def meanfieldupdate(self):
J_init, h_init = np.linalg.inv(self.sigma_init), \
np.linalg.solve(self.sigma_init, self.mu_init)
J_pair_22, J_pair_21, J_pair_11, _ = \
self.dynamics_distn._mf_expected_statistics()
_, J_yx, J_node, _ = self.emission_distn._mf_expected_statistics()
h_node = np.einsum('ti,ij->tj', self.data, J_yx)
self._normalizer, self.smoothed_mus, self.smoothed_sigmas, \
E_xtp1_xtT = info_E_step(
J_init,h_init,J_pair_11,J_pair_21,J_pair_22,J_node,h_node)
self._set_expected_stats(self.smoothed_mus, self.smoothed_sigmas,
E_xtp1_xtT)
def meanfieldupdate(self):
J_init = np.linalg.inv(self.sigma_init)
h_init = np.linalg.solve(self.sigma_init, self.mu_init)
def get_params(distn):
return mniw_expectedstats(
*distn._natural_to_standard(distn.mf_natural_hypparam))
J_pair_22, J_pair_21, J_pair_11, logdet_pair = \
get_params(self.dynamics_distn)
J_yy, J_yx, J_node, logdet_node = get_params(self.emission_distn)
h_node = self.data.dot(J_yx)
self._normalizer, self.smoothed_mus, self.smoothed_sigmas, \
E_xtp1_xtT = info_E_step(
J_init,h_init,J_pair_11,-J_pair_21,J_pair_22,J_node,h_node)
self._normalizer += self._info_extra_loglike_terms(
J_init, h_init, logdet_pair, J_yy, logdet_node, self.data)
self._set_expected_stats(
self.smoothed_mus,self.smoothed_sigmas,E_xtp1_xtT)
def meanfieldupdate(self):
J_init = np.linalg.inv(self.sigma_init)
h_init = np.linalg.solve(self.sigma_init, self.mu_init)
def get_params(distn):
return mniw_expectedstats(
*distn._natural_to_standard(distn.mf_natural_hypparam))
J_pair_22, J_pair_21, J_pair_11, logdet_pair = \
get_params(self.dynamics_distn)
J_yy, J_yx, J_node, logdet_node = get_params(self.emission_distn)
h_node = self.data.dot(J_yx)
self._normalizer, self.smoothed_mus, self.smoothed_sigmas, \
E_xtp1_xtT = info_E_step(
J_init,h_init,J_pair_11,-J_pair_21,J_pair_22,J_node,h_node)
self._normalizer += self._info_extra_loglike_terms(
J_init, h_init, logdet_pair, J_yy, logdet_node, self.data)
self._set_expected_stats(self.smoothed_mus, self.smoothed_sigmas,
E_xtp1_xtT)
def info_E_step(self):
data = self.data
A, sigma_states, C, sigma_obs = \
self.A, self.sigma_states, self.C, self.sigma_obs
J_init = np.linalg.inv(self.sigma_init)
h_init = np.linalg.solve(self.sigma_init, self.mu_init)
J_pair_11 = A.T.dot(np.linalg.solve(sigma_states, A))
J_pair_21 = -np.linalg.solve(sigma_states, A)
J_pair_22 = np.linalg.inv(sigma_states)
J_node = C.T.dot(np.linalg.solve(sigma_obs, C))
h_node = np.einsum('ik,ij,tj->tk', C, np.linalg.inv(sigma_obs), data)
self._normalizer, self.smoothed_mus, self.smoothed_sigmas, \
E_xtp1_xtT = info_E_step(
J_init,h_init,J_pair_11,J_pair_21,J_pair_22,J_node,h_node)
self._normalizer += self._extra_loglike_terms(
self.A, self.sigma_states, self.C, self.sigma_obs,
self.mu_init, self.sigma_init, self.data)
self._set_expected_stats(
self.smoothed_mus,self.smoothed_sigmas,E_xtp1_xtT)
def info_E_step(self):
data = self.data
A, sigma_states, C, sigma_obs = \
self.A, self.sigma_states, self.C, self.sigma_obs
J_init = np.linalg.inv(self.sigma_init)
h_init = np.linalg.solve(self.sigma_init, self.mu_init)
J_pair_11 = A.T.dot(np.linalg.solve(sigma_states, A))
J_pair_21 = -np.linalg.solve(sigma_states, A)
J_pair_22 = np.linalg.inv(sigma_states)
J_node = C.T.dot(np.linalg.solve(sigma_obs, C))
h_node = np.einsum('ik,ij,tj->tk', C, np.linalg.inv(sigma_obs), data)
self._normalizer, self.smoothed_mus, self.smoothed_sigmas, \
E_xtp1_xtT = info_E_step(
J_init,h_init,J_pair_11,J_pair_21,J_pair_22,J_node,h_node)
self._normalizer += self._extra_loglike_terms(
self.A, self.sigma_states, self.C, self.sigma_obs, self.mu_init,
self.sigma_init, self.data)
self._set_expected_stats(self.smoothed_mus, self.smoothed_sigmas,
E_xtp1_xtT)
