API Reference¶

This is the class and function reference of hmmlearn.

Please refer to the full user guide for further details, as the class and function raw specifications may not be enough to give full guidelines on their uses.

hmmlearn.base¶

ConvergenceMonitor¶

class hmmlearn.base.ConvergenceMonitor(tol, n_iter, verbose)[source]¶

Monitors and reports convergence to sys.stderr.

Parameters:

tol : double

Convergence threshold. EM has converged either if the maximum number of iterations is reached or the log probability improvement between the two consecutive iterations is less than threshold.

n_iter : int

Maximum number of iterations to perform.

verbose : bool

If True then per-iteration convergence reports are printed, otherwise the monitor is mute.

Attributes

history	(deque) The log probability of the data for the last two training iterations. If the values are not strictly increasing, the model did not converge.
iter	(int) Number of iterations performed while training the model.

converged¶: True if the EM algorithm converged and False otherwise.

report(logprob)[source]¶

Reports convergence to sys.stderr.

The output consists of three columns: iteration number, log probability of the data at the current iteration and convergence rate. At the first iteration convergence rate is unknown and is thus denoted by NaN.

Parameters:

logprob : float

The log probability of the data as computed by EM algorithm in the current iteration.

_BaseHMM¶

class hmmlearn.base._BaseHMM(n_components=1, startprob_prior=1.0, transmat_prior=1.0, algorithm='viterbi', random_state=None, n_iter=10, tol=0.01, verbose=False, params='abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ', init_params='abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ')[source]¶

Base class for Hidden Markov Models.

This class allows for easy evaluation of, sampling from, and maximum-likelihood estimation of the parameters of a HMM.

See the instance documentation for details specific to a particular object.

Parameters:

n_components : int

Number of states in the model.

startprob_prior : array, shape (n_components, )

Initial state occupation prior distribution.

transmat_prior : array, shape (n_components, n_components)

Matrix of prior transition probabilities between states.

algorithm : string

Decoder algorithm. Must be one of “viterbi” or “map”. Defaults to “viterbi”.

random_state: RandomState or an int seed

A random number generator instance.

n_iter : int, optional

Maximum number of iterations to perform.

tol : float, optional

Convergence threshold. EM will stop if the gain in log-likelihood is below this value.

verbose : bool, optional

When True per-iteration convergence reports are printed to sys.stderr. You can diagnose convergence via the monitor_ attribute.

params : string, optional

Controls which parameters are updated in the training process. Can contain any combination of ‘s’ for startprob, ‘t’ for transmat, and other characters for subclass-specific emission parameters. 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, and other characters for subclass-specific emission parameters. Defaults to all parameters.

Attributes

monitor_	(ConvergenceMonitor) Monitor object used to check the convergence of EM.
startprob_	(array, shape (n_components, )) Initial state occupation distribution.
transmat_	(array, shape (n_components, n_components)) Matrix of transition probabilities between states.

_accumulate_sufficient_statistics(stats, X, framelogprob, posteriors, fwdlattice, bwdlattice)[source]¶

Updates sufficient statistics from a given sample.

Parameters:

stats : dict

Sufficient statistics as returned by _initialize_sufficient_statistics.

X : array, shape (n_samples, n_features)

Sample sequence.

framelogprob : array, shape (n_samples, n_components)

Log-probabilities of each sample under each of the model states.

posteriors : array, shape (n_samples, n_components)

Posterior probabilities of each sample being generated by each of the model states.

fwdlattice, bwdlattice : array, shape (n_samples, n_components)

Log-forward and log-backward probabilities.

_check()[source]¶

Validates model parameters prior to fitting.

Raises:

ValueError

If any of the parameters are invalid, e.g. if startprob_ don’t sum to 1.

_compute_log_likelihood(X)[source]¶

Computes per-component log probability under the model.

Parameters:

X : array-like, shape (n_samples, n_features)

Feature matrix of individual samples.

Returns:

logprob : array, shape (n_samples, n_components)

Log probability of each sample in X for each of the model states.

_do_mstep(stats)[source]¶

Performs the M-step of EM algorithm.

