def __init__(self, scaling=True, reflection=True, reduction=True,
oblique=False, oblique_rcond=-1, **kwargs):
"""Initialize the ProcrusteanMapper
:Parameters:
scaling: bool
Scale data for the transformation (no longer rigid body
transformation)
reflection: bool
Allow for the data to be reflected (so it might not be a rotation).
Effective only for non-oblique transformations
reduction: bool
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
oblique: bool
Either to allow non-orthogonal transformation -- might heavily overfit
the data if there is less samples than dimensions. Use `oblique_rcond`.
oblique_rcond: float
Cutoff for 'small' singular values to regularize the inverse. See
:class:`~numpy.linalg.lstsq` for more information.
"""
ProjectionMapper.__init__(self, **kwargs)
self._scaling = scaling
"""Either to determine the scaling factor"""
self._reduction = reduction
self._reflection = reflection
self._oblique = oblique
self._oblique_rcond = oblique_rcond
self._scale = None
"""Estimated scale"""
def __init__(self,
baselinelabels=None,
**kwargs):
"""Initialize ZScoreMapper
:Parameters:
baselinelabels : None or list of int or string
Used to compute mean and variance
TODO: not in effect now and need to be adherent to all
`ProjectionMapper`s
"""
ProjectionMapper.__init__(self, **kwargs)
if baselinelabels is not None:
raise NotImplementedError, "Support for baseline labels " \
"is not yet implemented in ZScoreMapper"
self.baselinelabels = baselinelabels
def __init__(self, **kwargs):
"""Initialize the SVDMapper
:Parameters:
**kwargs:
All keyword arguments are passed to the ProjectionMapper
constructor.
Note, that for the 'selector' argument this class also supports
passing a `ElementSelector` instance, which will be used to
determine the to be selected features, based on the singular
values of each component.
"""
ProjectionMapper.__init__(self, **kwargs)
self._sv = None
"""Singular values of the training matrix."""
def __init__(self, transpose=False, **kwargs):
ProjectionMapper.__init__(self, **kwargs)
self._var = None
def __init__(self, algorithm='cubica', transpose=False, **kwargs):
ProjectionMapper.__init__(self, **kwargs)
self._algorithm = algorithm
self._transpose = transpose