Mapper to project data onto PCA components estimated from some dataset.
After the mapper has been instantiated, it has to be train first. When
train() is called with a 2D (samples x features) matrix the PCA
components are determined by performing singular value decomposition
on the covariance matrix.
The PCA mapper only handle 2D data matrices.
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__deepcopy__(self,
memo=None)
Yes, this is it. |
source code
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train(self,
dataset)
Determine the projection matrix onto the PCA components from
a 2D samples x feature data matrix. |
source code
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forward(self,
data)
Project a 2D samples x features matrix onto the PCA components. |
source code
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reverse(self,
data)
Projects feature vectors or matrices with feature vectors back
onto the original features. |
source code
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Inherited from base.Mapper :
__call__
Inherited from object :
__delattr__ ,
__getattribute__ ,
__hash__ ,
__new__ ,
__reduce__ ,
__reduce_ex__ ,
__repr__ ,
__setattr__ ,
__str__
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