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## iof-tools / networkxMiCe / networkx-master / networkx / algorithms / centrality / tests / test_current_flow_closeness.py @ 5cef0f13

 1 ```#!/usr/bin/env python ``` ```from nose.tools import * ``` ```from nose import SkipTest ``` ```import networkx as nx ``` ```class TestFlowClosenessCentrality(object): ``` ``` numpy = 1 # nosetests attribute, use nosetests -a 'not numpy' to skip test ``` ``` @classmethod ``` ``` def setupClass(cls): ``` ``` global np ``` ``` try: ``` ``` import numpy as np ``` ``` import scipy ``` ``` except ImportError: ``` ``` raise SkipTest('NumPy not available.') ``` ``` def test_K4(self): ``` ``` """Closeness centrality: K4""" ``` ``` G = nx.complete_graph(4) ``` ``` b = nx.current_flow_closeness_centrality(G) ``` ``` b_answer = {0: 2.0 / 3, 1: 2.0 / 3, 2: 2.0 / 3, 3: 2.0 / 3} ``` ``` for n in sorted(G): ``` ``` assert_almost_equal(b[n], b_answer[n]) ``` ``` def test_P4(self): ``` ``` """Closeness centrality: P4""" ``` ``` G = nx.path_graph(4) ``` ``` b = nx.current_flow_closeness_centrality(G) ``` ``` b_answer = {0: 1.0 / 6, 1: 1.0 / 4, 2: 1.0 / 4, 3: 1.0 / 6} ``` ``` for n in sorted(G): ``` ``` assert_almost_equal(b[n], b_answer[n]) ``` ``` def test_star(self): ``` ``` """Closeness centrality: star """ ``` ``` G = nx.Graph() ``` ``` nx.add_star(G, ['a', 'b', 'c', 'd']) ``` ``` b = nx.current_flow_closeness_centrality(G) ``` ``` b_answer = {'a': 1.0 / 3, 'b': 0.6 / 3, 'c': 0.6 / 3, 'd': 0.6 / 3} ``` ``` for n in sorted(G): ``` ``` assert_almost_equal(b[n], b_answer[n]) ``` ```class TestWeightedFlowClosenessCentrality(object): ``` ``` pass ```