Parameters:

stats : dict

Sufficient statistics updated from all available samples.

_generate_sample_from_state(state, random_state=None)[source]¶

Generates a random sample from a given component.

Parameters:

state : int

Index of the component to condition on.

random_state: RandomState or an int seed

A random number generator instance. If None, the object’s random_state is used.

Returns:

X : array, shape (n_features, )

A random sample from the emission distribution corresponding to a given component.

_init(X, lengths)[source]¶

Initializes model parameters prior to fitting.

Parameters:

X : array-like, shape (n_samples, n_features)

Feature matrix of individual samples.

lengths : array-like of integers, shape (n_sequences, )

Lengths of the individual sequences in X. The sum of these should be n_samples.

_initialize_sufficient_statistics()[source]¶

Initializes sufficient statistics required for M-step.

The method is pure, meaning that it doesn’t change the state of the instance. For extensibility computed statistics are stored in a dictionary.

Returns:

nobs : int

Number of samples in the data.

start : array, shape (n_components, )

An array where the i-th element corresponds to the posterior probability of the first sample being generated by the i-th state.

trans : array, shape (n_components, n_components)

An array where the (i, j)-th element corresponds to the posterior probability of transitioning between the i-th to j-th states.

decode(X, lengths=None, algorithm=None)[source]¶

Find most likely state sequence corresponding to X.

Parameters:

X : array-like, shape (n_samples, n_features)

Feature matrix of individual samples.

lengths : array-like of integers, shape (n_sequences, ), optional

Lengths of the individual sequences in X. The sum of these should be n_samples.

algorithm : string

Decoder algorithm. Must be one of “viterbi” or “map”. If not given, decoder is used.

Returns:

logprob : float

Log probability of the produced state sequence.

state_sequence : array, shape (n_samples, )

Labels for each sample from X obtained via a given decoder algorithm.

See also

score_samples: Compute the log probability under the model and posteriors.
score: Compute the log probability under the model.

fit(X, lengths=None)[source]¶

Estimate model parameters.

An initialization step is performed before entering the EM algorithm. If you want to avoid this step for a subset of the parameters, pass proper init_params keyword argument to estimator’s constructor.

Parameters:

X : array-like, shape (n_samples, n_features)

Feature matrix of individual samples.

lengths : array-like of integers, shape (n_sequences, )

Lengths of the individual sequences in X. The sum of these should be n_samples.

Returns:

self : object

Returns self.

predict(X, lengths=None)[source]¶

Find most likely state sequence corresponding to X.

Parameters:

X : array-like, shape (n_samples, n_features)

Feature matrix of individual samples.

lengths : array-like of integers, shape (n_sequences, ), optional

Lengths of the individual sequences in X. The sum of these should be n_samples.

Returns:

state_sequence : array, shape (n_samples, )

Labels for each sample from X.

predict_proba(X, lengths=None)[source]¶

Compute the posterior probability for each state in the model.

X : array-like, shape (n_samples, n_features): Feature matrix of individual samples.
lengths : array-like of integers, shape (n_sequences, ), optional: Lengths of the individual sequences in X. The sum of these should be n_samples.

Returns:

posteriors : array, shape (n_samples, n_components)

State-membership probabilities for each sample from X.

sample(n_samples=1, random_state=None)[source]¶

Generate random samples from the model.

Parameters:

n_samples : int

Number of samples to generate.

random_state : RandomState or an int seed

A random number generator instance. If None, the object’s random_state is used.

Returns:

X : array, shape (n_samples, n_features)

Feature matrix.

state_sequence : array, shape (n_samples, )

State sequence produced by the model.

score(X, lengths=None)[source]¶

Compute the log probability under the model.

Parameters:

X : array-like, shape (n_samples, n_features)

Feature matrix of individual samples.

lengths : array-like of integers, shape (n_sequences, ), optional

Lengths of the individual sequences in X. The sum of these should be n_samples.

Returns:

logprob : float

Log likelihood of X.

See also

score_samples: Compute the log probability under the model and posteriors.
decode: Find most likely state sequence corresponding to X.

score_samples(X, lengths=None)[source]¶

Compute the log probability under the model and compute posteriors.

