def testCentreArray(self):
numExamples = 10
numFeatures = 3
preprocessor = Standardiser()
#Test an everyday matrix
X = numpy.random.rand(numExamples, numFeatures)
Xc = preprocessor.centreArray(X)
centreV = preprocessor.getCentreVector()
self.assertAlmostEquals(numpy.sum(Xc), 0, places=3)
self.assertTrue((X-centreV == Xc).all())
#Now take out 3 rows of X, normalise and compare to normalised X
Xs = X[0:3, :]
Xsc = preprocessor.centreArray(Xs)
self.assertTrue((Xsc == Xc[0:3, :]).all())
X, X2, Xs, XOpls, YList, ages, df = MetabolomicsUtils.loadData()
waveletStr = 'db4'
mode = "cpd"
maxLevel = 10
errors = numpy.zeros(maxLevel)
numFeatures = numpy.zeros(maxLevel)
level = 10
waveletStrs = ["haar", "db4", "db8"]
#The variances are very similar across different wavelets
for waveletStr in waveletStrs:
Xw = MetabolomicsUtils.getWaveletFeatures(X, waveletStr, level, mode)
standardiser = Standardiser()
Xw = standardiser.centreArray(Xw)
w, V = numpy.linalg.eig(Xw.dot(Xw.T))
w = numpy.flipud(numpy.sort(w))
variances = []
variances.append(numpy.sum(w[0:1])/numpy.sum(w))
variances.append(numpy.sum(w[0:5])/numpy.sum(w))
variances.append(numpy.sum(w[0:10])/numpy.sum(w))
variances.append(numpy.sum(w[0:15])/numpy.sum(w))
variances.append(numpy.sum(w[0:20])/numpy.sum(w))
variances.append(numpy.sum(w[0:25])/numpy.sum(w))
variances.append(numpy.sum(w[0:50])/numpy.sum(w))
variances.append(numpy.sum(w[0:100])/numpy.sum(w))
variances.append(numpy.sum(w[0:150])/numpy.sum(w))
variances.append(numpy.sum(w[0:200])/numpy.sum(w))
#print(variances)
