def cluster(self):
# We cluster for each argument independently!
retval = ClusterResult()
curOffset = 0
argNum = 0
for symbolsForArg in self.contentProvider.getSourceAPISymbols():
D = self._calculateDistanceMatrix(symbolsForArg)
curOffset = len(retval.clusterIdToDatapoint.keys())
if len(symbolsForArg) == 0:
argNum += 1
continue
if len(symbolsForArg) == 1:
retval.register(curOffset, symbolsForArg[0], argNum)
argNum += 1
continue
Z = linkage(D, method=self.linkageMethod)
clustering = fcluster(Z, self.maxDistInCluster, criterion = 'distance')
retval.registerSet(symbolsForArg, clustering, curOffset, argNum)
argNum += 1
return retval
def cluster(self, models):
retval = ClusterResult()
for model in models:
for argNum in range(model.getNumberOfArguments()):
invocs = model.members
curOffset = len(retval.clusterIdToDatapoint.keys())
embedder = ConditionEmbedder(self.contentProvider)
embedder.embed(invocs, argNum)
# TODO: we need to be able to pass a distance parameter to joern-cluster
clusterLines = [x.rstrip() for x in launch('joern-cluster')]
clustering = []
datapoints = []
for line in clusterLines:
(nodeId, clusterId) = line.split('\t')
clustering.append(int(clusterId))
datapoints.append(nodeId)
retval.registerSet(datapoints, clustering, curOffset, argNum)
os.system('rm -rf embedding')
return retval
def cluster(self, models):
retval = ClusterResult()
for model in models:
for argNum in range(model.getNumberOfArguments()):
invocs = model.members
curOffset = len(retval.clusterIdToDatapoint.keys())
embedder = ConditionEmbedder(self.contentProvider)
embedder.embed(invocs, argNum)
# TODO: we need to be able to pass a distance parameter to joern-cluster
clusterLines = [x.rstrip() for x in launch("joern-cluster")]
clustering = []
datapoints = []
for line in clusterLines:
(nodeId, clusterId) = line.split("\t")
clustering.append(int(clusterId))
datapoints.append(nodeId)
retval.registerSet(datapoints, clustering, curOffset, argNum)
os.system("rm -rf embedding")
return retval
def cluster(self):
# We cluster for each argument independently!
retval = ClusterResult()
curOffset = 0
argNum = 0
for symbolsForArg in self.contentProvider.getSourceAPISymbols():
D = self._calculateDistanceMatrix(symbolsForArg)
curOffset = len(retval.clusterIdToDatapoint.keys())
if len(symbolsForArg) == 0:
argNum += 1
continue
if len(symbolsForArg) == 1:
retval.register(curOffset, symbolsForArg[0], argNum)
argNum += 1
continue
Z = linkage(D, method=self.linkageMethod)
clustering = fcluster(Z,
self.maxDistInCluster,
criterion='distance')
retval.registerSet(symbolsForArg, clustering, curOffset, argNum)
argNum += 1
return retval
def cluster(self, sourceClusters):
self.defStmts = self.contentProvider.getAllDefStmtsPerArg()
self.sClusters = sourceClusters
converter = InvocationsToDataMatrix()
dataMatrix = converter.convert(self.defStmts, self.sClusters)
if dataMatrix.T.shape == (1,1):
return ClusterResult()
D = pdist(dataMatrix.T, METRIC)
Z = linkage(D, method= LINKAGE_METHOD)
clustering = fcluster(Z, self.maxDistInCluster, criterion = 'distance')
result = ClusterResult()
result.setNumberOfArguments(len(self.defStmts[0]) if len(self.defStmts) > 0 else 0)
result.registerSet(range(len(self.defStmts)), clustering)
result.dataMatrix = dataMatrix
result.callSiteIds = self.contentProvider.getInvocationCallSiteIds()
return result