Parameters:

X : array-like, shape (n_samples, n_features)

Feature matrix of individual samples.

lengths : array-like of integers, shape (n_sequences, ), optional

Lengths of the individual sequences in X. The sum of these should be n_samples.

Returns:

logprob : float

Log likelihood of X.

posteriors : array, shape (n_samples, n_components)

State-membership probabilities for each sample in X.

See also

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

hmmlearn.hmm¶

GaussianHMM¶

class hmmlearn.hmm.GaussianHMM(n_components=1, covariance_type='diag', min_covar=0.001, startprob_prior=1.0, transmat_prior=1.0, means_prior=0, means_weight=0, covars_prior=0.01, covars_weight=1, algorithm='viterbi', random_state=None, n_iter=10, tol=0.01, verbose=False, params='stmc', init_params='stmc')[source]¶

Hidden Markov Model with Gaussian emissions.

Parameters:

n_components : int

Number of states.

covariance_type : string

String describing the type of covariance parameters to use. Must be one of

“spherical” — each state uses a single variance value that applies to all features;

“diag” — each state uses a diagonal covariance matrix;

“full” — each state uses a full (i.e. unrestricted) covariance matrix;

“tied” — all states use the same full covariance matrix.

Defaults to “diag”.

min_covar : float

Floor on the diagonal of the covariance matrix to prevent overfitting. Defaults to 1e-3.

startprob_prior : array, shape (n_components, )

Initial state occupation prior distribution.

transmat_prior : array, shape (n_components, n_components)

Matrix of prior transition probabilities between states.

algorithm : string

Decoder algorithm. Must be one of “viterbi” or “map”. Defaults to “viterbi”.

random_state: RandomState or an int seed

A random number generator instance.

n_iter : int, optional

Maximum number of iterations to perform.

tol : float, optional

Convergence threshold. EM will stop if the gain in log-likelihood is below this value.

verbose : bool, optional

When True per-iteration convergence reports are printed to sys.stderr. You can diagnose convergence via the monitor_ attribute.

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.

Examples

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

Attributes

n_features	(int) Dimensionality of the Gaussian emissions.
monitor_	(ConvergenceMonitor) Monitor object used to check the convergence of EM.
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’

decode(X, lengths=None, algorithm=None)[source]¶

Find most likely state sequence corresponding to X.

Parameters:

X : array-like, shape (n_samples, n_features)

Feature matrix of individual samples.

lengths : array-like of integers, shape (n_sequences, ), optional

Lengths of the individual sequences in X. The sum of these should be n_samples.

algorithm : string

Decoder algorithm. Must be one of “viterbi” or “map”. If not given, decoder is used.

Returns:

logprob : float

Log probability of the produced state sequence.

state_sequence : array, shape (n_samples, )

Labels for each sample from X obtained via a given decoder algorithm.

See also

score_samples: Compute the log probability under the model and posteriors.
score: Compute the log probability under the model.

fit(X, lengths=None)[source]¶

Estimate model parameters.

An initialization step is performed before entering the EM algorithm. If you want to avoid this step for a subset of the parameters, pass proper init_params keyword argument to estimator’s constructor.

Parameters:

X : array-like, shape (n_samples, n_features)

Feature matrix of individual samples.

lengths : array-like of integers, shape (n_sequences, )

Lengths of the individual sequences in X. The sum of these should be n_samples.

Returns:

self : object

Returns self.

predict(X, lengths=None)[source]¶

Find most likely state sequence corresponding to X.

Parameters:

X : array-like, shape (n_samples, n_features)

Feature matrix of individual samples.

lengths : array-like of integers, shape (n_sequences, ), optional

Lengths of the individual sequences in X. The sum of these should be n_samples.

Returns:

state_sequence : array, shape (n_samples, )

Labels for each sample from X.

predict_proba(X, lengths=None)[source]¶

Compute the posterior probability for each state in the model.

