B (bx @s&ddlmZdgZGdddeZdS))_PLSCCAcs"eZdZdZdfdd ZZS) ra CCA Canonical Correlation Analysis. CCA inherits from PLS with mode="B" and deflation_mode="canonical". Read more in the :ref:`User Guide `. 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 Tư>c s&tt|j||dddd|||d dS)N canonicalBTZnipals) n_componentsscaleZdeflation_modemodeZnorm_y_weights algorithmmax_itertolcopy)superr__init__)selfr r r rr) __class__O/home/dcms/DCMS/lib/python3.7/site-packages/sklearn/cross_decomposition/cca_.pyrfsz CCA.__init__)rTrrT)__name__ __module__ __qualname____doc__r __classcell__rr)rrrs^N)Zpls_r__all__rrrrrs