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venv/Lib/site-packages/sklearn/cross_decomposition/_cca.py

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+from ._pls import _PLS
+from ..base import _UnstableArchMixin
+from ..utils.validation import _deprecate_positional_args
+
+__all__ = ['CCA']
+
+
+class CCA(_UnstableArchMixin, _PLS):
+    """CCA Canonical Correlation Analysis.
+
+    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.
+
+    coef_ : array of shape (p, q)
+        The coefficients of the linear model: ``Y = X coef_ + Err``
+
+    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)
+    CCA(n_components=1)
+    >>> 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
+    """
+
+    @_deprecate_positional_args
+    def __init__(self, n_components=2, *, scale=True,
+                 max_iter=500, tol=1e-06, copy=True):
+        super().__init__(n_components=n_components, scale=scale,
+                         deflation_mode="canonical", mode="B",
+                         norm_y_weights=True, algorithm="nipals",
+                         max_iter=max_iter, tol=tol, copy=copy)