def _compute_log_likelihood(self, obs):
"""Compute the log likelihood of each observation in each state
Parameters
----------
obs : np.array, shape (`n_samples`, `n_features`)
Returns
-------
logl : np.array, shape (`n_samples`, `n_components`)
"""
return _vmhmm._compute_log_likelihood(obs, self._means_, self._kappas_)
def _compute_log_likelihood(self, obs):
"""Compute the log likelihood of each observation in each state
Parameters
----------
obs : np.array, shape (`n_samples`, `n_features`)
Returns
-------
logl : np.array, shape (`n_samples`, `n_components`)
"""
return _vmhmm._compute_log_likelihood(obs, self._means_, self._kappas_)
def test_log_likelihood():
n_samples, n_states, n_features = 1000, 27, 16
obs = np.random.rand(n_samples, n_features)
vm = VonMisesHMM(n_states=n_states)
vm.fit([obs])
t0 = time.time()
from scipy.stats.distributions import vonmises
reference = np.array([np.sum(vonmises.logpdf(obs, vm.kappas_[i], vm.means_[i]), axis=1) for i in range(n_states)]).T
t1 = time.time()
value = _vmhmm._compute_log_likelihood(obs, vm.means_, vm.kappas_)
t2 = time.time()
print("Log likeihood timings")
print('reference time ', t1-t0)
print('c time ', t2-t1)
np.testing.assert_array_almost_equal(reference, value)
def test_log_likelihood():
n_samples, n_states, n_features = 1000, 27, 16
obs = np.random.rand(n_samples, n_features)
vm = VonMisesHMM(n_states=n_states)
vm.fit([obs])
t0 = time.time()
from scipy.stats.distributions import vonmises
reference = np.array([
np.sum(vonmises.logpdf(obs, vm.kappas_[i], vm.means_[i]), axis=1)
for i in range(n_states)
]).T
t1 = time.time()
value = _vmhmm._compute_log_likelihood(obs, vm.means_, vm.kappas_)
t2 = time.time()
print("Log likeihood timings")
print('reference time ', t1 - t0)
print('c time ', t2 - t1)
np.testing.assert_array_almost_equal(reference, value)