def testWriteToFile3(self):
"""
We will test out writing out some random graphs to Pajek
"""
numVertices = 20
numFeatures = 0
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
p = 0.1
generator = ErdosRenyiGenerator(p)
graph = generator.generate(graph)
pw = PajekWriter()
directory = PathDefaults.getOutputDir() + "test/"
pw.writeToFile(directory + "erdosRenyi20", graph)
#Now write a small world graph
p = 0.2
k = 3
graph.removeAllEdges()
generator = SmallWorldGenerator(p, k)
graph = generator.generate(graph)
pw.writeToFile(directory + "smallWorld20", graph)
def testWriteToFile3(self):
"""
We will test out writing out some random graphs to Pajek
"""
numVertices = 20
numFeatures = 0
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
p = 0.1
generator = ErdosRenyiGenerator(p)
graph = generator.generate(graph)
pw = PajekWriter()
directory = PathDefaults.getOutputDir() + "test/"
pw.writeToFile(directory + "erdosRenyi20", graph)
#Now write a small world graph
p = 0.2
k = 3
graph.removeAllEdges()
generator = SmallWorldGenerator(p, k)
graph = generator.generate(graph)
pw.writeToFile(directory + "smallWorld20", graph)
def generateGraph(self, alpha, p, dim):
Parameter.checkFloat(alpha, 0.0, float('inf'))
Parameter.checkFloat(p, 0.0, 1.0)
Parameter.checkInt(dim, 0, float('inf'))
numVertices = self.graph.getNumVertices()
self.X = numpy.random.rand(numVertices, dim)
D = KernelUtils.computeDistanceMatrix(numpy.dot(self.X, self.X.T))
P = numpy.exp(-alpha * D)
diagIndices = numpy.array(list(range(0, numVertices)))
P[(diagIndices, diagIndices)] = numpy.zeros(numVertices)
B = numpy.random.rand(numVertices, numVertices) <= P
#Note that B is symmetric - could just go through e.g. upper triangle
for i in range(numpy.nonzero(B)[0].shape[0]):
v1 = numpy.nonzero(B)[0][i]
v2 = numpy.nonzero(B)[1][i]
self.graph.addEdge(v1, v2)
erdosRenyiGenerator = ErdosRenyiGenerator(p)
self.graph = erdosRenyiGenerator.generate(self.graph, False)
return self.graph
def generateGraph(self, alpha, p, dim):
Parameter.checkFloat(alpha, 0.0, float('inf'))
Parameter.checkFloat(p, 0.0, 1.0)
Parameter.checkInt(dim, 0, float('inf'))
numVertices = self.graph.getNumVertices()
self.X = numpy.random.rand(numVertices, dim)
D = KernelUtils.computeDistanceMatrix(numpy.dot(self.X, self.X.T))
P = numpy.exp(-alpha * D)
diagIndices = numpy.array(list(range(0, numVertices)))
P[(diagIndices, diagIndices)] = numpy.zeros(numVertices)
B = numpy.random.rand(numVertices, numVertices) <= P
#Note that B is symmetric - could just go through e.g. upper triangle
for i in range(numpy.nonzero(B)[0].shape[0]):
v1 = numpy.nonzero(B)[0][i]
v2 = numpy.nonzero(B)[1][i]
self.graph.addEdge(v1, v2)
erdosRenyiGenerator = ErdosRenyiGenerator(p)
self.graph = erdosRenyiGenerator.generate(self.graph, False)
return self.graph
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)
p = 0.2
generator = ErdosRenyiGenerator(p)
graph = generator.generate(graph)
logging.debug((graph.getNumEdges()))
nxGraph = graph.toNetworkXGraph()
nodePositions = networkx.spring_layout(nxGraph)
nodesAndEdges = networkx.draw_networkx(nxGraph, pos=nodePositions)
ax = matplotlib.pyplot.axes()
ax.set_xticklabels([])
ax.set_yticklabels([])
def testMeanSeqScalarStats(self):
numFeatures = 1
numVertices = 10
vList = VertexList(numVertices, numFeatures)
p = 0.1
generator = ErdosRenyiGenerator(p)
numGraphs = 50
graphList = []
subgraphIndices = [list(range(3)), list(range(6)), list(range(10))]
for i in range(numGraphs):
graph = generator.generate(SparseGraph(vList))
graphList.append((graph, subgraphIndices))
growthStatistics = GraphStatistics()
meanStats, stdStats = growthStatistics.meanSeqScalarStats(graphList)
#Check some of the stats
