def setUp(self): np.random.seed(2) n_points = 100 normal_noise = np.random.normal(0, 1.0, n_points) points = np.linspace(0, 10, n_points) points = points.reshape([n_points, 1]) kernel = Matern52.define_kernel_from_array(1, np.array([2.0])) self.sigma2 = ParameterEntity(SIGMA2_NAME, np.array([1.0]), None) kernel = ScaledKernel(1, kernel, self.sigma2) function = SampleFunctions.sample_from_gp(points, kernel) function = function[0, :] evaluations = function + normal_noise + 10.0 self.training_data_gp = { "evaluations": list(evaluations), "points": points, "var_noise": [] } bounds = None self.gp_gaussian = GPFittingGaussian([MATERN52_NAME], self.training_data_gp, [1], bounds, max_steps_out=1000) self.gp_gaussian_2 = GPFittingGaussian([SCALED_KERNEL, MATERN52_NAME], self.training_data_gp, [1], bounds, max_steps_out=1000)
def test_cross_validation_mle_parameters(self): type_kernel = [MATERN52_NAME] np.random.seed(5) n_points = 10 normal_noise = np.random.normal(0, 0.01, n_points) points = np.linspace(0, 100, n_points) points = points.reshape([n_points, 1]) kernel = Matern52.define_kernel_from_array(1, np.array([100.0])) function = SampleFunctions.sample_from_gp(points, kernel) function = function[0, :] evaluations = function + normal_noise training_data = { "evaluations": evaluations, "points": points, "var_noise": None} dimensions = [1] problem_name = 'a' result = \ ValidationGPModel.cross_validation_mle_parameters(type_kernel, training_data, dimensions, problem_name, start=np.array([0.01**2, 0.0, 100.0])) compare = 'results/diagnostic_kernel/a/validation_kernel_histogram_a_' + MATERN52_NAME + \ '_same_correlation_False_10_None.png' assert result['filename_histogram'] == compare assert np.all(result['y_eval'] == evaluations) assert result['n_data'] == n_points assert result['filename_plot'] == 'results/diagnostic_kernel/a/' \ 'validation_kernel_mean_vs_observations_a_' + \ MATERN52_NAME + '_same_correlation_False_10_None' + '.png' assert result['success_proportion'] >= 0.9 noise = np.random.normal(0, 0.000001, n_points) evaluations_noisy = evaluations + noise training_data_2 = { "evaluations": evaluations_noisy, "points": points, "var_noise": np.array(n_points * [0.000001**2])} result_2 = \ ValidationGPModel.cross_validation_mle_parameters(type_kernel, training_data_2, dimensions, problem_name, start=np.array([0.01**2, 0.0, 100.0])) compare = 'results/diagnostic_kernel/a/validation_kernel_histogram_a_' + MATERN52_NAME + \ '_same_correlation_False_10_None.png' assert result_2['filename_histogram'] == compare assert np.all(result_2['y_eval'] == evaluations_noisy) assert result_2['n_data'] == n_points compare = 'results/diagnostic_kernel/a/validation_kernel_mean_vs_observations_a_' + \ MATERN52_NAME + '_same_correlation_False_10_None.png' assert result_2['filename_plot'] == compare assert result_2['success_proportion'] >= 0.9
def setUp(self): np.random.seed(5) n_points = 100 points = np.linspace(0, 100, n_points) points = points.reshape([n_points, 1]) tasks = np.random.randint(2, size=(n_points, 1)) add = [-10, 10] kernel = Matern52.define_kernel_from_array(1, np.array([100.0, 1.0])) function = SampleFunctions.sample_from_gp(points, kernel) self.function = function for i in xrange(n_points): function[0, i] += add[tasks[i, 0]] points = np.concatenate((points, tasks), axis=1) self.points = points self.evaluations = function[0, :] function = function[0, :] training_data = { 'evaluations': list(function), 'points': points, "var_noise": [], } gaussian_p = GPFittingGaussian( [PRODUCT_KERNELS_SEPARABLE, MATERN52_NAME, TASKS_KERNEL_NAME], training_data, [2, 1, 2], bounds_domain=[[0, 100], [0, 1]], type_bounds=[0, 1]) gaussian_p = gaussian_p.fit_gp_regression(random_seed=1314938) quadrature = BayesianQuadrature(gaussian_p, [0], UNIFORM_FINITE, parameters_distribution={TASKS: 2}, model_only_x=True) self.mt = MultiTasks(quadrature, quadrature.parameters_distribution.get(TASKS))
