/usr/lib/python2.7/dist-packages/networkx/algorithms/assortativity/tests/test_correlation.py is in python-networkx 1.8.1-0ubuntu3.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | #!/usr/bin/env python
from nose.tools import *
from nose import SkipTest
import networkx as nx
from base_test import BaseTestAttributeMixing,BaseTestDegreeMixing
from networkx.algorithms.assortativity.correlation import attribute_ac
class TestDegreeMixingCorrelation(BaseTestDegreeMixing):
@classmethod
def setupClass(cls):
global np
global npt
try:
import numpy as np
import numpy.testing as npt
except ImportError:
raise SkipTest('NumPy not available.')
try:
import scipy
import scipy.stats
except ImportError:
raise SkipTest('SciPy not available.')
def test_degree_assortativity_undirected(self):
r=nx.degree_assortativity_coefficient(self.P4)
npt.assert_almost_equal(r,-1.0/2,decimal=4)
def test_degree_assortativity_directed(self):
r=nx.degree_assortativity_coefficient(self.D)
npt.assert_almost_equal(r,-0.57735,decimal=4)
def test_degree_assortativity_multigraph(self):
r=nx.degree_assortativity_coefficient(self.M)
npt.assert_almost_equal(r,-1.0/7.0,decimal=4)
def test_degree_assortativity_undirected(self):
r=nx.degree_pearson_correlation_coefficient(self.P4)
npt.assert_almost_equal(r,-1.0/2,decimal=4)
def test_degree_assortativity_directed(self):
r=nx.degree_pearson_correlation_coefficient(self.D)
npt.assert_almost_equal(r,-0.57735,decimal=4)
def test_degree_assortativity_multigraph(self):
r=nx.degree_pearson_correlation_coefficient(self.M)
npt.assert_almost_equal(r,-1.0/7.0,decimal=4)
class TestAttributeMixingCorrelation(BaseTestAttributeMixing):
@classmethod
def setupClass(cls):
global np
global npt
try:
import numpy as np
import numpy.testing as npt
except ImportError:
raise SkipTest('NumPy not available.')
def test_attribute_assortativity_undirected(self):
r=nx.attribute_assortativity_coefficient(self.G,'fish')
assert_equal(r,6.0/22.0)
def test_attribute_assortativity_directed(self):
r=nx.attribute_assortativity_coefficient(self.D,'fish')
assert_equal(r,1.0/3.0)
def test_attribute_assortativity_multigraph(self):
r=nx.attribute_assortativity_coefficient(self.M,'fish')
assert_equal(r,1.0)
def test_attribute_assortativity_coefficient(self):
# from "Mixing patterns in networks"
a=np.array([[0.258,0.016,0.035,0.013],
[0.012,0.157,0.058,0.019],
[0.013,0.023,0.306,0.035],
[0.005,0.007,0.024,0.016]])
r=attribute_ac(a)
npt.assert_almost_equal(r,0.623,decimal=3)
def test_attribute_assortativity_coefficient2(self):
a=np.array([[0.18,0.02,0.01,0.03],
[0.02,0.20,0.03,0.02],
[0.01,0.03,0.16,0.01],
[0.03,0.02,0.01,0.22]])
r=attribute_ac(a)
npt.assert_almost_equal(r,0.68,decimal=2)
def test_attribute_assortativity(self):
a=np.array([[50,50,0],[50,50,0],[0,0,2]])
r=attribute_ac(a)
npt.assert_almost_equal(r,0.029,decimal=3)
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