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class sklearn.cross_decomposition.PLSSVD(n_components=2, scale=True, copy=True)[source] -
Partial Least Square SVD
Simply perform a svd on the crosscovariance matrix: X?Y There are no iterative deflation here.
Read more in the User Guide.
Parameters: n_components : int, default 2
Number of components to keep.
scale : boolean, default True
Whether to scale X and Y.
copy : boolean, default True
Whether to copy X and Y, or perform in-place computations.
Attributes: x_weights_ : array, [p, n_components]
X block weights vectors.
y_weights_ : array, [q, n_components]
Y block weights vectors.
x_scores_ : array, [n_samples, n_components]
X scores.
y_scores_ : array, [n_samples, n_components]
Y scores.
See also
Methods
fit(X, Y)fit_transform(X[, y])Learn and apply the dimension reduction on the train data. get_params([deep])Get parameters for this estimator. set_params(\*\*params)Set the parameters of this estimator. transform(X[, Y])Apply the dimension reduction learned on the train data. -
__init__(n_components=2, scale=True, copy=True)[source]
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fit_transform(X, y=None, **fit_params)[source] -
Learn and apply the dimension reduction on the train data.
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.
Returns: x_scores if Y is not given, (x_scores, y_scores) otherwise. :
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get_params(deep=True)[source] -
Get parameters for this estimator.
Parameters: deep : boolean, optional
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.
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set_params(**params)[source] -
Set 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.Returns: self :
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transform(X, Y=None)[source] -
Apply the dimension reduction learned on the train data.
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cross_decomposition.PLSSVD()
2017-01-15 04:21:02
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