added ridge regression

machine_learning/ridge_regression/model.py

@@ -2,14 +2,14 @@ import numpy as np
 import pandas as pd
 
 class RidgeRegression:
-    def __init__(self, alpha=0.001, regularization_param=0.1, num_iterations=1000):
-        self.alpha = alpha
-        self.regularization_param = regularization_param
-        self.num_iterations = num_iterations
-        self.theta = 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):
+    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)
         
@@ -20,7 +20,7 @@ class RidgeRegression:
         return X_scaled, mean, std
     
 
-    def fit(self, X, y):
+    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
@@ -34,12 +34,12 @@ class RidgeRegression:
             self.theta -= self.alpha * gradient  # updating weights
 
 
-    def predict(self, X):
+    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, y):
+    def compute_cost(self, X:np.ndarray, y:np.ndarray) -> float:
         X_scaled, _, _ = self.feature_scaling(X)  
         m = len(y)
         
@@ -48,7 +48,7 @@ class RidgeRegression:
         return cost
     
 
-    def mean_absolute_error(self, y_true, y_pred):
+    def mean_absolute_error(self, y_true:np.ndarray, y_pred:np.ndarray) -> float:
         return np.mean(np.abs(y_true - y_pred))
     
 
@@ -59,10 +59,10 @@ if __name__ == "__main__":
     y = df["ADR"].values
     y = (y - np.mean(y)) / np.std(y)
 
-    # Add bias term (intercept) to the feature matrix
+    # added bias term to the feature matrix
     X = np.c_[np.ones(X.shape[0]), X] 
 
-    # initialize and train the Ridge Regression model
+    # initialize and train the ridge regression model
     model = RidgeRegression(alpha=0.01, regularization_param=0.1, num_iterations=1000)
     model.fit(X, y)