dim = 1
seed = 22
method = 'brute' # possible methods: 'brute', 'vi', 'means', 'svi'
parametrization = 'natural' # possible parametrizations for svi method: cholesky, natural
ind_inputs_num = 5
max_iter = 100
lbfgsb_options = {'maxiter': max_iter, 'disp': False}
np.random.seed(seed)
x_tr = np.random.rand(dim, num)
if dim == 1:
x_test = np.linspace(0, 1, test_num)
x_test = x_test.reshape(1, test_num)
else:
x_test = np.random.rand(dim, test_num)
y_tr, y_test = gp.generate_data(x_tr, x_test, seed=seed)
data_points = []
data_targets = []
fig = plt.figure()
gp_plot_reg_data(x_test, y_test, 'y-')
means_gp = GPR(model_covariance_obj, method='means')
means_gp.fit(x_tr, y_tr, num_inputs=ind_inputs_num, optimizer_options=lbfgsb_options)
print(model_covariance_obj.get_params())
means_inducing_points, means_mean, means_cov = means_gp.inducing_inputs
means_y_test, means_high, means_low = means_gp.predict(x_test)
def onclick(event):
