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: 


Attributes: 

See also
>>> import numpy as np >>> from sklearn.cross_decomposition import PLSSVD >>> X = np.array([[0., 0., 1.], ... [1.,0.,0.], ... [2.,2.,2.], ... [2.,5.,4.]]) >>> Y = np.array([[0.1, 0.2], ... [0.9, 1.1], ... [6.2, 5.9], ... [11.9, 12.3]]) >>> plsca = PLSSVD(n_components=2) >>> plsca.fit(X, Y) PLSSVD(copy=True, n_components=2, scale=True) >>> X_c, Y_c = plsca.transform(X, Y) >>> X_c.shape, Y_c.shape ((4, 2), (4, 2))
fit (X, Y)  Fit model to data. 
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(X, Y)
[source]
Fit model to data.
Parameters: 


fit_transform(X, y=None)
[source]
Learn and apply the dimension reduction on the train data.
Parameters: 


Returns: 

get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: 


Returns: 

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: 


transform(X, Y=None)
[source]
Apply the dimension reduction learned on the train data.
Parameters: 


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Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.cross_decomposition.PLSSVD.html