def __init__(self):
#Total number of people in population
self.M = 1000
numInitialInfected = 5
#The graph is one in which edges represent a contact
undirected = True
self.graph = HIVGraph(self.M, undirected)
for i in range(self.M):
vertex = self.graph.getVertex(i)
#Set the infection time of a number of individuals to 0
if i < numInitialInfected:
vertex[HIVVertices.stateIndex] = HIVVertices.infected
p = 0.01
generator = ErdosRenyiGenerator(p)
self.graph = generator.generate(self.graph)
perm1 = numpy.random.permutation(self.M)
perm2 = numpy.random.permutation(self.M)
sizes = [200, 300, 500, 1000]
self.summary1 = []
self.summary2 = []
for size in sizes:
self.summary1.append(self.graph.subgraph(perm1[0:size]))
self.summary2.append(self.graph.subgraph(perm2[0:int(size/10)]))
print(self.graph)
sigmaSqSum = (sigma[0:r2]**2).sum()
bound = gammaSqSum + lmbdaSqSum - 2*sigmaSqSum
print("r=" + str(r))
print("gammaSqSum=" + str(gammaSqSum))
print("lmbdaSqSum=" + str(lmbdaSqSum))
print("sigmaSqSum=" + str(sigmaSqSum))
return bound
#Change to work with real Laplancian
numRows = 100
graph = SparseGraph(GeneralVertexList(numRows))
p = 0.1
generator = ErdosRenyiGenerator(p)
graph = generator.generate(graph)
print(graph)
AA = graph.normalisedLaplacianSym()
p = 0.001
generator.setP(p)
graph = generator.generate(graph, requireEmpty=False)
AA2 = graph.normalisedLaplacianSym()
""" Name: Generate Graph: Author: Jia_qiu Wang(王佳秋) Data: December, 2016 function: """ from apgl.graph.DenseGraph import DenseGraph from apgl.graph.GeneralVertexList import GeneralVertexList from apgl.generator.ErdosRenyiGenerator import * numVertices = 20 graph = DenseGraph(GeneralVertexList(numVertices)) p = 0.2 generator = ErdosRenyiGenerator(p) graph = generator.generate(graph)
bound = gammaSqSum + lmbdaSqSum - 2 * sigmaSqSum
print("r=" + str(r))
print("gammaSqSum=" + str(gammaSqSum))
print("lmbdaSqSum=" + str(lmbdaSqSum))
print("sigmaSqSum=" + str(sigmaSqSum))
return bound
#Change to work with real Laplancian
numRows = 100
graph = SparseGraph(GeneralVertexList(numRows))
p = 0.1
generator = ErdosRenyiGenerator(p)
graph = generator.generate(graph)
print(graph)
AA = graph.normalisedLaplacianSym()
p = 0.001
generator.setP(p)
graph = generator.generate(graph, requireEmpty=False)
AA2 = graph.normalisedLaplacianSym()
U = AA2 - AA
#print(U)