def clusterLinkAgg(dom, para=1):
md = ldi.linkDistance(dom, para)
linkList = getLink(dom)
linkClusterList = []
for link in linkList:
linkClusterList.append([link])
while True:
if len(linkClusterList) <= 1:
break
first = 0
second = 1
try:
minGap = singleLink(linkClusterList[first],
linkClusterList[second], md)
#minGap = singleLink2D(linkClusterList[first], linkClusterList[second], md)
except:
print(len(linkClusterList))
return
for i in range(0, len(linkClusterList)):
for j in range(i + 1, len(linkClusterList)):
gap = singleLink(linkClusterList[i], linkClusterList[j], md)
#gap = singleLink2D(linkClusterList[i], linkClusterList[j], md)
if gap < minGap:
minGap = gap
first = i
second = j
if minGap > md.dt:
#if minGap > md.dt*1.414:
break
linkClusterList[first] += linkClusterList[second]
linkClusterList.pop(second)
return linkClusterList
def clusterLinkKM(dom, k=2):
md = ldi.linkDistance(dom, 1)
linkList = getLink(dom)
a = [[link['index']] for link in linkList]
#a = [[link['index'], link.parent['index']] for link in linkList]
#a = [[link['index'], link['link_color'], link['link_size']] for link in linkList]
#a = [[link['index'], link['link_color'], link['link_size'], link.parent['index']] for link in linkList]
if not a:
return []
cluster = KMeans(k)
labelList = cluster.fit_predict(array(a))
linkClusterList = [[] for i in range(0, k)]
for i in range(0, len(labelList)):
linkClusterList[labelList[i]].append(linkList[i])
return linkClusterList
def clusterLink1D(dom, para=1):
md = ldi.linkDistance(dom, para)
linkList = getLink(dom)
if not linkList:
return []
linkClusterList = []
tmp = [linkList[0]]
for i in range(1, len(linkList)):
if md.getDis(linkList[i], linkList[i - 1]) > md.dt:
linkClusterList.append(tmp)
tmp = []
else:
tmp.append(linkList[i])
if tmp:
linkClusterList.append(tmp)
linkClusterList = [
linkCluster for linkCluster in linkClusterList if linkCluster
]
return linkClusterList
def clusterLinkSC(dom,
k=2,
affinity='nearest_neighbors',
n_neighbors=1,
gamma=0.021):
md = ldi.linkDistance(dom, 1)
linkList = getLink(dom)
a = [[link['index']] for link in linkList]
#a = [[link['index'], link.parent['index']] for link in linkList]
#a = [[link['index'], link['link_color'], link['link_size']] for link in linkList]
#a = [[link['index'], link['link_color'], link['link_size'], link.parent['index']] for link in linkList]
if not a:
return []
cluster = SpectralClustering(k, gamma=gamma)
labelList = cluster.fit_predict(array(a))
linkClusterList = [[] for i in range(0, k)]
for i in range(0, len(labelList)):
linkClusterList[labelList[i]].append(linkList[i])
return linkClusterList
def clusterLinkDB(dom, para=0.5):
md = ldi.linkDistance(dom, para)
linkList = getLink(dom)
a = [[link['index']] for link in linkList]
#a = [[link['index'], link.parent['index']] for link in linkList]
#a = [[link['index'], link['link_color'], link['link_size']] for link in linkList]
#a = [[link['index'], link['link_color'], link['link_size'], link.parent['index']] for link in linkList]
if not a:
return []
if not md.dt:
return linkList
#cluster = DBSCAN(md.dt*1.414, min_samples=1)
cluster = DBSCAN(md.dt, min_samples=1)
labelList = cluster.fit_predict(array(a))
tmpSet = set()
for label in labelList:
tmpSet.add(label)
linkClusterList = [[] for i in range(0, len(tmpSet))]
for i in range(0, len(labelList)):
linkClusterList[labelList[i]].append(linkList[i])
linkClusterList = [
linkCluster for linkCluster in linkClusterList if linkCluster
]
return linkClusterList
def chdLD(dom, para=1):
md1 = ldi.linkDistance(dom, para)
md2 = lde.linkDensity(dom, 1, 0.00001)
return runClusterLink(dom, md1, md2)