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() X_ = [] for i in range(3): X_.append([i+0.5, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1])