def _generate_sample_from_state(self, state, random_state=None):
if self._cvtype == 'tied':
cv = self._covars
else:
cv = self._covars[state]
return sample_gaussian(self._means[state], cv, self._cvtype,
random_state=random_state)
def _test_sample_gaussian_diag(self, ndim, n=10000):
mu = np.random.randint(10) * np.random.rand(ndim)
cv = (np.random.rand(ndim) + 1.0)**2
samples = gmm.sample_gaussian(mu, cv, cvtype='diag', n=n)
if ndim > 1:
axis = 1
else:
axis = None
assert_array_almost_equal(samples.mean(axis), mu, decimal=1)
assert_array_almost_equal(samples.var(axis), cv, decimal=1)
def _test_sample_gaussian_diag(self, ndim, n=10000):
mu = np.random.randint(10) * np.random.rand(ndim)
cv = (np.random.rand(ndim) + 1.0)**2
samples = gmm.sample_gaussian(mu, cv, cvtype='diag', n=n)
if ndim > 1:
axis = 1
else:
axis = None
assert_array_almost_equal(samples.mean(axis), mu, decimal=1)
assert_array_almost_equal(samples.var(axis), cv, decimal=1)
def _test_sample_gaussian_full(self, ndim, n=10000):
mu = np.random.randint(10) * np.random.rand(ndim)
cv = _generate_random_spd_matrix(ndim)
samples = gmm.sample_gaussian(mu, cv, cvtype='full', n=n)
if ndim > 1:
axis = 1
else:
axis = None
assert_array_almost_equal(samples.mean(axis), mu, decimal=1)
assert_array_almost_equal(np.cov(samples), cv, decimal=1)
def _test_sample_gaussian_full(self, ndim, n=10000):
mu = np.random.randint(10) * np.random.rand(ndim)
cv = _generate_random_spd_matrix(ndim)
samples = gmm.sample_gaussian(mu, cv, cvtype='full', n=n)
if ndim > 1:
axis = 1
else:
axis = None
assert_array_almost_equal(samples.mean(axis), mu, decimal=1)
assert_array_almost_equal(np.cov(samples), cv, decimal=1)