X : array-like, shape (n_samples, n_features): Feature matrix of individual samples.
lengths : array-like of integers, shape (n_sequences, ), optional: Lengths of the individual sequences in X. The sum of these should be n_samples.

Returns:

posteriors : array, shape (n_samples, n_components)

State-membership probabilities for each sample from X.

sample(n_samples=1, random_state=None)[source]¶

Generate random samples from the model.

Parameters:

n_samples : int

Number of samples to generate.

random_state : RandomState or an int seed

A random number generator instance. If None, the object’s random_state is used.

Returns:

X : array, shape (n_samples, n_features)

Feature matrix.

state_sequence : array, shape (n_samples, )

State sequence produced by the model.

score(X, lengths=None)[source]¶

Compute the log probability under the model.

Parameters:

X : array-like, shape (n_samples, n_features)

Feature matrix of individual samples.

lengths : array-like of integers, shape (n_sequences, ), optional

Lengths of the individual sequences in X. The sum of these should be n_samples.

Returns:

logprob : float

Log likelihood of X.

See also

score_samples: Compute the log probability under the model and posteriors.
decode: Find most likely state sequence corresponding to X.

score_samples(X, lengths=None)[source]¶

Compute the log probability under the model and compute posteriors.

Parameters:

X : array-like, shape (n_samples, n_features)

Feature matrix of individual samples.

lengths : array-like of integers, shape (n_sequences, ), optional

Lengths of the individual sequences in X. The sum of these should be n_samples.

Returns:

logprob : float

Log likelihood of X.

posteriors : array, shape (n_samples, n_components)

State-membership probabilities for each sample in X.

See also

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

GMMHMM¶

class hmmlearn.hmm.GMMHMM(n_components=1, n_mix=1, startprob_prior=1.0, transmat_prior=1.0, covariance_type='diag', covars_prior=0.01, algorithm='viterbi', random_state=None, n_iter=10, tol=0.01, verbose=False, params='stmcw', init_params='stmcw')[source]¶

Hidden Markov Model with Gaussian mixture emissions.

Parameters:

n_components : int

Number of states in the model.

n_mix : int

Number of states in the GMM.

covariance_type : string

String describing the type of covariance parameters to use. Must be one of

“spherical” — each state uses a single variance value that applies to all features;

“diag” — each state uses a diagonal covariance matrix;

“full” — each state uses a full (i.e. unrestricted) covariance matrix;

“tied” — all states use the same full covariance matrix.

Defaults to “diag”.

startprob_prior : array, shape (n_components, )

Initial state occupation prior distribution.

transmat_prior : array, shape (n_components, n_components)

Matrix of prior transition probabilities between states.

algorithm : string

Decoder algorithm. Must be one of “viterbi” or “map”. Defaults to “viterbi”.

random_state: RandomState or an int seed

A random number generator instance.

n_iter : int, optional

Maximum number of iterations to perform.

tol : float, optional

Convergence threshold. EM will stop if the gain in log-likelihood is below this value.

verbose : bool, optional

When True per-iteration convergence reports are printed to sys.stderr. You can diagnose convergence via the monitor_ attribute.

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, ‘c’ for covars, and ‘w’ for GMM mixing weights. 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, and ‘w’ for GMM mixing weights. Defaults to all parameters.

Examples

>>> from hmmlearn.hmm import GMMHMM
>>> GMMHMM(n_components=2, n_mix=10, covariance_type='diag')
... 
GMMHMM(algorithm='viterbi', covariance_type='diag',...

Attributes

monitor_	(ConvergenceMonitor) Monitor object used to check the convergence of EM.
startprob_	(array, shape (n_components, )) Initial state occupation distribution.
transmat_	(array, shape (n_components, n_components)) Matrix of transition probabilities between states.
gmms_	(list of GMM objects, length n_components) GMM emission distributions for each state.

decode(X, lengths=None, algorithm=None)[source]¶

Find most likely state sequence corresponding to X.

Parameters:

X : array-like, shape (n_samples, n_features)

Feature matrix of individual samples.

lengths : array-like of integers, shape (n_sequences, ), optional

Lengths of the individual sequences in X. The sum of these should be n_samples.

algorithm : string

Decoder algorithm. Must be one of “viterbi” or “map”. If not given, decoder is used.