def test_cross_validation_mle_parameters_2(self): type_kernel = [MATERN52_NAME] np.random.seed(5) n_points = 10 normal_noise = np.random.normal(0, 0.01, n_points) points = np.linspace(0, 100, n_points) points = points.reshape([n_points, 1]) kernel = Matern52.define_kernel_from_array(1, np.array([100.0])) function = SampleFunctions.sample_from_gp(points, kernel) function = function[0, :] evaluations = function + normal_noise training_data = { "evaluations": evaluations, "points": points, "var_noise": None} dimensions = [1] problem_name = 'a' result = \ ValidationGPModel.cross_validation_mle_parameters(type_kernel, training_data, dimensions, problem_name, start=np.array([-1])) assert result['success_proportion'] == -1
def test_sample_new_observations(self): np.random.seed(5) n_points = 10 normal_noise = np.random.normal(0, 0.5, n_points) points = np.linspace(0, 500, n_points) points = points.reshape([n_points, 1]) kernel = Matern52.define_kernel_from_array(1, np.array([100.0, 1.0])) function = SampleFunctions.sample_from_gp(points, kernel) function = function[0, :] evaluations = function + normal_noise training_data_gp = { "evaluations": list(evaluations[1:]), "points": points[1:, :], "var_noise": []} gp = GPFittingGaussian([MATERN52_NAME], training_data_gp, [1], kernel_values=[100.0, 1.0], mean_value=[0.0], var_noise_value=[0.5**2]) n_samples = 100 samples = gp.sample_new_observations(np.array([[30.0]]), n_samples, random_seed=1) new_point = np.array([[30.0]]) z = gp.compute_posterior_parameters(new_point) mean = z['mean'] cov = z['cov'] npt.assert_almost_equal(mean, np.mean(samples), decimal=1) npt.assert_almost_equal(cov, np.var(samples), decimal=1)
def test_compute_posterior_parameters(self): np.random.seed(5) n_points = 10 normal_noise = np.random.normal(0, 0.5, n_points) points = np.linspace(0, 500, n_points) points = points.reshape([n_points, 1]) kernel = Matern52.define_kernel_from_array(1, np.array([100.0, 1.0])) function = SampleFunctions.sample_from_gp(points, kernel) function = function[0, :] evaluations = function + normal_noise training_data_gp = { "evaluations": list(evaluations[1:]), "points": points[1:, :], "var_noise": []} gp = GPFittingGaussian([MATERN52_NAME], training_data_gp, [1], kernel_values=[100.0, 1.0], mean_value=[0.0], var_noise_value=[0.5**2]) new_point = np.array([points[0], points[1]]) z = gp.compute_posterior_parameters(new_point) mean = z['mean'] cov = z['cov'] assert mean[1] - 2.0 * np.sqrt(cov[1, 1]) <= function[1] assert function[1] <= mean[1] + 2.0 * np.sqrt(cov[1, 1]) assert mean[0] - 2.0 * np.sqrt(cov[0, 0]) <= function[0] assert function[0] <= mean[0] + 2.0 * np.sqrt(cov[0, 0]) # Values obtained from GPy npt.assert_almost_equal(mean, np.array([0.30891226, 0.60256237])) npt.assert_almost_equal(cov, np.array([[0.48844879, 0.16799927], [0.16799927, 0.16536313]]))
