GaussianHMM

class GaussianHMM(n_components: int = 1, covariance_type: str = 'diag', min_covar: float = 0.001, startprob_prior: float = 1.0, transmat_prior: float = 1.0, means_prior: float = 0, means_weight: float = 0, covars_prior: float = 0.01, covars_weight: float = 1, algorithm: str = 'viterbi', random_state: float = None, n_iter: int = 10, tol: float = 0.01, verbose: bool = False, params: str = 'stmc', init_params: str = 'stmc', implementation: str = 'log')[source]

Hidden Markov Model with Gaussian emissions.

Parameters:

n_componentsint

Number of states

covariance_type{“spherical”, “diag”, “full”, “tied”}, optional

The type of covariance parameters to use:

“spherical” — each state uses a single variance value that
applies to all features.
“diag” — each state uses a diagonal covariance matrix
(default).
“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).

min_covarfloat, optional

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

means_prior, means_weightarray, shape (n_mix, ), optional

Mean and precision of the Normal prior distribution for means_.

covars_prior, covars_weightarray, 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.

startprob_priorarray, shape (n_components, ), optional

Parameters of the Dirichlet prior distribution for startprob_.

transmat_priorarray, shape (n_components, n_components), optional

Parameters of the Dirichlet prior distribution for each row of the transition probabilities transmat_.

algorithm{“viterbi”, “map”}, optional

Decoder algorithm.

random_state: RandomState or an int seed, optional

A random number generator instance.

n_iterint, optional

Maximum number of iterations to perform.

tolfloat, optional

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

verbosebool, optional

Whether per-iteration convergence reports are printed to sys.stderr. Convergence can also be diagnosed using the monitor_ attribute.

params, init_paramsstring, optional

The parameters that get updated during (params) or initialized before (init_params) the training. Can contain any combination of ‘s’ for startprob, ‘t’ for transmat, ‘m’ for means and ‘c’ for covars. Defaults to all parameters.

implementation: string, optional

Determines if the forward-backward algorithm is implemented with logarithms (“log”), or using scaling (“scaling”). The default is to use logarithms for backwards compatibility.

Attributes:

n_featuresint

Dimensionality of the Gaussian emissions.

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.

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_components, n_features) if “diag”,
(n_components, n_features, n_features) if “full”,
(n_features, n_features) if “tied”.

Examples

>>> from sktime.detection.hmm_learn import GaussianHMM
>>> from sktime.detection.datagen import piecewise_normal
>>> data = piecewise_normal(
...    means=[2, 4, 1], lengths=[10, 35, 40], random_state=7
...    ).reshape((-1, 1))
>>> model = GaussianHMM(algorithm='viterbi', n_components=2)
>>> model = model.fit(data)
>>> labeled_data = model.predict(data)

Methods

`change_points_to_segments`(y_sparse[, start, end])	Convert an series of change point indexes to segments.
`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`dense_to_sparse`(y_dense)	Convert the dense output from an detector to a sparse format.
`fit`(X[, y])	Fit to training data.
`fit_predict`(X[, y])	Fit to data, then predict it.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`predict`(X)	Create labels on test/deployment data.
`predict_points`(X)	Predict changepoints/anomalies on test/deployment data.
`predict_scores`(X)	Return scores for predicted labels on test/deployment data.
`predict_segments`(X)	Predict segments on test/deployment data.
`reset`()	Reset the object to a clean post-init state.
`sample`([n_samples, random_state, currstate])	Interface class which allows users to sample from their HMM.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`segments_to_change_points`(y_sparse)	Convert segments to change points.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.
`sparse_to_dense`(y_sparse, index)	Convert the sparse output from an detector to a dense format.
`transform`(X)	Create labels on test/deployment data.
`transform_scores`(X)	Return scores for predicted labels on test/deployment data.
`update`(X[, y])	Update model with new data and optional ground truth labels.
`update_predict`(X[, y])	Update model with new data and create labels for it.