Class CCAScikitsLearnNode

CCA Canonical Correlation Analysis.

This node has been automatically generated by wrapping the ``sklearn.cross_decomposition.cca_.CCA`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

CCA inherits from PLS with mode="B" and deflation_mode="canonical".

Read more in the :ref:`User Guide <cross_decomposition>`.

**Parameters**

n_components : int, (default 2).
    number of components to keep.

scale : boolean, (default True)
    whether to scale the data?

max_iter : an integer, (default 500)
    the maximum number of iterations of the NIPALS inner loop

tol : non-negative real, default 1e-06.
    the tolerance used in the iterative algorithm

copy : boolean
    Whether the deflation be done on a copy. Let the default value
    to True unless you don't care about side effects

**Attributes**

``x_weights_`` : array, [p, n_components]
    X block weights vectors.

``y_weights_`` : array, [q, n_components]
    Y block weights vectors.

``x_loadings_`` : array, [p, n_components]
    X block loadings vectors.

``y_loadings_`` : array, [q, n_components]
    Y block loadings vectors.

``x_scores_`` : array, [n_samples, n_components]
    X scores.

``y_scores_`` : array, [n_samples, n_components]
    Y scores.

``x_rotations_`` : array, [p, n_components]
    X block to latents rotations.

``y_rotations_`` : array, [q, n_components]
    Y block to latents rotations.

``n_iter_`` : array-like
    Number of iterations of the NIPALS inner loop for each
    component.

**Notes**

For each component k, find the weights u, v that maximizes
max corr(Xk u, Yk v), such that ``|u| = |v| = 1``

Note that it maximizes only the correlations between the scores.

The residual matrix of X (Xk+1) block is obtained by the deflation on the
current X score: x_score.

The residual matrix of Y (Yk+1) block is obtained by deflation on the
current Y score.

**Examples**

>>> from sklearn.cross_decomposition import CCA
>>> X = [[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [3.,5.,4.]]
>>> Y = [[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]]
>>> cca = CCA(n_components=1)
>>> cca.fit(X, Y)
... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
CCA(copy=True, max_iter=500, n_components=1, scale=True, tol=1e-06)
>>> X_c, Y_c = cca.transform(X, Y)

**References**


Jacob A. Wegelin. A survey of Partial Least Squares (PLS) methods, with
emphasis on the two-block case. Technical Report 371, Department of
Statistics, University of Washington, Seattle, 2000.

In french but still a reference:

Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris:

Editions Technic.

See also

PLSCanonical
PLSSVD

Overrides: object.__init__

Apply the dimension reduction learned on the train data.

This node has been automatically generated by wrapping the sklearn.cross_decomposition.cca_.CCA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : array-like of predictors, shape = [n_samples, p]

Training vectors, where n_samples in the number of samples and p is the number of predictors.

Y : array-like of response, shape = [n_samples, q], optional

Training vectors, where n_samples in the number of samples and q is the number of response variables.

copy : boolean, default True

Whether to copy X and Y, or perform in-place normalization.

Returns

x_scores if Y is not given, (x_scores, y_scores) otherwise.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Fit model to data.

This node has been automatically generated by wrapping the sklearn.cross_decomposition.cca_.CCA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : array-like, shape = [n_samples, n_features]

Training vectors, where n_samples in the number of samples and n_features is the number of predictors.

Y : array-like of response, shape = [n_samples, n_targets]

Target vectors, where n_samples in the number of samples and n_targets is the number of response variables.

Overrides: Node.stop_training