gemmr.estimators.NIPALSCCA

class gemmr.estimators.NIPALSCCA(n_components=2, *, scale=True, max_iter=500, tol=1e-06, copy=True)

Identical to sklearn.cross_decomposition.CCA, except that fit creates additional attributes for compatibility with SVDPLS and SVDCCA:

corrs_

contains the canonical correlations. This is the quantity that’s maximized by CCA

Type:: np.ndarray (n_components,)

assocs_

Identical to corrs_. assocs_ is the common identifier used in in SVDPLS, SVDCCA, NIPALSPLS and NIPALSCCA for the association strength that is optimized by each particular method

Type:: np.ndarray (n_components,)

__init__(n_components=2, *, scale=True, max_iter=500, tol=1e-06, copy=True)

Methods

`__init__`([n_components, scale, max_iter, ...])
`fit`(X, Y)	Fit model to data.
`fit_transform`(X[, y])	Learn and apply the dimension reduction on the train data.
`get_feature_names_out`([input_features])	Get output feature names for transformation.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`inverse_transform`(X[, Y])	Transform data back to its original space.
`predict`(X[, copy])	Predict targets of given samples.
`score`(X, y[, sample_weight])	Return the coefficient of determination of the prediction.
`set_output`(*[, transform])	Set output container.
`set_params`(**params)	Set the parameters of this estimator.
`set_predict_request`(*[, copy])	Request metadata passed to the `predict` method.
`set_score_request`(*[, sample_weight])	Request metadata passed to the `score` method.
`set_transform_request`(*[, copy])	Request metadata passed to the `transform` method.
`transform`(X[, Y, copy])	Apply the dimension reduction.

Attributes

param