resolved conflicts

machine_learning/ridge_regression/model.py

@@ -3,22 +3,21 @@
 
 
 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
+        self, x: np.ndarray
     ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        mean = np.mean(X, axis=0)
-        std = np.std(X, axis=0)
+        mean = np.mean(x, axis=0)
+        std = np.std(x, axis=0)
 
         # avoid division by zero for constant features (std = 0)
         std[std == 0] = 1  # set std=1 for constant features to avoid NaN
@@ -31,7 +30,7 @@ def fit(self, x: np.ndarray, y: np.ndarray) -> None:
         m, n = x_scaled.shape
         self.theta = np.zeros(n)  # initializing weights to zeros
 
-        for i in range(self.num_iterations):
+        for _ in range(self.num_iterations):
             predictions = x_scaled.dot(self.theta)
             error = predictions - y
 
@@ -41,18 +40,19 @@ def fit(self, x: np.ndarray, y: np.ndarray) -> None:
             ) / 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 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:
         x_scaled, _, _ = self.feature_scaling(x)
         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 @@ def mean_absolute_error(self, y_true: np.ndarray, y_pred: np.ndarray) -> float:
 
 # Example usage
 if __name__ == "__main__":
-    df = pd.read_csv("ADRvsRating.csv")
-    x = df[["Rating"]].values
-    y = df["ADR"].values
+    data = pd.read_csv("ADRvsRating.csv")
+    x = data[["Rating"]].to_numpy()
+    y = data["ADR"].to_numpy()
     y = (y - np.mean(y)) / np.std(y)
 
     # added bias term to the feature matrix