def testParameterEstimation(self):
mu, shape = 1, 3
data = numpy.random.wald(mu, shape, 100000)
pdf = InverseGaussian(1, 1)
pdf.estimate(data)
self.assertAlmostEqual(pdf.mu, mu, places=2)
self.assertAlmostEqual(pdf.shape, shape, delta=0.05)
def testParameterEstimation(self):
mu, shape = 1, 3
data = numpy.random.wald(mu, shape, 100000)
pdf = InverseGaussian(1, 1)
pdf.estimate(data)
self.assertAlmostEqual(pdf.mu, mu, places=2)
self.assertAlmostEqual(pdf.shape, shape, delta=0.05)
def testParameters(self):
pdf = InverseGaussian(1.5, 2.5)
pdf.mu = 3
pdf.shape = 4
self.assertEqual(pdf.mu, 3)
self.assertEqual(pdf.shape, 4)
self.assertEqual(pdf.mu, pdf['mu'])
def propertyAssignment():
pdf.shape = -1
def directAssignment():
pdf['shape'] = -1
self.assertRaises(ParameterValueError, propertyAssignment)
self.assertRaises(ParameterValueError, directAssignment)
def testParameters(self):
pdf = InverseGaussian(1.5, 2.5)
pdf.mu = 3
pdf.shape = 4
self.assertEqual(pdf.mu, 3)
self.assertEqual(pdf.shape, 4)
self.assertEqual(pdf.mu, pdf['mu'])
def propertyAssignment():
pdf.shape = -1
def directAssignment():
pdf['shape'] = -1
self.assertRaises(ParameterValueError, propertyAssignment)
self.assertRaises(ParameterValueError, directAssignment)
def testRandom(self):
ig = InverseGaussian(1., 1.)
samples = ig.random(1000000)
mu = numpy.mean(samples)
var = numpy.var(samples)
self.assertAlmostEqual(ig.mu, mu, delta=1e-1)
self.assertAlmostEqual(ig.mu ** 3 / ig.shape, var, delta=1e-1)
ig = InverseGaussian(3., 6.)
samples = ig.random(1000000)
mu = numpy.mean(samples)
var = numpy.var(samples)
self.assertAlmostEqual(ig.mu, mu, delta=1e-1)
self.assertAlmostEqual(ig.mu ** 3 / ig.shape, var, delta=5e-1)
def testLogProb(self):
ig = InverseGaussian(1., 1.)
self.assertAlmostEqual(ig(1.),
numpy.sqrt(1 / (2 * numpy.pi)),
delta=1e-5)
self.assertAlmostEqual(
ig(2.),
(numpy.sqrt(1 / (2 * numpy.pi * 2**3)) * numpy.exp(-1. / 4.)),
delta=1e-5)
def testParameterValidation(self):
"""
@see: [CSB 0000132]
"""
self.assertRaises(ParameterValueError, InverseGaussian, -1, 1)
pdf = InverseGaussian()
data = [3.1, 1.15, 6.86, 0.69, -1.73, -2.91, -2.2800744]
def testMu(v):
pdf['mu'] = v
pdf.mu = v
self.assertRaises(ParameterValueError, testMu, -1)
self.assertRaises(EstimationFailureError, pdf.estimate, data)
def testRandom(self):
ig = InverseGaussian(1., 1.)
samples = ig.random(1000000)
mu = numpy.mean(samples)
var = numpy.var(samples)
self.assertAlmostEqual(ig.mu, mu, delta=1e-1)
self.assertAlmostEqual(ig.mu**3 / ig.shape, var, delta=1e-1)
ig = InverseGaussian(3., 6.)
samples = ig.random(1000000)
mu = numpy.mean(samples)
var = numpy.var(samples)
self.assertAlmostEqual(ig.mu, mu, delta=1e-1)
self.assertAlmostEqual(ig.mu**3 / ig.shape, var, delta=5e-1)
