def testParameterEstimation(self):
d = 3
mu = numpy.ones(d)
sigma = numpy.eye(d)
pdf = MultivariateGaussian(mu, sigma)
samples = pdf.random(100000)
pdf.estimate(samples)
for i in range(d):
self.assertAlmostEqual(pdf.mu[i], mu[i], delta=1e-1)
for j in range(d):
self.assertAlmostEqual(pdf.sigma[i, j],
sigma[i, j],
delta=1e-1)
d = 3
mu = numpy.array([0., 1., 2.])
sigma = numpy.random.random((d, d))
sigma = numpy.dot(sigma, sigma.T)
pdf = MultivariateGaussian(mu, sigma)
samples = pdf.random(1000000)
pdf.estimate(samples)
for i in range(d):
self.assertAlmostEqual(pdf.mu[i], mu[i], delta=1e-1)
for j in range(d):
self.assertAlmostEqual(pdf.sigma[i, j],
sigma[i, j],
delta=1e-1)
def testParameterEstimation(self):
d = 3
mu = numpy.ones(d)
sigma = numpy.eye(d)
pdf = MultivariateGaussian(mu, sigma)
samples = pdf.random(100000)
pdf.estimate(samples)
for i in range(d):
self.assertAlmostEqual(pdf.mu[i], mu[i], delta=1e-1)
for j in range(d):
self.assertAlmostEqual(pdf.sigma[i, j], sigma[i, j], delta=1e-1)
d = 3
mu = numpy.array([0., 1., 2.])
sigma = numpy.random.random((d, d))
sigma = numpy.dot(sigma, sigma.T)
pdf = MultivariateGaussian(mu, sigma)
samples = pdf.random(1000000)
pdf.estimate(samples)
for i in range(d):
self.assertAlmostEqual(pdf.mu[i], mu[i], delta=1e-1)
for j in range(d):
self.assertAlmostEqual(pdf.sigma[i, j], sigma[i, j], delta=1e-1)
