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)¶ 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.

history
¶ The log probability of the data for the last two training iterations. If the values are not strictly increasing, the model did not converge.
 Type
deque

iter
¶ Number of iterations performed while training the model.
 Type
int
Examples
Use custom convergence criteria by subclassing
ConvergenceMonitor
and redefining theconverged
method. The resulting subclass can be used by creating an instance and pointing a model’smonitor_
attribute to it prior to fitting.>>> from hmmlearn.base import ConvergenceMonitor >>> from hmmlearn import hmm >>> >>> class ThresholdMonitor(ConvergenceMonitor): ... @property ... def converged(self): ... return (self.iter == self.n_iter or ... self.history[1] >= self.tol) >>> >>> model = hmm.GaussianHMM(n_components=2, tol=5, verbose=True) >>> model.monitor_ = ThresholdMonitor(model.monitor_.tol, ... model.monitor_.n_iter, ... model.monitor_.verbose)

property
converged
¶ True
if the EM algorithm converged andFalse
otherwise.

report
(logprob)¶ 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')¶ Base class for Hidden Markov Models.
This class allows for easy evaluation of, sampling from, and maximum a posteriori 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, ), optional) – Parameters of the Dirichlet prior distribution for
startprob_
.transmat_prior (array, shape (n_components, n_components), optional) – Parameters of the Dirichlet prior distribution for each row of the transition probabilities
transmat_
.algorithm (string, optional) – Decoder algorithm. Must be one of “viterbi” or “map”. Defaults to “viterbi”.
random_state (RandomState or an int seed, optional) – 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.

monitor\_
Monitor object used to check the convergence of EM.
 Type

startprob\_
Initial state occupation distribution.
 Type
array, shape (n_components, )

transmat\_
Matrix of transition probabilities between states.
 Type
array, shape (n_components, n_components)

_accumulate_sufficient_statistics
(stats, X, framelogprob, posteriors, fwdlattice, bwdlattice)¶ 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 (array, shape (n_samples, n_components)) – Logforward and logbackward probabilities.
bwdlattice (array, shape (n_samples, n_components)) – Logforward and logbackward probabilities.

_check
()¶ 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)¶ Computes percomponent log probability under the model.
 Parameters
X (arraylike, shape (n_samples, n_features)) – Feature matrix of individual samples.
 Returns
logprob – Log probability of each sample in
X
for each of the model states. Return type
array, shape (n_samples, n_components)

_do_mstep
(stats)¶ Performs the Mstep of EM algorithm.
 Parameters
stats (dict) – Sufficient statistics updated from all available samples.

_generate_sample_from_state
(state, random_state=None)¶ 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 – A random sample from the emission distribution corresponding to a given component.
 Return type
array, shape (n_features, )

_get_n_fit_scalars_per_param
()¶ Return a mapping of fittable parameter name (as in
self.params
) to the number of corresponding scalar parameters that will actually be fitted.This is used to detect whether the user did not pass enough data points for a nondegenerate fit.

_init
(X, lengths)¶ 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
()¶ 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)¶ 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)¶ 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 – Returns self.
 Return type
object

get_stationary_distribution
()¶ Compute the stationary distribution of states.

predict
(X, lengths=None)¶ 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 – Labels for each sample from
X
. Return type
array, shape (n_samples, )

predict_proba
(X, lengths=None)¶ Compute the posterior probability for each state in the model.
 Xarraylike, shape (n_samples, n_features)
Feature matrix of individual samples.
 lengthsarraylike of integers, shape (n_sequences, ), optional
Lengths of the individual sequences in
X
. The sum of these should ben_samples
.
 Returns
posteriors – Statemembership probabilities for each sample from
X
. Return type
array, shape (n_samples, n_components)

sample
(n_samples=1, random_state=None)¶ 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)¶ 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 – Log likelihood of
X
. Return type
float
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)¶ 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')¶ Hidden Markov Model with Gaussian emissions.
 Parameters
n_components (int) – Number of states.
covariance_type (string, optional) –
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, optional) – Floor on the diagonal of the covariance matrix to prevent overfitting. Defaults to 1e3.
startprob_prior (array, shape (n_components, ), optional) – Parameters of the Dirichlet prior distribution for
startprob_
.transmat_prior (array, shape (n_components, n_components), optional) – Parameters of the Dirichlet prior distribution for each row of the transition probabilities
transmat_
.means_prior (array, shape (n_components, ), optional) – Mean and precision of the Normal prior distribtion for
means_
.means_weight (array, shape (n_components, ), optional) – Mean and precision of the Normal prior distribtion for
means_
.covars_prior (array, shape (n_components, ), optional) –
Parameters of the prior distribution for the covariance matrix
covars_
.If
covariance_type
is “spherical” or “diag” the prior is the inverse gamma distribution, otherwise — the inverse Wishart distribution.covars_weight (array, shape (n_components, ), optional) –
Parameters of the prior distribution for the covariance matrix
covars_
.If
covariance_type
is “spherical” or “diag” the prior is the inverse gamma distribution, otherwise — the inverse Wishart distribution.algorithm (string, optional) – Decoder algorithm. Must be one of “viterbi” or`”map”. Defaults to “viterbi”.
random_state (RandomState or an int seed, optional) – 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.

n_features
¶ Dimensionality of the Gaussian emissions.
 Type
int

monitor\_
Monitor object used to check the convergence of EM.
 Type

startprob\_
Initial state occupation distribution.
 Type
array, shape (n_components, )

transmat\_
Matrix of transition probabilities between states.
 Type
array, shape (n_components, n_components)

means\_
Mean parameters for each state.
 Type
array, shape (n_components, n_features)

covars\_
Covariance parameters for each state.
The shape depends on
covariance_type
:(n_components, ) if "spherical", (n_components, n_features) if "diag", (n_components, n_features, n_features) if "full" (n_features, n_features) if "tied",
 Type
array
Examples
>>> from hmmlearn.hmm import GaussianHMM >>> GaussianHMM(n_components=2) GaussianHMM(algorithm='viterbi',...

decode
(X, lengths=None, algorithm=None)¶ 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)¶ 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 – Returns self.
 Return type
object

get_stationary_distribution
()¶ Compute the stationary distribution of states.

predict
(X, lengths=None)¶ 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 – Labels for each sample from
X
. Return type
array, shape (n_samples, )

predict_proba
(X, lengths=None)¶ Compute the posterior probability for each state in the model.
 Xarraylike, shape (n_samples, n_features)
Feature matrix of individual samples.
 lengthsarraylike of integers, shape (n_sequences, ), optional
Lengths of the individual sequences in
X
. The sum of these should ben_samples
.
 Returns
posteriors – Statemembership probabilities for each sample from
X
. Return type
array, shape (n_samples, n_components)

sample
(n_samples=1, random_state=None)¶ 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)¶ 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 – Log likelihood of
X
. Return type
float
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)¶ 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, min_covar=0.001, startprob_prior=1.0, transmat_prior=1.0, weights_prior=1.0, means_prior=0.0, means_weight=0.0, covars_prior=None, covars_weight=None, algorithm='viterbi', covariance_type='diag', random_state=None, n_iter=10, tol=0.01, verbose=False, params='stmcw', init_params='stmcw')¶ 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, optional) –
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 mixture components of each state use the same full covariance matrix (note that this is not the same as for
GaussianHMM
).
Defaults to “diag”.
min_covar (float, optional) – Floor on the diagonal of the covariance matrix to prevent overfitting. Defaults to 1e3.
startprob_prior (array, shape (n_components, ), optional) – Parameters of the Dirichlet prior distribution for
startprob_
.transmat_prior (array, shape (n_components, n_components), optional) – Parameters of the Dirichlet prior distribution for each row of the transition probabilities
transmat_
.weights_prior (array, shape (n_mix, ), optional) – Parameters of the Dirichlet prior distribution for
weights_
.means_prior (array, shape (n_mix, ), optional) – Mean and precision of the Normal prior distribtion for
means_
.means_weight (array, shape (n_mix, ), optional) – Mean and precision of the Normal prior distribtion for
means_
.covars_prior (array, shape (n_mix, ), optional) –
Parameters of the prior distribution for the covariance matrix
covars_
.If
covariance_type
is “spherical” or “diag” the prior is the inverse gamma distribution, otherwise — the inverse Wishart distribution.covars_weight (array, shape (n_mix, ), optional) –
Parameters of the prior distribution for the covariance matrix
covars_
.If
covariance_type
is “spherical” or “diag” the prior is the inverse gamma distribution, otherwise — the inverse Wishart distribution.algorithm (string, optional) – Decoder algorithm. Must be one of “viterbi” or “map”. Defaults to “viterbi”.
random_state (RandomState or an int seed, optional) – 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.

monitor\_
Monitor object used to check the convergence of EM.
 Type

startprob\_
Initial state occupation distribution.
 Type
array, shape (n_components, )

transmat\_
Matrix of transition probabilities between states.
 Type
array, shape (n_components, n_components)

weights\_
Mixture weights for each state.
 Type
array, shape (n_components, n_mix)

means\_
Mean parameters for each mixture component in each state.
 Type
array, shape (n_components, n_mix)

covars\_
Covariance parameters for each mixture components in each state.
The shape depends on
covariance_type
:(n_components, n_mix) if "spherical", (n_components, n_mix, n_features) if "diag", (n_components, n_mix, n_features, n_features) if "full" (n_components, n_features, n_features) if "tied",
 Type
array

decode
(X, lengths=None, algorithm=None)¶ 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)¶ 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 – Returns self.
 Return type
object

get_stationary_distribution
()¶ Compute the stationary distribution of states.

predict
(X, lengths=None)¶ 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 – Labels for each sample from
X
. Return type
array, shape (n_samples, )

predict_proba
(X, lengths=None)¶ Compute the posterior probability for each state in the model.
 Xarraylike, shape (n_samples, n_features)
Feature matrix of individual samples.
 lengthsarraylike of integers, shape (n_sequences, ), optional
Lengths of the individual sequences in
X
. The sum of these should ben_samples
.
 Returns
posteriors – Statemembership probabilities for each sample from
X
. Return type
array, shape (n_samples, n_components)

sample
(n_samples=1, random_state=None)¶ 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)¶ 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 – Log likelihood of
X
. Return type
float
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)¶ 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')¶ Hidden Markov Model with multinomial (discrete) emissions.
 Parameters
n_components (int) – Number of states.
startprob_prior (array, shape (n_components, ), optional) – Parameters of the Dirichlet prior distribution for
startprob_
.transmat_prior (array, shape (n_components, n_components), optional) – Parameters of the Dirichlet prior distribution for each row of the transition probabilities
transmat_
.algorithm (string, optional) – Decoder algorithm. Must be one of “viterbi” or “map”. Defaults to “viterbi”.
random_state (RandomState or an int seed, optional) – 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.

n_features
¶ Number of possible symbols emitted by the model (in the samples).
 Type
int

monitor\_
Monitor object used to check the convergence of EM.
 Type

startprob\_
Initial state occupation distribution.
 Type
array, shape (n_components, )

transmat\_
Matrix of transition probabilities between states.
 Type
array, shape (n_components, n_components)

emissionprob\_
Probability of emitting a given symbol when in each state.
 Type
array, shape (n_components, n_features)
Examples
>>> from hmmlearn.hmm import MultinomialHMM >>> MultinomialHMM(n_components=2) MultinomialHMM(algorithm='viterbi',...

decode
(X, lengths=None, algorithm=None)¶ Find most likely state sequence corresponding to
X
. Parameters
X (arraylike, shape (n_samples, 1)) – 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.
Notes
Unlike other HMM classes,
MultinomialHMM
X
arrays have shape(n_samples, 1)
(instead of(n_samples, n_features)
). Consider usingsklearn.preprocessing.LabelEncoder
to transform your input to the right format.

fit
(X, lengths=None)¶ 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, 1)) – 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 – Returns self.
 Return type
object
Notes
Unlike other HMM classes,
MultinomialHMM
X
arrays have shape(n_samples, 1)
(instead of(n_samples, n_features)
). Consider usingsklearn.preprocessing.LabelEncoder
to transform your input to the right format.

get_stationary_distribution
()¶ Compute the stationary distribution of states.

predict
(X, lengths=None)¶ Find most likely state sequence corresponding to
X
. Parameters
X (arraylike, shape (n_samples, 1)) – 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 – Labels for each sample from
X
. Return type
array, shape (n_samples, )
Notes
Unlike other HMM classes,
MultinomialHMM
X
arrays have shape(n_samples, 1)
(instead of(n_samples, n_features)
). Consider usingsklearn.preprocessing.LabelEncoder
to transform your input to the right format.

predict_proba
(X, lengths=None)¶ Compute the posterior probability for each state in the model.
 Xarraylike, shape (n_samples, 1)
Feature matrix of individual samples.
 lengthsarraylike of integers, shape (n_sequences, ), optional
Lengths of the individual sequences in
X
. The sum of these should ben_samples
.
 Returns
posteriors – Statemembership probabilities for each sample from
X
. Return type
array, shape (n_samples, n_components)
Notes
Unlike other HMM classes,
MultinomialHMM
X
arrays have shape(n_samples, 1)
(instead of(n_samples, n_features)
). Consider usingsklearn.preprocessing.LabelEncoder
to transform your input to the right format.

sample
(n_samples=1, random_state=None)¶ 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, 1)) – Feature matrix.
state_sequence (array, shape (n_samples, )) – State sequence produced by the model.
Notes
Unlike other HMM classes,
MultinomialHMM
X
arrays have shape(n_samples, 1)
(instead of(n_samples, n_features)
). Consider usingsklearn.preprocessing.LabelEncoder
to transform your input to the right format.

score
(X, lengths=None)¶ Compute the log probability under the model.
 Parameters
X (arraylike, shape (n_samples, 1)) – 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 – Log likelihood of
X
. Return type
float
See also
score_samples
Compute the log probability under the model and posteriors.
decode
Find most likely state sequence corresponding to
X
.
Notes
Unlike other HMM classes,
MultinomialHMM
X
arrays have shape(n_samples, 1)
(instead of(n_samples, n_features)
). Consider usingsklearn.preprocessing.LabelEncoder
to transform your input to the right format.

score_samples
(X, lengths=None)¶ Compute the log probability under the model and compute posteriors.
 Parameters
X (arraylike, shape (n_samples, 1)) – 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
.
See also
Notes
Unlike other HMM classes,
MultinomialHMM
X
arrays have shape(n_samples, 1)
(instead of(n_samples, n_features)
). Consider usingsklearn.preprocessing.LabelEncoder
to transform your input to the right format.