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)
def test_pickle(self):
model0 = GSM(7, 9)
model0.mean = randn(7, 1)
model0.covariance = cov(randn(7, 20))
tmp_file = mkstemp()[1]
# store model
with open(tmp_file, 'w') as handle:
dump({'model': model0}, handle)
# load model
with open(tmp_file) as handle:
model1 = load(handle)['model']
# make sure parameters haven't changed
self.assertEqual(model0.dim, model1.dim)
self.assertEqual(model0.num_scales, model1.num_scales)
self.assertLess(max(abs(model0.scales - model0.scales)), 1e-10)
self.assertLess(max(abs(model0.priors - model1.priors)), 1e-10)
self.assertLess(max(abs(model0.mean - model1.mean)), 1e-10)
self.assertLess(max(abs(model0.covariance - model1.covariance)), 1e-10)
def test_pickle(self):
model0 = GSM(7, 9)
model0.mean = randn(7, 1)
model0.covariance = cov(randn(7, 20))
tmp_file = mkstemp()[1]
# store model
with open(tmp_file, 'w') as handle:
dump({'model': model0}, handle)
# load model
with open(tmp_file) as handle:
model1 = load(handle)['model']
# make sure parameters haven't changed
self.assertEqual(model0.dim, model1.dim)
self.assertEqual(model0.num_scales, model1.num_scales)
self.assertLess(max(abs(model0.scales - model0.scales)), 1e-10)
self.assertLess(max(abs(model0.priors - model1.priors)), 1e-10)
self.assertLess(max(abs(model0.mean - model1.mean)), 1e-10)
self.assertLess(max(abs(model0.covariance - model1.covariance)), 1e-10)
