8.11.3. sklearn.hmm.GMMHMM¶
- class sklearn.hmm.GMMHMM(n_components=1, n_mix=1, startprob=None, transmat=None, startprob_prior=None, transmat_prior=None, gmms=None, covariance_type=None)¶
Hidden Markov Model with Gaussin mixture emissions
See also
- GaussianHMM
- HMM with Gaussian emissions
Examples
>>> from sklearn.hmm import GMMHMM >>> GMMHMM(n_components=2, n_mix=10, covariance_type='diag') ... GMMHMM(covariance_type=None, gmms=[GMM(covariance_type=None, min_covar=0.001, n_components=10, random_state=None, thresh=0.01), GMM(covariance_type=None, min_covar=0.001, n_components=10, random_state=None, thresh=0.01)], n_components=2, n_mix=10, startprob=None, startprob_prior=1.0, transmat=None, transmat_prior=1.0)
Attributes
n_components int Number of states in the model. transmat array, shape (n_components, n_components) Matrix of transition probabilities between states. startprob array, shape (‘n_components`,) Initial state occupation distribution. gmms array of GMM objects, length n_components GMM emission distributions for each state. Methods
decode(obs[, maxrank, beamlogprob]) Find most likely state sequence corresponding to obs. eval(obs[, maxrank, beamlogprob]) Compute the log probability under the model and compute posteriors fit(obs[, n_iter, thresh, params, ...]) Estimate model parameters. get_params([deep]) Get parameters for the estimator predict(obs, **kwargs) Find most likely state sequence corresponding to obs. predict_proba(obs, **kwargs) Compute the posterior probability for each state in the model rvs([n, random_state]) Generate random samples from the model. score(obs[, maxrank, beamlogprob]) Compute the log probability under the model. set_params(**params) Set the parameters of the estimator. - __init__(n_components=1, n_mix=1, startprob=None, transmat=None, startprob_prior=None, transmat_prior=None, gmms=None, covariance_type=None)¶
Create a hidden Markov model with GMM emissions.
Parameters : n_components : int
Number of states.
- decode(obs, maxrank=None, beamlogprob=-inf)¶
Find most likely state sequence corresponding to obs.
Uses the Viterbi algorithm.
Parameters : obs : array_like, shape (n, n_features)
List of n_features-dimensional data points. Each row corresponds to a single data point.
maxrank : int
Maximum rank to evaluate for rank pruning. If not None, only consider the top maxrank states in the inner sum of the forward algorithm recursion. Defaults to None (no rank pruning). See The HTK Book for more details.
beamlogprob : float
Width of the beam-pruning beam in log-probability units. Defaults to -numpy.Inf (no beam pruning). See The HTK Book for more details.
Returns : viterbi_logprob : float
Log probability of the maximum likelihood path through the HMM
states : array_like, shape (n,)
Index of the most likely states for each observation
- eval(obs, maxrank=None, beamlogprob=-inf)¶
Compute the log probability under the model and compute posteriors
Implements rank and beam pruning in the forward-backward algorithm to speed up inference in large models.
Parameters : obs : array_like, shape (n, n_features)
Sequence of n_features-dimensional data points. Each row corresponds to a single point in the sequence.
maxrank : int
Maximum rank to evaluate for rank pruning. If not None, only consider the top maxrank states in the inner sum of the forward algorithm recursion. Defaults to None (no rank pruning). See The HTK Book for more details.
beamlogprob : float
Width of the beam-pruning beam in log-probability units. Defaults to -numpy.Inf (no beam pruning). See The HTK Book for more details.
Returns : logprob : array_like, shape (n,)
Log probabilities of the sequence obs
posteriors: array_like, shape (n, n_components) :
Posterior probabilities of each state for each observation
- fit(obs, n_iter=10, thresh=0.01, params='abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ', init_params='abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ', maxrank=None, beamlogprob=-inf, **kwargs)¶
Estimate model parameters.
An initialization step is performed before entering the EM algorithm. If you want to avoid this step, set the keyword argument init_params to the empty string ‘’. Likewise, if you would like just to do an initialization, call this method with n_iter=0.
Parameters : obs : list
List of array-like observation sequences (shape (n_i, n_features)).
n_iter : int, optional
Number of iterations to perform.
thresh : float, optional
Convergence threshold.
params : string, optional
Controls which parameters are updated in the training process. Can contain any combination of ‘s’ for startprob, ‘t’ for transmat, ‘m’ for means, and ‘c’ for covars, etc. Defaults to all parameters.
init_params : string, optional
Controls which parameters are initialized prior to training. Can contain any combination of ‘s’ for startprob, ‘t’ for transmat, ‘m’ for means, and ‘c’ for covars, etc. Defaults to all parameters.
maxrank : int, optional
Maximum rank to evaluate for rank pruning. If not None, only consider the top maxrank states in the inner sum of the forward algorithm recursion. Defaults to None (no rank pruning). See “The HTK Book” for more details.
beamlogprob : float, optional
Width of the beam-pruning beam in log-probability units. Defaults to -numpy.Inf (no beam pruning). See “The HTK Book” for more details.
Notes
In general, logprob should be non-decreasing unless aggressive pruning is used. Decreasing logprob is generally a sign of overfitting (e.g. a covariance parameter getting too small). You can fix this by getting more training data, or decreasing covars_prior.
- get_params(deep=True)¶
Get parameters for the estimator
Parameters : deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- predict(obs, **kwargs)¶
Find most likely state sequence corresponding to obs.
Parameters : obs : array_like, shape (n, n_features)
List of n_features-dimensional data points. Each row corresponds to a single data point.
maxrank : int
Maximum rank to evaluate for rank pruning. If not None, only consider the top maxrank states in the inner sum of the forward algorithm recursion. Defaults to None (no rank pruning). See The HTK Book for more details.
beamlogprob : float
Width of the beam-pruning beam in log-probability units. Defaults to -numpy.Inf (no beam pruning). See The HTK Book for more details.
Returns : states : array_like, shape (n,)
Index of the most likely states for each observation
- predict_proba(obs, **kwargs)¶
Compute the posterior probability for each state in the model
Parameters : obs : array_like, shape (n, n_features)
List of n_features-dimensional data points. Each row corresponds to a single data point.
See eval() for a list of accepted keyword arguments. :
Returns : T : array-like, shape (n, n_components)
Returns the probability of the sample for each state in the model.
- rvs(n=1, random_state=None)¶
Generate random samples from the model.
Parameters : n : int
Number of samples to generate.
Returns : obs : array_like, length n
List of samples
- score(obs, maxrank=None, beamlogprob=-inf)¶
Compute the log probability under the model.
Parameters : obs : array_like, shape (n, n_features)
Sequence of n_features-dimensional data points. Each row corresponds to a single data point.
maxrank : int
Maximum rank to evaluate for rank pruning. If not None, only consider the top maxrank states in the inner sum of the forward algorithm recursion. Defaults to None (no rank pruning). See The HTK Book for more details.
beamlogprob : float
Width of the beam-pruning beam in log-probability units. Defaults to -numpy.Inf (no beam pruning). See The HTK Book for more details.
Returns : logprob : array_like, shape (n,)
Log probabilities of each data point in obs
- set_params(**params)¶
Set the parameters of the estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns : self :