class KernelEDMD

class deeptime.decomposition.KernelEDMD(kernel: Kernel, epsilon: float = 0.0, n_eigs: Optional[int] = None)

Estimator implementing kernel extended mode decomposition. [1] [2] [3].

Parameters:

• kernel (Kernel) – The kernel to use. See deeptime.kernels for a list of available kernels.

• epsilon (float, optional, default=0) – Regularization parameter.

• n_eigs (int, optional, default=None) – Number of eigenvalue/eigenvector pairs to compute.

References

[1]

Matthew O Williams, Clarence W Rowley, and Ioannis G Kevrekidis. A kernel-based method for data-driven Koopman spectral analysis. Journal of Computational Dynamics, 2(2):247–265, 2015.

[2]

Stefan Klus, Ingmar Schuster, and Krikamol Muandet. Eigendecompositions of transfer operators in reproducing kernel Hilbert spaces. Journal of Nonlinear Science, 30:283–315, 2019. doi:10.1007/s00332-019-09574-z.

[3]

Stefan Klus, Andreas Bittracher, Ingmar Schuster, and Christof Schütte. A kernel-based approach to molecular conformation analysis. The Journal of Chemical Physics, 149(24):244109, 2018.

Attributes

has_model	Property reporting whether this estimator contains an estimated model.
model	Shortcut to fetch_model().

Methods

fetch_model()	Yields the estimated model or None.
fit(data, **kwargs)	Fit this estimator instance onto data.
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[KernelEDMDModel]

Yields the estimated model or None.

Returns:

model – The model.

Return type:

KernelEDMDModel or None

fit(data, **kwargs)

Fit this estimator instance onto data.

Parameters:

• data – Input data, see to_dataset for options.

• **kwargs – Kwargs, may contain lagtime.

Returns:

self – Reference to self.

Return type:

KernelEDMD

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 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().