def _update_group_vars(args):
c, _, lu, zeta, counts_norm, (log_phi, log_mu, beta, lw) = args
##
nfeatures, nreplicas = counts_norm.shape
##
loglik = _compute_loglik(counts_norm[:, :, np.newaxis],
log_phi.reshape(-1, 1, 1),
log_mu.reshape(-1, 1, 1), beta)
loglik = loglik.sum(1)
# update d
logw = loglik[:, c] + lu
logw = ut.normalize_log_weights(logw.T)
d = st.sample_categorical(np.exp(logw)).ravel()
# update d: merge null
d_ = np.zeros(nfeatures, dtype='int')
ll = _compute_loglik(counts_norm, log_phi.reshape(-1, 1),
log_mu.reshape(-1, 1), beta[c[d]].reshape(-1,
1)).sum(-1)
ll_ = _compute_loglik(counts_norm, log_phi.reshape(-1, 1),
log_mu.reshape(-1, 1),
beta[c[d_]].reshape(-1, 1)).sum(-1)
idxs = (ll_ >= ll) | (rn.rand(nfeatures) < np.exp(ll_ - ll))
d[idxs] = d_[idxs]
##
occ = np.bincount(d, minlength=lu.size)
iact = occ > 0
kact = np.sum(iact)
# update c
logw = np.vstack([loglik[d == k].sum(0) for k in np.nonzero(iact)[0]]) + lw
logw = ut.normalize_log_weights(logw.T)
c[iact] = st.sample_categorical(np.exp(logw)).ravel()
c[~iact] = rn.choice(lw.size, c.size - kact, p=np.exp(lw))
c[0] = 0
# update zeta
zeta = st.sample_eta_west(zeta, kact, nfeatures)
# update lu
lu[:], _ = st.sample_stick(occ, zeta)
##
return c, d, c[d], lu, zeta
def update(self, data, pool):
"""Implements a single step of the blocked Gibbs sampler"""
##
self.iter += 1
##
# self._update_phi_global(data)
# self._update_phi_local(data)
# self._update_mu(data)
# self._update_beta_global(data)
# self._update_beta_local(data)
if rn.rand() < 0.5:
_update_phi_global(self, data)
_update_mu(self, data)
_update_beta_global(self, data)
else:
_update_phi_local(self, data)
_update_mu(self, data)
_update_beta_local(self, data)
# update group-specific variables
counts_norm, _ = data
common_args = it.repeat(
(self.log_phi, self.log_mu, self.beta, self.lw))
args = zip(self.c[1:], self.d[1:], self.lu[1:], self.zeta[1:],
counts_norm[1:], common_args)
if pool is None:
self.c[1:], self.d[1:], self.z[1:], self.lu[1:], self.zeta[
1:] = zip(*map(_update_group_vars, args))
else:
self.c[1:], self.d[1:], self.z[1:], self.lu[1:], self.zeta[
1:] = zip(*pool.map(_update_group_vars, args))
# update occupancies
self.occ[:] = np.bincount(self.z[1:].ravel(), minlength=self.lw.size)
self.iact[:] = self.occ > 0
self.nact = np.sum(self.iact)
# update eta
self.eta = st.sample_eta_west(self.eta, self.nact, self.occ.sum())
# update weights
self.lw[:], _ = st.sample_stick(self.occ, self.eta)
# update hyper-parameters
_update_hpars(self)
def update(self, data, pool):
"""Implements a single step of the blocked Gibbs sampler"""
##
self.iter += 1
##
# self._update_phi_global(data)
# self._update_phi_local(data)
# self._update_mu(data)
# self._update_beta_global(data)
# self._update_beta_local(data)
if rn.rand() < 0.5:
_update_phi_global(self, data)
_update_mu(self, data)
_update_beta_global(self, data)
else:
_update_phi_local(self, data)
_update_mu(self, data)
_update_beta_local(self, data)
# update group-specific variables
counts_norm, _ = data
common_args = it.repeat((self.log_phi, self.log_mu, self.beta, self.lw))
args = zip(self.c[1:], self.d[1:], self.lu[1:], self.zeta[1:], counts_norm[1:], common_args)
if pool is None:
self.c[1:], self.d[1:], self.z[1:], self.lu[1:], self.zeta[1:] = zip(*map(_update_group_vars, args))
else:
self.c[1:], self.d[1:], self.z[1:], self.lu[1:], self.zeta[1:] = zip(*pool.map(_update_group_vars, args))
# update occupancies
self.occ[:] = np.bincount(self.z[1:].ravel(), minlength=self.lw.size)
self.iact[:] = self.occ > 0
self.nact = np.sum(self.iact)
# update eta
self.eta = st.sample_eta_west(self.eta, self.nact, self.occ.sum())
# update weights
self.lw[:], _ = st.sample_stick(self.occ, self.eta)
# update hyper-parameters
_update_hpars(self)
def _update_group_vars(args):
c, _, lu, zeta, counts_norm, (log_phi, log_mu, beta, lw) = args
##
nfeatures, nreplicas = counts_norm.shape
##
loglik = _compute_loglik(counts_norm[:, :, np.newaxis], log_phi.reshape(-1, 1, 1), log_mu.reshape(-1, 1, 1), beta)
loglik = loglik.sum(1)
# update d
logw = loglik[:, c] + lu
logw = ut.normalize_log_weights(logw.T)
d = st.sample_categorical(np.exp(logw)).ravel()
# update d: merge null
d_ = np.zeros(nfeatures, dtype='int')
ll = _compute_loglik(counts_norm, log_phi.reshape(-1, 1), log_mu.reshape(-1, 1), beta[c[d]].reshape(-1, 1)).sum(-1)
ll_ = _compute_loglik(counts_norm, log_phi.reshape(-1, 1), log_mu.reshape(-1, 1), beta[c[d_]].reshape(-1, 1)).sum(-1)
idxs = np.log(rn.rand(nfeatures)) < ll_ - ll
d[idxs] = d_[idxs]
##
occ = np.bincount(d, minlength=lu.size)
iact = occ > 0
kact = np.sum(iact)
# update c
logw = np.vstack([loglik[d == k].sum(0) for k in np.nonzero(iact)[0]]) + lw
logw = ut.normalize_log_weights(logw.T)
c[iact] = st.sample_categorical(np.exp(logw)).ravel()
c[~iact] = rn.choice(lw.size, c.size-kact, p=np.exp(lw))
c[0] = 0
# update zeta
zeta = st.sample_eta_west(zeta, kact, nfeatures)
# update lu
lu[:], _ = st.sample_stick(occ, zeta)
##
return c, d, c[d], lu, zeta