Returns:

logprob : float

Log probability of the produced state sequence.

state_sequence : array, shape (n_samples, )

Labels for each sample from X obtained via a given decoder algorithm.

See also

score_samples: Compute the log probability under the model and posteriors.
score: Compute the log probability under the model.

fit(X, lengths=None)[source]¶

Estimate model parameters.

An initialization step is performed before entering the EM algorithm. If you want to avoid this step for a subset of the parameters, pass proper init_params keyword argument to estimator’s constructor.

Parameters:

X : array-like, shape (n_samples, n_features)

Feature matrix of individual samples.

lengths : array-like of integers, shape (n_sequences, )

Lengths of the individual sequences in X. The sum of these should be n_samples.

Returns:

self : object

Returns self.

predict(X, lengths=None)[source]¶

Find most likely state sequence corresponding to X.

Parameters:

X : array-like, shape (n_samples, n_features)

Feature matrix of individual samples.

lengths : array-like of integers, shape (n_sequences, ), optional

Lengths of the individual sequences in X. The sum of these should be n_samples.

Returns:

state_sequence : array, shape (n_samples, )

Labels for each sample from X.

predict_proba(X, lengths=None)[source]¶

Compute the posterior probability for each state in the model.

X : array-like, shape (n_samples, n_features): Feature matrix of individual samples.
lengths : array-like of integers, shape (n_sequences, ), optional: Lengths of the individual sequences in X. The sum of these should be n_samples.

Returns:

posteriors : array, shape (n_samples, n_components)

State-membership probabilities for each sample from X.

sample(n_samples=1, random_state=None)[source]¶

Generate random samples from the model.

Parameters:

n_samples : int

Number of samples to generate.

random_state : RandomState or an int seed

A random number generator instance. If None, the object’s random_state is used.

Returns:

X : array, shape (n_samples, n_features)

Feature matrix.

state_sequence : array, shape (n_samples, )

State sequence produced by the model.

score(X, lengths=None)[source]¶

Compute the log probability under the model.

Parameters:

X : array-like, shape (n_samples, n_features)

Feature matrix of individual samples.

lengths : array-like of integers, shape (n_sequences, ), optional

Lengths of the individual sequences in X. The sum of these should be n_samples.

Returns:

logprob : float

Log likelihood of X.

See also

score_samples: Compute the log probability under the model and posteriors.
decode: Find most likely state sequence corresponding to X.

score_samples(X, lengths=None)[source]¶

Compute the log probability under the model and compute posteriors.

Parameters:

X : array-like, shape (n_samples, n_features)

Feature matrix of individual samples.

lengths : array-like of integers, shape (n_sequences, ), optional

Lengths of the individual sequences in X. The sum of these should be n_samples.

Returns:

logprob : float

Log likelihood of X.

posteriors : array, shape (n_samples, n_components)

State-membership probabilities for each sample in X.

See also

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

MultinomialHMM¶

class hmmlearn.hmm.MultinomialHMM(n_components=1, startprob_prior=1.0, transmat_prior=1.0, algorithm='viterbi', random_state=None, n_iter=10, tol=0.01, verbose=False, params='ste', init_params='ste')[source]¶

Hidden Markov Model with multinomial (discrete) emissions

Parameters:

n_components : int

Number of states.

startprob_prior : array, shape (n_components, )

Initial state occupation prior distribution.

transmat_prior : array, shape (n_components, n_components)

Matrix of prior transition probabilities between states.

algorithm : string

Decoder algorithm. Must be one of “viterbi” or “map”. Defaults to “viterbi”.

random_state: RandomState or an int seed

A random number generator instance.

n_iter : int, optional

Maximum number of iterations to perform.

tol : float, optional

Convergence threshold. EM will stop if the gain in log-likelihood is below this value.

verbose : bool, optional

When True per-iteration convergence reports are printed to sys.stderr. You can diagnose convergence via the monitor_ attribute.

params : string, optional

Controls which parameters are updated in the training process. Can contain any combination of ‘s’ for startprob, ‘t’ for transmat, ‘e’ for emissionprob. 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, ‘e’ for emissionprob. Defaults to all parameters.

