def test_loglikelihood(self): gsm = GSM(3, 1) samples = gsm.sample(100000) # compute entropy analytically entropy = 0.5 * slogdet(2. * pi * e * gsm.covariance / gsm.scales)[1] # compare with estimated entropy self.assertAlmostEqual(entropy, -mean(gsm.loglikelihood(samples)), 1)
def test_loglikelihood(self):
gsm = GSM(3, 1)
samples = gsm.sample(100000)
# compute entropy analytically
entropy = 0.5 * slogdet(2. * pi * e * gsm.covariance / gsm.scales)[1]
# compare with estimated entropy
self.assertAlmostEqual(entropy, -mean(gsm.loglikelihood(samples)), 1)
def test_train(self):
gsm0 = GSM(3, 2)
gsm0.mean = [1, 1, 1]
gsm0.scales = [1, 5]
gsm0.priors = [0.7, 0.3]
gsm0.covariance = gsm0.covariance / power(det(gsm0.covariance),
1. / gsm0.dim)
samples = gsm0.sample(50000)
# try to recover parameters
gsm1 = GSM(3, 2)
gsm1.train(samples, parameters={'max_iter': 50})
# normalize
f = power(det(gsm1.covariance), 1. / gsm1.dim)
gsm1.covariance = gsm1.covariance / f
gsm1.scales = gsm1.scales / f
self.assertLess(max(abs(gsm1.mean - gsm0.mean)), 0.2)
self.assertLess(
max(abs(1. -
sort(gsm1.priors.ravel()) / sort(gsm0.priors.ravel()))),
0.2)
self.assertLess(
max(abs(1. -
sort(gsm1.scales.ravel()) / sort(gsm0.scales.ravel()))),
0.2)
self.assertLess(max(abs(gsm1.covariance - gsm0.covariance)), 0.2)
weights = rand(1, samples.shape[1])
weights /= sum(weights)
gsm1 = GSM(3, 2)
gsm1.train(samples, weights=weights, parameters={'max_iter': 100})
# normalize
f = power(det(gsm1.covariance), 1. / gsm1.dim)
gsm1.covariance = gsm1.covariance / f
gsm1.scales = gsm1.scales / f
self.assertLess(max(abs(gsm1.mean - gsm0.mean)), 0.2)
self.assertLess(
max(abs(1. -
sort(gsm1.priors.ravel()) / sort(gsm0.priors.ravel()))),
0.2)
self.assertLess(
max(abs(1. -
sort(gsm1.scales.ravel()) / sort(gsm0.scales.ravel()))),
0.2)
self.assertLess(max(abs(gsm1.covariance - gsm0.covariance)), 0.2)
def test_train(self):
gsm0 = GSM(3, 2)
gsm0.mean = [1, 1, 1]
gsm0.scales = [1, 5]
gsm0.priors = [0.7, 0.3]
gsm0.covariance = gsm0.covariance / power(det(gsm0.covariance), 1. / gsm0.dim)
samples = gsm0.sample(50000)
# try to recover parameters
gsm1 = GSM(3, 2)
gsm1.train(samples, parameters={'max_iter': 50})
# normalize
f = power(det(gsm1.covariance), 1. / gsm1.dim)
gsm1.covariance = gsm1.covariance / f
gsm1.scales = gsm1.scales / f
self.assertLess(max(abs(gsm1.mean - gsm0.mean)), 0.2)
self.assertLess(max(abs(1. - sort(gsm1.priors.ravel()) / sort(gsm0.priors.ravel()))), 0.2)
self.assertLess(max(abs(1. - sort(gsm1.scales.ravel()) / sort(gsm0.scales.ravel()))), 0.2)
self.assertLess(max(abs(gsm1.covariance - gsm0.covariance)), 0.2)
weights = rand(1, samples.shape[1])
weights /= sum(weights)
gsm1 = GSM(3, 2)
gsm1.train(samples, weights=weights, parameters={'max_iter': 100})
# normalize
f = power(det(gsm1.covariance), 1. / gsm1.dim)
gsm1.covariance = gsm1.covariance / f
gsm1.scales = gsm1.scales / f
self.assertLess(max(abs(gsm1.mean - gsm0.mean)), 0.2)
self.assertLess(max(abs(1. - sort(gsm1.priors.ravel()) / sort(gsm0.priors.ravel()))), 0.2)
self.assertLess(max(abs(1. - sort(gsm1.scales.ravel()) / sort(gsm0.scales.ravel()))), 0.2)
self.assertLess(max(abs(gsm1.covariance - gsm0.covariance)), 0.2)
