folie.EMEstimator

class folie.EMEstimator(transition, *args, tol=1e-05, max_iter=100, n_init=1, warm_start=False, no_stop=False, verbose=0, verbose_interval=10, **kwargs)

Maximize the likelihood using Expectation-maximization algorithm TODO: Replace all history by a callback

fetch_model() → Model | None

Yields the estimated model. Can be None if fit() was not called.

Returns:

modelModel or None

The estimated model or None.

fit(data, minimizer=None, coefficients0=None, use_jac=True, callback=None, **kwargs)

In this do a loop that alternatively minimize and compute expectation

fit_fetch(data, **kwargs)

Fits the internal model on data and subsequently fetches it in one call.

Parameters:

dataarray_like

Data that is used to fit the model.

**kwargs

Additional arguments to fit().

Returns:

model

The estimated model.

get_params(deep=False)

Get the parameters.

Returns:

paramsmapping of string to any

Parameter names mapped to their values.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfobject

Estimator instance.

property has_model: bool

Property reporting whether this estimator contains an estimated model. This assumes that the model is initialized with None otherwise.

Type:

bool

property model

Shortcut to fetch_model().

Examples using folie.EMEstimator

Hidden Overdamped Langevin Estimation

Hidden Overdamped Langevin Estimation