val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?n_components:int ->
?scale:bool ->
?max_iter:Py.Object.t ->
?tol:Py.Object.t ->
?copy:bool ->
unit ->
tCCA 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.
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
Fit model to data.
Parameters ---------- X : array-like of shape (n_samples, n_features) Training vectors, where n_samples is the number of samples and n_features is the number of predictors.
Y : array-like of shape (n_samples, n_targets) Target vectors, where n_samples is the number of samples and n_targets is the number of response variables.
Learn and apply the dimension reduction on the train data.
Parameters ---------- X : array-like of shape (n_samples, n_features) Training vectors, where n_samples is the number of samples and n_features is the number of predictors.
y : array-like of shape (n_samples, n_targets) Target vectors, where n_samples is the number of samples and n_targets is the number of response variables.
Returns ------- x_scores if Y is not given, (x_scores, y_scores) otherwise.
Get parameters for this estimator.
Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns ------- params : mapping of string to any Parameter names mapped to their values.
Transform data back to its original space.
Parameters ---------- X : array-like of shape (n_samples, n_components) New data, where n_samples is the number of samples and n_components is the number of pls components.
Returns ------- x_reconstructed : array-like of shape (n_samples, n_features)
Notes ----- This transformation will only be exact if n_components=n_features
Apply the dimension reduction learned on the train data.
Parameters ---------- X : array-like of shape (n_samples, n_features) Training vectors, where n_samples is the number of samples and n_features is the number of predictors.
copy : boolean, default True Whether to copy X and Y, or perform in-place normalization.
Notes ----- This call requires the estimation of a p x q matrix, which may be an issue in high dimensional space.
Return the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.
Parameters ---------- X : array-like of shape (n_samples, n_features) Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
y : array-like of shape (n_samples,) or (n_samples, n_outputs) True values for X.
sample_weight : array-like of shape (n_samples,), default=None Sample weights.
Returns ------- score : float R^2 of self.predict(X) wrt. y.
Notes ----- The R2 score used when calling ``score`` on a regressor will use ``multioutput='uniform_average'`` from version 0.23 to keep consistent with :func:`~sklearn.metrics.r2_score`. This will influence the ``score`` method of all the multioutput regressors (except for :class:`~sklearn.multioutput.MultiOutputRegressor`). To specify the default value manually and avoid the warning, please either call :func:`~sklearn.metrics.r2_score` directly or make a custom scorer with :func:`~sklearn.metrics.make_scorer` (the built-in scorer ``'r2'`` uses ``multioutput='uniform_average'``).
val set_params : ?params:(string * Py.Object.t) list -> t -> tSet the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.
Parameters ---------- **params : dict Estimator parameters.
Returns ------- self : object Estimator instance.
Apply the dimension reduction learned on the train data.
Parameters ---------- X : array-like of shape (n_samples, n_features) Training vectors, where n_samples is the number of samples and n_features is the number of predictors.
Y : array-like of shape (n_samples, n_targets) Target vectors, where n_samples is the number of samples and n_targets 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.
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.