def init_gaussian_hmm(observations, nstates, lag=1, reversible=True):
""" Use a heuristic scheme to generate an initial model.
Parameters
----------
observations : list of ndarray((T_i))
list of arrays of length T_i with observation data
nstates : int
The number of states.
Examples
--------
Generate initial model for a gaussian output model.
>>> import bhmm
>>> [model, observations, states] = bhmm.testsystems.generate_synthetic_observations(output='gaussian')
>>> initial_model = init_gaussian_hmm(observations, model.nstates)
"""
from bhmm.init import gaussian
if lag > 1:
observations = lag_observations(observations, lag)
hmm0 = gaussian.init_model_gaussian1d(observations,
nstates,
reversible=reversible)
hmm0._lag = lag
return hmm0
def init_gaussian_hmm(observations, nstates, lag=1, reversible=True):
""" Use a heuristic scheme to generate an initial model.
Parameters
----------
observations : list of ndarray((T_i))
list of arrays of length T_i with observation data
nstates : int
The number of states.
Examples
--------
Generate initial model for a gaussian output model.
>>> import bhmm
>>> [model, observations, states] = bhmm.testsystems.generate_synthetic_observations(output='gaussian')
>>> initial_model = init_gaussian_hmm(observations, model.nstates)
"""
from bhmm.init import gaussian
if lag > 1:
observations = lag_observations(observations, lag)
hmm0 = gaussian.init_model_gaussian1d(observations, nstates, reversible=reversible)
hmm0._lag = lag
return hmm0
def test_init(self):
initial_model = init_model_gaussian1d(self._observations, self._nstates)
assert initial_model.nstates == self._nstates
assert msmana.is_transition_matrix(initial_model.transition_matrix)