def testLogProb(self):
fx = [
2.7939e-24, 1.5305e-03, 2.3028e-02, 6.6302e-02, 1.1759e-01,
1.6810e-01, 2.1365e-01, 2.5255e-01, 2.8438e-01, 3.0937e-01,
3.2806e-01, 3.4111e-01, 3.4922e-01, 3.5307e-01, 3.5329e-01,
3.5048e-01, 3.4515e-01, 3.3778e-01, 3.2878e-01, 3.1850e-01,
3.0726e-01, 2.9531e-01, 2.8289e-01, 2.7018e-01, 2.5736e-01,
2.4454e-01, 2.3185e-01, 2.1937e-01, 2.0717e-01, 1.9532e-01,
1.8385e-01, 1.7280e-01, 1.6220e-01, 1.5205e-01, 1.4236e-01,
1.3315e-01, 1.2440e-01, 1.1611e-01, 1.0828e-01, 1.0088e-01,
9.3917e-02, 8.7363e-02, 8.1207e-02, 7.5432e-02, 7.0021e-02,
6.4958e-02, 6.0224e-02, 5.5804e-02
]
x = numpy.arange(0.01, 5., 0.1)
a = 2.
b = 1.
p = 2
gig = GeneralizedInverseGaussian(a, b, p)
fx2 = gig(x)
for i in range(len(fx)):
self.assertAlmostEqual(fx[i], fx2[i], delta=1e-1)
def testRandom(self):
from scipy.special import kv
from numpy import sqrt
a = 2.
b = 1.
p = 1
gig = GeneralizedInverseGaussian(a, b, p)
samples = gig.random(10000)
mu_analytical = sqrt(b) * kv(p + 1, sqrt(a * b)) / (sqrt(a) * kv(p, sqrt(a * b)))
var_analytical = b * kv(p + 2, sqrt(a * b)) / a / kv(p, sqrt(a * b)) - mu_analytical ** 2
mu = numpy.mean(samples)
var = numpy.var(samples)
self.assertAlmostEqual(mu_analytical, mu, delta=1e-1)
self.assertAlmostEqual(var_analytical, var, delta=1e-1)
def testParameters(self):
pdf = GeneralizedInverseGaussian(1, 1, 1)
pdf.a = 2
pdf.b = 3
pdf.p = 4
self.assertEqual(pdf.a, 2)
self.assertEqual(pdf.b, 3)
self.assertEqual(pdf.p, 4)
self.assertEqual(pdf.p, pdf['p'])
def propertyAssignment():
pdf.a = -1
def directAssignment(p):
pdf[p] = -1
self.assertRaises(ParameterValueError, propertyAssignment)
self.assertRaises(ParameterValueError, directAssignment, 'a')
self.assertRaises(ParameterValueError, directAssignment, 'b')
self.assertRaises(ParameterValueError, directAssignment, 'p')
def testRandom(self):
from scipy.special import kv
from numpy import sqrt
a = 2.
b = 1.
p = 1
gig = GeneralizedInverseGaussian(a, b, p)
samples = gig.random(10000)
mu_analytical = sqrt(b) * kv(p + 1, sqrt(
a * b)) / (sqrt(a) * kv(p, sqrt(a * b)))
var_analytical = b * kv(p + 2, sqrt(a * b)) / a / kv(p, sqrt(
a * b)) - mu_analytical**2
mu = numpy.mean(samples)
var = numpy.var(samples)
self.assertAlmostEqual(mu_analytical, mu, delta=1e-1)
self.assertAlmostEqual(var_analytical, var, delta=1e-1)
def testParameters(self):
pdf = GeneralizedInverseGaussian(1, 1, 1)
pdf.a = 2
pdf.b = 3
pdf.p = 4
self.assertEqual(pdf.a, 2)
self.assertEqual(pdf.b, 3)
self.assertEqual(pdf.p, 4)
self.assertEqual(pdf.p, pdf['p'])
def propertyAssignment():
pdf.a = -1
def directAssignment(p):
pdf[p] = -1
self.assertRaises(ParameterValueError, propertyAssignment)
self.assertRaises(ParameterValueError, directAssignment, 'a')
self.assertRaises(ParameterValueError, directAssignment, 'b')
self.assertRaises(ParameterValueError, directAssignment, 'p')
