`folie`.KramersMoyalEstimator¶

class folie.KramersMoyalEstimator(model, **kwargs)[source]¶

Kramers-Moyal estimator

Parameters:

modelModel, optional, default=None: A model which can be used for initialization. In case an estimator is capable of online learning, i.e., capable of updating models, this can be used to resume the estimation process.

fetch_model() → Model | None[source]¶

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

Returns:

modelModel or None: The estimated model or None.

fit(data, estimator=LinearRegression(copy_X=False, fit_intercept=False, n_jobs=1), **kwargs)[source]¶

Fits data to the estimator’s internal Model and overwrites it. This way, every call to fetch_model() yields an autonomous model instance.

Parameters:

dataarray_like: Data that is used to fit a model.
estimator: sklearn compatible estimator: Defaut to sklearn.linear_model.LinearRegression(copy_X=False, fit_intercept=False) but any compatible estimator can be used. Estimator should have a coef attibutes after fitting
**kwargs: Additional kwargs.

Returns:

selfEstimator: Reference to self.

fit_fetch(data, **kwargs)[source]¶

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)[source]¶

Get the parameters.

Returns:

paramsmapping of string to any: Parameter names mapped to their values.

preprocess_traj(trj, **kwargs)[source]¶: Basic preprocessing

set_params(**params)[source]¶

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: