def testDegreeDistribution(self): numFeatures = 0 numVertices = 100 vList = VertexList(numVertices, numFeatures) graph = SparseGraph(vList) alpha = 10.0 p = 0.01 dim = 2 generator = GeometricRandomGenerator(graph) graph = generator.generateGraph(alpha, p, dim) logging.debug((graph.degreeDistribution()))
def testGenerateGraph(self): numFeatures = 0 numVertices = 20 vList = VertexList(numVertices, numFeatures) graph = SparseGraph(vList) alpha1 = 10.0 alpha2 = 20.0 p = 0.001 dim = 2 generator = GeometricRandomGenerator(graph) graph = generator.generateGraph(alpha1, p, dim) numEdges1 = graph.getNumEdges() #Check no self edges for i in range(numVertices): self.assertTrue(graph.getEdge(i, i) == None) graph.removeAllEdges() graph = generator.generateGraph(alpha2, p, dim) numEdges2 = graph.getNumEdges() #self.assertTrue(numEdges1 >= numEdges2) logging.debug(numEdges1) logging.debug(numEdges2) for i in range(numVertices): self.assertTrue(graph.getEdge(i, i) == None) #Test case with p=0 and alpha huge p = 0.0 alpha = 100.0 graph.removeAllEdges() graph = generator.generateGraph(alpha, p, dim) self.assertEquals(graph.getNumEdges(), 0) #When alpha=0, should get max edges alpha = 0.0 graph.removeAllEdges() graph = generator.generateGraph(alpha, p, dim) #self.assertEquals(graph.getNumEdges(), int(0.5*(numVertices + numVertices**2) - numVertices)) #TODO: Test variations in dimension """