class ThreeClustIterator(object):
def __init__(self, p=0.1, numClusters=3, seed=21):
numpy.random.seed(seed)
self.numClusters = numClusters
self.startClusterSize = 20
self.endClusterSize = 60
self.clusterStep = 5
self.pClust = 0.3
self.numVertices = self.numClusters*self.endClusterSize
vList = GeneralVertexList(self.numVertices)
subgraphIndicesList = [range(0, self.startClusterSize)]
subgraphIndicesList[0].extend(range(self.endClusterSize, self.endClusterSize+self.startClusterSize))
subgraphIndicesList[0].extend(range(2*self.endClusterSize, 2*self.endClusterSize+self.startClusterSize))
for i in range(self.startClusterSize, self.endClusterSize-self.clusterStep+1, self.clusterStep):
subgraphIndices = copy.copy(subgraphIndicesList[-1])
subgraphIndices.extend(range(i, i+self.clusterStep))
subgraphIndices.extend(range(self.endClusterSize+i, self.endClusterSize+i+self.clusterStep))
subgraphIndices.extend(range(2*self.endClusterSize+i, 2*self.endClusterSize+i+self.clusterStep))
subgraphIndicesList.append(subgraphIndices)
# to test removing
# - increasing graph
# do nothing
# - decreasing graph
#subgraphIndicesList.reverse()
# - increasing and decreasing graph
tmp = copy.copy(subgraphIndicesList[:-1])
tmp.reverse()
subgraphIndicesList.extend(tmp)
self.subgraphIndicesList = subgraphIndicesList
self.p = p
W = numpy.ones((self.numVertices, self.numVertices))*self.p
for i in range(numClusters):
W[self.endClusterSize*i:self.endClusterSize*(i+1), self.endClusterSize*i:self.endClusterSize*(i+1)] = self.pClust
P = numpy.random.rand(self.numVertices, self.numVertices)
W = numpy.array(P < W, numpy.float)
upTriInds = numpy.triu_indices(self.numVertices)
W[upTriInds] = 0
W = W + W.T
self.graph = SparseGraph(vList)
self.graph.setWeightMatrix(W)
def getIterator(self):
return IncreasingSubgraphListIterator(self.graph, self.subgraphIndicesList)
def run():
W = createDataset()
numVertices = W.shape[0]
graph = SparseGraph(GeneralVertexList(numVertices))
graph.setWeightMatrix(W)
L = graph.normalisedLaplacianSym()
#L = GraphUtils.shiftLaplacian(scipy.sparse.csr_matrix(W)).todense()
n = 100
omega, Q = numpy.linalg.eigh(L)
omega2, Q2 = Nystrom.eigpsd(L, n)
print(omega)
print(omega2)
plt.figure(1)
plt.plot(numpy.arange(omega.shape[0]), omega)
plt.plot(numpy.arange(omega2.shape[0]), omega2)
plt.show()
#run()
#There is no eigengap in this case so bound does poorly W = scipy.sparse.csr_matrix(createDataset(sigma=1.5)) nystromNs = numpy.arange(20, 151, 10) k = 2 errors = numpy.zeros((len(nystromNs), numMethods)) innerProds = numpy.zeros((len(nystromNs), numMethods)) L = GraphUtils.shiftLaplacian(W) L2 = GraphUtils.normalisedLaplacianSym(W) print(L2.todense()) #Find connected components graph = SparseGraph(GeneralVertexList(W.shape[0])) graph.setWeightMatrix(W) components = graph.findConnectedComponents() print(len(components)) #Compute exact eigenvalues omega, Q = numpy.linalg.eigh(L.todense()) inds = numpy.flipud(numpy.argsort(omega)) omega, Q = omega[inds], Q[:, inds] omegak, Qk = omega[0:k], Q[:, 0:k] print(omega) print(Q) omegaHat, Qhat = numpy.linalg.eigh(L2.todense()) inds = numpy.argsort(omegaHat) omegaHat, Qhat = omegaHat[inds], Qhat[:, inds]
