# third_algorithm_label = 'Half-log-scaled SE'
np.random.seed(seed)
x_tr = np.random.rand(dim, num)
if dim == 2:
x_1 = np.linspace(0, 1, test_density)
x_2 = np.linspace(0, 1, test_density)
x_1, x_2 = np.meshgrid(x_1, x_2)
x_test = np.array(list(zip(x_1.reshape(-1).tolist(), x_2.reshape(-1).tolist())))
x_test = x_test.T
else:
x_test = np.random.rand(dim, test_density**2)
print("Generating Data")
y_tr, y_test = gp.generate_data(x_tr, x_test, seed=seed)
print("Data generated")
# First method
model_params = np.array([np.log(2.2), np.log(1.73), np.log(0.2)])
model_covariance_obj = ExpScaledSquaredExponential(model_params)
# model_params = np.array([2.2, 1.73, 0.2])
# model_covariance_obj = SquaredExponential(model_params)
first_gp = GaussianProcess(model_covariance_obj, lambda x: 0, 'class')
w_a_list, time_a_list = first_gp.find_hyper_parameters(x_tr, y_tr, max_iter=iterations_1, alternate=True)
w_a_list = [np.exp(w) for w in w_a_list]
w_a_opt = w_a_list[-1]
w_a_list = w_a_list[:plot_iterations_1]
np.random.seed(seed)
x_tr = np.random.rand(dim, num)
if dim == 2:
x_1 = np.linspace(0, 1, test_density)
x_2 = np.linspace(0, 1, test_density)
x_1, x_2 = np.meshgrid(x_1, x_2)
x_test = np.array(
list(zip(x_1.reshape(-1).tolist(),
x_2.reshape(-1).tolist())))
x_test = x_test.T
else:
x_test = np.random.rand(dim, test_density**2)
print("Generating Data")
y_tr, y_test = gp.generate_data(x_tr, x_test, seed=seed)
print("Data generated")
# First method
model_params = np.array([np.log(2.2), np.log(1.73), np.log(0.2)])
model_covariance_obj = ExpScaledSquaredExponential(model_params)
# model_params = np.array([2.2, 1.73, 0.2])
# model_covariance_obj = SquaredExponential(model_params)
first_gp = GaussianProcess(model_covariance_obj, lambda x: 0, 'class')
w_a_list, time_a_list = first_gp.find_hyper_parameters(x_tr,
y_tr,
max_iter=iterations_1,
alternate=True)
w_a_list = [np.exp(w) for w in w_a_list]
