Update app.py

app.py

@@ -46,7 +46,8 @@ k_train_train = rbf_kernel(x_train, x_train, lengthscale, variance)
 k_test_train = rbf_kernel(x_test, x_train, lengthscale, variance)
 k_test_test = rbf_kernel(x_test, x_test, lengthscale, variance)
 
-c = np.linalg.inv(np.linalg.cholesky(k_train_train + noise_variance * np.eye(3)))
+k_train_with_noise = k_train_train + noise_variance * np.eye(3)
+c = np.linalg.inv(np.linalg.cholesky())
 k_inv = np.dot(c.T,c)
 
 pred_mean = k_test_train@k_inv@y_train
@@ -58,6 +59,7 @@ ax.fill_between(x_test.ravel(), pred_mean.ravel()-pred_std2.ravel(),
                 pred_mean.ravel()+pred_std2.ravel(), alpha=0.5, label='$\\mu \\pm 2\\sigma$')
 ax.set_xlabel('x')
 ax.set_ylabel('y')
+ax.set_title(f"Negative Log Marginal Likelihood: {scipy.stats.multivariate_normal(np.zeros(3), k_train_with_noise).pdf(y_train.ravel()):.4f}")
 ax.legend()
 
 with st_col: