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 periteration 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 andFalse
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 maximumlikelihood 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 loglikelihood is below this value.
verbose : bool, optional
When
True
periteration convergence reports are printed tosys.stderr
. You can diagnose convergence via themonitor_
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 subclassspecific 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 subclassspecific 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)
Logprobabilities 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)
Logforward and logbackward 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 percomponent log probability under the model.
Parameters: X : arraylike, 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 Mstep 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’srandom_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 : arraylike, shape (n_samples, n_features)
Feature matrix of individual samples.
lengths : arraylike of integers, shape (n_sequences, )
Lengths of the individual sequences in
X
. The sum of these should ben_samples
.

_initialize_sufficient_statistics
()[source]¶ Initializes sufficient statistics required for Mstep.
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 ith element corresponds to the posterior probability of the first sample being generated by the ith 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 ith to jth states.

decode
(X, lengths=None, algorithm=None)[source]¶ Find most likely state sequence corresponding to
X
.Parameters: X : arraylike, shape (n_samples, n_features)
Feature matrix of individual samples.
lengths : arraylike of integers, shape (n_sequences, ), optional
Lengths of the individual sequences in
X
. The sum of these should ben_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 decoderalgorithm
.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 : arraylike, shape (n_samples, n_features)
Feature matrix of individual samples.
lengths : arraylike of integers, shape (n_sequences, )
Lengths of the individual sequences in
X
. The sum of these should ben_samples
.Returns: self : object
Returns self.

predict
(X, lengths=None)[source]¶ Find most likely state sequence corresponding to
X
.Parameters: X : arraylike, shape (n_samples, n_features)
Feature matrix of individual samples.
lengths : arraylike of integers, shape (n_sequences, ), optional
Lengths of the individual sequences in
X
. The sum of these should ben_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 : arraylike, shape (n_samples, n_features)
 Feature matrix of individual samples.
 lengths : arraylike of integers, shape (n_sequences, ), optional
 Lengths of the individual sequences in
X
. The sum of these should ben_samples
.
Returns: posteriors : array, shape (n_samples, n_components)
Statemembership 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’srandom_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 : arraylike, shape (n_samples, n_features)
Feature matrix of individual samples.
lengths : arraylike of integers, shape (n_sequences, ), optional
Lengths of the individual sequences in
X
. The sum of these should ben_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 : arraylike, shape (n_samples, n_features)
Feature matrix of individual samples.
lengths : arraylike of integers, shape (n_sequences, ), optional
Lengths of the individual sequences in
X
. The sum of these should ben_samples
.Returns: logprob : float
Log likelihood of
X
.posteriors : array, shape (n_samples, n_components)
Statemembership probabilities for each sample in
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 1e3.
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 loglikelihood is below this value.
verbose : bool, optional
When
True
periteration convergence reports are printed tosys.stderr
. You can diagnose convergence via themonitor_
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 : arraylike, shape (n_samples, n_features)
Feature matrix of individual samples.
lengths : arraylike of integers, shape (n_sequences, ), optional
Lengths of the individual sequences in
X
. The sum of these should ben_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 decoderalgorithm
.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 : arraylike, shape (n_samples, n_features)
Feature matrix of individual samples.
lengths : arraylike of integers, shape (n_sequences, )
Lengths of the individual sequences in
X
. The sum of these should ben_samples
.Returns: self : object
Returns self.