def test_train(self): np.random.seed(5) n_points = 10 normal_noise = np.random.normal(0, 0.5, n_points) points = np.linspace(0, 500, n_points) points = points.reshape([n_points, 1]) kernel = Matern52.define_kernel_from_array(1, np.array([100.0, 1.0])) function = SampleFunctions.sample_from_gp(points, kernel) function = function[0, :] evaluations = function + normal_noise training_data_gp = { "evaluations": list(evaluations), "points": points, "var_noise": []} new_gp = GPFittingGaussian.train([MATERN52_NAME], [1], True, training_data_gp, None, random_seed=1314938) gp_gaussian = GPFittingGaussian([MATERN52_NAME], training_data_gp, [1]) gp_2 = gp_gaussian.fit_gp_regression(random_seed=1314938) npt.assert_almost_equal(new_gp.var_noise.value[0], gp_2.var_noise.value[0], decimal=6) npt.assert_almost_equal(new_gp.mean.value[0], gp_2.mean.value[0], decimal=6) npt.assert_almost_equal(new_gp.kernel_values, gp_2.kernel_values) gp_gaussian = GPFittingGaussian([MATERN52_NAME], training_data_gp, [1]) new_gp_2 = GPFittingGaussian.train([MATERN52_NAME], [1], False, training_data_gp, None) npt.assert_almost_equal(new_gp_2.var_noise.value[0], gp_gaussian.var_noise.value[0]) npt.assert_almost_equal(new_gp_2.mean.value[0], gp_gaussian.mean.value[0], decimal=6) npt.assert_almost_equal(new_gp_2.kernel_values, gp_gaussian.kernel_values)
def test_sample_from_gp(self): x = np.linspace(0, 10, 50) x = x.reshape([50, 1]) kernel = Matern52.define_kernel_from_array(1, np.array([3.0, 5.0])) function = SampleFunctions.sample_from_gp(x, kernel, n_samples=100000) mean = np.mean(function, axis=0) cov = np.cov(function.transpose()) cov_ = kernel.cov(x) npt.assert_almost_equal(mean, np.zeros(len(mean)), decimal=1) npt.assert_almost_equal(cov, cov_, decimal=1) function_2 = SampleFunctions.sample_from_gp(x, kernel, n_samples=100000, random_seed=10) mean = np.mean(function_2, axis=0) cov = np.cov(function_2.transpose()) cov_ = kernel.cov(x) npt.assert_almost_equal(mean, np.zeros(len(mean)), decimal=1) npt.assert_almost_equal(cov, cov_, decimal=1)
def test_optimize_posterior_mean_samples(self): np.random.seed(5) n_points = 100 points = np.linspace(0, 100, n_points) points = points.reshape([n_points, 1]) tasks = np.random.randint(2, size=(n_points, 1)) add = [10, -10] kernel = Matern52.define_kernel_from_array(1, np.array([100.0, 1.0])) function = SampleFunctions.sample_from_gp(points, kernel) max_value = function[0, np.argmax(function)] max_point = points[np.argmax(function), 0] for i in xrange(n_points): function[0, i] += add[tasks[i, 0]] points = np.concatenate((points, tasks), axis=1) function = function[0, :] training_data = { 'evaluations': list(function), 'points': points, "var_noise": [], } gaussian_p = GPFittingGaussian( [PRODUCT_KERNELS_SEPARABLE, MATERN52_NAME, TASKS_KERNEL_NAME], training_data, [2, 1, 2], bounds_domain=[[0, 100]], max_steps_out=1000) gaussian_p = gaussian_p.fit_gp_regression(random_seed=1314938) gp = BayesianQuadrature(gaussian_p, [0], UNIFORM_FINITE, {TASKS: 2}) random_seed = 10 n_samples_parameters = 15 gp.gp.thinning = 10 gp.gp.n_burning = 500 sol_2 = gp.optimize_posterior_mean( random_seed=random_seed, n_best_restarts=2, n_samples_parameters=n_samples_parameters, start_new_chain=True) assert max_point == sol_2['solution'] npt.assert_almost_equal(max_value, sol_2['optimal_value'], decimal=3)
def test_gradient_vector_b(self): np.random.seed(5) n_points = 10 points = np.linspace(0, 100, n_points) points = points.reshape([n_points, 1]) tasks = np.random.randint(2, size=(n_points, 1)) add = [10, -10] kernel = Matern52.define_kernel_from_array(1, np.array([100.0, 1.0])) function = SampleFunctions.sample_from_gp(points, kernel) for i in xrange(n_points): function[0, i] += add[tasks[i, 0]] points = np.concatenate((points, tasks), axis=1) function = function[0, :] training_data = { 'evaluations': list(function), 'points': points, "var_noise": [], } gaussian_p = GPFittingGaussian( [PRODUCT_KERNELS_SEPARABLE, MATERN52_NAME, TASKS_KERNEL_NAME], training_data, [2, 1, 2], bounds_domain=[[0, 100]]) gaussian_p = gaussian_p.fit_gp_regression(random_seed=1314938) gp = BayesianQuadrature(gaussian_p, [0], UNIFORM_FINITE, {TASKS: 