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()
