def testFitDiscretePowerLaw2(self):
try:
import networkx
except ImportError:
logging.debug("Networkx not found, can't run test")
return
nxGraph = networkx.barabasi_albert_graph(1000, 2)
graph = SparseGraph.fromNetworkXGraph(nxGraph)
degreeSeq = graph.outDegreeSequence()
output = Util.fitDiscretePowerLaw(degreeSeq)
def testFitDiscretePowerLaw2(self):
try:
import networkx
except ImportError:
logging.debug("Networkx not found, can't run test")
return
nxGraph = networkx.barabasi_albert_graph(1000, 2)
graph = SparseGraph.fromNetworkXGraph(nxGraph)
degreeSeq = graph.outDegreeSequence()
output = Util.fitDiscretePowerLaw(degreeSeq)
def testFitDiscretePowerLaw(self):
#Test with small x
x = numpy.array([5])
ks, alpha2, xmin = Util.fitDiscretePowerLaw(x)
self.assertEquals(ks, -1)
self.assertEquals(alpha2, -1)
x = numpy.array([5, 2])
ks, alpha2, xmin = Util.fitDiscretePowerLaw(x)
#Test with a large vector x
alpha = 2.5
exponent = (1/(alpha-1))
numPoints = 15000
x = 10*numpy.random.rand(numPoints)**-exponent
x = numpy.array(numpy.round(x), numpy.int)
x = x[x<=500]
x = x[x>=1]
xmins = numpy.arange(1, 15)
ks, alpha2, xmin = Util.fitDiscretePowerLaw(x, xmins)
self.assertAlmostEqual(alpha, alpha2, places=1)
def testFitDiscretePowerLaw(self):
#Test with small x
x = numpy.array([5])
ks, alpha2, xmin = Util.fitDiscretePowerLaw(x)
self.assertEquals(ks, -1)
self.assertEquals(alpha2, -1)
x = numpy.array([5, 2])
ks, alpha2, xmin = Util.fitDiscretePowerLaw(x)
#Test with a large vector x
alpha = 2.5
exponent = (1 / (alpha - 1))
numPoints = 15000
x = 10 * numpy.random.rand(numPoints)**-exponent
x = numpy.array(numpy.round(x), numpy.int)
x = x[x <= 500]
x = x[x >= 1]
xmins = numpy.arange(1, 15)
ks, alpha2, xmin = Util.fitDiscretePowerLaw(x, xmins)
self.assertAlmostEqual(alpha, alpha2, places=1)
