def testDropEdgesByProbability(self):
dummy_hypergraph = Hypergraph(example_graphs.gt_dummy_graph)
edges_count = dummy_hypergraph.number_of_edges()
for p in [0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.]:
new_hypergraph = algorithms.drop_edges_by_probability(dummy_hypergraph, p)
new_graph = algorithms.drop_edges_by_probability(example_graphs.gt_dummy_graph, p)
edges_prop_exp = ((1. - p) * edges_count) / float(edges_count)
edges_prop_hyper = float(new_hypergraph.number_of_edges()) / float(edges_count)
edges_prop = float(new_graph.number_of_edges()) / float(edges_count)
msg = "The proportion of edges remaining after being dropping deviate too much from the expected."
self.assertAlmostEquals(edges_prop_exp, edges_prop_hyper, delta=0.2, msg=msg)
self.assertAlmostEquals(edges_prop_exp, edges_prop, delta=0.2, msg=msg)
def testDropEdgesByProbability(self):
dummy_hypergraph = Hypergraph(example_graphs.gt_dummy_graph)
edges_count = dummy_hypergraph.number_of_edges()
for p in [0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.]:
new_hypergraph = algorithms.drop_edges_by_probability(
dummy_hypergraph, p)
new_graph = algorithms.drop_edges_by_probability(
example_graphs.gt_dummy_graph, p)
edges_prop_exp = ((1. - p) * edges_count) / float(edges_count)
edges_prop_hyper = float(
new_hypergraph.number_of_edges()) / float(edges_count)
edges_prop = float(
new_graph.number_of_edges()) / float(edges_count)
msg = "The proportion of edges remaining after being dropping deviate too much from the expected."
self.assertAlmostEquals(edges_prop_exp,
edges_prop_hyper,
delta=0.2,
msg=msg)
self.assertAlmostEquals(edges_prop_exp,
edges_prop,
delta=0.2,
msg=msg)
