#Now assign the percolation decays to the edges for both graphs
graph.addEdges(allEdges, y)
graph2.addEdges(allEdges, predY)
logging.info("min(y)= " + str(numpy.min(y)))
logging.info("max(y)= " + str(numpy.max(y)))
P = graph.maxProductPaths()
P = P + numpy.eye(numVertices)
P2 = graph2.maxProductPaths()
P2 = P2 + numpy.eye(numVertices)
logging.info("Computing max influence for learnt percolations")
inds = numpy.array(influence.maxInfluence(P, maxK))
logging.info("Computing max influence for real percolations")
inds2 = numpy.array(influence.maxInfluence(P2, maxK))
for j in range(len(ks)):
k = ks[j]
#print(numpy.sum(numpy.max(P[inds[0:k], :], 0)))
influenceErrors[i, j] = numpy.setdiff1d(inds[0:k], inds2[0:k]).shape[0]/float(k)
meanInfluenceErrors = numpy.mean(influenceErrors, 0)
stdInfluenceErrors = numpy.std(influenceErrors, 0)
logging.info(meanInfluenceErrors)
logging.info(stdInfluenceErrors)
fileName = outputDir + "influenceErrors" + classifier + "_n=" + str(noise)
influenceArray = numpy.zeros((k, 2))
outputDirectory = PathDefaults.getOutputDir()
outputDir = outputDirectory + "influence/"
for i in range(len(generators)):
fileName = outputDir + "FullTransGraph" + str(generators[i]) + ".spg"
graph = SparseGraph.load(fileName)
numVertices = graph.getNumVertices()
logging.info(graph.degreeDistribution())
logging.info("Computing max product paths")
#Make sure the diagonal entrices have information 1
P = graph.maxProductPaths()
P = P + numpy.eye(numVertices)
logging.info("Computing max influence")
influence = GreedyInfluence()
inds = influence.maxInfluence(P, maxIks)
for m in range(len(iks)):
ik = iks[m]
influenceArray[m, 0, i] = numpy.sum(numpy.max(P[inds[0:ik], :], 0))
influenceArray[m, 1, i] = numpy.sum(numpy.max(P[inds2[0:ik], :], 0))
influenceArray[m, 2, i] = numpy.sum(numpy.max(P[inds3[0:ik], :], 0))
#Now save the results
numpy.save(outputDir + "InfluenceArraySW1", influenceArray[:,:,0])
numpy.save(outputDir + "InfluenceArrayER", influenceArray[:,:,1])
logging.info("All done.")
numVertices = graph.getNumVertices()
#Rewrite edges to have a decay rate of d
edges = graph.getAllEdges()
for m in range(edges.shape[0]):
d = numpy.random.rand()
graph.addEdge(edges[m, 0], edges[m, 1], d)
logging.info("Computing max product paths")
#Make sure the diagonal entrices have information 1
P = graph.maxProductPaths()
P = P + numpy.eye(numVertices)
P2 = numpy.array(P != 0, numpy.int32)
logging.info("Computing max influence")
influence = GreedyInfluence()
inds = influence.maxInfluence(P, maxIks)
logging.info("Computing Kempes max influence")
inds2 = influence.maxInfluence(P2, maxIks)
inds3 = numpy.random.permutation(numVertices)[0:maxIks]
for m in range(len(iks)):
ik = iks[m]
influenceArray[m, 0] = numpy.sum(numpy.max(P[inds[0:ik], :], 0))
influenceArray[m, 1] = numpy.sum(numpy.max(P[inds2[0:ik], :], 0))
influenceArray[m, 2] = numpy.sum(numpy.max(P[inds3[0:ik], :], 0))
#Now save the results
numpy.save(outputDir + "influenceArraySW2", influenceArray)
logging.info("All done.")
