def demo():
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.}
#initial_params = {'mean':1.783386007372333,
# 'sigma_n':-1.922677410742645,
# 'sigma_f':0.3390422274608337,
# 'l_k':-1.0909797052150225}
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(x_val, y_val, num_epoch = 100,
lr = 1e-1,decay=0.99,opt_method='rmsprop',
momentum=0., nesterov=False,batch_size=x_val.shape[0])
#model.train(x_val, y_val, num_epoch = 200,
# lr = 1e-2,decay=0.99,opt_method='SGD', batch_size=x_val.shape[0])
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()
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()
