ass5.1

Author: sirapaop

Regularization/usingLibs/Ridge_ass1.py

@@ -1,12 +1,20 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
+<<<<<<< HEAD:Regularization/usingLibs/Ridge_ass1.py
 
+=======
+# Set seed for reproducibility
+>>>>>>> dce2152 (ass5.1):Linear Model Selection and Regularization/Ridge_ass1.py
 np.random.seed(0)
 X = np.random.rand(100, 1) * 10
 y = 2 * X.squeeze() + 1 + np.random.randn(100) * 2
 
+<<<<<<< HEAD:Regularization/usingLibs/Ridge_ass1.py
 
+=======
+# Ridge Regression function
+>>>>>>> dce2152 (ass5.1):Linear Model Selection and Regularization/Ridge_ass1.py
 def ridge_regression(X, y, alpha):
     n = len(X)
     mean_X = sum(X) / n
@@ -17,16 +25,28 @@ def ridge_regression(X, y, alpha):
     denominator = sum((X[i] - mean_X) ** 2 for i in range(n)) + alpha
     b1 = numerator / denominator
 
+<<<<<<< HEAD:Regularization/usingLibs/Ridge_ass1.py
     # bias (b0)
+=======
+    # intercept (b0)
+>>>>>>> dce2152 (ass5.1):Linear Model Selection and Regularization/Ridge_ass1.py
     b0 = mean_y - b1 * mean_X
 
     return b0, b1
 
+<<<<<<< HEAD:Regularization/usingLibs/Ridge_ass1.py
 
 def predict(X, b0, b1):
     return [b0 + b1 * x for x in X]
 
 # SSR + λ * slope^2 
+=======
+# Prediction function
+def predict(X, b0, b1):
+    return [b0 + b1 * x for x in X]
+
+# SSR + λ * slope^2 function
+>>>>>>> dce2152 (ass5.1):Linear Model Selection and Regularization/Ridge_ass1.py
 def ssr_plus_reg(X, y, slope, alpha):
     n = len(X)
     predicted_y = [slope * x + 1 for x in X]
@@ -35,15 +55,25 @@ def ssr_plus_reg(X, y, slope, alpha):
     alpha_slop2 = alpha * slope ** 2
     return squared_residuals + alpha_slop2
 
+<<<<<<< HEAD:Regularization/usingLibs/Ridge_ass1.py
 
+=======
+# Plotting Data
+>>>>>>> dce2152 (ass5.1):Linear Model Selection and Regularization/Ridge_ass1.py
 plt.figure(figsize=(14, 10))
 plt.subplot(2, 3, 1)
 plt.scatter(X, y, color='blue', edgecolor='k')
 plt.title('Data')
 
+<<<<<<< HEAD:Regularization/usingLibs/Ridge_ass1.py
 alphas = [0, 10, 20, 40, 400]
 
 # Ridge Regression for different alpha values 
+=======
+alphas = [0, 1, 10, 100, 1000]
+
+# Perform Ridge Regression for different alpha values and plot results
+>>>>>>> dce2152 (ass5.1):Linear Model Selection and Regularization/Ridge_ass1.py
 for i, alpha in enumerate(alphas):
     b0, b1 = ridge_regression(X, y, alpha)
     y_pred = predict(X, b0, b1)
@@ -64,7 +94,11 @@ def ssr_plus_reg(X, y, slope, alpha):
     cost_ridge = [(slope - 0.5) ** 2 + alpha * slope ** 2 for slope in slope_values]
     plt.plot(slope_values, cost_ridge, label=f'λ = {alpha}')
 
+<<<<<<< HEAD:Regularization/usingLibs/Ridge_ass1.py
 plt.title('Sum of Squared Residuals + λ * Slope^2')
+=======
+plt.title('Sum of Squared Residuals + λ * Slope² (Ridge)')
+>>>>>>> dce2152 (ass5.1):Linear Model Selection and Regularization/Ridge_ass1.py
 plt.xlabel('Slope Values')
 plt.ylabel('Cost')
 plt.legend()