class KVAD

class deeptime.decomposition.KVAD(kernel: ~deeptime.kernels._base.Kernel, lagtime: ~typing.Optional[int] = None, dim: ~typing.Optional[int] = None, epsilon: float = 1e-06, observable_transform: ~typing.Callable[[~numpy.ndarray], ~numpy.ndarray] = <deeptime.basis._monomials.Identity object>)

An estimator for the “Kernel embedding based variational approach for dynamical systems” (KVAD).

Theory and introduction into the method can be found in [1].

Parameters:

  • kernel (Kernel) – The kernel to be used, see deeptime.kernels for a selection of predefined kernels.

  • lagtime (int, optional, default=None) – Lagtime if data is not a list of instantaneous and time-lagged data pairs but a trajectory instead.

  • dim (int, optional, default=None) – Dimension cutoff parameter.

  • epsilon (float, default=1e-6) – Regularization parameter for truncated SVD.

  • observable_transform (callable, optional, default=Identity) – A feature transformation on the raw data which is used to estimate the model.

See also

KVADModel

References

Attributes

dim

Dimension cutoff for the decomposition.

epsilon

Regularization parameter for truncated SVD.

has_model

Property reporting whether this estimator contains an estimated model.

model

Shortcut to fetch_model().

observable_transform

Transforms observable instantaneous and time-lagged data into feature space.

Methods

fetch_model()

Yields the estimated model.

fit(data, **kwargs)

Fits data to the estimator's internal Model and overwrites it.

fit_fetch(data, **kwargs)

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

fit_transform(data[, fit_options, ...])

Fits a model which simultaneously functions as transformer and subsequently transforms the input data.

get_params([deep])

Get the parameters.

set_params(**params)

Set the parameters of this estimator.

transform(data, **kwargs)

Transforms data with the encapsulated model.
__call__(*args, **kwargs)

Call self as a function.

fetch_model() → Optional[Model]

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

Returns:

model – The estimated model or None.

Return type:

Model or None

fit(data, **kwargs)

Fits data to the estimator’s internal Model and overwrites it. This way, every call to fetch_model() yields an autonomous model instance. Sometimes a partial_fit method is available, in which case the model can get updated by the estimator.

Parameters:

  • data (array_like) – Data that is used to fit a model.

  • **kwargs – Additional kwargs.

Returns:

self – Reference to self.

Return type:

Estimator

fit_fetch(data, **kwargs)

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

Parameters:

  • data (array_like) – Data that is used to fit the model.

  • **kwargs – Additional arguments to fit().

Returns:

The estimated model.

Return type:

model

fit_transform(data, fit_options=None, transform_options=None)

Fits a model which simultaneously functions as transformer and subsequently transforms the input data. The estimated model can be accessed by calling fetch_model().

Parameters:

  • data (array_like) – The input data.

  • fit_options (dict, optional, default=None) – Optional keyword arguments passed on to the fit method.

  • transform_options (dict, optional, default=None) – Optional keyword arguments passed on to the transform method.

Returns:

output – Transformed data.

Return type:

array_like

get_params(deep=False)

Get the parameters.

Returns:

params – Parameter names mapped to their values.

Return type:

mapping of string to any

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:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

object

transform(data, **kwargs)

Transforms data with the encapsulated model.

Parameters:

  • data (array_like) – Input data

  • **kwargs – Optional arguments.

Returns:

output – Transformed data.

Return type:

array_like

property dim: Optional[int]

Dimension cutoff for the decomposition.

Type:

int or None

property epsilon

Regularization parameter for truncated SVD.

Type:

float

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().

property observable_transform: Callable[[ndarray], ndarray]

Transforms observable instantaneous and time-lagged data into feature space.

Type:

Callable[[ndarray], ndarray]