mlpy.stats.dbn.hmm.StudentHMM

class mlpy.stats.dbn.hmm.StudentHMM(ncomponents=1, startprob_prior=None, startprob=None, transmat_prior=None, transmat=None, emission_prior=None, emission=None, n_iter=None, thresh=None, verbose=None)[source]

Bases: mlpy.stats.dbn.hmm.HMM

Hidden Markov Model with Student emissions

Representation of a hidden Markov model probability distribution. This class allows for easy evaluation of, sampling from, and maximum-likelihood estimation of the parameters of a HMM.

Parameters:

ncomponents : int

Number of states in the model.

startprob_prior : array, shape (ncomponents,)

Initial state occupation prior distribution.

startprob : array, shape (ncomponents,)

Initial state occupation distribution.

transmat_prior : array, shape (ncomponents, ncomponents)

Matrix of prior transition probabilities between states.

transmat : array, shape (ncomponents, ncomponents)

Matrix of transition probabilities between states.

emission : conditional_student_frozen

The conditional probability distribution used for the emission.

emission_prior : normal_invwishart

Initial emission parameters, a normal-inverse Wishart distribution.

n_iter : int

Number of iterations to perform during training, optional.

thresh : float

Convergence threshold, optional.

verbose : bool

Controls if debug information is printed to the console, optional.

Examples

>>> from mlpy.stats.dbn.hmm import StudentHMM
>>> StudentHMM(ncomponents=2)
...

Note

Adapted from Matlab:

Copyright (2010) Kevin Murphy and Matt Dunham
License: MIT

Attributes

startprob_prior Vector of initial probabilities for each state.
transmat_prior Transition probability matrix.
ncomponents (int) The number of hidden states.
nfeatures (int) Dimensionality of the Gaussian emission.
startprob (array, shape (ncomponents,)) Initial state occupation distribution.
transmat (array, shape (ncomponents, ncomponents)) Matrix of transition probabilities between states.
emission_prior (normal_invwishart) Initial emission parameters, a normal-inverse Wishart distribution.
emission (cond_rv_frozen) The conditional probability distribution used for the emission.

Methods

decode(obs[, algorithm]) Find the most likely state sequence.
fit(obs[, n_init]) Estimate model parameters.
predict_proba(obs) Compute the posterior probability for each state in the model.
sample(length[, size]) Generates random samples from the model.
score(obs) Compute log probability of the evidence (likelihood) under the model.
score_samples(obs) Compute the log probability of the evidence.