# display data
a = input('Do you want to display test data ? ')
if a == 'y':
print 'X: ', XT
print 'y: ', y
# initialize the kernel
kernel_test = RBFBoolS(length_scale = [2, 2, 2, 2, 2, 2, 2], length_scale_bounds = [0.1, 10])
print 'after kernel init'
# train the model
gp_test = GaussianProcessRegressor(kernel=kernel_test, alpha=0.1, normalize_y=True)
print 'after GP regressor'
gp_test.InitKernel()
print("GPML kernel: %s" % gp_test.kernel_)
print("Log-marginal-likelihood: %.3f"
% gp_test.log_marginal_likelihood_data(XT, y))
gp_test.fit(XT, y)
print("GPML kernel: %s" % gp_test.kernel_)
print("Log-marginal-likelihood: %.3f"
% gp_test.log_marginal_likelihood(gp_test.kernel_.theta))
print("GPML kernel: %s" % gp_test.kernel_)
print("Log-marginal-likelihood: %.3f"
% gp_test.log_marginal_likelihood_data(XT, y))
start_time = time()
