PoissonHMM

class PoissonHMM(n_components: int = 1, startprob_prior: float = 1.0, transmat_prior: float = 1.0, lambdas_prior: float = 0.0, lambdas_weight: float = 0.0, algorithm: str = 'viterbi', random_state: int = None, n_iter: int = 10, tol: float = 0.01, verbose: bool = False, params: str = 'stl', init_params: str = 'stl', implementation: str = 'log')[source]

Hidden Markov Model with Poisson emissions.

Parameters:

n_componentsint: Number of states.
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_.
lambdas_prior, lambdas_weightarray, shape (n_components,), optional: The gamma prior on the lambda values using alpha-beta notation, respectively. If None, will be set based on the method of moments.
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, and ‘l’ for lambdas. 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:

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.
lambdas_array, shape (n_components, n_features): The expectation value of the waiting time parameters for each feature in a given state.

Examples

>>> from sktime.detection.hmm_learn import PoissonHMM
>>> from sktime.detection.datagen import piecewise_poisson
>>> data = piecewise_poisson(
...    lambdas=[1, 2, 3], lengths=[2, 4, 8], random_state=7
...    ).reshape((-1, 1))
>>> model = PoissonHMM(n_components=3)
>>> 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.