def learn_sum_gaussians(events, end_time, return_learner=False, verbose=False, **kwargs): learner_mle = HawkesSumGaussians(**kwargs, verbose=verbose) learner_mle.fit(events, end_time) if return_learner: return learner_mle return learner_mle.baseline, learner_mle.amplitudes
def test_hawkes_sumgaussians_solution(self): """...Test solution obtained by HawkesSumGaussians on toy timestamps """ events = [[ np.array([1, 1.2, 3.4, 5.8, 10.3, 11, 13.4]), np.array([2, 5, 8.3, 9.10, 15, 18, 20, 33]) ], [ np.array([2, 3.2, 11.4, 12.8, 45]), np.array([2, 3, 8.8, 9, 15.3, 19]) ]] n_nodes = len(events[0]) n_gaussians = 3 max_mean_gaussian = 5 step_size = 1e-3 C = 10 lasso_grouplasso_ratio = 0.7 baseline_start = np.zeros(n_nodes) + .2 amplitudes_start = np.zeros((n_nodes, n_nodes, n_gaussians)) + .2 learner = HawkesSumGaussians( n_gaussians=n_gaussians, max_mean_gaussian=max_mean_gaussian, step_size=step_size, C=C, lasso_grouplasso_ratio=lasso_grouplasso_ratio, n_threads=3, max_iter=11, verbose=False, em_max_iter=3) learner.fit(events[0], baseline_start=baseline_start, amplitudes_start=amplitudes_start) baseline = np.array([0.0979586, 0.15552228]) amplitudes = np.array([[[0.20708954, -0.00627318, 0.08388442], [-0.00341803, 0.34805652, -0.00687372]], [[-0.00341635, 0.1608013, 0.05531324], [-0.00342652, -0.00685425, 0.19046195]]]) np.testing.assert_array_almost_equal(learner.baseline, baseline, decimal=6) np.testing.assert_array_almost_equal(learner.amplitudes, amplitudes, decimal=6) kernel_values = np.array([ -0.00068796, 0.01661161, 0.08872543, 0.21473618, 0.25597692, 0.15068586, 0.04194497, 0.00169372, -0.00427233, -0.00233042 ]) kernels_norm = np.array([[0.28470077, 0.33776477], [0.21269818, 0.18018118]]) np.testing.assert_almost_equal( learner.get_kernel_values(0, 1, np.linspace(0, 4, 10)), kernel_values) np.testing.assert_almost_equal(learner.get_kernel_norms(), kernels_norm) means_gaussians = np.array([0., 1.66666667, 3.33333333]) std_gaussian = 0.5305164769729844 np.testing.assert_array_almost_equal(learner.means_gaussians, means_gaussians) self.assertEqual(learner.std_gaussian, std_gaussian) learner.n_gaussians = learner.n_gaussians + 1 means_gaussians = np.array([0., 1.25, 2.5, 3.75]) std_gaussian = 0.3978873577297384 np.testing.assert_array_almost_equal(learner.means_gaussians, means_gaussians) self.assertEqual(learner.std_gaussian, std_gaussian)