def demo_optimizer(): demoData = np.load('regression_data.npz') x_val = demoData['x'] # training data y_val = demoData['y'] # training target x_test_val = demoData['xstar'] # test data from gptheano_model import GP_Theano initial_params = {'mean':np.mean(y_val), 'sigma_n':np.log(.1), 'sigma_f':0., 'l_k':0.} model = GP_Theano(initial_params) outputs = model.get_outputs(x_val, y_val, x_test_val) plot_regression(x_val, y_val, x_test_val, outputs['y_test_mu'],outputs['y_test_var'],'Before Optimization') model.train_by_optimizer(x_val, y_val, number_epoch=100,batch_size=20) outputs = model.get_outputs(x_val, y_val, x_test_val) plot_regression(x_val, y_val, x_test_val, outputs['y_test_mu'],outputs['y_test_var'],'After Optimization') plt.show()