def test_diffusion_engine(): hyper_graph = generators.generic_hypergraph(10, ((3, 2), (4, 3), (5, 3))) t_max = 1000 number_of_walkers = 1 offset = 10 markov_matrix = create_markov_matrix_model_nodes(hyper_graph) chosen_states = [] for x in range(10, t_max, offset): engine = DiffusionEngine(markov_matrix) frequencies, states = engine.simulate(offset) chosen_states += states[0] nt.assert_equals(len(chosen_states), t_max / number_of_walkers - 10)
def test_generator(): number_of_nodes = 10 edges_params = ((2, 3), (3, 4), (4, 8)) hypergraph = generators.generic_hypergraph(number_of_nodes, edges_params) number_of_edges = sum(edge_count for _, edge_count in edges_params) nt.assert_equals(number_of_edges, len(hypergraph.hyper_edges()))