: float = 0.1,
-            num_iterations: int = 1000,
-            ) -> None:
+    def __init__(
+        self,
+        alpha: float = 0.001,
+        regularization_param: float = 0.1,
+        num_iterations: int = 1000,
+    ) -> None:
         self.alpha: float = alpha
         self.regularization_param: float = regularization_param
         self.num_iterations: int = num_iterations
         self.theta: np.ndarray = None
 
     def feature_scaling(
         self, X: np.ndarray
     ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
         mean = np.mean(X, axis=0)
         std = np.std(X, axis=0)
@@ -30,7 +31,7 @@
         m, n = x_scaled.shape
         self.theta = np.zeros(n)  # initializing weights to zeros
 
         for i in range(self.num_iterations):
             predictions = x_scaled.dot(self.theta)
             error = predictions - y
 
@@ -40,8 +41,8 @@
             ) / m
             self.theta -= self.alpha * gradient  # updating weights
 
     def predict(self, X: np.ndarray) -> np.ndarray:
         X_scaled, _, _ = self.feature_scaling(X)
         return X_scaled.dot(self.theta)
 
     def compute_cost(self, x: np.ndarray, y: np.ndarray) -> float:
@@ -49,10 +50,9 @@
         m = len(y)
 
         predictions = x_scaled.dot(self.theta)
-        cost = (
-            1 / (2 * m)) * np.sum((predictions - y) ** 2) + (
-                self.regularization_param / (2 * m)
-                ) * np.sum(self.theta**2)
+        cost = (1 / (2 * m)) * np.sum((predictions - y) ** 2) + (
+            self.regularization_param / (2 * m)
+        ) * np.sum(self.theta**2)
         return cost
 
     def mean_absolute_error(self, y_true: np.ndarray, y_pred: np.ndarray) -> float:
@@ -61,9 +61,9 @@
 
 # Example usage
 if __name__ == "__main__":
     df = pd.read_csv("ADRvsRating.csv")
     x = df[["Rating"]].values
     y = df["ADR"].values
     y = (y - np.mean(y)) / np.std(y)
 
     # added bias term to the feature matrix