scaling :
Estimate a global scaling factor for the transformation (no longer
rigid body). (Default: True)
reflection :
Allow for the data to be reflected (so it might not be a rotation.
Effective only for non-oblique transformations. (Default: True)
reduction :
If true, it is allowed to map into lower-dimensional space. Forward
transformation might be suboptimal then and reverse transformation
might not recover all original variance. (Default: True)
oblique :
Either to allow non-orthogonal transformation – might heavily
overfit the data if there is less samples than dimensions. Use
oblique_rcond. (Default: False)
oblique_rcond :
Cutoff for ‘small’ singular values to regularize the inverse. See
lstsq for more information. (Default: -1)
svd :
Implementation of SVD to use. dgesvd requires ctypes to be
available. (Default: ‘numpy’)
enable_ca : None or list of str
Names of the conditional attributes which should be enabled in addition
to the default ones
disable_ca : None or list of str
Names of the conditional attributes which should be disabled
demean : bool
Either data should be demeaned while computing
projections and applied back while doing reverse()
auto_train : bool
Flag whether the learner will automatically train itself on the input
dataset when called untrained.
force_train : bool
Flag whether the learner will enforce training on the input dataset
upon every call.
space: str, optional :
Name of the ‘processing space’. The actual meaning of this argument
heavily depends on the sub-class implementation. In general, this is
a trigger that tells the node to compute and store information about
the input data that is “interesting” in the context of the
corresponding processing in the output dataset.
postproc : Node instance, optional
Node to perform post-processing of results. This node is applied
in __call__() to perform a final processing step on the to be
result dataset. If None, nothing is done.
descr : str
Description of the instance
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