ML之HierarchicalClustering：自定义HierarchicalClustering层次聚类算法

ML之HierarchicalClustering：自定义HierarchicalClustering层次聚类算法

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输出结果

更新……

实现代码

# -*- encoding=utf-8 -*-

from numpy import *

class cluster_node:  #定义cluster_node类，类似Java中的构造函数
    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 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)

        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) 

        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):  #(clust上边的树形结构，dist阈值)
    # 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):  #用于取出算好聚类的元素
    # 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):  #将值打印出来
    # 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])

    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):  #树的高度，递归方法
    # 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):   #树的深度，递归方法
    if clust.left==None and clust.right==None: return 0

    return max(getdepth(clust.left),getdepth(clust.right))+clust.distance

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