113 lines
3.2 KiB
Python
113 lines
3.2 KiB
Python
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from ._pls import _PLS
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from ..base import _UnstableArchMixin
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from ..utils.validation import _deprecate_positional_args
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__all__ = ['CCA']
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class CCA(_UnstableArchMixin, _PLS):
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"""CCA Canonical Correlation Analysis.
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CCA inherits from PLS with mode="B" and deflation_mode="canonical".
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Read more in the :ref:`User Guide <cross_decomposition>`.
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Parameters
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----------
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n_components : int, (default 2).
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number of components to keep.
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scale : boolean, (default True)
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whether to scale the data?
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max_iter : an integer, (default 500)
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the maximum number of iterations of the NIPALS inner loop
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tol : non-negative real, default 1e-06.
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the tolerance used in the iterative algorithm
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copy : boolean
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Whether the deflation be done on a copy. Let the default value
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to True unless you don't care about side effects
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Attributes
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----------
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x_weights_ : array, [p, n_components]
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X block weights vectors.
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y_weights_ : array, [q, n_components]
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Y block weights vectors.
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x_loadings_ : array, [p, n_components]
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X block loadings vectors.
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y_loadings_ : array, [q, n_components]
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Y block loadings vectors.
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x_scores_ : array, [n_samples, n_components]
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X scores.
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y_scores_ : array, [n_samples, n_components]
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Y scores.
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x_rotations_ : array, [p, n_components]
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X block to latents rotations.
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y_rotations_ : array, [q, n_components]
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Y block to latents rotations.
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coef_ : array of shape (p, q)
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The coefficients of the linear model: ``Y = X coef_ + Err``
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n_iter_ : array-like
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Number of iterations of the NIPALS inner loop for each
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component.
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Notes
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-----
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For each component k, find the weights u, v that maximizes
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max corr(Xk u, Yk v), such that ``|u| = |v| = 1``
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Note that it maximizes only the correlations between the scores.
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The residual matrix of X (Xk+1) block is obtained by the deflation on the
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current X score: x_score.
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The residual matrix of Y (Yk+1) block is obtained by deflation on the
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current Y score.
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Examples
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--------
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>>> from sklearn.cross_decomposition import CCA
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>>> X = [[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [3.,5.,4.]]
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>>> Y = [[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]]
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>>> cca = CCA(n_components=1)
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>>> cca.fit(X, Y)
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CCA(n_components=1)
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>>> X_c, Y_c = cca.transform(X, Y)
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References
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----------
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Jacob A. Wegelin. A survey of Partial Least Squares (PLS) methods, with
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emphasis on the two-block case. Technical Report 371, Department of
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Statistics, University of Washington, Seattle, 2000.
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In french but still a reference:
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Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris:
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Editions Technic.
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See also
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--------
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PLSCanonical
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PLSSVD
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"""
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@_deprecate_positional_args
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def __init__(self, n_components=2, *, scale=True,
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max_iter=500, tol=1e-06, copy=True):
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super().__init__(n_components=n_components, scale=scale,
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deflation_mode="canonical", mode="B",
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norm_y_weights=True, algorithm="nipals",
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max_iter=max_iter, tol=tol, copy=copy)
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