def _wmvnpdf(x, pi, mu, va, d=1, logged=False, use_gpu=True, **kwargs):
if len(x.shape) == 1:
x = x.reshape((1, x.shape))
if len(mu.shape) == 1:
mu = mu.reshape((1, mu.shape))
if len(va.shape) == 2:
va = va.reshape(1, va.shape[0], va.shape[1])
if len(va.shape) == 1:
va = va.reshape(va.shape[0], 1, 1)
if isinstance(pi, float) or isinstance(pi, int):
pi = array([pi])
elif isinstance(pi, ndarray):
if len(pi.shape) == 0:
pi = pi.reshape((1))
if has_gpu and use_gpu:
if 'device' in kwargs:
dev = kwargs['device']
else:
dev = 0
select_gpu(dev)
return mvnpdf_multi(x, mu, va, weights=pi, logged=logged, order='C').astype('float64')
else:
if logged:
return mvn_weighted_logged(x, mu, va, pi)
else:
return exp(mvn_weighted_logged(x, mu, va, pi))
def _mvnpdf(x, mu, va, n=1, logged=False, use_gpu=True, **kwargs):
if len(x.shape) == 1:
x = x.reshape((1, x.shape[0]))
if len(mu.shape) == 1:
mu = mu.reshape((1, mu.shape[0]))
if len(va.shape) == 2:
va = va.reshape(1, va.shape[0], va.shape[1])
if has_gpu and use_gpu:
if 'device' in kwargs:
dev = kwargs['device']
else:
dev = 0
select_gpu(dev)
return mvnpdf_multi(
x,
mu,
va,
weights=ones(
mu.shape[0]),
logged=logged,
order='C').astype('float64')
else:
if logged:
return mvn_weighted_logged(x, mu, va, ones(mu.shape[0]))
else:
return exp(mvn_weighted_logged(x, mu, va, ones(mu.shape[0])))
def _mvnpdf(x, mu, va, n=1, logged=False, use_gpu=True, **kwargs):
if len(x.shape) == 1:
x = x.reshape((1, x.shape[0]))
if len(mu.shape) == 1:
mu = mu.reshape((1, mu.shape[0]))
if len(va.shape) == 2:
va = va.reshape(1, va.shape[0], va.shape[1])
if has_gpu and use_gpu:
if 'device' in kwargs:
dev = kwargs['device']
else:
dev = 0
select_gpu(dev)
return mvnpdf_multi(x,
mu,
va,
weights=ones(mu.shape[0]),
logged=logged,
order='C').astype('float64')
else:
if logged:
return mvn_weighted_logged(x, mu, va, ones(mu.shape[0]))
else:
return exp(mvn_weighted_logged(x, mu, va, ones(mu.shape[0])))
def update_labels(data, mus, sigmas, pis):
densities = mvn_weighted_logged(data, mus, sigmas, pis)
Z = np.asarray(densities.argmax(1), dtype='i')
return sample_discrete(densities).squeeze(), Z