Examples

>>> from hmmlearn.hmm import MultinomialHMM
>>> MultinomialHMM(n_components=2)
...                             
MultinomialHMM(algorithm='viterbi',...

Attributes

n_features	(int) Number of possible symbols emitted by the model (in the samples).
monitor_	(ConvergenceMonitor) Monitor object used to check the convergence of EM.
transmat_	(array, shape (n_components, n_components)) Matrix of transition probabilities between states.
startprob_	(array, shape (n_components, )) Initial state occupation distribution.
emissionprob_	(array, shape (n_components, n_features)) Probability of emitting a given symbol when in each state.

decode(X, lengths=None, algorithm=None)[source]¶

Find most likely state sequence corresponding to X.

Parameters:

X : array-like, shape (n_samples, n_features)

Feature matrix of individual samples.

lengths : array-like of integers, shape (n_sequences, ), optional

Lengths of the individual sequences in X. The sum of these should be n_samples.

algorithm : string

Decoder algorithm. Must be one of “viterbi” or “map”. If not given, decoder is used.

Returns:

logprob : float

Log probability of the produced state sequence.

state_sequence : array, shape (n_samples, )

Labels for each sample from X obtained via a given decoder algorithm.

See also

score_samples: Compute the log probability under the model and posteriors.
score: Compute the log probability under the model.

fit(X, lengths=None)[source]¶

Estimate model parameters.

An initialization step is performed before entering the EM algorithm. If you want to avoid this step for a subset of the parameters, pass proper init_params keyword argument to estimator’s constructor.

Parameters:

X : array-like, shape (n_samples, n_features)

Feature matrix of individual samples.

lengths : array-like of integers, shape (n_sequences, )

Lengths of the individual sequences in X. The sum of these should be n_samples.

Returns:

self : object

Returns self.

predict(X, lengths=None)[source]¶

Find most likely state sequence corresponding to X.

Parameters:

X : array-like, shape (n_samples, n_features)

Feature matrix of individual samples.

lengths : array-like of integers, shape (n_sequences, ), optional

Lengths of the individual sequences in X. The sum of these should be n_samples.

Returns:

state_sequence : array, shape (n_samples, )

Labels for each sample from X.

predict_proba(X, lengths=None)[source]¶

Compute the posterior probability for each state in the model.

X : array-like, shape (n_samples, n_features): Feature matrix of individual samples.
lengths : array-like of integers, shape (n_sequences, ), optional: Lengths of the individual sequences in X. The sum of these should be n_samples.

Returns:

posteriors : array, shape (n_samples, n_components)

State-membership probabilities for each sample from X.

sample(n_samples=1, random_state=None)[source]¶

Generate random samples from the model.

Parameters:

n_samples : int

Number of samples to generate.

random_state : RandomState or an int seed

A random number generator instance. If None, the object’s random_state is used.

Returns:

X : array, shape (n_samples, n_features)

Feature matrix.

state_sequence : array, shape (n_samples, )

State sequence produced by the model.

score(X, lengths=None)[source]¶

Compute the log probability under the model.

Parameters:

X : array-like, shape (n_samples, n_features)

Feature matrix of individual samples.

lengths : array-like of integers, shape (n_sequences, ), optional

Lengths of the individual sequences in X. The sum of these should be n_samples.

Returns:

logprob : float

Log likelihood of X.

See also

score_samples: Compute the log probability under the model and posteriors.
decode: Find most likely state sequence corresponding to X.

score_samples(X, lengths=None)[source]¶

Compute the log probability under the model and compute posteriors.

Parameters:

X : array-like, shape (n_samples, n_features)

Feature matrix of individual samples.

lengths : array-like of integers, shape (n_sequences, ), optional

Lengths of the individual sequences in X. The sum of these should be n_samples.

Returns:

logprob : float

Log likelihood of X.

posteriors : array, shape (n_samples, n_components)

State-membership probabilities for each sample in X.

See also

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