def testFromCoefficients(self):
'''Test we can create a generating function from explicit coefficients.'''
coeffs = [0, 0.05, 0.7, 0.25]
gf = gf_from_coefficients(coeffs)
self.assertEqual(gf(1.0), 1.0)
for k in range(len(coeffs)):
self.assertEqual(coeffs[k], gf[k])
self.assertEqual(gf[k + 1], 0.0)
def testFromFunction(self):
'''Test we can create a generating function from a function returning the coefficients.'''
coeffs = [0, 0.05, 0.7, 0.25]
def f(k):
if k < len(coeffs):
return coeffs[k]
else:
return 0
gf = gf_from_coefficient_function(f)
self.assertEqual(gf(1.0), 1.0)
for k in range(len(coeffs)):
self.assertEqual(coeffs[k], gf[k])
self.assertEqual(gf[k + 1], 0.0)
def testDiscreteProbabilities(self):
'''Test that the coefficients of a discrete GF sum to 1.'''
g = networkx.gnp_random_graph(5000, 0.005)
gf = DiscreteGF(g=g)
self.assertAlmostEqual(gf(1.0), 1.0, places=2)
def testPLCProbabilities(self):
'''Test that the PLC GF evaluates to 1.'''
gf = gf_plc(3.0, 60)
self.assertAlmostEqual(gf(1.0), 1.0, places=2)
def testPLProbabilities(self):
'''Test that the powerlaw GF evaluates to 1.'''
gf = gf_powerlaw(3.0)
self.assertAlmostEqual(gf(1.0), 1.0, places=2)
def testERProbabilities(self):
'''Test that the ER GF evaluates to 1.'''
gf = gf_er(5000, 0.005)
self.assertAlmostEqual(gf(1.0), 1.0, places=2)