for i in range(len(subgraphIndices)):
self.assertEquals(meanStats[i, growthStatistics.numVerticesIndex], len(subgraphIndices[i]))
self.assertEquals(stdStats[i, growthStatistics.numVerticesIndex], 0)
self.assertAlmostEquals(meanStats[i, growthStatistics.numEdgesIndex], 0.5*(len(subgraphIndices[i])*(len(subgraphIndices[i])-1))*p, places=0)
def testGenerate2(self):
numVertices = 20
graph = SparseGraph(GeneralVertexList(numVertices))
p = 0.2
generator = ErdosRenyiGenerator(p)
graph = generator.generate(graph)
self.assertTrue((graph.getNumEdges() - p*numVertices*numVertices/2) < 8)
def testLearnModel(self):
numVertices = 100
numFeatures = 1
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
p = 0.2
generator = ErdosRenyiGenerator(p)
graph = generator.generate(graph)
vertexIndices = list(range(0, numVertices))
k = 2
learner = GrowthLearner(k)
tol = 10**-1
#Lets test the values of alpha on a series of Erdos-Renyi graphs
for i in range(1, 6):
p = float(i)/10
graph.removeAllEdges()
graph = generator.generate(graph)
alpha = learner.learnModel(graph, vertexIndices)
logging.debug((numpy.linalg.norm(alpha - numpy.array([p, 0]))))
#self.assertTrue(numpy.linalg.norm(alpha - numpy.array([p, 0])) < tol)
#Now test the learning on some preferencial attachment graphs
ell = 10
m = 8
vertexIndices = list(range(ell, numVertices))
graph.removeAllEdges()
generator = BarabasiAlbertGenerator(ell, m)
graph = generator.generate(graph)
alpha = learner.learnModel(graph, vertexIndices)
logging.debug(alpha)
def __init__(self):
numVertices = 1000
graph = SparseGraph(GeneralVertexList(numVertices))
p = 0.1
generator = ErdosRenyiGenerator(p)
graph = generator.generate(graph)
subgraphIndicesList = []
for i in range(100, numVertices, 10):
subgraphIndicesList.append(range(i))
k1 = 5
k2 = 100
self.graph = graph
self.subgraphIndicesList = subgraphIndicesList
self.clusterer = IterativeSpectralClustering(k1, k2, T=10, alg="IASC")
def __init__(self):
numVertices = 1000
graph = SparseGraph(GeneralVertexList(numVertices))
p = 0.1
generator = ErdosRenyiGenerator(p)
graph = generator.generate(graph)
subgraphIndicesList = []
for i in range(100, numVertices, 10):
subgraphIndicesList.append(range(i))
k1 = 5
k2 = 100
self.graph = graph
self.subgraphIndicesList = subgraphIndicesList
self.clusterer = IterativeSpectralClustering(k1, k2, T=10, alg="IASC")
class ErdoRenyiGeneratorTest(unittest.TestCase):
def setUp(self):
numpy.set_printoptions(suppress=True, linewidth=200, precision=5)
self.numVertices = 10;
self.numFeatures = 2;
self.vList = VertexList(self.numVertices, self.numFeatures)
self.graph = SparseGraph(self.vList)
self.p = 0.1
self.erg = ErdosRenyiGenerator(self.p)
def testGenerate(self):
#undirected = True
p = 0.0
self.graph.removeAllEdges()
self.erg.setP(p)
graph = self.erg.generate(self.graph)
self.assertEquals(graph.getNumEdges(), 0)
undirected = False
self.graph = SparseGraph(self.vList, undirected)
self.graph.removeAllEdges()
self.erg = ErdosRenyiGenerator(p)
graph = self.erg.generate(self.graph)
self.assertEquals(graph.getNumEdges(), 0)
p = 1.0
undirected = True
self.graph = SparseGraph(self.vList, undirected)
self.graph.removeAllEdges()
self.erg = ErdosRenyiGenerator(p)
graph = self.erg.generate(self.graph)
self.assertEquals(graph.getNumEdges(), (self.numVertices*self.numVertices-self.numVertices)/2)
p = 1.0
undirected = False
self.graph = SparseGraph(self.vList, undirected)
self.graph.removeAllEdges()
self.erg = ErdosRenyiGenerator(p)
graph = self.erg.generate(self.graph)
self.assertEquals(graph.getNumEdges(), self.numVertices*self.numVertices-self.numVertices)
self.assertEquals(graph.getEdge(1, 2), 1)
self.assertEquals(graph.getEdge(1, 1), None)
p = 0.5
