def _update_labels(self, mu, Sigma, weights, ident=False):
if self.gpu:
# GPU business happens?
return get_labelsGPU(self.gpu_workers, weights, mu, Sigma, relabel=ident)
else:
densities = mvn_weighted_logged(self.data, mu, Sigma, weights)
if ident:
Z = np.asarray(densities.argmax(1), dtype='i')
else:
Z = None
return sample_discrete(densities).squeeze(), Z
def _update_labels(self, mu, Sigma, weights):
# gets the latent classifications .. easily done with current multigpu
zhat = []
if self.gpu:
return get_hdp_labels_GPU(self.gpu_workers, weights, mu, Sigma, self._ident)
else:
labels = [np.zeros(self.nobs[j]) for j in range(self.ngroups)]
for j in xrange(self.ngroups):
densities = mvn_weighted_logged(self.data[j], mu, Sigma, weights[j])
labels[j] = sample_discrete(densities).squeeze()
if self._ident:
zhat.append(np.asarray(densities.argmax(1), dtype='i'))
return labels, zhat
def _update_labels(self, mu, Sigma, weights):
# gets the latent classifications .. easily done with current multigpu
zhat = []
if self.gpu:
return get_hdp_labels_GPU(self.gpu_workers, weights, mu, Sigma, self._ident)
else:
labels = [np.zeros(self.nobs[j]) for j in range(self.ngroups)]
for j in xrange(self.ngroups):
densities = mvn_weighted_logged(self.data[j], mu, Sigma, weights[j])
labels[j] = sample_discrete(densities).squeeze()
if self._ident:
zhat.append(np.asarray(densities.argmax(1), dtype='i'))
return labels, zhat
def _update_labels(self, mu, Sigma, weights, ident=False):
if self.gpu:
# GPU business happens?
return get_labelsGPU(self.gpu_workers,
weights,
mu,
Sigma,
relabel=ident)
else:
densities = mvn_weighted_logged(self.data, mu, Sigma, weights)
if ident:
Z = np.asarray(densities.argmax(1), dtype='i')
else:
Z = None
return sample_discrete(densities).squeeze(), Z
