HierarchicalClustering：编写HierarchicalClustering层次聚类算法—Jason niu

from numpy import *

class cluster_node:  
    def __init__(self,vec,left=None,right=None,distance=0.0,id=None,count=1):
        self.left=left  
        self.right=right
        self.vec=vec
        self.id=id
        self.distance=distance
        self.count=count 

def L2dist(v1,v2): 
    return sqrt(sum((v1-v2)**2))
    
def L1dist(v1,v2):   
    return sum(abs(v1-v2))

def hcluster(features,distance=L2dist):  
    distances={}     
    currentclustid=-1 

    clust=[cluster_node(array(features[i]),id=i) for i in range(len(features))]

    while len(clust)>1: 
        lowestpair=(0,1) 
        closest=distance(clust[0].vec,clust[1].vec)
    
        for i in range(len(clust)):
            for j in range(i+1,len(clust)):
                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)  

        mergevec=[(clust[lowestpair[0]].vec[i]+clust[lowestpair[1]].vec[i])/2.0             for i in range(len(clust[0].vec))]
        
        newcluster=cluster_node(array(mergevec),left=clust[lowestpair[0]],
                             right=clust[lowestpair[1]],
                             distance=closest,id=currentclustid)
        
        currentclustid-=1  
        del clust[lowestpair[1]]
        del clust[lowestpair[0]]
        clust.append(newcluster)

    return clust[0]

def extract_clusters(clust,dist):
    clusters = {}
    if clust.distance<dist: 
        return [clust] 
    else:
        cl = []  
        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):  
    if clust.id>=0:  
        return [clust.id]
    else:
        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): 
    for i in range(n): print (‘ ‘),
    if clust.id<0: 
        print (‘-‘)
    else:         
        if labels==None: print (clust.id)
        else: print (labels[clust.id])

    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): 
    if clust.left==None and clust.right==None: return 1
    return getheight(clust.left)+getheight(clust.right)

def getdepth(clust):  
    if clust.left==None and clust.right==None: return 0
    return max(getdepth(clust.left),getdepth(clust.right))+clust.distance