def test_mogsm(self):
mcgsm = MCGSM(
dim_in=0,
dim_out=3,
num_components=2,
num_scales=2,
num_features=0)
p0 = 0.3
p1 = 0.7
N = 20000
m0 = array([[2], [0], [0]])
m1 = array([[0], [2], [1]])
C0 = cov(randn(mcgsm.dim_out, mcgsm.dim_out**2))
C1 = cov(randn(mcgsm.dim_out, mcgsm.dim_out**2))
input = zeros([0, N])
output = hstack([
dot(cholesky(C0), randn(mcgsm.dim_out, round(p0 * N))) + m0,
dot(cholesky(C1), randn(mcgsm.dim_out, round(p1 * N))) + m1]) * (rand(1, N) + 0.5)
mcgsm.train(input, output, parameters={
'verbosity': 0,
'max_iter': 10,
'train_means': True})
mogsm = MoGSM(3, 2, 2)
# translate parameters from MCGSM to MoGSM
mogsm.priors = sum(exp(mcgsm.priors), 1) / sum(exp(mcgsm.priors))
for k in range(mogsm.num_components):
mogsm[k].mean = mcgsm.means[:, k]
mogsm[k].covariance = inv(dot(mcgsm.cholesky_factors[k], mcgsm.cholesky_factors[k].T))
mogsm[k].scales = exp(mcgsm.scales[k, :])
mogsm[k].priors = exp(mcgsm.priors[k, :]) / sum(exp(mcgsm.priors[k, :]))
self.assertAlmostEqual(mcgsm.evaluate(input, output), mogsm.evaluate(output), 5)
mogsm_samples = mogsm.sample(N)
mcgsm_samples = mcgsm.sample(input)
# generated samples should have the same distribution
for i in range(mogsm.dim):
self.assertTrue(ks_2samp(mogsm_samples[i], mcgsm_samples[0]) > 0.0001)
self.assertTrue(ks_2samp(mogsm_samples[i], mcgsm_samples[1]) > 0.0001)
self.assertTrue(ks_2samp(mogsm_samples[i], mcgsm_samples[2]) > 0.0001)
posterior = mcgsm.posterior(input, mcgsm_samples)
# average posterior should correspond to prior
for k in range(mogsm.num_components):
self.assertLess(abs(1 - mean(posterior[k]) / mogsm.priors[k]), 0.1)
def test_basics(self): model = MoGSM(1, 4, 1) model.priors = arange(model.num_components) + 1. model.priors = model.priors / sum(model.priors) for k in range(model.num_components): model[k].mean = [[k]] model[k].scales = [[1000.]] n = 1000 samples = asarray(model.sample(n) + .5, dtype=int) for k in range(model.num_components): p = model.priors.ravel()[k] x = sum(samples == k) c = binom.cdf(x, n, p) self.assertGreater(c, 1e-5) self.assertGreater(1. - c, 1e-5)
def test_mogsm(self):
mcgsm = MCGSM(dim_in=0,
dim_out=3,
num_components=2,
num_scales=2,
num_features=0)
p0 = 0.3
p1 = 0.7
N = 20000
m0 = array([[2], [0], [0]])
m1 = array([[0], [2], [1]])
C0 = cov(randn(mcgsm.dim_out, mcgsm.dim_out**2))
C1 = cov(randn(mcgsm.dim_out, mcgsm.dim_out**2))
input = zeros([0, N])
output = hstack([
dot(cholesky(C0), randn(mcgsm.dim_out, round(p0 * N))) + m0,
dot(cholesky(C1), randn(mcgsm.dim_out, round(p1 * N))) + m1
]) * (rand(1, N) + 0.5)
mcgsm.train(input,
output,
parameters={
'verbosity': 0,
'max_iter': 10,
'train_means': True
})
mogsm = MoGSM(3, 2, 2)
# translate parameters from MCGSM to MoGSM
mogsm.priors = sum(exp(mcgsm.priors), 1) / sum(exp(mcgsm.priors))
for k in range(mogsm.num_components):
mogsm[k].mean = mcgsm.means[:, k]
mogsm[k].covariance = inv(
dot(mcgsm.cholesky_factors[k], mcgsm.cholesky_factors[k].T))
mogsm[k].scales = exp(mcgsm.scales[k, :])
mogsm[k].priors = exp(mcgsm.priors[k, :]) / sum(
exp(mcgsm.priors[k, :]))
self.assertAlmostEqual(mcgsm.evaluate(input, output),
mogsm.evaluate(output), 5)
mogsm_samples = mogsm.sample(N)
mcgsm_samples = mcgsm.sample(input)
# generated samples should have the same distribution
for i in range(mogsm.dim):
self.assertTrue(
ks_2samp(mogsm_samples[i], mcgsm_samples[0]) > 0.0001)
self.assertTrue(
ks_2samp(mogsm_samples[i], mcgsm_samples[1]) > 0.0001)
self.assertTrue(
ks_2samp(mogsm_samples[i], mcgsm_samples[2]) > 0.0001)
posterior = mcgsm.posterior(input, mcgsm_samples)
# average posterior should correspond to prior
for k in range(mogsm.num_components):
self.assertLess(abs(1 - mean(posterior[k]) / mogsm.priors[k]), 0.1)
