def testEvaluate(self):
numVertices = 6
numFeatures = 1
vList = VertexList(numVertices, numFeatures)
g1 = DenseGraph(vList)
g1.addEdge(0, 1)
g1.addEdge(2, 1)
g1.addEdge(3, 1)
g1.addEdge(4, 1)
g1.addEdge(5, 2)
g2 = DenseGraph(vList)
g2.addEdge(0, 2)
g2.addEdge(1, 2)
g2.addEdge(3, 2)
g2.addEdge(4, 2)
g2.addEdge(5, 1)
g3 = DenseGraph(vList)
g3.addEdge(0, 1)
g4 = SparseGraph(vList)
g4.addEdge(0, 1)
g4.addEdge(2, 1)
g4.addEdge(3, 1)
g4.addEdge(4, 1)
g4.addEdge(5, 2)
lmbda = 0.01
pgk = RandWalkGraphKernel(lmbda)
logging.debug((pgk.evaluate(g1, g1)))
logging.debug((pgk.evaluate(g1, g2)))
logging.debug((pgk.evaluate(g2, g1)))
logging.debug((pgk.evaluate(g2, g2)))
logging.debug((pgk.evaluate(g1, g3)))
logging.debug((pgk.evaluate(g3, g3)))
#Tests - graph kernel is symmetric, permutations of indices are identical
self.assertAlmostEquals(pgk.evaluate(g1, g2),
pgk.evaluate(g2, g1),
places=6)
self.assertAlmostEquals(pgk.evaluate(g1, g3),
pgk.evaluate(g3, g1),
places=6)
self.assertAlmostEquals(pgk.evaluate(g1, g1),
pgk.evaluate(g1, g2),
places=6)
self.assertAlmostEquals(pgk.evaluate(g2, g4),
pgk.evaluate(g1, g2),
places=6)
#All evaluation of themselves are above zero
self.assertTrue(pgk.evaluate(g1, g1) >= 0)
self.assertTrue(pgk.evaluate(g2, g2) >= 0)
self.assertTrue(pgk.evaluate(g3, g3) >= 0)
def setUp(self):
#Let's set up a very simple graph
numVertices = 5
numFeatures = 1
edges = []
vList = VertexList(numVertices, numFeatures)
#An undirected dense graph
self.dGraph1 = DenseGraph(vList, True)
self.dGraph1.addEdge(0, 1, 1)
self.dGraph1.addEdge(0, 2, 1)
self.dGraph1.addEdge(2, 4, 1)
self.dGraph1.addEdge(2, 3, 1)
self.dGraph1.addEdge(3, 4, 1)
#A directed sparse graph
self.dGraph2 = DenseGraph(vList, False)
self.dGraph2.addEdge(0, 1, 1)
self.dGraph2.addEdge(0, 2, 1)
self.dGraph2.addEdge(2, 4, 1)
self.dGraph2.addEdge(2, 3, 1)
self.dGraph2.addEdge(3, 4, 1)
#Now try sparse graphs
vList = VertexList(numVertices, numFeatures)
self.sGraph1 = SparseGraph(vList, True)
self.sGraph1.addEdge(0, 1, 1)
self.sGraph1.addEdge(0, 2, 1)
self.sGraph1.addEdge(2, 4, 1)
self.sGraph1.addEdge(2, 3, 1)
self.sGraph1.addEdge(3, 4, 1)
self.sGraph2 = SparseGraph(vList, False)
self.sGraph2.addEdge(0, 1, 1)
self.sGraph2.addEdge(0, 2, 1)
self.sGraph2.addEdge(2, 4, 1)
self.sGraph2.addEdge(2, 3, 1)
self.sGraph2.addEdge(3, 4, 1)
#Finally, try DictGraphs
self.dctGraph1 = DictGraph(True)
self.dctGraph1.addEdge(0, 1, 1)
self.dctGraph1.addEdge(0, 2, 2)
self.dctGraph1.addEdge(2, 4, 8)
self.dctGraph1.addEdge(2, 3, 1)
self.dctGraph1.addEdge(12, 4, 1)
self.dctGraph2 = DictGraph(False)
self.dctGraph2.addEdge(0, 1, 1)
self.dctGraph2.addEdge(0, 2, 1)
self.dctGraph2.addEdge(2, 4, 1)
self.dctGraph2.addEdge(2, 3, 1)
self.dctGraph2.addEdge(12, 4, 1)
def testVertexLabelPairs(self):
numVertices = 6
numFeatures = 1
vList = VertexList(numVertices, numFeatures)
vList.setVertices(numpy.array([numpy.arange(0, 6)]).T)
graph = DenseGraph(vList, True)
graph.addEdge(0, 1, 0.1)
graph.addEdge(1, 3, 0.1)
graph.addEdge(0, 2, 0.2)
graph.addEdge(2, 3, 0.5)
graph.addEdge(0, 4, 0.1)
graph.addEdge(3, 4, 0.1)
tol = 10**-6
edges = graph.getAllEdges()
X = GraphUtils.vertexLabelPairs(graph, edges)
self.assertTrue(numpy.linalg.norm(X - edges) < tol)
