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())
edgeTypeIndex1 = 0
edgeTypeIndex2 = 1
sGraphContact = graph.getSparseGraph(edgeTypeIndex1)
sGraphInfect = graph.getSparseGraph(edgeTypeIndex2)
numpy.set_printoptions(precision=3, suppress=True)
logging.info("Statistics over Verticies ")
logging.info("===============================")
#Other measures : infection period of tree, infection types
#PCA to find variance of data, correlation matrix, center matrix
X = graph.getVertexList().copy().getVertices(list(range(0, graph.getNumVertices())))
standardiser = Standardiser()
X = standardiser.standardiseArray(X)
centerArray = standardiser.getCentreVector()
C = numpy.dot(X.T, X)
print((Latex.array2DToRows(numpy.reshape(centerArray, (5, 8)))))
C2 = numpy.abs(C - numpy.eye(X.shape[1]))
C2[numpy.tril_indices(C.shape[0])] = 0
inds = numpy.flipud(numpy.argsort(C2, None))
numEls = 10
for i in range(numEls):
corr = "%.3f" % C[numpy.unravel_index(inds[i], C2.shape)]
print((str(numpy.unravel_index(inds[i], C2.shape)) + " & " + corr + "\\\\"))
