batuhan-basoglu/Regularization-and-Model-Evaluation
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Update A2.py

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ShaaniBel 2025-10-18 13:52:55 -04:00
parent 65fe618f93
commit 9b51861470
1 changed files with 6 additions and 6 deletions
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A2.py
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@ -55,7 +55,7 @@ def gaussian_features(x, D, sigma=1.0):
features = np.ones((len(x), D + 1)) # with bias term features = np.ones((len(x), D + 1)) # with bias term
for i, mu in enumerate(mu_i): for i, mu in enumerate(mu_i):
features[:, i+1] = gaussian_basis(x, mu, sigma) features[:, i+1] = gaussian_basis(x, mu, sigma).flatten()
return features return features
@ -120,14 +120,14 @@ def true_function(x):
return np.log(x + 1) * np.cos(x) + np.sin(2*x) return np.log(x + 1) * np.cos(x) + np.sin(2*x)
# fit models with different numbers of basis functions and plot # fit models with different numbers of basis functions and plot
D_i = [0, 2, 5, 10, 13, 15, 17, 20, 25, 30, 35, 45] D_values = [0, 5, 10, 13, 15, 17, 20, 25, 30, 45]
x_plot = np.linspace(0, 10, 300) x_plot = np.linspace(0, 10, 300)
plt.figure(figsize=(18, 12)) plt.figure(figsize=(20, 8))
for i, D in enumerate(D_values):
plt.subplot(2, 5, i+1)
for i, D in enumerate(D_i):
plt.subplot(4, 3, i+1)
# Create new model for each D value, fit and get predictions # Create new model for each D value, fit and get predictions
model = GaussianRegression(sigma=1.0) model = GaussianRegression(sigma=1.0)
model.fit(x, y_noisy, D) model.fit(x, y_noisy, D)