def test_nonsingleton_different_old_class(self): x, y, _ = BaseTest.get_data() imgpe = iMGPE(x, y, initial_k=6) BaseTest.divide_data(imgpe, singleton=False) BaseTest.prepare_kernels(imgpe) target_cluster = 4 BaseTest.boost_target_affinity(imgpe, 43, target_cluster) BaseTest.destroy_priors(imgpe) pre_c, pre_class_indices, pre_param_vals = BaseTest.extract_sample_state(imgpe) imgpe.gibbs(43) post_c, post_class_indices, post_param_vals = BaseTest.extract_sample_state(imgpe) self.assertEqual(len(pre_c), len(post_c)) for index, (pre_c_i, post_c_i) in enumerate(zip(pre_c, post_c)): if index == 43: self.assertEqual(post_c_i, target_cluster) else: self.assertEqual(pre_c_i, post_c_i) pre_class_indices_compare = [pre_class_indices_j for index, pre_class_indices_j in enumerate(pre_class_indices) if index != 2 and index != target_cluster] post_class_indices_compare = [pre_class_indices_j for index, pre_class_indices_j in enumerate(post_class_indices) if index != 2 and index != target_cluster] self.assertEqual(len(pre_class_indices), len(post_class_indices)) for pre_class_indices_j, post_class_indices_j in zip(pre_class_indices_compare, post_class_indices_compare): self.assertTrue(np.array_equal(pre_class_indices_j, post_class_indices_j)) self.assertTrue(len(pre_class_indices[2]) - 1, len(post_class_indices[2])) self.assertTrue(np.array_equal(pre_class_indices[2][pre_class_indices[2] != 43], post_class_indices[2])) self.assertTrue(len(pre_class_indices[target_cluster] + 1), len(post_class_indices[target_cluster])) self.assertTrue(np.array_equal(np.append(pre_class_indices[target_cluster], 43), post_class_indices[target_cluster])) self.assertEqual(pre_param_vals, post_param_vals)
