def testNormalisedLaplacianRw(self):
numVertices = 10
numFeatures = 0
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
ell = 2
m = 2
generator = BarabasiAlbertGenerator(ell, m)
graph = generator.generate(graph)
k = 10
W = graph.getSparseWeightMatrix()
L = GraphUtils.normalisedLaplacianRw(W)
L2 = graph.normalisedLaplacianRw()
tol = 10**-6
self.assertTrue(numpy.linalg.norm(L - L2) < tol)
#Test zero rows/cols
W = scipy.sparse.csr_matrix((5, 5))
W[1, 0] = 1
W[0, 1] = 1
L = GraphUtils.normalisedLaplacianRw(W)
for i in range(2, 5):
self.assertEquals(L[i, i], 0)
def testNormalisedLaplacianRw(self):
numVertices = 10
numFeatures = 0
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
ell = 2
m = 2
generator = BarabasiAlbertGenerator(ell, m)
graph = generator.generate(graph)
k = 10
W = graph.getSparseWeightMatrix()
L = GraphUtils.normalisedLaplacianRw(W)
L2 = graph.normalisedLaplacianRw()
tol = 10**-6
self.assertTrue(numpy.linalg.norm(L - L2) < tol)
#Test zero rows/cols
W = scipy.sparse.csr_matrix((5, 5))
W[1, 0] = 1
W[0, 1] = 1
L = GraphUtils.normalisedLaplacianRw(W)
for i in range(2, 5):
self.assertEquals(L[i, i], 0)
def testShiftLaplacian(self):
numVertices = 10
numFeatures = 0
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
ell = 2
m = 2
generator = BarabasiAlbertGenerator(ell, m)
graph = generator.generate(graph)
k = 10
W = graph.getSparseWeightMatrix()
L = GraphUtils.shiftLaplacian(W)
L2 = 2*numpy.eye(numVertices) - graph.normalisedLaplacianSym()
tol = 10**-6
self.assertTrue(numpy.linalg.norm(L - L2) < tol)
def testShiftLaplacian(self):
numVertices = 10
numFeatures = 0
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
ell = 2
m = 2
generator = BarabasiAlbertGenerator(ell, m)
graph = generator.generate(graph)
k = 10
W = graph.getSparseWeightMatrix()
L = GraphUtils.shiftLaplacian(W)
L2 = 2 * numpy.eye(numVertices) - graph.normalisedLaplacianSym()
tol = 10**-6
self.assertTrue(numpy.linalg.norm(L - L2) < tol)