X = GraphUtils.vertexLabelPairs(graph, edges[[5, 2, 1], :])
self.assertTrue(numpy.linalg.norm(X - edges[[5, 2, 1], :]) < tol)
#Try a bigger graph
numVertices = 6
numFeatures = 2
vList = VertexList(numVertices, numFeatures)
vList.setVertices(numpy.random.randn(numVertices, numFeatures))
graph = DenseGraph(vList, True)
graph.addEdge(0, 1, 0.1)
graph.addEdge(1, 3, 0.1)
edges = graph.getAllEdges()
X = GraphUtils.vertexLabelPairs(graph, edges)
self.assertTrue(
numpy.linalg.norm(X[0, 0:numFeatures] - vList.getVertex(1)) < tol)
self.assertTrue(
numpy.linalg.norm(X[0, numFeatures:numFeatures * 2] -
vList.getVertex(0)) < tol)
self.assertTrue(
numpy.linalg.norm(X[1, 0:numFeatures] - vList.getVertex(3)) < tol)
self.assertTrue(
numpy.linalg.norm(X[1, numFeatures:numFeatures * 2] -
vList.getVertex(1)) < tol)
#Try directed graphs
graph = DenseGraph(vList, False)
graph.addEdge(0, 1, 0.1)
graph.addEdge(1, 3, 0.1)
edges = graph.getAllEdges()
X = GraphUtils.vertexLabelPairs(graph, edges)
self.assertTrue(
numpy.linalg.norm(X[0, 0:numFeatures] - vList.getVertex(0)) < tol)
self.assertTrue(
numpy.linalg.norm(X[0, numFeatures:numFeatures * 2] -
vList.getVertex(1)) < tol)
self.assertTrue(
numpy.linalg.norm(X[1, 0:numFeatures] - vList.getVertex(1)) < tol)
self.assertTrue(
numpy.linalg.norm(X[1, numFeatures:numFeatures * 2] -
vList.getVertex(3)) < tol)
import sys
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
""" 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)
def testInit(self):
numVertices = 0
numFeatures = 1
vList = VertexList(numVertices, numFeatures)
graph = DenseGraph(vList)
self.assertEquals(graph.weightMatrixDType(), numpy.float64)
graph = DenseGraph(vList, dtype=numpy.int16)
self.assertEquals(graph.weightMatrixDType(), numpy.int16)
numVertices = 0
numFeatures = 1
vList = VertexList(numVertices, numFeatures)
graph = DenseGraph(vList, dtype=numpy.int16)
numVertices = 10
numFeatures = 1
vList = VertexList(numVertices, numFeatures)
graph = DenseGraph(vList, dtype=numpy.int16)
self.assertEquals(type(graph.W), numpy.ndarray)
self.assertRaises(ValueError, DenseGraph, [])
self.assertRaises(ValueError, DenseGraph, vList, 1)
self.assertRaises(ValueError, DenseGraph, vList, True, 1)
#Now test invalid values of W
W = scipy.sparse.csr_matrix((numVertices, numVertices))
self.assertRaises(ValueError, DenseGraph, vList, True, W)
W = numpy.zeros((numVertices + 1, numVertices))
self.assertRaises(ValueError, DenseGraph, vList, True, W)
W = numpy.zeros((numVertices, numVertices))
W[0, 1] = 1
self.assertRaises(ValueError, DenseGraph, vList, True, W)
W = numpy.zeros((numVertices, numVertices))
graph = DenseGraph(vList, W=W)
self.assertEquals(type(graph.W), numpy.ndarray)
#Test intialising with non-empty graph
numVertices = 10
W = numpy.zeros((numVertices, numVertices))
W[1, 0] = 1.1
W[0, 1] = 1.1
graph = DenseGraph(numVertices, W=W)
self.assertEquals(graph[1, 0], 1.1)
#Test just specifying number of vertices
graph = DenseGraph(numVertices)
self.assertEquals(graph.size, numVertices)
#Try creating a sparse matrix of dtype int
graph = DenseGraph(numVertices, dtype=numpy.int)
self.assertEquals(graph.W.dtype, numpy.int)
graph[0, 0] = 1.2
self.assertEquals(graph[0, 0], 1)