predict
(X, lengths=None)[source]¶ Find most likely state sequence corresponding to
X
.Parameters: X : arraylike, shape (n_samples, n_features)
Feature matrix of individual samples.
lengths : arraylike of integers, shape (n_sequences, ), optional
Lengths of the individual sequences in
X
. The sum of these should ben_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 : arraylike, shape (n_samples, n_features)
 Feature matrix of individual samples.
 lengths : arraylike of integers, shape (n_sequences, ), optional
 Lengths of the individual sequences in
X
. The sum of these should ben_samples
.
Returns: posteriors : array, shape (n_samples, n_components)
Statemembership 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’srandom_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 : arraylike, shape (n_samples, n_features)
Feature matrix of individual samples.
lengths : arraylike of integers, shape (n_sequences, ), optional
Lengths of the individual sequences in
X
. The sum of these should ben_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 : arraylike, shape (n_samples, n_features)
Feature matrix of individual samples.
lengths : arraylike of integers, shape (n_sequences, ), optional
Lengths of the individual sequences in
X
. The sum of these should ben_samples
.Returns: logprob : float
Log likelihood of
X
.posteriors : array, shape (n_samples, n_components)
Statemembership probabilities for each sample in
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 loglikelihood is below this value.
verbose : bool, optional
When
True
periteration convergence reports are printed tosys.stderr
. You can diagnose convergence via themonitor_
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 : arraylike, shape (n_samples, n_features)
Feature matrix of individual samples.
lengths : arraylike of integers, shape (n_sequences, ), optional
Lengths of the individual sequences in
X
. The sum of these should ben_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 decoderalgorithm
.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 : arraylike, shape (n_samples, n_features)
Feature matrix of individual samples.
lengths : arraylike of integers, shape (n_sequences, )
Lengths of the individual sequences in
X
. The sum of these should ben_samples
.Returns: self : object
Returns self.

predict
(X, lengths=None)[source]¶ Find most likely state sequence corresponding to
X
.Parameters: X : arraylike, shape (n_samples, n_features)
Feature matrix of individual samples.
lengths : arraylike of integers, shape (n_sequences, ), optional
Lengths of the individual sequences in
X
. The sum of these should ben_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 : arraylike, shape (n_samples, n_features)
 Feature matrix of individual samples.
 lengths : arraylike of integers, shape (n_sequences, ), optional
 Lengths of the individual sequences in
X
. The sum of these should ben_samples
.
Returns: posteriors : array, shape (n_samples, n_components)
Statemembership 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’srandom_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 : arraylike, shape (n_samples, n_features)
Feature matrix of individual samples.
lengths : arraylike of integers, shape (n_sequences, ), optional
Lengths of the individual sequences in
X
. The sum of these should ben_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 : arraylike, shape (n_samples, n_features)
Feature matrix of individual samples.
lengths : arraylike of integers, shape (n_sequences, ), optional
Lengths of the individual sequences in
X
. The sum of these should ben_samples
.Returns: logprob : float
Log likelihood of
X
.posteriors : array, shape (n_samples, n_components)
Statemembership probabilities for each sample in
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 loglikelihood is below this value.
verbose : bool, optional
When
True
periteration convergence reports are printed tosys.stderr
. You can diagnose convergence via themonitor_
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 : arraylike, shape (n_samples, n_features)
Feature matrix of individual samples.
lengths : arraylike of integers, shape (n_sequences, ), optional
Lengths of the individual sequences in
X
. The sum of these should ben_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 decoderalgorithm
.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 : arraylike, shape (n_samples, n_features)
Feature matrix of individual samples.
lengths : arraylike of integers, shape (n_sequences, )
Lengths of the individual sequences in
X
. The sum of these should ben_samples
.Returns: self : object
Returns self.

predict
(X, lengths=None)[source]¶ Find most likely state sequence corresponding to
X
.Parameters: X : arraylike, shape (n_samples, n_features)
Feature matrix of individual samples.
lengths : arraylike of integers, shape (n_sequences, ), optional
Lengths of the individual sequences in
X
. The sum of these should ben_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 : arraylike, shape (n_samples, n_features)
 Feature matrix of individual samples.
 lengths : arraylike of integers, shape (n_sequences, ), optional
 Lengths of the individual sequences in
X
. The sum of these should ben_samples
.
Returns: posteriors : array, shape (n_samples, n_components)
Statemembership 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’srandom_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 : arraylike, shape (n_samples, n_features)
Feature matrix of individual samples.
lengths : arraylike of integers, shape (n_sequences, ), optional
Lengths of the individual sequences in
X
. The sum of these should ben_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 : arraylike, shape (n_samples, n_features)
Feature matrix of individual samples.
lengths : arraylike of integers, shape (n_sequences, ), optional
Lengths of the individual sequences in
X
. The sum of these should ben_samples
.Returns: logprob : float
Log likelihood of
X
.posteriors : array, shape (n_samples, n_components)
Statemembership probabilities for each sample in
X
.
