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()))
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()))
