def discrete_hmm(P, pout, pi=None, stationary=True, reversible=True):
""" Initializes a discrete HMM
Parameters
----------
P : ndarray(nstates,nstates)
Hidden transition matrix
pout : ndarray(nstates,nsymbols)
Output matrix from hidden states to observable symbols
pi : ndarray(nstates, )
Fixed initial (if stationary=False) or fixed stationary distribution (if stationary=True).
stationary : bool, optional, default=True
If True: initial distribution is equal to stationary distribution of transition matrix
reversible : bool, optional, default=True
If True: transition matrix will fulfill detailed balance constraints.
"""
from bhmm.hmm.discrete_hmm import DiscreteHMM
from bhmm.output_models.discrete import DiscreteOutputModel
# initialize output model
output_model = DiscreteOutputModel(pout)
# initialize general HMM
from bhmm.hmm.generic_hmm import HMM as _HMM
dhmm = _HMM(P,
output_model,
Pi=pi,
stationary=stationary,
reversible=reversible)
# turn it into a Gaussian HMM
dhmm = DiscreteHMM(dhmm)
return dhmm
def gaussian_hmm(pi, P, means, sigmas):
""" Initializes a 1D-Gaussian HMM
Parameters
----------
pi : ndarray(nstates, )
Initial distribution.
P : ndarray(nstates,nstates)
Hidden transition matrix
means : ndarray(nstates, )
Means of Gaussian output distributions
sigmas : ndarray(nstates, )
Standard deviations of Gaussian output distributions
stationary : bool, optional, default=True
If True: initial distribution is equal to stationary distribution of transition matrix
reversible : bool, optional, default=True
If True: transition matrix will fulfill detailed balance constraints.
"""
from bhmm.hmm.gaussian_hmm import GaussianHMM
from bhmm.output_models.gaussian import GaussianOutputModel
# count states
nstates = _np.array(P).shape[0]
# initialize output model
output_model = GaussianOutputModel(nstates, means, sigmas)
# initialize general HMM
from bhmm.hmm.generic_hmm import HMM as _HMM
ghmm = _HMM(pi, P, output_model)
# turn it into a Gaussian HMM
ghmm = GaussianHMM(ghmm)
return ghmm
def gaussian_hmm(P, means, sigmas, pi=None, stationary=True, reversible=True):
""" Initializes a 1D-Gaussian HMM
Parameters
----------
P : ndarray(nstates,nstates)
Hidden transition matrix
means : ndarray(nstates, )
Means of Gaussian output distributions
sigmas : ndarray(nstates, )
Standard deviations of Gaussian output distributions
pi : ndarray(nstates, )
Fixed initial (if stationary=False) or fixed stationary distribution (if stationary=True).
stationary : bool, optional, default=True
If True: initial distribution is equal to stationary distribution of transition matrix
reversible : bool, optional, default=True
If True: transition matrix will fulfill detailed balance constraints.
"""
from bhmm.hmm.gaussian_hmm import GaussianHMM
from bhmm.output_models.gaussian import GaussianOutputModel
# count states
nstates = _np.array(P).shape[0]
# initialize output model
output_model = GaussianOutputModel(nstates, means, sigmas)
# initialize general HMM
from bhmm.hmm.generic_hmm import HMM as _HMM
ghmm = _HMM(P,
output_model,
Pi=pi,
stationary=stationary,
reversible=reversible)
# turn it into a Gaussian HMM
ghmm = GaussianHMM(ghmm)
return ghmm
def discrete_hmm(pi, P, pout):
""" Initializes a discrete HMM
Parameters
----------
pi : ndarray(nstates, )
Initial distribution.
P : ndarray(nstates,nstates)
Hidden transition matrix
pout : ndarray(nstates,nsymbols)
Output matrix from hidden states to observable symbols
pi : ndarray(nstates, )
Fixed initial (if stationary=False) or fixed stationary distribution (if stationary=True).
stationary : bool, optional, default=True
If True: initial distribution is equal to stationary distribution of transition matrix
reversible : bool, optional, default=True
If True: transition matrix will fulfill detailed balance constraints.
"""
from bhmm.hmm.discrete_hmm import DiscreteHMM
from bhmm.output_models.discrete import DiscreteOutputModel
# initialize output model
output_model = DiscreteOutputModel(pout)
# initialize general HMM
from bhmm.hmm.generic_hmm import HMM as _HMM
dhmm = _HMM(pi, P, output_model)
# turn it into a Gaussian HMM
dhmm = DiscreteHMM(dhmm)
return dhmm
