from numpy import * """ Code for hierarchical clustering, modified from Programming Collective Intelligence by Toby Segaran (O'Reilly Media 2007, page 33). """ class cluster_node: def __init__(self,vec,left=None,right=None,distance=0.0,id=None,count=1): #vec 数据的每一行都是向量，左节点右节点 self.left=left self.right=right self.vec=vec self.id=id self.distance=distance self.count=count #only used for weighted average def L2dist(v1,v2): return sqrt(sum((v1-v2)**2)) def L1dist(v1,v2): return sum(abs(v1-v2)) # def Chi2dist(v1,v2): # return sqrt(sum((v1-v2)**2)) def hcluster(features,distance=L2dist): #features是一个矩阵即numpy array，每一行都是一个实例点 #cluster the rows of the "features" matrix distances={} currentclustid=-1 #当前类的id # clusters are initially just the individual rows clust=[cluster_node(array(features[i]),id=i) for i in range(len(features))] #1,2,3,4,5分别对应features中的每一行，len(features)是features矩阵的行数 while len(clust)>1: lowestpair=(0,1) #距离最近的两个点坐标即一类，先设成前两个 closest=distance(clust[0].vec,clust[1].vec) #最小距离 # loop through every pair looking for the smallest distance for i in range(len(clust)): for j in range(i+1,len(clust)): # distances is the cache of distance calculations if (clust[i].id,clust[j].id) not in distances: distances[(clust[i].id,clust[j].id)]=distance(clust[i].vec,clust[j].vec) d=distances[(clust[i].id,clust[j].id)] if d<closest: closest=d lowestpair=(i,j) # calculate the average of the two clusters mergevec=[(clust[lowestpair[0]].vec[i]+clust[lowestpair[1]].vec[i])/2.0 \ for i in range(len(clust[0].vec))] # create the new cluster newcluster=cluster_node(array(mergevec),left=clust[lowestpair[0]], #新节点 right=clust[lowestpair[1]], distance=closest,id=currentclustid) # cluster ids that weren't in the original set are negative currentclustid-=1 del clust[lowestpair[1]] del clust[lowestpair[0]] clust.append(newcluster) return clust[0] def extract_clusters(clust,dist): #dist阈值，，树状结构用递归 # extract list of sub-tree clusters from hcluster tree with distance<dist clusters = {} if clust.distance<dist: # we have found a cluster subtree return [clust] else: # check the right and left branches cl = [] #左边 cr = [] if clust.left!=None: cl = extract_clusters(clust.left,dist=dist) if clust.right!=None: cr = extract_clusters(clust.right,dist=dist) return cl+cr def get_cluster_elements(clust): # return ids for elements in a cluster sub-tree if clust.id>=0: # positive id means that this is a leaf return [clust.id] else: # check the right and left branches cl = [] cr = [] if clust.left!=None: cl = get_cluster_elements(clust.left) if clust.right!=None: cr = get_cluster_elements(clust.right) return cl+cr def printclust(clust,labels=None,n=0): # indent to make a hierarchy layout for i in range(n): print ' ', if clust.id<0: # negative id means that this is branch print '-' else: # positive id means that this is an endpoint if labels==None: print clust.id else: print labels[clust.id] # now print the right and left branches if clust.left!=None: printclust(clust.left,labels=labels,n=n+1) if clust.right!=None: printclust(clust.right,labels=labels,n=n+1) def getheight(clust): # Is this an endpoint? Then the height is just 1 if clust.left==None and clust.right==None: return 1 # clust.left左儿子 # Otherwise the height is the same of the heights of # each branch return getheight(clust.left)+getheight(clust.right) def getdepth(clust): # The distance of an endpoint is 0.0 if clust.left==None and clust.right==None: return 0 # The distance of a branch is the greater of its two sides # plus its own distance return max(getdepth(clust.left),getdepth(clust.right))+clust.distance
