def test_lmvnpdftied_consistent_with_lmvnpdffull(self):
nstates = 4
ndim = 20
nobs = 200
mu = np.random.randint(10) * np.random.rand(nstates, ndim)
tiedcv = _generate_random_spd_matrix(ndim)
obs = np.random.randint(10) * np.random.rand(nobs, ndim)
cv = np.tile(tiedcv, (nstates, 1, 1))
reference = gmm.lmvnpdf(obs, mu, cv, 'full')
lpr = gmm.lmvnpdf(obs, mu, tiedcv, 'tied')
assert_array_almost_equal(lpr, reference)
def test_lmvnpdftied_consistent_with_lmvnpdffull(self):
nstates = 4
ndim = 20
nobs = 200
mu = np.random.randint(10) * np.random.rand(nstates, ndim)
tiedcv = _generate_random_spd_matrix(ndim)
obs = np.random.randint(10) * np.random.rand(nobs, ndim)
cv = np.tile(tiedcv, (nstates, 1, 1))
reference = gmm.lmvnpdf(obs, mu, cv, 'full')
lpr = gmm.lmvnpdf(obs, mu, tiedcv, 'tied')
assert_array_almost_equal(lpr, reference)
def _test_lmvnpdfspherical(self, ndim, nstates, nobs=100):
mu = np.random.randint(10) * np.random.rand(nstates, ndim)
spherecv = np.random.rand(nstates, 1)**2 + 1
obs = np.random.randint(10) * np.random.rand(nobs, ndim)
cv = np.tile(spherecv, (ndim, 1))
reference = self._slow_lmvnpdfdiag(obs, mu, cv)
lpr = gmm.lmvnpdf(obs, mu, spherecv, 'spherical')
assert_array_almost_equal(lpr, reference)
def _test_lmvnpdfspherical(self, ndim, nstates, nobs=100):
mu = np.random.randint(10) * np.random.rand(nstates, ndim)
spherecv = np.random.rand(nstates, 1)**2 + 1
obs = np.random.randint(10) * np.random.rand(nobs, ndim)
cv = np.tile(spherecv, (ndim, 1))
reference = self._slow_lmvnpdfdiag(obs, mu, cv)
lpr = gmm.lmvnpdf(obs, mu, spherecv, 'spherical')
assert_array_almost_equal(lpr, reference)
def _test_lmvnpdftied_with_diagonal_covariance(self, ndim, nstates, nobs=100):
mu = np.random.randint(10) * np.random.rand(nstates, ndim)
tiedcv = (np.random.rand(ndim) + 1.0)**2
obs = np.random.randint(10) * np.random.rand(nobs, ndim)
cv = np.tile(tiedcv, (nstates, 1))
reference = self._slow_lmvnpdfdiag(obs, mu, cv)
lpr = gmm.lmvnpdf(obs, mu, np.diag(tiedcv), 'tied')
assert_array_almost_equal(lpr, reference)
def _test_lmvnpdffull_with_diagonal_covariance(self, ndim, nstates, nobs=100):
mu = np.random.randint(10) * np.random.rand(nstates, ndim)
cv = (np.random.rand(nstates, ndim) + 1.0)**2
obs = np.random.randint(10) * np.random.rand(nobs, ndim)
fullcv = np.array([np.diag(x) for x in cv])
reference = self._slow_lmvnpdfdiag(obs, mu, cv)
lpr = gmm.lmvnpdf(obs, mu, fullcv, 'full')
assert_array_almost_equal(lpr, reference)
def _test_lmvnpdfdiag(self, ndim, nstates, nobs=100):
# test the slow and naive implementation of lmvnpdf and
# compare it to the vectorized version (gmm.lmvnpdf) to test
# for correctness
mu = np.random.randint(10) * np.random.rand(nstates, ndim)
cv = (np.random.rand(nstates, ndim) + 1.0)**2
obs = np.random.randint(10) * np.random.rand(nobs, ndim)
reference = self._slow_lmvnpdfdiag(obs, mu, cv)
lpr = gmm.lmvnpdf(obs, mu, cv, 'diag')
assert_array_almost_equal(lpr, reference)
def _test_lmvnpdfdiag(self, ndim, nstates, nobs=100):
# test the slow and naive implementation of lmvnpdf and
# compare it to the vectorized version (gmm.lmvnpdf) to test
# for correctness
mu = np.random.randint(10) * np.random.rand(nstates, ndim)
cv = (np.random.rand(nstates, ndim) + 1.0)**2
obs = np.random.randint(10) * np.random.rand(nobs, ndim)
reference = self._slow_lmvnpdfdiag(obs, mu, cv)
lpr = gmm.lmvnpdf(obs, mu, cv, 'diag')
assert_array_almost_equal(lpr, reference)
def _test_lmvnpdftied_with_diagonal_covariance(self,
ndim,
nstates,
nobs=100):
mu = np.random.randint(10) * np.random.rand(nstates, ndim)
tiedcv = (np.random.rand(ndim) + 1.0)**2
obs = np.random.randint(10) * np.random.rand(nobs, ndim)
cv = np.tile(tiedcv, (nstates, 1))
reference = self._slow_lmvnpdfdiag(obs, mu, cv)
lpr = gmm.lmvnpdf(obs, mu, np.diag(tiedcv), 'tied')
assert_array_almost_equal(lpr, reference)
def _test_lmvnpdffull_with_diagonal_covariance(self,
ndim,
nstates,
nobs=100):
mu = np.random.randint(10) * np.random.rand(nstates, ndim)
cv = (np.random.rand(nstates, ndim) + 1.0)**2
obs = np.random.randint(10) * np.random.rand(nobs, ndim)
fullcv = np.array([np.diag(x) for x in cv])
reference = self._slow_lmvnpdfdiag(obs, mu, cv)
lpr = gmm.lmvnpdf(obs, mu, fullcv, 'full')
assert_array_almost_equal(lpr, reference)
def _compute_log_likelihood(self, obs):
return lmvnpdf(obs, self._means, self._covars, self._cvtype)
