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

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ShaaniBel 2025-10-18 13:31:41 -04:00
parent 3b94b39368
commit 65fe618f93
1 changed files with 3 additions and 3 deletions
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A2.py
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@ -16,7 +16,7 @@ def generate_data(n_samples=100, noise_std=1.0):
x = np.linspace(0, 10, n_samples) x = np.linspace(0, 10, n_samples)
#y values without noise #y values without noise
y_clean = (np.log(x + 1e-10) + 1) * np.cos(x) + np.sin(2*x) y_clean = np.log(x + 1) * np.cos(x) + np.sin(2*x)
#noise #noise
noise = np.random.normal(0, noise_std, n_samples) noise = np.random.normal(0, noise_std, n_samples)
@ -117,7 +117,7 @@ class GaussianRegression:
def true_function(x): def true_function(x):
return (np.log(x + 1e-10) + 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_i = [0, 2, 5, 10, 13, 15, 17, 20, 25, 30, 35, 45]
@ -178,7 +178,7 @@ for D in D_values:
# predict on training then validation # predict on training then validation
yh_train = model.predict(x_train) yh_train = model.predict(x_train)
yh_train = yh_train.flatten() if yh_train.ndim > 1 else yh_train yh_train = yh_train.flatten() if yh_train.ndim > 1 else yh_train
yh_val = model.predict(x_val) yh_val = model.predict(x_val)
yh_val = yh_val.flatten() if yh_val.ndim > 1 else yh_val yh_val = yh_val.flatten() if yh_val.ndim > 1 else yh_val