第27节--层次聚类（下）

层次聚类代码：

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):        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):    #cluster the rows of the "features" matrix    distances={}    currentclustid=-1    # clusters are initially just the individual rows    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)        # 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]],                             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):    # 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 = []        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+crdef 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    # 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