def __next__(self):
if self.emit == False:
# We need a "do while" loop here to manage situations where self.graphIterator is already exhausted
while True:
W = next(self.graphIterator)
graph = SparseGraph(W.shape[0], W=W)
numComponents = len(graph.findConnectedComponents())
if __debug__:
logging.debug("graph size = " + str(graph.size) + " num components = " + str(numComponents))
if numComponents < self.maxComponents:
# self.emit = True
break
else:
W = self.graphIterator.next()
return W
def __next__(self):
if self.emit == False:
# We need a "do while" loop here to manage situations where self.graphIterator is already exhausted
while True:
W = next(self.graphIterator)
graph = SparseGraph(W.shape[0], W=W)
numComponents = len(graph.findConnectedComponents())
if __debug__:
logging.debug("graph size = " + str(graph.size) +
" num components = " + str(numComponents))
if numComponents < self.maxComponents:
# self.emit = True
break
else:
W = self.graphIterator.next()
return W
W = scipy.sparse.csr_matrix(createDataset(sigma=1.5)) nystromNs = numpy.arange(20, 151, 10) k = 2 errors = numpy.zeros((len(nystromNs), numMethods)) innerProds = numpy.zeros((len(nystromNs), numMethods)) L = GraphUtils.shiftLaplacian(W) L2 = GraphUtils.normalisedLaplacianSym(W) print(L2.todense()) #Find connected components graph = SparseGraph(GeneralVertexList(W.shape[0])) graph.setWeightMatrix(W) components = graph.findConnectedComponents() print(len(components)) #Compute exact eigenvalues omega, Q = numpy.linalg.eigh(L.todense()) inds = numpy.flipud(numpy.argsort(omega)) omega, Q = omega[inds], Q[:, inds] omegak, Qk = omega[0:k], Q[:, 0:k] print(omega) print(Q) omegaHat, Qhat = numpy.linalg.eigh(L2.todense()) inds = numpy.argsort(omegaHat) omegaHat, Qhat = omegaHat[inds], Qhat[:, inds] omegaHatk, Qhatk = omegaHat[0:k], Qhat[:, 0:k]
class CitationIterGenerator(object):
"""
A class to load the high energy physics data and generate an iterator. The
dataset is found in http://snap.stanford.edu/data/cit-HepTh.html
"""
def __init__(self, minGraphSize=500, maxGraphSize=None, dayStep=30):
dataDir = PathDefaults.getDataDir() + "cluster/"
edgesFilename = dataDir + "Cit-HepTh.txt"
dateFilename = dataDir + "Cit-HepTh-dates.txt"
#Note the IDs are integers but can start with zero so we prefix "1" to each ID
edges = []
file = open(edgesFilename, 'r')
file.readline()
file.readline()
file.readline()
file.readline()
for line in file:
(vertex1, sep, vertex2) = line.partition("\t")
vertex1 = vertex1.strip()
vertex2 = vertex2.strip()
edges.append([vertex1, vertex2])
#if vertex1 == vertex2:
# print(vertex1)
file.close()
logging.info("Loaded edge file " + str(edgesFilename) + " with " + str(len(edges)) + " edges")
#Keep an edge graph
graph = DictGraph(False)
graph.addEdges(edges)
logging.info("Created directed citation graph with " + str(graph.getNumEdges()) + " edges and " + str(graph.getNumVertices()) + " vertices")
#Read in the dates articles appear in a dict which used the year and month
#as the key and the value is a list of vertex ids. For each month we include
#all papers uploaded that month and those directed cited by those uploads.
startDate = datetime.date(1990, 1, 1)
file = open(dateFilename, 'r')
file.readline()
numLines = 0
subgraphIds = []
for line in file:
(id, sep, date) = line.partition("\t")
id = id.strip()
date = date.strip()
inputDate = datetime.datetime.strptime(date.strip(), "%Y-%m-%d")
inputDate = inputDate.date()
if graph.vertexExists(id):
tDelta = inputDate - startDate
graph.vertices[id] = tDelta.days
subgraphIds.append(id)
#If a paper cites another, it must have been written before
#the citing paper - enforce this rule.
for neighbour in graph.neighbours(id):
if graph.getVertex(neighbour) == None:
graph.setVertex(neighbour, tDelta.days)
subgraphIds.append(neighbour)
elif tDelta.days < graph.getVertex(neighbour):
graph.setVertex(neighbour, tDelta.days)
numLines += 1
file.close()
subgraphIds = set(subgraphIds)
graph = graph.subgraph(list(subgraphIds))
logging.debug(graph)
logging.info("Loaded date file " + str(dateFilename) + " with " + str(len(subgraphIds)) + " dates and " + str(numLines) + " lines")
W = graph.getSparseWeightMatrix()
W = W + W.T
vList = VertexList(W.shape[0], 1)
vList.setVertices(numpy.array([graph.getVertices(graph.getAllVertexIds())]).T)
#Note: we have 16 self edges and some two-way citations so this graph has fewer edges than the directed one
self.graph = SparseGraph(vList, W=W)
logging.debug(self.graph)
#Now pick the max component
components = self.graph.findConnectedComponents()
self.graph = self.graph.subgraph(components[0])
logging.debug("Largest component graph: " + str(self.graph))
self.minGraphSize = minGraphSize
self.maxGraphSize = maxGraphSize
self.dayStep = dayStep
def getIterator(self):
"""
Return an iterator which outputs the citation graph for each month. Note
that the graphs are undirected but we make them directed.
"""
vertexArray = self.graph.getVertexList().getVertices()
dates = vertexArray[:, 0]
firstVertex = numpy.argmin(dates)
dayList = range(int(numpy.min(dates)), int(numpy.max(dates)), self.dayStep)
dayList.append(numpy.max(dates))
subgraphIndicesList = []
#Generate subgraph indices list
for i in dayList:
subgraphIndices = numpy.nonzero(dates <= i)[0]
#Check subgraphIndices are sorted
subgraphIndices = numpy.sort(subgraphIndices)
currentSubgraph = self.graph.subgraph(subgraphIndices)
compIndices = currentSubgraph.depthFirstSearch(list(subgraphIndices).index(firstVertex))
subgraphIndices = subgraphIndices[compIndices]
if self.maxGraphSize != None and subgraphIndices.shape[0] > self.maxGraphSize:
break
print(subgraphIndices.shape[0])
if subgraphIndices.shape[0] >= self.minGraphSize:
subgraphIndicesList.append(subgraphIndices)
iterator = IncreasingSubgraphListIterator(self.graph, subgraphIndicesList)
return iterator
""" Name: Generate Graph: Author: Jia_qiu Wang(王佳秋) Data: December, 2016 function: """ from apgl.graph import GeneralVertexList, SparseGraph import numpy numVertices = 5 # 顶点个数 graph = SparseGraph(numVertices) # 具有5个顶点个数的图数据结构 graph[0, 1] = 1 graph[0, 2] = 3 graph[1, 2] = 0.1 graph[3, 4] = 2 graph.setVertex(0, "abc") graph.setVertex(1, 123) print(graph.findConnectedComponents()) # 输出联通分量 print(graph.getWeightMatrix()) # 输出图的邻接矩阵 # print(graph.degreeDistribution()) print(graph.neighbours(0)) print(graph)
