def testNormalisedLaplacianSym(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 = graph.normalisedLaplacianSym()
tol = 10**-6
self.assertTrue(
numpy.linalg.norm(L + L2 - 2 * numpy.eye(numVertices)) < tol)
#Test zero rows/cols
W = scipy.sparse.csr_matrix((5, 5))
W[1, 0] = 1
W[0, 1] = 1
L = GraphUtils.normalisedLaplacianSym(W)
for i in range(2, 5):
self.assertEquals(L[i, i], 0)
def testNormalisedLaplacianSym(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 = graph.normalisedLaplacianSym()
tol = 10**-6
self.assertTrue(numpy.linalg.norm(L + L2 - 2*numpy.eye(numVertices)) < tol)
#Test zero rows/cols
W = scipy.sparse.csr_matrix((5, 5))
W[1, 0] = 1
W[0, 1] = 1
L = GraphUtils.normalisedLaplacianSym(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)
cluster1 = numpy.array([[0,1], [0,2], [1,3], [2,3], [3,4], [4,5]]) graph1.addEdges(cluster1) cluster2 = numpy.array([[5,7], [5,8], [7,9], [8,9], [6,7]]) graph1.addEdges(cluster2) cluster3 = numpy.array([[6,10], [10,11], [10,12], [11,12], [11,13], [12,14]]) graph1.addEdges(cluster3) graph2 = SparseGraph(GeneralVertexList(numVertices)) cluster1 = numpy.array([[0,1], [0,2], [1,3], [2,3], [3,4], [0,3],[4,5]]) graph2.addEdges(cluster1) cluster2 = numpy.array([[5,7], [5,8], [7,9], [8,9], [6,7]]) graph2.addEdges(cluster2) cluster3 = numpy.array([[6,10], [10,11], [10,12], [11,12], [11,13], [12,14]]) graph2.addEdges(cluster3) L1 = graph1.normalisedLaplacianSym() L2 = graph2.normalisedLaplacianSym() l1, U = numpy.linalg.eig(L1) inds = numpy.argsort(l1)[0:k] U = U[:, inds] U = Standardiser().normaliseArray(U.T).T l2, V = numpy.linalg.eig(L2) inds = numpy.argsort(l2)[0:k] V = V[:, inds] V = Standardiser().normaliseArray(V.T).T kmeans = KMeans(k) kmeans.fit(U) C = FeatureGenerator().categoricalToIndicator(numpy.array([kmeans.labels_]).T, numpy.array([0]))
from apgl.graph.SparseGraph import SparseGraph
from apgl.graph.GeneralVertexList import GeneralVertexList
numpy.set_printoptions(suppress=True, precision=4, linewidth=100)
numpy.random.seed(21)
p = 0.001
numVertices = 10000
generator = ErdosRenyiGenerator(p)
graph = SparseGraph(GeneralVertexList(numVertices))
graph = generator.generate(graph)
print("Num vertices = " + str(graph.getNumVertices()))
print("Num edges = " + str(graph.getNumEdges()))
L = graph.normalisedLaplacianSym(sparse=True)
# L = csr_matrix(L)
print("Created Laplacian")
# ml = smoothed_aggregation_solver(L)
# M = ml.aspreconditioner()
start = time.clock()
w, V = scipy.sparse.linalg.eigsh(L, k=20)
totalTime = time.clock() - start
print(totalTime)
# print(w)
# Eigsh is quite fast
from apgl.generator.ErdosRenyiGenerator import ErdosRenyiGenerator
from apgl.graph.SparseGraph import SparseGraph
from apgl.graph.GeneralVertexList import GeneralVertexList
numpy.set_printoptions(suppress=True, precision=4, linewidth=100)
numpy.random.seed(21)
p = 0.001
numVertices = 10000
generator = ErdosRenyiGenerator(p)
graph = SparseGraph(GeneralVertexList(numVertices))
graph = generator.generate(graph)
print("Num vertices = " + str(graph.getNumVertices()))
print("Num edges = " + str(graph.getNumEdges()))
L = graph.normalisedLaplacianSym(sparse=True)
#L = csr_matrix(L)
print("Created Laplacian")
#ml = smoothed_aggregation_solver(L)
#M = ml.aspreconditioner()
start = time.clock()
w, V = scipy.sparse.linalg.eigsh(L, k=20)
totalTime = time.clock() - start
print(totalTime)
#print(w)
#Eigsh is quite fast
