Performs principal components analysis on a set of images, features,
or vectors.
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__init__(self,
center_points=True,
one_std=True)
Create a PCA object |
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addFeature(self,
feature)
Add a feature vector to the analysis. |
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train(self,
drop_front=None,
number=None,
energy=None)
Compute the PCA basis vectors using the SVD |
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project(self,
feature,
whiten=False)
Transform a feature into its low dimentional representation |
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reconstruct(self,
feat)
return the eigen values for this computation |
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getBasis(self)
return the eigen vectors returned by the computation |
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getValues(self)
return the bases used for transforming features |
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