diff options
| -rw-r--r-- | lj_matrix/do_ml.py | 18 |
1 files changed, 9 insertions, 9 deletions
diff --git a/lj_matrix/do_ml.py b/lj_matrix/do_ml.py index ba88a6fd8..bb954a0ae 100644 --- a/lj_matrix/do_ml.py +++ b/lj_matrix/do_ml.py @@ -79,17 +79,17 @@ def do_ml(desc_data, printc('\tTest size: {}'.format(test_size), 'CYAN') printc('\tSigma: {}'.format(sigma), 'CYAN') - Xcm_training = desc_data[:training_size] - Ycm_training = energy_data[:training_size] - Kcm_training = gauss_kernel(Xcm_training, Xcm_training, sigma) - alpha_cm = cholesky_solve(Kcm_training, Ycm_training) + X_training = desc_data[:training_size] + Y_training = energy_data[:training_size] + K_training = gauss_kernel(X_training, X_training, sigma) + alpha_ = cholesky_solve(K_training, Y_training) - Xcm_test = desc_data[-test_size:] - Ycm_test = energy_data[-test_size:] - Kcm_test = gauss_kernel(Xcm_test, Xcm_training, sigma) - Ycm_predicted = np.dot(Kcm_test, alpha_cm) + X_test = desc_data[-test_size:] + Y_test = energy_data[-test_size:] + K_test = gauss_kernel(X_test, X_training, sigma) + Y_predicted = np.dot(K_test, alpha_) - mae = np.mean(np.abs(Ycm_predicted - Ycm_test)) + mae = np.mean(np.abs(Y_predicted - Y_test)) if show_msgs: printc('\tMAE for {}: {:.4f}'.format(desc_type, mae), 'GREEN') |