sklearn.hmm.GMMHMM¶
- class sklearn.hmm.GMMHMM(n_components=1, n_mix=1, startprob=None, transmat=None, startprob_prior=None, transmat_prior=None, algorithm='viterbi', gmms=None, covariance_type='diag', covars_prior=0.01, random_state=None, n_iter=10, thresh=0.01, params='abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ', init_params='abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ')¶
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(algorithm='viterbi', covariance_type='diag',...
Attributes
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. 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. 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. random_state RandomState or an int seed (0 by default) A random number generator instance n_iter int, optional Number of iterations to perform. thresh float, optional Convergence threshold. Methods
decode(obs[, algorithm]) Find most likely state sequence corresponding to obs. eval(*args, **kwargs) DEPRECATED: HMM.eval was renamed to HMM.score_samples in 0.14 and will be removed in 0.16. fit(obs) Estimate model parameters. get_params([deep]) Get parameters for this estimator. predict(obs[, algorithm]) Find most likely state sequence corresponding to obs. predict_proba(obs) Compute the posterior probability for each state in the model sample([n, random_state]) Generate random samples from the model. score(obs) Compute the log probability under the model. score_samples(obs) Compute the log probability under the model and compute posteriors. set_params(**params) Set the parameters of this estimator. - __init__(n_components=1, n_mix=1, startprob=None, transmat=None, startprob_prior=None, transmat_prior=None, algorithm='viterbi', gmms=None, covariance_type='diag', covars_prior=0.01, random_state=None, n_iter=10, thresh=0.01, params='abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ', init_params='abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ')¶
Create a hidden Markov model with GMM emissions.
Parameters : n_components : int
Number of states.
- algorithm¶
decoder algorithm
- covariance_type¶
Covariance type of the model.
Must be one of ‘spherical’, ‘tied’, ‘diag’, ‘full’.
- decode(obs, algorithm='viterbi')¶
Find most likely state sequence corresponding to obs. Uses the selected algorithm for decoding.
Parameters : obs : array_like, shape (n, n_features)
List of n_features-dimensional data points. Each row corresponds to a single data point.
algorithm : string, one of the decoder_algorithms
decoder algorithm to be used
Returns : logprob : float
Log probability of the maximum likelihood path through the HMM
state_sequence : array_like, shape (n,)
Index of the most likely states for each observation
See also
- score_samples
- Compute the log probability under the model and posteriors.
- score
- Compute the log probability under the model.
- eval(*args, **kwargs)¶
DEPRECATED: HMM.eval was renamed to HMM.score_samples in 0.14 and will be removed in 0.16.
- fit(obs)¶
Estimate model parameters.
An initialization step is performed before entering the EM algorithm. If you want to avoid this step, pass proper init_params keyword argument to estimator’s constructor.
Parameters : obs : list
List of array-like observation sequences (shape (n_i, n_features)).
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 this estimator.
Parameters : deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns : params : mapping of string to any
Parameter names mapped to their values.
- predict(obs, algorithm='viterbi')¶
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.
Returns : state_sequence : array_like, shape (n,)
Index of the most likely states for each observation
- predict_proba(obs)¶
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.
Returns : T : array-like, shape (n, n_components)
Returns the probability of the sample for each state in the model.
- sample(n=1, random_state=None)¶
Generate random samples from the model.
Parameters : n : int
Number of samples to generate.
random_state: RandomState or an int seed (0 by default) :
A random number generator instance. If None is given, the object’s random_state is used
Returns : (obs, hidden_states) :
obs : array_like, length n List of samples
hidden_states : array_like, length n List of hidden states
- score(obs)¶
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.
Returns : logprob : float
Log likelihood of the obs.
See also
- score_samples
- Compute the log probability under the model and posteriors
- decode
- Find most likely state sequence corresponding to a obs
- score_samples(obs)¶
Compute the log probability under the model and compute posteriors.
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.
Returns : logprob : float
Log likelihood of the sequence obs.
posteriors : array_like, shape (n, n_components)
Posterior probabilities of each state for each observation
- set_params(**params)¶
Set the parameters of this 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 :
- startprob_¶
Mixing startprob for each state.
- transmat_¶
Matrix of transition probabilities.