def __init__(self, space='targets', **kwargs):
ProjectionMapper.__init__(self, space=space, **kwargs)
self._scale = None
"""Estimated scale"""
if self.params.svd == 'dgesvd' and not externals.exists('liblapack.so'):
warning("Reverting choice of svd for ProcrusteanMapper to be default "
"'numpy' since liblapack.so seems not to be available for "
"'dgesvd'")
self.params.svd = 'numpy'
def __init__(self, space='targets', **kwargs):
ProjectionMapper.__init__(self, space=space, **kwargs)
self._scale = None
"""Estimated scale"""
if self.params.svd == 'dgesvd' and not externals.exists('liblapack.so'):
warning("Reverting choice of svd for ProcrusteanMapper to be default "
"'numpy' since liblapack.so seems not to be available for "
"'dgesvd'")
self.params.svd = 'numpy'
def __init__(self, proj, **kwargs):
"""Initialize the StaticProjectionMapper
Parameters
----------
**kwargs:
All keyword arguments are passed to the ProjectionMapper
constructor.
"""
ProjectionMapper.__init__(self, auto_train=True, **kwargs)
self._proj = proj
def __init__(self, proj, **kwargs):
"""Initialize the StaticProjectionMapper
Parameters
----------
**kwargs:
All keyword arguments are passed to the ProjectionMapper
constructor.
"""
ProjectionMapper.__init__(self, auto_train=True, **kwargs)
self._proj = proj
def __init__(self, **kwargs):
"""Initialize the SVDMapper
Parameters
----------
**kwargs:
All keyword arguments are passed to the ProjectionMapper
constructor.
"""
ProjectionMapper.__init__(self, **kwargs)
self._sv = None
"""Singular values of the training matrix."""
def __init__(self, **kwargs):
"""Initialize the SVDMapper
Parameters
----------
**kwargs:
All keyword arguments are passed to the ProjectionMapper
constructor.
"""
ProjectionMapper.__init__(self, **kwargs)
self._sv = None
"""Singular values of the training matrix."""
def __init__(self, similarities, prototypes=None, **kwargs):
"""
Parameters
----------
similarities : list
A list of similarity functions.
prototypes : Dataset or list
A dataset or a list of instances (e.g., streamlines)?
**kwargs:
All keyword arguments are passed to the ProjectionMapper
constructor
"""
ProjectionMapper.__init__(self, **kwargs)
self.similarities = similarities
self.prototypes = prototypes
def __init__(self, similarities, prototypes=None, **kwargs):
"""
Parameters
----------
similarities : list
A list of similarity functions.
prototypes : Dataset or list
A dataset or a list of instances (e.g., streamlines)?
**kwargs:
All keyword arguments are passed to the ProjectionMapper
constructor
"""
ProjectionMapper.__init__(self, **kwargs)
self.similarities = similarities
self.prototypes = prototypes
def __repr__(self):
s = ProjectionMapper.__repr__(self).rstrip(' )')
if not s[-1] == '(': s += ', '
s += "scaling=%r, reflection=%r, reduction=%r, " \
"oblique=%r, oblique_rcond=%r)" % \
(self._scaling, self._reflection, self._reduction,
self._oblique, self._oblique_rcond)
return s
def __init__(self, proj, recon=None, **kwargs):
"""Initialize the StaticProjectionMapper
Parameters
----------
proj : 2-D array
Projection matrix to be used for forward projection.
recon: 2-D array
Projection matrix to be used for reverse projection.
If this is not given, `numpy.linalg.pinv` of proj
will be used by default.
**kwargs:
All keyword arguments are passed to the ProjectionMapper
constructor.
"""
ProjectionMapper.__init__(self, auto_train=True, **kwargs)
self._proj = proj
self._recon = recon
def __init__(self, proj, recon=None, **kwargs):
"""Initialize the StaticProjectionMapper
Parameters
----------
proj : 2-D array
Projection matrix to be used for forward projection.
recon: 2-D array
Projection matrix to be used for reverse projection.
If this is not given, `numpy.linalg.pinv` of proj
will be used by default.
**kwargs:
All keyword arguments are passed to the ProjectionMapper
constructor.
"""
ProjectionMapper.__init__(self, auto_train=True, **kwargs)
self._proj = proj
self._recon = recon
def __init__(self, **kwargs):
"""Initialize the SVDMapper
Parameters
----------
**kwargs:
All keyword arguments are passed to the ProjectionMapper
constructor.
Notes
-----
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, scaling=True, reflection=True, reduction=True,
oblique=False, oblique_rcond=-1, svd='numpy', **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.
svd : string (numpy, scipy, dgesvd), optional
Implementation of SVD to use. dgesvd requires ctypes to be available.
**kwargs
To be passed to ProjectionMapper
"""
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"""
self._svd = svd
