层次聚类算法

层次聚类算法很简单
类似于树的结构
树自下而上更新
即n->1个簇的更新
具体见代码
增加了树的几点操作函数
应用暂时没有
后续会更新

# -*- coding:utf-8 _*_

import numpy


def getMax(x, y):
    if x > y:
        return x
    else:
        return y


def getMin(x, y):
    if x > y:
        return y
    else:
        return x


def loadData(fileName):
    data = []
    with open(fileName) as txtFile:
        for line in txtFile.readlines():
            init = map(float, line.split())  # 全部转换为float类型
            data.append(init)
    return data


# 树的节点
class clusterNode:
    # vec为坐标 lr左右节点 目前没有get到distance的作用
    def __init__(self, vec, left=None, right=None, id=None, distance=0):
        self.vec = vec
        self.left = left
        self.right = right
        self.id = id
        self.distance = distance


def L2Distance(v1, v2):
    return numpy.sqrt(sum(pow(v1 - v2, 2)))


deep = 0  # 得到树的深度
def getDeep(node, high):
    if node.left is None and node.right is None:
        global deep
        deep = getMax(deep, high)
    else:
        print node.vec
        getDeep(node.left, high + 1)
        getDeep(node.right, high + 1)


leavesNum = 0  # 得到树的叶子树
def getLeaves(node):
    if node.left is None and node.right is None:
        global leavesNum
        leavesNum += 1
    if node.left is not None:
        getLeaves(node.left)
    if node.right is not None:
        getLeaves(node.right)


def hiCluster(dataSet, calDistance=L2Distance):
    # 利用list建立树形clust 在中括号内循环
    clust = [clusterNode(numpy.array(dataSet[i]), id=i)
             for i in range(len(dataSet))]
    while len(clust) > 1:    # 没有合并完成
        lowestpair = (0, 1)  # 默认01最近 并求距离
        closest = calDistance(clust[0].vec, clust[1].vec)
        # 两两求距离 打擂台找最近的"点" (其实是簇)
        for i in range(len(clust)):
            for j in range(i + 1, len(clust)):
                temp = calDistance(clust[i].vec, clust[j].vec)
                if closest > temp:
                    closest = temp
                    lowestpair = (i, j)# 重新声明tuple (元组无法赋值)
        # 新的簇 中值加和得来
        mergeVec = [(clust[lowestpair[0]].vec[i] + clust[lowestpair[1]].vec[i]) / 2.0
                    for i in range(len(clust[0].vec))]
        # 合并后的"质心"
        newcluster = clusterNode(numpy.array(mergeVec), left=clust[lowestpair[0]],
                                 right=clust[lowestpair[1]], distance=closest)
        # 删除两个距离最近的已经合并的簇 从大的开始删
        Max = getMax(lowestpair[0], lowestpair[1])
        Min = getMin(lowestpair[0], lowestpair[1])
        del (clust[Max])
        del (clust[Min])
        # 添加新的簇
        clust.append(newcluster)
    # 返回的是类的对象 clust[0]是树根
    return clust[0]


if __name__ == "__main__":
    dataSet = loadData("testSet.txt")
    root = hiCluster(dataSet)
    print "node is"
    getDeep(root, 0)
    print "deep is", deep
    getLeaves(root)
    print "leavesNum is", leavesNum