2}) # gp = self.gp_complete candidate_point = np.array([[84.0, 1]]) points = np.array([[99.5], [12.1], [70.2]]) value = gp.gradient_vector_b(candidate_point, points, cache=False) dh_ = 0.0000001 dh = [dh_] finite_diff = FiniteDifferences.forward_difference( lambda point: gp.compute_posterior_parameters_kg( points, point.reshape((1, len(point))), cache=False)['b'], candidate_point[0, :], np.array(dh)) npt.assert_almost_equal(finite_diff[0], value[:, 0], decimal=5) assert np.all(finite_diff[1] == value[:, 1]) value_2 = gp.gradient_vector_b(candidate_point, points, cache=True) assert np.all(value_2 == value)
def test_mle_parameters(self): # Results compared with the ones given by GPy np.random.seed(1) add = -45.946926660233636 llh = self.gp_gaussian.log_likelihood(1.0, 0.0, np.array([100.0, 1.0])) npt.assert_almost_equal(llh + add, -59.8285565516, decimal=6) opt = self.gp_gaussian.mle_parameters(start=np.array([1.0, 0.0, 14.0, 0.9])) assert opt['optimal_value'] + add >= -67.1494227694 compare = self.gp_gaussian.log_likelihood(9, 10.0, np.array([100.2, 1.1])) assert self.gp_gaussian_central.log_likelihood(9, 0.0, np.array([100.2, 1.1])) == compare np.random.seed(5) n_points = 10 normal_noise = np.random.normal(0, 0.5, n_points) points = np.linspace(0, 500, n_points) points = points.reshape([n_points, 1]) kernel = Matern52.define_kernel_from_array(1, np.array([100.0, 1.0])) function = SampleFunctions.sample_from_gp(points, kernel) function = function[0, :] evaluations = function + normal_noise training_data_gp = { "evaluations": list(evaluations), "points": points, "var_noise": []} gp_gaussian = GPFittingGaussian([MATERN52_NAME], training_data_gp, [1]) opt_3 = gp_gaussian.mle_parameters(random_seed=1314938) np.random.seed(1314938) start = gp_gaussian.sample_parameters_posterior(1)[0, :] opt_4 = gp_gaussian.mle_parameters(start) npt.assert_almost_equal(opt_3['optimal_value'], opt_4['optimal_value']) npt.assert_almost_equal(opt_3['solution'], opt_4['solution'], decimal=4)
def setUp(self): self.bounds_domain_x = BoundsEntity({ 'lower_bound': 0, 'upper_bound': 100, }) spec_domain = { 'dim_x': 1, 'choose_noise': True, 'bounds_domain_x': [self.bounds_domain_x], 'number_points_each_dimension': [100], 'problem_name': 'a', } self.domain = DomainService.from_dict(spec_domain) dict = { 'problem_name': 'test_problem_with_tasks', 'dim_x': 1, 'choose_noise': True, 'bounds_domain_x': [BoundsEntity({ 'lower_bound': 0, 'upper_bound': 100 })], 'number_points_each_dimension': [100], 'method_optimization': 'sbo', 'training_name': 'test_bgo', 'bounds_domain': [[0, 100], [0, 1]], 'n_training': 4, 'type_kernel': [PRODUCT_KERNELS_SEPARABLE, MATERN52_NAME, TASKS_KERNEL_NAME], 'noise': False, 'random_seed': 5, 'parallel': False, 'type_bounds': [0, 1], 'dimensions': [2, 1, 2], 'name_model': 'gp_fitting_gaussian', 'mle': True, 'thinning': 0, 'n_burning': 0, 'max_steps_out': 1, 'training_data': None, 'x_domain': [0], 'distribution': UNIFORM_FINITE, 'parameters_distribution': None, 'minimize': False, 'n_iterations': 5, } self.spec = RunSpecEntity(dict) self.acquisition_function = None self.gp_model = None ###Define other BGO object np.random.seed(5) n_points = 100 points = np.linspace(0, 100, n_points) points = points.reshape([n_points, 1]) tasks = np.random.randint(2, size=(n_points, 1)) add = [10, -10] kernel = Matern52.define_kernel_from_array(1, np.array([100.0, 1.0])) function = SampleFunctions.sample_from_gp(points, kernel) for i in xrange(n_points): function[0, i] += add[tasks[i, 0]] points = np.concatenate((points, tasks), axis=1) self.points = points function = function[0, :] points_ls = [list(points[i, :]) for i in xrange(n_points)] training_data_med = { 'evaluations': list(function[0:5]), 'points': points_ls[0:5], "var_noise": [], } self.training_data = training_data_med training_data = { 'evaluations': list(function), 'points': points, "var_noise": [], } gaussian_p = GPFittingGaussian( [PRODUCT_KERNELS_SEPARABLE, MATERN52_NAME, TASKS_KERNEL_NAME], training_data, [2, 1, 2], bounds_domain=[[0, 100], [0, 1]], type_bounds=[0, 1]) gaussian_p = gaussian_p.fit_gp_regression(random_seed=1314938) params = gaussian_p.get_value_parameters_model self.params = params dict = { 'problem_name': 'test_simulated_gp', 'dim_x': 1, 'choose_noise': True, 'bounds_domain_x': [BoundsEntity({ 'lower_bound': 0, 'upper_bound': 100 })], 'number_points_each_dimension': [100], 'method_optimization': 'sbo', 'training_name': 'test_sbo', 'bounds_domain': [[0, 100], [0, 1]], 'n_training': 5, 'type_kernel': [PRODUCT_KERNELS_SEPARABLE, MATERN52_NAME, TASKS_KERNEL_NAME], 'noise': False, 'random_seed': 5, 'parallel': False, 'type_bounds': [0, 1], 'dimensions': [2, 1, 2], 'name_model': 'gp_fitting_gaussian', 'mle': False, 'thinning': 0, 'n_burning': 0, 'max_steps_out': 1, 'training_data': training_data_med, 'x_domain': [0], 'distribution': UNIFORM_FINITE, 'parameters_distribution': None, 'minimize': False, 'n_iterations': 1, 'var_noise_value': [params[0]], 'mean_value': [params[1]], 'kernel_values': list(params[2:]), 'cache': False, 'debug': False, } self.spec_2 = RunSpecEntity(dict)
def setUp(self): self.training_data_complex = { "evaluations": [1.0, 1.1], "points": [[42.2851784656, 0], [42.3851784656, 0]], "var_noise": [] } self.complex_gp = GPFittingGaussian( [PRODUCT_KERNELS_SEPARABLE, MATERN52_NAME, TASKS_KERNEL_NAME], self.training_data_complex, [2, 1, 1]) self.gp = BayesianQuadrature(self.complex_gp, [0], UNIFORM_FINITE, {TASKS: 1}) training_data_complex = { "evaluations": [1.0, 1.1], "points": [[42.2851784656, 0], [42.3851784656, 1]], "var_noise": [] } self.complex_gp_2 = GPFittingGaussian( [PRODUCT_KERNELS_SEPARABLE, MATERN52_NAME, TASKS_KERNEL_NAME], training_data_complex, [3, 1, 2]) self.gp_2 = BayesianQuadrature(self.complex_gp_2, [0], UNIFORM_FINITE, {TASKS: 2}) np.random.seed(5) n_points = 100 points = np.linspace(0, 100, n_points) points = points.reshape([n_points, 1]) tasks = np.random.randint(2, size=(n_points, 1)) add = [10, -10] kernel = Matern52.define_kernel_from_array(1, np.array([100.0, 1.0])) function = SampleFunctions.sample_from_gp(points, kernel) self.original_function = function self.max_value = function[0, np.argmax(function)] self.max_point = points[np.argmax(function), 0] for i in xrange(n_points): function[0, i] += add[tasks[i, 0]] points = np.concatenate((points, tasks), axis=1) function = function[0, :] training_data = { 'evaluations': list(function), 'points': points, "var_noise": [], } training_data_2 = { 'evaluations': list(function[[0, 30, 50, 90, 99]]), 'points': points[[0, 30, 50, 90, 99], :], "var_noise": [], } gaussian_p = GPFittingGaussian( [PRODUCT_KERNELS_SEPARABLE, MATERN52_NAME, TASKS_KERNEL_NAME], training_data, [2, 1, 2], bounds_domain=[[0, 100]]) gaussian_p = gaussian_p.fit_gp_regression(random_seed=1314938) gaussian_p_2 = GPFittingGaussian( [PRODUCT_KERNELS_SEPARABLE, MATERN52_NAME, TASKS_KERNEL_NAME], training_data_2, [2, 1, 2], bounds_domain=[[0, 100]]) gaussian_p_2 = gaussian_p.fit_gp_regression(random_seed=1314938) self.gp_complete_2 = BayesianQuadrature(gaussian_p_2, [0], UNIFORM_FINITE, {TASKS: 2}) self.gp_complete = BayesianQuadrature(gaussian_p, [0], UNIFORM_FINITE, {TASKS: 2})
