linear regression for polynomials and implemented Lasso and Ridge from scratch

02 Linear Regression/Lasso&RidgeRegression.py

@@ -0,0 +1,35 @@
+import numpy as np
+
+class Lasso_Ridge_Regression:
+
+    def __init__(self, lr=0.001, n_iters=1000, alpha=0.1):
+        self.lr = lr
+        self.n_iters = n_iters
+        self.alpha = alpha  # regularization strength
+        self.weights = None
+        self.bias = None
+
+    def fit(self, X, y):
+        n_samples, n_features = X.shape
+        self.weights = np.zeros(n_features)
+        self.bias = 0
+
+        for _ in range(self.n_iters):
+            y_pred = np.dot(X, self.weights) + self.bias
+
+            dw = (1 / n_samples) * np.dot(X.T, (y_pred - y)) + self.alpha * np.sign(self.weights)
+            '''
+            dw = (1 / n_samples) * np.dot(X.T, (y_pred - y)) + 2 * self.alpha * self.weights 
+
+            this is for RidgeRegression
+
+            '''
+
+            db = (1 / n_samples) * np.sum(y_pred - y)
+
+            self.weights -= self.lr * dw
+            self.bias -= self.lr * db
+
+    def predict(self, X):
+        y_pred = np.dot(X, self.weights) + self.bias
+        return y_pred