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 )
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)
