sklearn.hmm.GaussianHMM¶

class sklearn.hmm.GaussianHMM(n_components=1, covariance_type='diag', startprob=None, transmat=None, startprob_prior=None, transmat_prior=None, algorithm='viterbi', means_prior=None, means_weight=0, covars_prior=0.01, covars_weight=1, random_state=None, n_iter=10, thresh=0.01, params='abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ', init_params='abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ')¶

Hidden Markov Model with Gaussian 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 :

n_components : int

Number of states.

``_covariance_type`` : string

String describing the type of covariance parameters to use. Must be one of ‘spherical’, ‘tied’, ‘diag’, ‘full’. Defaults to ‘diag’.

See also

GMM: Gaussian mixture model

Examples

>>> from sklearn.hmm import GaussianHMM
>>> GaussianHMM(n_components=2)
...                             
GaussianHMM(algorithm='viterbi',...

Attributes

`_covariance_type`	string	String describing the type of covariance parameters used by the model. Must be one of ‘spherical’, ‘tied’, ‘diag’, ‘full’.
n_features	int	Dimensionality of the Gaussian emissions.
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.
means	array, shape (n_components, n_features)	Mean parameters for each state.
covars	array	Covariance parameters for each state. The shape depends on `_covariance_type`: (`n_components`,) if 'spherical', (`n_features`, `n_features`) if 'tied', (`n_components`, `n_features`) if 'diag', (`n_components`, `n_features`, `n_features`) if 'full'
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.
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. 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. Defaults to all parameters.

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, covariance_type='diag', startprob=None, transmat=None, startprob_prior=None, transmat_prior=None, algorithm='viterbi', means_prior=None, means_weight=0, covars_prior=0.01, covars_weight=1, random_state=None, n_iter=10, thresh=0.01, params='abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ', init_params='abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ')¶

algorithm¶: decoder algorithm

covariance_type¶

Covariance type of the model.

Must be one of ‘spherical’, ‘tied’, ‘diag’, ‘full’.

covars_¶: Return covars as a full matrix.

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)

Sequence of n_features-dimensional data points. Each row corresponds to a single point in the sequence.

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, each of which has shape (n_i, n_features), where n_i is the length of the i_th observation.

Notes

In general, logprob should be non-decreasing unless aggressive pruning is used. Decreasing logprob is generally a sign of overfitting (e.g. the covariance parameter on one or more components becomminging too small). You can fix this by getting more training data, or increasing 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.

means_¶: Mean parameters for each state.

predict(obs, algorithm='viterbi')¶

Find most likely state sequence corresponding to obs.

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 :

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)

Sequence of n_features-dimensional data points. Each row corresponds to a single point in the sequence.

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

See also

score: Compute the log probability under the model
decode: Find most likely state sequence corresponding to a obs

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.