class KernelCCA

class deeptime.decomposition.KernelCCA(kernel: Kernel, n_eigs: int, epsilon: float = 1e-06)

Estimator implementing the kernelized version of canonical correlation analysis. [1] (CCA [2])

Parameters:

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

• n_eigs (int) – Number of eigenvalue/eigenvector pairs to use for low-rank approximation.

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

See also

KernelCCAModel

References

[1]

Francis R Bach and Michael I Jordan. Kernel independent component analysis. Journal of machine learning research, 3(Jul):1–48, 2002.

[2]

Harold Hotelling. Relations between two sets of variates. In Breakthroughs in statistics, pages 162–190. Springer, 1992.

Attributes

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

Methods

fetch_model()	Yields the latest 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.
get_params([deep])	Get the parameters.
set_params(**params)	Set the parameters of this estimator.

fetch_model() → Optional[KernelCCAModel]

Yields the latest estimated model or None.

Returns:

model – The latest estimated model or None.

Return type:

KernelCCAModel 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:

KernelCCA

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

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

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().