/usr/share/pyshared/mvpa/mappers/pca.py is in python-mvpa 0.4.8-3.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
#
# See COPYING file distributed along with the PyMVPA package for the
# copyright and license terms.
#
### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
"""Data mapper"""
__docformat__ = 'restructuredtext'
import numpy as N
from mvpa.base import warning
from mvpa.base.dochelpers import enhancedDocString
from mvpa.mappers.base import ProjectionMapper
import mvpa.base.externals as externals
if externals.exists('mdp', raiseException=True):
from mdp.nodes import NIPALSNode
class PCAMapper(ProjectionMapper):
"""Mapper to project data onto PCA components estimated from some dataset.
After the mapper has been instantiated, it has to be train first. The PCA
mapper only handles 2D data matrices.
"""
def __init__(self, transpose=False, **kwargs):
ProjectionMapper.__init__(self, **kwargs)
self._var = None
__doc__ = enhancedDocString('PCAMapper', locals(), ProjectionMapper)
def _train(self, dataset):
"""Determine the projection matrix onto the components from
a 2D samples x feature data matrix.
"""
samples = dataset.samples
dtype = samples.dtype
if str(dtype).startswith('uint') \
or str(dtype).startswith('int'):
warning("PCA: input data is in integers. " + \
"MDP's NIPALSNode operates only on floats, thus "+\
"coercing to double")
dtype = N.double
samples = samples.astype(N.double)
node = NIPALSNode(dtype=dtype)
node.train(samples)
self._proj = N.asmatrix(node.get_projmatrix())
self._recon = N.asmatrix(node.get_recmatrix())
# store variance per PCA component
self._var = node.d
var = property(fget=lambda self: self._var, doc='Variances per component')
|