numVertices = 1000
numFeatures = 0
vList = VertexList(numVertices, numFeatures)
undirected = False
self.graph = SparseGraph(vList, undirected)
self.erg = ErdosRenyiGenerator(p)
graph = self.erg.generate(self.graph)
self.assertAlmostEquals(graph.getNumEdges()/float(numVertices**2 - numVertices), p, places=2)
p = 0.1
self.graph = SparseGraph(vList, undirected)
self.erg = ErdosRenyiGenerator(p)
graph = self.erg.generate(self.graph)
self.assertAlmostEquals(graph.getNumEdges()/float(numVertices**2 - numVertices), p, places=2)
#Test the case in which we have a graph with edges
p = 0.5
numVertices = 10
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList, undirected)
graph.addEdge(0, 1, 5)
graph.addEdge(0, 2)
graph.addEdge(0, 5, 0.7)
graph.addEdge(1, 8)
graph.addEdge(2, 9)
numEdges = graph.getNumEdges()
graph = self.erg.generate(graph, False)
self.assertTrue(graph.getNumEdges() > numEdges)
@apgl.skip("")
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)
p = 0.2
generator = ErdosRenyiGenerator(p)
graph = generator.generate(graph)
logging.debug((graph.getNumEdges()))
nxGraph = graph.toNetworkXGraph()
nodePositions = networkx.spring_layout(nxGraph)
nodesAndEdges = networkx.draw_networkx(nxGraph, pos=nodePositions)
ax = matplotlib.pyplot.axes()
ax.set_xticklabels([])
ax.set_yticklabels([])
#matplotlib.pyplot.show()
def testGenerate2(self):
numVertices = 20
graph = SparseGraph(GeneralVertexList(numVertices))
p = 0.2
generator = ErdosRenyiGenerator(p)
graph = generator.generate(graph)
self.assertTrue((graph.getNumEdges() - p*numVertices*numVertices/2) < 8)
def testErdosRenyiGenerations(self):
numVertices = 20
graph = DenseGraph(GeneralVertexList(numVertices))
p = 0.2
generator = ErdosRenyiGenerator(p)
graph = generator.generate(graph)
def testErdosRenyiGenerations(self):
numVertices = 20
graph = DenseGraph(GeneralVertexList(numVertices))
p = 0.2
generator = ErdosRenyiGenerator(p)
graph = generator.generate(graph)
import numpy
import scipy.sparse.linalg
from scipy.sparse import csr_matrix
from pyamg import smoothed_aggregation_solver
from apgl.generator.ErdosRenyiGenerator import ErdosRenyiGenerator
from apgl.graph.SparseGraph import SparseGraph
from apgl.graph.GeneralVertexList import GeneralVertexList
numpy.set_printoptions(suppress=True, precision=4, linewidth=100)
numpy.random.seed(21)
p = 0.001
numVertices = 10000
generator = ErdosRenyiGenerator(p)
graph = SparseGraph(GeneralVertexList(numVertices))
graph = generator.generate(graph)
print("Num vertices = " + str(graph.getNumVertices()))
print("Num edges = " + str(graph.getNumEdges()))
L = graph.normalisedLaplacianSym(sparse=True)
# L = csr_matrix(L)
print("Created Laplacian")
# ml = smoothed_aggregation_solver(L)
# M = ml.aspreconditioner()
start = time.clock()
w, V = scipy.sparse.linalg.eigsh(L, k=20)
totalTime = time.clock() - start
import struct
#from apgl.graph import *
from apgl.graph.DenseGraph import DenseGraph
from apgl.graph.VertexList import VertexList
from apgl.generator.ErdosRenyiGenerator import ErdosRenyiGenerator
import numpy
if len(sys.argv) != 2:
print('Usage {} <num-vertices>'.format(sys.argv[0]))
sys.exit(1)
# Generate a random graph
p = 0.2
graph = DenseGraph(VertexList(int(sys.argv[1]), 1))
generator = ErdosRenyiGenerator(p)
graph = generator.generate(graph)
numVertices = graph.getNumVertices()
numEdges = graph.getNumEdges()
print("{} vertices {} edges".format(numVertices, numEdges))
# Write packed adjacency list to a binary file
outfile = 'graph'
f = open(outfile, "wb")
try:
f.write(struct.pack('i', numVertices))
f.write(struct.pack('i', numEdges))
index = 0
# Vertices
for i in range(numVertices):
f.write(struct.pack('i', numVertices + index))