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#!/usr/bin/env python
from nose.tools import *
from networkx import *
from networkx.generators.random_graphs import *
class TestGeneratorsRandom():
def smoke_test_random_graph(self):
seed = 42
G=gnp_random_graph(100,0.25,seed)
G=binomial_graph(100,0.25,seed)
G=erdos_renyi_graph(100,0.25,seed)
G=fast_gnp_random_graph(100,0.25,seed)
G=gnm_random_graph(100,20,seed)
G=dense_gnm_random_graph(100,20,seed)
G=watts_strogatz_graph(10,2,0.25,seed)
assert_equal(len(G), 10)
assert_equal(G.number_of_edges(), 10)
G=connected_watts_strogatz_graph(10,2,0.1,seed)
assert_equal(len(G), 10)
assert_equal(G.number_of_edges(), 10)
G=watts_strogatz_graph(10,4,0.25,seed)
assert_equal(len(G), 10)
assert_equal(G.number_of_edges(), 20)
G=newman_watts_strogatz_graph(10,2,0.0,seed)
assert_equal(len(G), 10)
assert_equal(G.number_of_edges(), 10)
G=newman_watts_strogatz_graph(10,4,0.25,seed)
assert_equal(len(G), 10)
assert_true(G.number_of_edges() >= 20)
G=barabasi_albert_graph(100,1,seed)
G=barabasi_albert_graph(100,3,seed)
assert_equal(G.number_of_edges(),(97*3))
G=powerlaw_cluster_graph(100,1,1.0,seed)
G=powerlaw_cluster_graph(100,3,0.0,seed)
assert_equal(G.number_of_edges(),(97*3))
G=duplication_divergence_graph(100,1.0,seed)
assert_equal(len(G), 100)
assert_raises(networkx.exception.NetworkXError,
duplication_divergence_graph, 100, 2)
assert_raises(networkx.exception.NetworkXError,
duplication_divergence_graph, 100, -1)
G=random_regular_graph(10,20,seed)
assert_raises(networkx.exception.NetworkXError,
random_regular_graph, 3, 21)
constructor=[(10,20,0.8),(20,40,0.8)]
G=random_shell_graph(constructor,seed)
G=nx.random_lobster(10,0.1,0.5,seed)
def test_random_zero_regular_graph(self):
"""Tests that a 0-regular graph has the correct number of nodes and
edges.
"""
G = random_regular_graph(0, 10)
assert_equal(len(G), 10)
assert_equal(len(G.edges()), 0)
def test_gnp(self):
for generator in [gnp_random_graph, binomial_graph, erdos_renyi_graph,
fast_gnp_random_graph]:
G = generator(10, -1.1)
assert_equal(len(G), 10)
assert_equal(len(G.edges()), 0)
G = generator(10, 0.1)
assert_equal(len(G), 10)
G = generator(10, 0.1, seed=42)
assert_equal(len(G), 10)
G = generator(10, 1.1)
assert_equal(len(G), 10)
assert_equal(len(G.edges()), 45)
G = generator(10, -1.1, directed=True)
assert_true(G.is_directed())
assert_equal(len(G), 10)
assert_equal(len(G.edges()), 0)
G = generator(10, 0.1, directed=True)
assert_true(G.is_directed())
assert_equal(len(G), 10)
G = generator(10, 1.1, directed=True)
assert_true(G.is_directed())
assert_equal(len(G), 10)
assert_equal(len(G.edges()), 90)
# assert that random graphs generate all edges for p close to 1
edges = 0
runs = 100
for i in range(runs):
edges += len(generator(10, 0.99999, directed=True).edges())
assert_almost_equal(edges/float(runs), 90, delta=runs*2.0/100)
def test_gnm(self):
G=gnm_random_graph(10,3)
assert_equal(len(G),10)
assert_equal(len(G.edges()),3)
G=gnm_random_graph(10,3,seed=42)
assert_equal(len(G),10)
assert_equal(len(G.edges()),3)
G=gnm_random_graph(10,100)
assert_equal(len(G),10)
assert_equal(len(G.edges()),45)
G=gnm_random_graph(10,100,directed=True)
assert_equal(len(G),10)
assert_equal(len(G.edges()),90)
G=gnm_random_graph(10,-1.1)
assert_equal(len(G),10)
assert_equal(len(G.edges()),0)
def test_watts_strogatz_big_k(self):
assert_raises(networkx.exception.NetworkXError,
watts_strogatz_graph, 10, 10, 0.25)
assert_raises(networkx.exception.NetworkXError,
newman_watts_strogatz_graph, 10, 10, 0.25)
# could create an infinite loop, now doesn't
# infinite loop used to occur when a node has degree n-1 and needs to rewire
watts_strogatz_graph(10, 9, 0.25, seed=0)
newman_watts_strogatz_graph(10, 9, 0.5, seed=0)
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