def __init__(self, hmm):
# superclass constructors
if not isinstance(hmm.output_model, DiscreteOutputModel):
raise TypeError('Given hmm is not a discrete HMM, but has an output model of type: ' +
str(type(hmm.output_model)))
DiscreteOutputModel.__init__(self, hmm.output_model.output_probabilities)
HMM.__init__(self, hmm.initial_distribution, hmm.transition_matrix, self, lag=hmm.lag)
def __init__(self, hmm):
# superclass constructors
if not isinstance(hmm.output_model, GaussianOutputModel):
raise TypeError('Given hmm is not a Gaussian HMM, but has an output model of type: ' +
str(type(hmm.output_model)))
GaussianOutputModel.__init__(self, hmm.nstates, means=hmm.output_model.means, sigmas=hmm.output_model.sigmas)
HMM.__init__(self, hmm.initial_distribution, hmm.transition_matrix, self, lag=hmm.lag)
def __init__(self, estimated_hmm, sampled_hmms, conf=0.95):
# call superclass constructer with estimated_hmm
HMM.__init__(self, estimated_hmm.initial_distribution, estimated_hmm.transition_matrix,
estimated_hmm.output_model, lag=estimated_hmm.lag)
# save sampled HMMs to calculate statistical moments.
self._sampled_hmms = sampled_hmms
self._nsamples = len(sampled_hmms)
# save confindence interval
self._conf = conf
def __init__(self, estimated_hmm, sampled_hmms, conf=0.95):
# call superclass constructer with estimated_hmm
HMM.__init__(self,
estimated_hmm.initial_distribution,
estimated_hmm.transition_matrix,
estimated_hmm.output_model,
lag=estimated_hmm.lag)
# save sampled HMMs to calculate statistical moments.
self._sampled_hmms = sampled_hmms
self._nsamples = len(sampled_hmms)
# save confindence interval
self._conf = conf
def __init__(self, hmm):
# superclass constructors
if not isinstance(hmm.output_model, DiscreteOutputModel):
raise TypeError(
'Given hmm is not a discrete HMM, but has an output model of type: '
+ str(type(hmm.output_model)))
DiscreteOutputModel.__init__(self,
hmm.output_model.output_probabilities)
HMM.__init__(self,
hmm.initial_distribution,
hmm.transition_matrix,
self,
lag=hmm.lag)
def __init__(self, hmm):
# superclass constructors
if not isinstance(hmm.output_model, GaussianOutputModel):
raise TypeError(
'Given hmm is not a Gaussian HMM, but has an output model of type: '
+ str(type(hmm.output_model)))
GaussianOutputModel.__init__(self,
hmm.nstates,
means=hmm.output_model.means,
sigmas=hmm.output_model.sigmas)
HMM.__init__(self,
hmm.initial_distribution,
hmm.transition_matrix,
self,
lag=hmm.lag)