from stratified_bayesian_optimization.kernels.matern52 import Matern52 from stratified_bayesian_optimization.lib.sample_functions import SampleFunctions decimals = 10 random_seed = 5 np.random.seed(random_seed) n_points = 1000 points = np.linspace(0, 100, n_points) points = np.round(points, decimals=decimals) points = points.reshape([n_points, 1]) tasks = np.array([[0, 1]]) add = [10, -10] kernel = Matern52.define_kernel_from_array(1, np.array([100.0, 1.0])) function = SampleFunctions.sample_from_gp(points, kernel) function = function[0, :] final_function = {} for task in range(2): final_function[task] = [] for i in xrange(n_points): point = np.concatenate((points[i, :], np.array([task]))) final_function[task].append(function[i] + add[task]) filename = path.join('problems', 'test_simulated_gp', 'simulated_function_with_%d_%d' % (n_points, random_seed)) JSONFile.write({'function': final_function, 'points': points}, filename)
def setUp(self): type_kernel = [SCALED_KERNEL, MATERN52_NAME] self.training_data = { "evaluations": [42.2851784656, 72.3121248508, 1.0113231069, 30.9309246906, 15.5288331909], "points": [ [42.2851784656], [72.3121248508], [1.0113231069], [30.9309246906], [15.5288331909]], "var_noise": []} dimensions = [1] self.gp = GPFittingGaussian(type_kernel, self.training_data, dimensions, bounds_domain=[[0, 100]]) self.training_data_3 = { "evaluations": [42.2851784656, 72.3121248508, 1.0113231069, 30.9309246906, 15.5288331909], "points": [ [42.2851784656], [72.3121248508], [1.0113231069], [30.9309246906], [15.5288331909]], "var_noise": [0.5, 0.8, 0.7, 0.9, 1.0]} self.gp_3 = GPFittingGaussian(type_kernel, self.training_data_3, dimensions, bounds_domain=[[0, 100]]) self.training_data_simple = { "evaluations": [5], "points": [[5]], "var_noise": []} dimensions = [1] self.simple_gp = GPFittingGaussian(type_kernel, self.training_data_simple, dimensions, bounds_domain=[[0, 100]]) self.training_data_complex = { "evaluations": [1.0], "points": [[42.2851784656, 0]], "var_noise": [0.5]} self.complex_gp = GPFittingGaussian( [PRODUCT_KERNELS_SEPARABLE, MATERN52_NAME, TASKS_KERNEL_NAME], self.training_data_complex, [2, 1, 1], bounds_domain=[[0, 100], [0]]) self.training_data_complex_2 = { "evaluations": [1.0, 2.0, 3.0], "points": [[42.2851784656, 0], [10.532, 0], [9.123123, 1]], "var_noise": [0.5, 0.2, 0.1]} self.complex_gp_2 = GPFittingGaussian( [PRODUCT_KERNELS_SEPARABLE, MATERN52_NAME, TASKS_KERNEL_NAME], self.training_data_complex_2, [3, 1, 2], bounds_domain=[[0, 100], [0, 1]]) self.new_point = np.array([[80.0]]) self.evaluation = np.array([80.0]) self.training_data_noisy = { "evaluations": [41.0101845096], "points": [[42.2851784656]], "var_noise": [0.0181073779]} self.gp_noisy = GPFittingGaussian(type_kernel, self.training_data_noisy, dimensions, bounds_domain=[[0, 100]]) np.random.seed(2) n_points = 50 normal_noise = np.random.normal(0, 0.5, n_points) points = np.linspace(0, 500, n_points) points = points.reshape([n_points, 1]) kernel = Matern52.define_kernel_from_array(1, np.array([100.0, 1.0])) function = SampleFunctions.sample_from_gp(points, kernel) function = function[0, :] evaluations = function + normal_noise self.training_data_gp = { "evaluations": list(evaluations), "points": points, "var_noise": []} self.gp_gaussian = GPFittingGaussian([SCALED_KERNEL, MATERN52_NAME], self.training_data_gp, [1]) self.gp_gaussian_2 = GPFittingGaussian([MATERN52_NAME], self.training_data_gp, [1], bounds_domain=[[0, 100]]) self.training_data_gp_2 = { "evaluations": list(evaluations - 10.0), "points": points, "var_noise": []} self.gp_gaussian_central = GPFittingGaussian([SCALED_KERNEL, MATERN52_NAME], self.training_data_gp_2, [1], bounds_domain=[[0, 100]])