def init_rand_diag_gmm(gmm):
num_comp, dim = gmm.num_gauss(), gmm.dim()
weights = Vector([kaldi_math.rand_uniform() for _ in range(num_comp)])
tot_weigth = weights.sum()
for i, m in enumerate(weights):
weights[i] = m / tot_weigth
means = Matrix([[kaldi_math.rand_gauss() for _ in range(dim)] for _ in range(num_comp)])
vars_ = Matrix([[kaldi_math.exp(kaldi_math.rand_gauss()) for _ in range(dim)] for _ in range(num_comp)])
vars_.invert_elements_()
gmm.set_weights(weights)
gmm.set_inv_vars_and_means(vars_, means)
gmm.perturb(0.5 * kaldi_math.rand_uniform())
gmm.compute_gconsts()
def testFullGmm(self):
dim = 1 + np.random.randint(low=0, high=9)
nMix = 1 + np.random.randint(low=0, high=9)
print("Testing NumGauss: {}, Dim: {}".format(nMix, dim))
feat = Vector([kaldi_math.rand_gauss() for _ in range(dim)])
weights = Vector([kaldi_math.rand_uniform() for _ in range(nMix)])
tot_weigth = weights.sum()
for i, m in enumerate(weights):
weights[i] = m / tot_weigth
means = Matrix([[kaldi_math.rand_gauss() for _ in range(dim)]
for _ in range(nMix)])
invcovars = [SpMatrix(dim) for _ in range(nMix)]
covars_logdet = []
for _ in range(nMix):
c, matrix_sqrt, logdet_out = RandPosdefSpMatrix(dim)
invcovars[_].copy_from_sp_(c)
invcovars[_].invert_double_()
covars_logdet.append(logdet_out)
# Calculate loglike for feature Vector
def auxLogLike(w, logdet, mean_row, invcovar):
return -0.5 * ( kaldi_math.M_LOG_2PI * dim \
+ logdet \
+ vec_mat_vec(mean_row, invcovar, mean_row) \
+ vec_mat_vec(feat, invcovar, feat)) \
+ vec_mat_vec(mean_row, invcovar, feat) \
+ np.log(w)
loglikes = [
auxLogLike(weights[m], covars_logdet[m], means[m, :], invcovars[m])
for m in range(nMix)
]
loglike = Vector(loglikes).log_sum_exp()
# new Gmm
gmm = FullGmm(nMix, dim)
gmm.set_weights(weights)
gmm.set_inv_covars_and_means(invcovars, means)
gmm.compute_gconsts()
loglike1, posterior1 = gmm.component_posteriors(feat)
self.assertAlmostEqual(loglike, loglike1, delta=0.01)
self.assertAlmostEqual(1.0, posterior1.sum(), delta=0.01)
weights_bak = gmm.weights()
means_bak = gmm.means()
invcovars_bak = gmm.covars()
for i in range(nMix):
invcovars_bak[i].invert_double_()
# Set all params one-by-one to new model
gmm2 = FullGmm(gmm.num_gauss(), gmm.dim())
gmm2.set_weights(weights_bak)
gmm2.set_means(means_bak)
gmm2.inv_covars_ = invcovars_bak
gmm2.compute_gconsts()
loglike_gmm2 = gmm2.log_likelihood(feat)
self.assertAlmostEqual(loglike1, loglike_gmm2, delta=0.01)
loglikes = gmm2.log_likelihoods(feat)
self.assertAlmostEqual(loglikes.log_sum_exp(), loglike_gmm2)
indices = list(range(gmm2.num_gauss()))
loglikes = gmm2.log_likelihoods_preselect(feat, indices)
self.assertAlmostEqual(loglikes.log_sum_exp(), loglike_gmm2)
# Simple component mean accessor + mutator
gmm3 = FullGmm(gmm.num_gauss(), gmm.dim())
gmm3.set_weights(weights_bak)
means_bak.set_zero_()
for i in range(nMix):
gmm.get_component_mean(i, means_bak[i, :])
gmm3.set_means(means_bak)
gmm3.inv_covars_ = invcovars_bak
gmm3.compute_gconsts()
loglike_gmm3 = gmm3.log_likelihood(feat)
self.assertAlmostEqual(loglike1, loglike_gmm3, delta=0.01)
gmm4 = FullGmm(gmm.num_gauss(), gmm.dim())
gmm4.set_weights(weights_bak)
invcovars_bak, means_bak = gmm.get_covars_and_means()
for i in range(nMix):
invcovars_bak[i].invert_double_()
gmm4.set_inv_covars_and_means(invcovars_bak, means_bak)
gmm4.compute_gconsts()
loglike_gmm4 = gmm4.log_likelihood(feat)
self.assertAlmostEqual(loglike1, loglike_gmm4, delta=0.01)
# TODO: I/O tests
# CopyFromFullGmm
gmm4 = FullGmm()
gmm4.copy_from_full(gmm)
loglike5, _ = gmm4.component_posteriors(feat)
self.assertAlmostEqual(loglike, loglike5, delta=0.01)
# CopyFromDiag
gmm_diag = DiagGmm(nMix, dim)
init_rand_diag_gmm(gmm_diag)
loglike_diag = gmm_diag.log_likelihood(feat)
gmm_full = FullGmm().copy(gmm_diag)
loglike_full = gmm_full.log_likelihood(feat)
gmm_diag2 = DiagGmm().copy(gmm_full)
loglike_diag2 = gmm_diag2.log_likelihood(feat)
self.assertAlmostEqual(loglike_diag, loglike_full, delta=0.01)
self.assertAlmostEqual(loglike_diag, loglike_diag2, delta=0.01)