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Merged
merged 2 commits into from
Feb 24, 2023
Merged

Develop care underflow #64

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49936b2
avoid overflow in gaussianmixture
yuta-nakahara Feb 23, 2023
c70cc99
avoid underflow in hiddenmarkovnormal
yuta-nakahara Feb 24, 2023
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18 changes: 13 additions & 5 deletions bayesml/gaussianmixture/_gaussianmixture.py
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Original file line number Diff line number Diff line change
Expand Up @@ -724,11 +724,19 @@ def _calc_vl(self):

def _calc_n_x_bar_s(self,x):
self.ns[:] = self.r_vecs.sum(axis=0)
self.x_bar_vecs[:] = (self.r_vecs[:,:,np.newaxis] * x[:,np.newaxis,:]).sum(axis=0) / self.ns[:,np.newaxis]
self.s_mats[:] = np.sum(self.r_vecs[:,:,np.newaxis,np.newaxis]
* ((x[:,np.newaxis,:] - self.x_bar_vecs)[:,:,:,np.newaxis]
@ (x[:,np.newaxis,:] - self.x_bar_vecs)[:,:,np.newaxis,:]),
axis=0) / self.ns[:,np.newaxis,np.newaxis]
indices = self.ns.astype(bool)
if np.all(indices):
self.x_bar_vecs[:] = (self.r_vecs[:,:,np.newaxis] * x[:,np.newaxis,:]).sum(axis=0) / self.ns[:,np.newaxis]
self.s_mats[:] = np.sum(self.r_vecs[:,:,np.newaxis,np.newaxis]
* ((x[:,np.newaxis,:] - self.x_bar_vecs)[:,:,:,np.newaxis]
@ (x[:,np.newaxis,:] - self.x_bar_vecs)[:,:,np.newaxis,:]),
axis=0) / self.ns[:,np.newaxis,np.newaxis]
else:
self.x_bar_vecs[indices] = (self.r_vecs[:,indices,np.newaxis] * x[:,np.newaxis,:]).sum(axis=0) / self.ns[indices,np.newaxis]
self.s_mats[indices] = np.sum(self.r_vecs[:,indices,np.newaxis,np.newaxis]
* ((x[:,np.newaxis,:] - self.x_bar_vecs[indices])[:,:,:,np.newaxis]
@ (x[:,np.newaxis,:] - self.x_bar_vecs[indices])[:,:,np.newaxis,:]),
axis=0) / self.ns[indices,np.newaxis,np.newaxis]

def _init_random_responsibility(self,x):
self.r_vecs[:] = self.rng.dirichlet(np.ones(self.c_num_classes),self.r_vecs.shape[0])
Expand Down
30 changes: 17 additions & 13 deletions bayesml/hiddenmarkovnormal/_hiddenmarkovnormal.py
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Original file line number Diff line number Diff line change
Expand Up @@ -837,23 +837,27 @@ def _calc_prior_char(self):
def _calc_n_m_x_bar_s(self,x):
self.ns[:] = self.gamma_vecs.sum(axis=0)
self.ms[:] = self.xi_mats.sum(axis=0) # xi must be initialized as a zero matrix
self.x_bar_vecs[:] = (self.gamma_vecs[:,:,np.newaxis] * x[:,np.newaxis,:]).sum(axis=0) / self.ns[:,np.newaxis]
self.s_mats[:] = np.sum(self.gamma_vecs[:,:,np.newaxis,np.newaxis]
* ((x[:,np.newaxis,:] - self.x_bar_vecs)[:,:,:,np.newaxis]
@ (x[:,np.newaxis,:] - self.x_bar_vecs)[:,:,np.newaxis,:]),
axis=0) / self.ns[:,np.newaxis,np.newaxis]
indices = self.ns.astype(bool)
if np.all(indices):
self.x_bar_vecs[:] = (self.gamma_vecs[:,:,np.newaxis] * x[:,np.newaxis,:]).sum(axis=0) / self.ns[:,np.newaxis]
self.s_mats[:] = np.sum(self.gamma_vecs[:,:,np.newaxis,np.newaxis]
* ((x[:,np.newaxis,:] - self.x_bar_vecs)[:,:,:,np.newaxis]
@ (x[:,np.newaxis,:] - self.x_bar_vecs)[:,:,np.newaxis,:]),
axis=0) / self.ns[:,np.newaxis,np.newaxis]
else:
self.x_bar_vecs[indices] = (self.gamma_vecs[:,indices,np.newaxis] * x[:,np.newaxis,:]).sum(axis=0) / self.ns[indices,np.newaxis]
self.s_mats[indices] = np.sum(self.gamma_vecs[:,indices,np.newaxis,np.newaxis]
* ((x[:,np.newaxis,:] - self.x_bar_vecs[indices])[:,:,:,np.newaxis]
@ (x[:,np.newaxis,:] - self.x_bar_vecs[indices])[:,:,np.newaxis,:]),
axis=0) / self.ns[indices,np.newaxis,np.newaxis]

def _calc_q_pi_char(self):
self._ln_pi_tilde_vec[:] = digamma(self.hn_eta_vec) - digamma(self.hn_eta_vec.sum())
self._pi_tilde_vec[:] = np.exp(self._ln_pi_tilde_vec)
# self._pi_tilde_vec[:] = np.exp(self._ln_pi_tilde_vec - self._ln_pi_tilde_vec.max())
# self._pi_tilde_vec[:] /= self._pi_tilde_vec.sum()
self._pi_tilde_vec[:] = np.exp(self._ln_pi_tilde_vec - self._ln_pi_tilde_vec.max())

def _calc_q_a_char(self):
self._ln_a_tilde_mat[:] = digamma(self.hn_zeta_vecs) - digamma(self.hn_zeta_vecs.sum(axis=1,keepdims=True))
self._a_tilde_mat[:] = np.exp(self._ln_a_tilde_mat)
# self._a_tilde_mat[:] = np.exp(self._ln_a_tilde_mat - self._ln_a_tilde_mat.max(axis=1,keepdims=True))
# self._a_tilde_mat[:] /= self._a_tilde_mat.sum(axis=1,keepdims=True)
self._a_tilde_mat[:] = np.exp(self._ln_a_tilde_mat - self._ln_a_tilde_mat.max())
self._ln_c_hn_zeta_vecs_sum = np.sum(gammaln(self.hn_zeta_vecs.sum(axis=1)) - gammaln(self.hn_zeta_vecs).sum(axis=1))

def _calc_q_lambda_char(self):
Expand Down Expand Up @@ -906,8 +910,8 @@ def _calc_vl(self):

# E[ln q(Z|pi)]
self._vl_q_z = (-(self.gamma_vecs * self._ln_rho).sum()
-(self.ms * self._ln_a_tilde_mat).sum()
-(self.gamma_vecs[0] * self._ln_pi_tilde_vec).sum()
-(self.ms * (self._ln_a_tilde_mat - self._ln_a_tilde_mat.max())).sum()
-(self.gamma_vecs[0] * (self._ln_pi_tilde_vec - self._ln_pi_tilde_vec.max())).sum()
+np.log(self._cs).sum())

# E[ln q(pi)]
Expand Down