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 expected_labels(self):
if self.gpu:
self.ll, self.ct, self.xbar, self.densities = get_expected_labels_GPU(
self.gpu_workers, self.weights, self.mu, self.Sigma)
else:
densities = mvn_weighted_logged(self.data, self.mu, self.Sigma, self.weights)
densities = np.exp(densities)
norm = densities.sum(1)
self.ll = np.sum(np.log(norm))
densities = (densities.T / norm).T
self.ct = densities.sum(0)
self.xbar = np.dot(densities.T, self.data)
self.densities = densities
def expected_labels(self):
if self.gpu:
self.ll, self.ct, self.xbar, self.densities = get_expected_labels_GPU(
self.gpu_workers, self.weights, self.mu, self.Sigma)
else:
densities = mvn_weighted_logged(self.data, self.mu, self.Sigma,
self.weights)
densities = np.exp(densities)
norm = densities.sum(1)
self.ll = np.sum(np.log(norm))
densities = (densities.T / norm).T
self.ct = densities.sum(0)
self.xbar = np.dot(densities.T, self.data)
self.densities = densities
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
