def __init__(self,
num_states=None,
alpha=None,
alphav=None,
trans_matrix=None):
self.N = num_states
if trans_matrix is not None:
self._row_distns = [
Multinomial(alpha_0=alpha,
K=self.N,
alphav_0=alphav,
weights=row) for row in trans_matrix
]
elif None not in (alpha, self.N) or (alphav is not None
and alphav.ndim < 2):
self._row_distns = [
Multinomial(alpha_0=alpha, K=self.N, alphav_0=alphav)
for n in range(self.N)
] # sample from prior
# for the case where we pass a matrix for the alpha (used mainly for adding the stickiness)
elif None not in (alpha, self.N) or (alphav is not None
and alphav.ndim == 2):
self._row_distns = [
Multinomial(alpha_0=alpha, K=self.N, alphav_0=alphav[n, :])
for n in range(self.N)
] # sample from prior
def __init__(self,num_states=None,alpha=None,alphav=None,trans_matrix=None):
self.N = num_states
if trans_matrix is not None:
self._row_distns = [Multinomial(alpha_0=alpha,K=self.N,alphav_0=alphav,
weights=row) for row in trans_matrix]
elif None not in (alpha,self.N) or alphav is not None:
self._row_distns = [Multinomial(alpha_0=alpha,K=self.N,alphav_0=alphav)
for n in xrange(self.N)] # sample from prior
def trans_matrix(self, trans_matrix):
N = self.N = trans_matrix.shape[0]
if self.alphav.ndim < 2:
self._row_distns = \
[Multinomial(alpha_0=self.alpha,K=N,alphav_0=self.alphav,weights=row)
for row in trans_matrix]
elif self.alphav.ndim == 2:
self._row_distns = \
[Multinomial(alpha_0=self.alpha,K=N,alphav_0=self.alphav[n,:],weights=row)
for n, row in enumerate(trans_matrix)]
def __init__(self,gamma,alpha,num_states=None,beta=None,trans_matrix=None):
if num_states is None:
assert beta is not None or trans_matrix is not None
self.N = len(beta) if beta is not None else trans_matrix.shape[0]
else:
self.N = num_states
self.alpha = alpha
if type(gamma) in [int,float]:
self.beta_obj = Multinomial(alpha_0=gamma,K=self.N,weights=beta)
else:
self.beta_obj = Multinomial(alphav_0=gamma,K=self.N,weights=beta)
super(_WeakLimitHDPHMMTransitionsBase,self).__init__(
num_states=self.N,alpha=alpha,
alphav=alpha*self.beta,trans_matrix=trans_matrix)
def trans_matrix(self, trans_matrix):
N = self.N = trans_matrix.shape[0]
self._row_distns = [
Multinomial(alpha_0=self.alpha,
K=N,
alphav_0=self.alphav,
weights=row) for row in trans_matrix
]
