def getSuccessors(self, points, groups):
successors = []
for g in groups:
memtup = cluster_util.totuple(g.members)
if len(self.state.intersection(memtup)) <= allow_intersection:
asd = BNode(self.state.union(memtup), self, g.uuid, g.cost)
if asd.gain > 0:
successors.append(asd)
return successors
def getSuccessors(self, points,groups):
successors = []
for g in groups:
memtup = cluster_util.totuple(g.members)
if len(self.state.intersection(memtup))<=allow_intersection:
asd=BNode(self.state.union(memtup),self,g.uuid,g.cost)
if asd.gain > 0:
successors.append(asd)
return successors
def sceneEval(inputObjectSet,params = ClusterParams(2,0.9,3,0.05,0.1,1,0,10)):
'''
find the clusters
evaulate the inside of the clusters as lines to see if they'd be better as lines than clusters
evaluate the outside of clusters for lines
concatenate the lists of clusters and lines
evaluate the whole thing with bundle search
'''
clusterCandidates = clustercost(dbscan(np.array(map(lambda x: (x.position,x.id),inputObjectSet))))
lineCandidates = findChains(inputObjectSet,params)
print'***'
allCandidates = clusterCandidates + lineCandidates
evali = bundleSearch(cluster_util.totuple(inputObjectSet), allCandidates, params.allow_intersection, 10)
print evali
return evali
def sceneEval(inputObjectSet,params = ClusterParams(2,0.9,3,0.05,0.1,1,0,11,False)):
'''
find the clusters
evaulate the inside of the clusters as lines to see if they'd be better as lines than clusters
evaluate the outside of clusters for lines
concatenate the lists of clusters and lines
evaluate the whole thing with bundle search
'''
reducedObjectSet = copy(inputObjectSet)
clusterCandidates = clustercost(dbscan(np.array(map(lambda x: (x.position,x.id),inputObjectSet))))
innerLines = []
#search for lines inside large clusters
if params.attempt_dnc==True:
for cluster in clusterCandidates[1]:
innerObjects = []
for id in cluster:
for x in inputObjectSet:
if x.id == id:
innerObjects.append(x)
innerChains = findChains(innerObjects,params)
for thing in innerChains:
innerLines.append(thing)
#remove core clusters
for cluster in clusterCandidates[0]:
for id in cluster:
for x in reducedObjectSet:
if x.id == id:
reducedObjectSet.remove(x)
lineCandidates = findChains(reducedObjectSet,params)
allCandidates = clusterCandidates[0]+clusterCandidates[1] + lineCandidates + innerLines
groupDictionary = dict()
for i in allCandidates:
groupDictionary[i.uuid]=i
evali = bundleSearch(cluster_util.totuple(inputObjectSet), allCandidates, params.allow_intersection, params.beam_width)
#find the things in evali that aren't in the dictionary ,and make a singleton group out of them, and add it to the output
output = map(lambda x: groupDictionary.get(x),evali)
return output