[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/ridge_regression/model.py

@@ -3,14 +3,20 @@
 
 
 class RidgeRegression:
-    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]:
+    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)
 
@@ -20,13 +26,11 @@ def feature_scaling(self, X:np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndar
         X_scaled = (X - mean) / std
         return X_scaled, mean, std
 
-
-    def fit(self, X:np.ndarray, y:np.ndarray) -> None:
+    def fit(self, X: np.ndarray, y: np.ndarray) -> None:
         X_scaled, mean, std = self.feature_scaling(X)
         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
@@ -37,13 +41,11 @@ 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:
+    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:
+    def compute_cost(self, X: np.ndarray, y: np.ndarray) -> float:
         X_scaled, _, _ = self.feature_scaling(X)
         m = len(y)
 
@@ -53,8 +55,7 @@ def compute_cost(self, X:np.ndarray, y:np.ndarray) -> float:
         ) * np.sum(self.theta**2)
         return cost
 
-
-    def mean_absolute_error(self, y_true:np.ndarray, y_pred:np.ndarray) -> float:
+    def mean_absolute_error(self, y_true: np.ndarray, y_pred: np.ndarray) -> float:
         return np.mean(np.abs(y_true - y_pred))
 
 
@@ -66,7 +67,7 @@ def mean_absolute_error(self, y_true:np.ndarray, y_pred:np.ndarray) -> float:
     y = (y - np.mean(y)) / np.std(y)
 
     # added bias term to the feature matrix
-    X = np.c_[np.ones(X.shape[0]), X] 
+    X = np.c_[np.ones(X.shape[0]), X]
 
     # initialize and train the ridge regression model
     model = RidgeRegression(alpha=0.01, regularization_param=0.1, num_iterations=1000)