def testGreedyInfluence(self):
numVertices = 4
P = numpy.zeros((numVertices, numVertices))
P[0, :] = numpy.array([1, 0.3, 0.4, 0.1])
P[1, :] = numpy.array([0, 1, 0.5, 0.2])
P[2, :] = numpy.array([0, 0.1, 1, 0])
P[3, :] = numpy.array([0.3, 0.6, 0.4, 1])
k = 2
influence = GreedyInfluence()
inds = influence.maxInfluence(P, k)
self.assertTrue(inds == [3, 0])
k = 4
influence = GreedyInfluence()
inds = influence.maxInfluence(P, k)
self.assertTrue(inds == [3, 0, 2, 1])
#Now test case in which we can get best influence in 1 vertex and we
#want to choose a larger set.
P[3, :] = numpy.array([1, 1, 1, 1])
P[1, :] = numpy.array([0, 1, 0.5, 0.2])
P[2, :] = numpy.array([0, 0.1, 1, 0])
P[0, :] = numpy.array([0.3, 0.6, 0.4, 1])
k = 3
inds = influence.maxInfluence(P, k)
self.assertTrue(inds == [3, 0, 1])
def testGraphInfuence(self):
#We test the influence using a real graph
numVertices = 5
numFeatures = 0
vList = VertexList(numVertices, numFeatures)
sGraph = SparseGraph(vList, False)
sGraph.addEdge(0, 1, 0.1)
sGraph.addEdge(0, 2, 0.5)
sGraph.addEdge(1, 3, 0.9)
sGraph.addEdge(2, 3, 0.7)
sGraph.addEdge(2, 4, 0.8)
P = sGraph.maxProductPaths()
self.assertTrue((P[0, :] == numpy.array([0,0.1, 0.5, 0.35, 0.4])).all())
self.assertTrue((P[1, :] == numpy.array([0,0,0,0.9,0])).all())
self.assertTrue((P[2, :] == numpy.array([0,0,0,0.7,0.8])).all())
self.assertTrue((P[3, :] == numpy.array([0,0,0,0,0])).all())
self.assertTrue((P[4, :] == numpy.array([0,0,0,0,0])).all())
k = 5
influence = GreedyInfluence()
inds = influence.maxInfluence(P, k)
def maxBudgetedInfluence(self, P, u, L):
Parameter.checkFloat(L, 0.0, float('inf'))
#Make the highest cost vertices appear first so we reach the budget faster
sortedInds = numpy.argsort(-u)
P2 = P[sortedInds, :]
u2 = u[sortedInds]
greedyInfluence = GreedyInfluence()
self.selectedIndices = greedyInfluence.maxBudgetedInfluence(P2, u2, L)
self.bestInfluence = numpy.sum(numpy.max(P2[self.selectedIndices, :], 0))
#Now start the branch and bound method
selectedIndices = []
self.__followBranch(selectedIndices, P2, u2, L, 0)
return numpy.sort(sortedInds[self.selectedIndices]).tolist()
def testMaxBudgetedInfluence(self):
#First test the case where the cost of each vertex is 1 and L = k
numVertices = 4
P = numpy.zeros((numVertices, numVertices))
u = numpy.ones(numVertices)
P[0, :] = numpy.array([1, 0.3, 0.4, 0.1])
P[1, :] = numpy.array([0, 1, 0.5, 0.2])
P[2, :] = numpy.array([0, 0.1, 1, 0])
P[3, :] = numpy.array([0.3, 0.6, 0.4, 1])
k = 2.0
influence = GreedyInfluence()
inds = influence.maxBudgetedInfluence(P, u, k)
self.assertTrue(inds == [3, 0])
k = 4.0
influence = GreedyInfluence()
inds = influence.maxBudgetedInfluence(P, u, k)
self.assertTrue(inds == [3, 0, 2, 1])
#Now test case in which we can get best influence in 1 vertex and we
#want to choose a larger set.
P[3, :] = numpy.array([1, 1, 1, 1])
P[1, :] = numpy.array([0, 1, 0.5, 0.2])
P[2, :] = numpy.array([0, 0.1, 1, 0])
P[0, :] = numpy.array([0.3, 0.6, 0.4, 1])
k = 3.0
inds = influence.maxBudgetedInfluence(P, u, k)
#This result differs from the unbudgeted version since we don't ask for
#k indices
self.assertTrue(inds == [3])
#Now test the budgeted case with varying cost vectors
numVertices = 5
P = numpy.zeros((numVertices, numVertices))
u = numpy.array([3,4,6,2,1])
P[0, :] = numpy.array([2, 8, 3, 4, 1])
P[1, :] = numpy.array([1, 9, 6, 2, 3])
P[2, :] = numpy.array([12, 8 ,9, 3, 1])
P[3, :] = numpy.array([0, 2, 1, 6, 9])
P[4, :] = numpy.array([1, 0, 0, 8, 2])
L = 7.0
inds = influence.maxBudgetedInfluence(P, u, L)
self.assertEquals(inds, [4, 3, 1])
L = 8.0
inds = influence.maxBudgetedInfluence(P, u, L)
self.assertEquals(inds, [4, 3, 1])
L = 9.0
inds = influence.maxBudgetedInfluence(P, u, L)
self.assertEquals(inds, [4, 3, 2])
#This is the maximum budget
L = 16.0
inds = influence.maxBudgetedInfluence(P, u, L)
inds2 = influence.maxInfluence((P.T/u).T, 4)
self.assertEquals(inds, [4, 3, 2, 0])
self.assertEquals(inds, inds2)
P[1, :] = numpy.array([2, 8, 6.5, 4, 1])
inds = influence.maxBudgetedInfluence(P, u, L)
self.assertEquals(inds, [4, 3, 2, 0, 1])
