def match(self, graph1, graph2):
"""
Take two graphs are match them. The two graphs must be AbstractMatrixGraphs
with VertexLists representing the vertices.
:param graph1: A graph object
:param graph2: The second graph object to match
:return permutation: A vector of indices representing the matching of elements of graph1 to graph2
:return distance: The graph distance list [graphDistance, fDistance, fDistanceExact]
"""
#Deal with case where at least one graph is emty
if graph1.size == 0 and graph2.size == 0:
permutation = numpy.array([], numpy.int)
distanceVector = [0, 0, 0]
time = 0
return permutation, distanceVector, time
elif graph1.size == 0 or graph2.size == 0:
if graph1.size == 0:
graph1 = SparseGraph(
VertexList(graph2.size,
graph2.getVertexList().getNumFeatures()))
else:
graph2 = SparseGraph(
VertexList(graph1.size,
graph1.getVertexList().getNumFeatures()))
numTempFiles = 5
tempFileNameList = []
for i in range(numTempFiles):
fileObj = tempfile.NamedTemporaryFile(delete=False)
tempFileNameList.append(fileObj.name)
fileObj.close()
configFileName = tempFileNameList[0]
graph1FileName = tempFileNameList[1]
graph2FileName = tempFileNameList[2]
similaritiesFileName = tempFileNameList[3]
outputFileName = tempFileNameList[4]
if self.useWeightM:
W1 = graph1.getWeightMatrix()
W2 = graph2.getWeightMatrix()
else:
W1 = graph1.adjacencyMatrix()
W2 = graph2.adjacencyMatrix()
numpy.savetxt(graph1FileName, W1, fmt='%.5f')
numpy.savetxt(graph2FileName, W2, fmt='%.5f')
#Compute matrix similarities
C = self.vertexSimilarities(graph1, graph2)
numpy.savetxt(similaritiesFileName, C, fmt='%.5f')
#Write config file
configFile = open(configFileName, 'w')
configStr = "graph_1=" + graph1FileName + " s\n"
configStr += "graph_2=" + graph2FileName + " s\n"
configStr += "C_matrix=" + similaritiesFileName + " s\n"
configStr += "algo=" + self.algorithm + " s\n"
configStr += "algo_init_sol=" + self.init + " s\n"
configStr += "alpha_ldh=" + str(self.alpha) + " d\n"
configStr += "cdesc_matrix=A c\n"
configStr += "cscore_matrix=A c\n"
configStr += "hungarian_max=10000 d\n"
configStr += "algo_fw_xeps=0.01 d\n"
configStr += "algo_fw_feps=0.01 d\n"
configStr += "dummy_nodes=0 i\n"
configStr += "dummy_nodes_fill=" + str(self.rho) + " d\n"
configStr += "dummy_nodes_c_coef=" + str(self.gamma) + " d\n"
configStr += "qcvqcc_lambda_M=" + str(self.lambdaM) + " d\n"
configStr += "qcvqcc_lambda_min=1e-3 d\n"
configStr += "blast_match=0 i\n"
configStr += "blast_match_proj=0 i\n"
configStr += "exp_out_file=" + outputFileName + " s\n"
configStr += "exp_out_format=Compact Permutation s\n"
configStr += "verbose_mode=0 i\n"
configStr += "verbose_file=cout s\n"
configFile.write(configStr)
configFile.close()
fnull = open(os.devnull, 'w')
home = expanduser("~")
argList = [home + "/.local/bin/graphm", configFileName]
subprocess.call(argList, stdout=fnull, stderr=fnull)
fnull.close()
#Next: parse input files
outputFile = open(outputFileName, 'r')
line = outputFile.readline()
line = outputFile.readline()
line = outputFile.readline()
line = outputFile.readline()
graphDistance = float(outputFile.readline().split()[2])
fDistance = float(outputFile.readline().split()[2])
fDistanceExact = float(outputFile.readline().split()[2])
time = float(outputFile.readline().split()[1])
line = outputFile.readline()
line = outputFile.readline()
permutation = numpy.zeros(
max(graph1.getNumVertices(), graph2.getNumVertices()), numpy.int)
i = 0
for line in outputFile:
permutation[i] = int(line.strip()) - 1
i += 1
#Delete files
os.remove(graph1FileName)
os.remove(graph2FileName)
os.remove(similaritiesFileName)
os.remove(configFileName)
os.remove(outputFileName)
distanceVector = [graphDistance, fDistance, fDistanceExact]
return permutation, distanceVector, time
def match(self, graph1, graph2):
"""
Take two graphs are match them. The two graphs must be AbstractMatrixGraphs
with VertexLists representing the vertices.
