/usr/share/pyshared/mvpa/mappers/svd.py is in python-mvpa 0.4.8-1.
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# See COPYING file distributed along with the PyMVPA package for the
# copyright and license terms.
#
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"""Singular-value decomposition mapper"""
__docformat__ = 'restructuredtext'
import numpy as N
from mvpa.base.dochelpers import enhancedDocString
from mvpa.mappers.base import ProjectionMapper
from mvpa.featsel.helpers import ElementSelector
if __debug__:
from mvpa.base import debug
class SVDMapper(ProjectionMapper):
"""Mapper to project data onto SVD components estimated from some dataset.
"""
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."""
__doc__ = enhancedDocString('SVDMapper', locals(), ProjectionMapper)
def _train(self, dataset):
"""Determine the projection matrix onto the SVD components from
a 2D samples x feature data matrix.
"""
X = N.asmatrix(dataset.samples)
X = self._demeanData(X)
# singular value decomposition
U, SV, Vh = N.linalg.svd(X, full_matrices=0)
# store the final matrix with the new basis vectors to project the
# features onto the SVD components. And store its .H right away to
# avoid computing it in forward()
self._proj = Vh.H
# also store singular values of all components
self._sv = SV
if __debug__:
debug("MAP", "SVD was done on %s and obtained %d SVs " %
(dataset, len(SV)) + " (%d non-0, max=%f)" %
(len(SV.nonzero()), SV[0]))
# .norm might be somewhat expensive to compute
if "MAP_" in debug.active:
debug("MAP_", "Mixing matrix has %s shape and norm=%f" %
(self._proj.shape, N.linalg.norm(self._proj)))
def selectOut(self, outIds):
"""Choose a subset of SVD components (and remove all others)."""
# handle ElementSelector operating on SV (base class has no idea about)
# XXX think about more generic interface, where some 'measure' is assigned
# per each projection dimension, like in _sv in case of SVD.
# May be selector could be parametrized with an instance + attribute as literal
# so later on it could extract necessary values?
if isinstance(self._selector, ElementSelector):
ProjectionMapper.selectOut(self, self._selector(self._sv))
else:
ProjectionMapper.selectOut(self, outIds)
def _computeRecon(self):
"""Since singular vectors are orthonormal, sufficient to take hermitian
"""
return self._proj.H
sv = property(fget=lambda self: self._sv, doc="Singular values")
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