def rwrw_size_estimate(graph, n_samples=-1, walk_length=200, thinning=40):
# determine the number of samples
if n_samples == -1: n_samples = graph.size() * 2
samples = RWRW(graph, n_samples, length=walk_length, thinning=thinning)
node_degrees = [graph.degree(node) for node in samples]
sum_of_degrees = sum(node_degrees)
sum_of_inverse_degrees = sum(graphsize.inverse_seq(node_degrees))
collisions = graphsize.collision_count(samples)
return graphsize.estimate_size(sum_of_degrees, sum_of_inverse_degrees, collisions)
def uis_wr_size_estimate(graph, n_samples=-1):
# determine the number of samples
if n_samples == -1: n_samples = graph.size() * 2
#uniform sample so degree ratios fixed to 1
sum_of_degrees = n_samples
sum_of_inverse_degrees = n_samples
node_samples = UIS_WR(graph.nodes(), n_samples)
collisions = graphsize.collision_count(node_samples)
return graphsize.estimate_size(sum_of_degrees, sum_of_inverse_degrees, collisions)
def wis_wr_size_estimate(graph, n_samples=-1):
# determine the number of samples
if n_samples == -1:
n_samples = graph.size() * 2
samples = graphsize.WIS_WR(graphsize.degree_weighted_nodes_for(graph), n_samples)
node_degrees = [graph.degree(node) for node in samples]
sum_of_degrees = sum(node_degrees)
sum_of_inverse_degrees = sum(graphsize.inverse_seq(node_degrees))
collisions = graphsize.collision_count(samples)
return graphsize.estimate_size(sum_of_degrees, sum_of_inverse_degrees, collisions)
def estimate_size_with_mhrw(graph, n_samples=-1, thinning=1, random_walk_length=20):
# determine the number of samples
if n_samples == -1: n_samples = graph.size() * 4
#sample the graph and process the results
node_samples = MHRW(graph, graph.nodes(), n_samples, length=random_walk_length, thinning=thinning)
degrees = [graph.degree(node) for node in node_samples]
sum_of_degrees = sum(degrees)
sum_of_inverse_degrees = sum(graphsize.inverse_seq(degrees))
collisions = graphsize.collision_count(node_samples)
print 'Sum of degrees: ', sum_of_degrees
print 'Sum of inverse degrees: ', sum_of_inverse_degrees
print 'Repeated samples: ', collisions
return graphsize.estimate_size(sum_of_degrees, sum_of_inverse_degrees, collisions)
def test_estimate_size_arithmetic(self):
"""degrees * inverse_degrees / 2 * identical_samples"""
self.assertEqual(10, graphsize.estimate_size(10, 10, 5))
self.assertEqual(100.4, graphsize.estimate_size(10, 100.4, 5))
