def BarabasiAlbertEdgeList(n):
graph = SparseGraph(n)
generator = BarabasiAlbertGenerator(10, 10)
graph = generator.generate(graph)
l, _ = graph.adjacencyList()
return convertAdjListToEdgeList(l)
def testGraphDisplay(self):
try:
import networkx
import matplotlib
except ImportError as error:
logging.debug(error)
return
#Show
numFeatures = 1
numVertices = 20
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
ell = 2
m = 2
generator = BarabasiAlbertGenerator(ell, m)
graph = generator.generate(graph)
logging.debug((graph.degreeDistribution()))
nxGraph = graph.toNetworkXGraph()
nodePositions = networkx.spring_layout(nxGraph)
nodesAndEdges = networkx.draw_networkx(nxGraph, pos=nodePositions)
def testGraphDisplay(self):
try:
import networkx
import matplotlib
except ImportError as error:
logging.debug(error)
return
#Show
numFeatures = 1
numVertices = 20
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
ell = 2
m = 2
generator = BarabasiAlbertGenerator(ell, m)
graph = generator.generate(graph)
logging.debug((graph.degreeDistribution()))
nxGraph = graph.toNetworkXGraph()
nodePositions = networkx.spring_layout(nxGraph)
nodesAndEdges = networkx.draw_networkx(nxGraph, pos=nodePositions)
def BarabasiAlbertEdgeList(n):
graph = SparseGraph(n)
generator = BarabasiAlbertGenerator(10, 10)
graph = generator.generate(graph)
l, _ = graph.adjacencyList()
return convertAdjListToEdgeList(l)
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 testEfficientNystrom(self):
numVertices = 50
graph = SparseGraph(GeneralVertexList(numVertices))
ell = 2
m = 2
generator = BarabasiAlbertGenerator(ell, m)
graph = generator.generate(graph)
indices = numpy.random.permutation(numVertices)
subgraphIndicesList = [indices[0:5], indices]
graphIterator = IncreasingSubgraphListIterator(graph, subgraphIndicesList)
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)
def testIncreasingSubgraphListIterator(self):
#Create a small graph and try the iterator increasing the number of vertices.
numVertices = 50
graph = SparseGraph(GeneralVertexList(numVertices))
ell = 2
m = 2
generator = BarabasiAlbertGenerator(ell, m)
graph = generator.generate(graph)
indices = numpy.random.permutation(numVertices)
subgraphIndicesList = [indices[0:5], indices]
graphIterator = IncreasingSubgraphListIterator(graph, subgraphIndicesList)
#Try a much longer sequence of vertices
subgraphIndicesList = []
for i in range(10, numVertices):
subgraphIndicesList.append(range(i))
graphIterator = IncreasingSubgraphListIterator(graph, subgraphIndicesList)
k1 = 3
k2 = 6
clusterer = IterativeSpectralClustering(k1, k2)
clustersList = clusterer.clusterFromIterator(graphIterator)
#Now test the Nystrom method
graphIterator = IncreasingSubgraphListIterator(graph, subgraphIndicesList)
clusterer = IterativeSpectralClustering(k1, alg="nystrom")
clustersList = clusterer.clusterFromIterator(graphIterator)
#Test efficient Nystrom method
graphIterator = IncreasingSubgraphListIterator(graph, subgraphIndicesList)
clusterer = IterativeSpectralClustering(k1, alg="efficientNystrom")
clustersList = clusterer.clusterFromIterator(graphIterator)
def testGenerate(self):
numFeatures = 1
numVertices = 20
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
ell = 2
m = 0
generator = BarabasiAlbertGenerator(ell, m)
graph = generator.generate(graph)
self.assertEquals(graph.getNumEdges(), 0)
ell = 5
graph.removeAllEdges()
generator.setEll(ell)
graph = generator.generate(graph)
self.assertEquals(graph.getNumEdges(), 0)
#Now test case where we m != 0
ell = 2
m = 1
graph.removeAllEdges()
generator.setEll(ell)
generator.setM(m)
graph = generator.generate(graph)
self.assertEquals(graph.getNumEdges(), (numVertices - ell) * m)
m = 2
graph.removeAllEdges()
generator.setM(m)
graph = generator.generate(graph)
self.assertEquals(graph.getNumEdges(), (numVertices - ell) * m)
def testGenerate(self):
numFeatures = 1
numVertices = 20
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
ell = 2
m = 0
generator = BarabasiAlbertGenerator(ell, m)
graph = generator.generate(graph)
self.assertEquals(graph.getNumEdges(), 0)
ell = 5
graph.removeAllEdges()
generator.setEll(ell)
graph = generator.generate(graph)
self.assertEquals(graph.getNumEdges(), 0)
#Now test case where we m != 0
ell = 2
m = 1
graph.removeAllEdges()
generator.setEll(ell)
generator.setM(m)
graph = generator.generate(graph)
self.assertEquals(graph.getNumEdges(), (numVertices-ell)*m)
m = 2
graph.removeAllEdges()
generator.setM(m)
graph = generator.generate(graph)
self.assertEquals(graph.getNumEdges(), (numVertices-ell)*m)
