/usr/lib/python2.7/dist-packages/PyMca/tests/PCAToolsTest.py is in pymca 4.7.1+dfsg-2.
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# Copyright (C) 2004-2012 European Synchrotron Radiation Facility
#
# This file is part of the PyMca X-ray Fluorescence Toolkit developed at
# the ESRF by the Software group.
#
# This file is free software; you can redistribute it and/or modify it
# under the terms of the GNU Lesser General Public License as published by the
# Free Software Foundation; either version 2 of the License, or (at your option)
# any later version.
#
# This file is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
# details.
#
#############################################################################*/
__author__ = "V.A. Sole - ESRF Data Analysis"
import unittest
import numpy
import numpy.linalg
try:
import mdp
MDP = True
except:
# MDP can give very weird errors
MDP = False
class testPCATools(unittest.TestCase):
def testPCAToolsImport(self):
from PyMca import PCATools
def testPCAToolsCovariance(self):
from PyMca.PCATools import getCovarianceMatrix
x = numpy.array([[0.0, 2.0, 3.0],
[3.0, 0.0, -1.0],
[4.0, -4.0, 4.0],
[4.0, 4.0, 4.0]])
nSpectra = x.shape[0]
# test just multiplication
tmpArray = numpy.dot(x.T, x)
for force in [True, False]:
pymcaCov, pymcaAvg, nData = getCovarianceMatrix(x,
force=force,
center=False)
self.assertTrue(numpy.allclose(tmpArray, pymcaCov * (nData - 1)))
# calculate covariance using numpy
numpyCov = numpy.cov(x.T)
numpyAvg = x.sum(axis=0).reshape(-1, 1) / nSpectra
tmpArray = x.T - numpyAvg
numpyCov2 = numpy.dot(tmpArray, tmpArray.T) / nSpectra
numpyAvg = numpyAvg.reshape(1, -1)
# calculate covariance using PCATools and 2D stack
# directly and dynamically loading data
for force in [False, True]:
pymcaCov, pymcaAvg, nData = getCovarianceMatrix(x,
force=force,
center=True)
self.assertTrue(numpy.allclose(numpyCov, pymcaCov))
self.assertTrue(numpy.allclose(numpyAvg, pymcaAvg))
self.assertTrue(nData == nSpectra)
# calculate covariance using PCATools and 3D stack
# directly and dynamically loading data
x.shape = 2, 2, -1
for force in [False, True]:
pymcaCov, pymcaAvg, nData = getCovarianceMatrix(x,
force=force,
center=True)
self.assertTrue(numpy.allclose(numpyCov, pymcaCov))
self.assertTrue(numpy.allclose(numpyAvg, pymcaAvg))
self.assertTrue(nData == nSpectra)
def testPCAToolsPCA(self):
from PyMca.PCATools import numpyPCA
x = numpy.array([[0.0, 2.0, 3.0],
[3.0, 0.0, -1.0],
[4.0, -4.0, 4.0],
[4.0, 4.0, 4.0]])
# that corresponds to 4 spectra of 3 channels
nSpectra = x.shape[0]
# calculate eigenvalues and eigenvectors with numpy
tmpArray = numpy.dot(x.T, x)/(nSpectra - 1)
numpyEigenvalues, numpyEigenvectors = numpy.linalg.eigh(tmpArray)
# sort from higher to lower
idx = list(range(numpyEigenvalues.shape[0]-1, -1 , -1))
numpyEigenvalues = numpy.take(numpyEigenvalues, idx)
numpyEigenvectors = numpyEigenvectors[:, ::-1].T
# now use PyMca
# centering has to be false to obtain the same results
for force in [True, False]:
images, eigenvalues, eigenvectors = numpyPCA(x,
ncomponents=x.shape[1],
force=force,
center=False,
scale=False)
self.assertTrue(numpy.allclose(eigenvalues, numpyEigenvalues))
self.assertTrue(numpy.allclose(eigenvectors, numpyEigenvectors))
# test with a different shape
x.shape = 2, 2, -1
for force in [True, False]:
images, eigenvalues, eigenvectors = numpyPCA(x,
ncomponents=3,
force=force,
center=False,
scale=False)
self.assertTrue(numpy.allclose(eigenvalues, numpyEigenvalues))
self.assertTrue(numpy.allclose(eigenvectors, numpyEigenvectors))
if MDP:
def testPCAToolsMDP(self):
from PyMca.PCATools import getCovarianceMatrix, numpyPCA
x = numpy.array([[0.0, 2.0, 3.0],
[3.0, 0.0, -1.0],
[4.0, -4.0, 4.0],
[4.0, 4.0, 4.0]])
# use mdp
pcaNode = mdp.nodes.PCANode()
pcaNode.train(x)
pcaNode.stop_training()
pcaEigenvectors = pcaNode.v.T
# and compare with PyMca
for force in [True, False]:
images, eigenvalues, eigenvectors = numpyPCA(x,
ncomponents=x.shape[1],
force=force,
center=True,
scale=False)
# the eigenvalues must be the same
self.assertTrue(numpy.allclose(eigenvalues, pcaNode.d))
# the eigenvectors can be multiplied by -1
for i in range(x.shape[1]):
if (eigenvectors[i,0] >= 0 and pcaEigenvectors[i,0] >=0) or\
(eigenvectors[i,0] <= 0 and pcaEigenvectors[i,0] <=0):
# both same sign
self.assertTrue(numpy.allclose(eigenvectors[i],
pcaEigenvectors[i]))
else:
self.assertTrue(numpy.allclose(-eigenvectors[i],
pcaEigenvectors[i]))
def getSuite(auto=True):
testSuite = unittest.TestSuite()
if auto:
testSuite.addTest(\
unittest.TestLoader().loadTestsFromTestCase(testPCATools))
else:
# use a predefined order
testSuite.addTest(testPCATools("testPCAToolsImport"))
testSuite.addTest(testPCATools("testPCAToolsCovariance"))
testSuite.addTest(testPCATools("testPCAToolsPCA"))
if MDP:
testSuite.addTest(testPCATools("testPCAToolsMDP"))
return testSuite
def test(auto=False):
unittest.TextTestRunner(verbosity=2).run(getSuite(auto=auto))
if __name__ == '__main__':
test()
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