:param graph1: A graph object
:param graph2: The second graph object to match
:return permutation: A vector of indices representing the matching of elements of graph1 to graph2
:return distance: The graph distance list [graphDistance, fDistance, fDistanceExact]
"""
# Deal with case where at least one graph is emty
if graph1.size == 0 and graph2.size == 0:
permutation = numpy.array([], numpy.int)
distanceVector = [0, 0, 0]
time = 0
return permutation, distanceVector, time
elif graph1.size == 0 or graph2.size == 0:
if graph1.size == 0:
graph1 = SparseGraph(VertexList(graph2.size, graph2.getVertexList().getNumFeatures()))
else:
graph2 = SparseGraph(VertexList(graph1.size, graph1.getVertexList().getNumFeatures()))
numTempFiles = 5
tempFileNameList = []
for i in range(numTempFiles):
fileObj = tempfile.NamedTemporaryFile(delete=False)
tempFileNameList.append(fileObj.name)
fileObj.close()
configFileName = tempFileNameList[0]
graph1FileName = tempFileNameList[1]
graph2FileName = tempFileNameList[2]
similaritiesFileName = tempFileNameList[3]
outputFileName = tempFileNameList[4]
if self.useWeightM:
W1 = graph1.getWeightMatrix()
W2 = graph2.getWeightMatrix()
else:
W1 = graph1.adjacencyMatrix()
W2 = graph2.adjacencyMatrix()
numpy.savetxt(graph1FileName, W1, fmt="%.5f")
numpy.savetxt(graph2FileName, W2, fmt="%.5f")
# Compute matrix similarities
C = self.vertexSimilarities(graph1, graph2)
numpy.savetxt(similaritiesFileName, C, fmt="%.5f")
# Write config file
configFile = open(configFileName, "w")
configStr = "graph_1=" + graph1FileName + " s\n"
configStr += "graph_2=" + graph2FileName + " s\n"
configStr += "C_matrix=" + similaritiesFileName + " s\n"
configStr += "algo=" + self.algorithm + " s\n"
configStr += "algo_init_sol=" + self.init + " s\n"
configStr += "alpha_ldh=" + str(self.alpha) + " d\n"
configStr += "cdesc_matrix=A c\n"
configStr += "cscore_matrix=A c\n"
configStr += "hungarian_max=10000 d\n"
configStr += "algo_fw_xeps=0.01 d\n"
configStr += "algo_fw_feps=0.01 d\n"
configStr += "dummy_nodes=0 i\n"
configStr += "dummy_nodes_fill=" + str(self.rho) + " d\n"
configStr += "dummy_nodes_c_coef=" + str(self.gamma) + " d\n"
configStr += "qcvqcc_lambda_M=" + str(self.lambdaM) + " d\n"
configStr += "qcvqcc_lambda_min=1e-3 d\n"
configStr += "blast_match=0 i\n"
configStr += "blast_match_proj=0 i\n"
configStr += "exp_out_file=" + outputFileName + " s\n"
configStr += "exp_out_format=Compact Permutation s\n"
configStr += "verbose_mode=0 i\n"
configStr += "verbose_file=cout s\n"
configFile.write(configStr)
configFile.close()
fnull = open(os.devnull, "w")
home = expanduser("~")
argList = [home + "/.local/bin/graphm", configFileName]
subprocess.call(argList, stdout=fnull, stderr=fnull)
fnull.close()
# Next: parse input files
outputFile = open(outputFileName, "r")
line = outputFile.readline()
line = outputFile.readline()
line = outputFile.readline()
line = outputFile.readline()
graphDistance = float(outputFile.readline().split()[2])
fDistance = float(outputFile.readline().split()[2])
fDistanceExact = float(outputFile.readline().split()[2])
time = float(outputFile.readline().split()[1])
line = outputFile.readline()
line = outputFile.readline()
permutation = numpy.zeros(max(graph1.getNumVertices(), graph2.getNumVertices()), numpy.int)
i = 0
for line in outputFile:
permutation[i] = int(line.strip()) - 1
i += 1
# Delete files
os.remove(graph1FileName)
os.remove(graph2FileName)
os.remove(similaritiesFileName)
os.remove(configFileName)
os.remove(outputFileName)
distanceVector = [graphDistance, fDistance, fDistanceExact]
return permutation, distanceVector, time
""" 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)
