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
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]
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. : |
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. |
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 : |
---|
transform(X, Y=None)
[source]
Apply the dimension reduction learned on the train data.
© 2007–2016 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.PLSSVD.html