机器学习实战之KMeans算法pandas实现

这次写的恶心死我了，第一次随机选中心点的代码有问题还是怎么的，导致第一轮过完，可能会出现某个中心点根本就完全不合适，没有任何一个点会标记为这个中心点，然后报错。导致我的这个代码时灵时不灵，开始完全想不到bug的原因。
而且虽然用pandas来自己实现确实能帮忙巩固pandas的知识，但我还是觉得以前是走入了误区。机器学习重要的部分应该是对理论的理解和轮子的使用，至于书中的代码，理解下理论实现的具体过程就行了，自己在这费心费力造轮子实在是意义不大，虽然在造的过程中也能加深理解，但事倍功半
所以还是决定以后学习就结合西瓜书的理论，实战中的实现思路和sklearn的代码实现了

import numpy as npimport pandas as pdfrom pandas import DataFrame,Seriespath = r'C:\Users\36955\Downloads\mlp\Ch10\testSet2.txt'data = pd.read_csv(path,sep='\t',header=None)def randCent(dataSet, k):    n = dataSet.shape[1]    centroids = DataFrame(np.zeros((k,n)))    data_min = dataSet.min(0)    data_range = dataSet.max(0)-data_min    for j in range(n):        minJ = data_min[j]        rangeJ = float(data_range[j])        centroids.iloc[:,j] = minJ + rangeJ * np.random.rand(k,1)    return centroidsdef distEclud(vecA, vecB):    return np.sqrt(np.sum(np.power(vecA - vecB, 2)))def kMeans(data,k):    m = data.shape[0]    clusterAssment = DataFrame(np.zeros((m,2)),columns=['clusterName','dist'])    centroids = randCent(data,k)    clusterChanged = True    while clusterChanged:        clusterChanged = False        for i in range(m):            minDist = np.inf;minIndex = -1            for j in range(k):                #print (i,j,m,len(centroids))                dist = distEclud(data.iloc[i,:],centroids.iloc[j,:])                if dist < minDist:                    minDist = dist;minIndex = j            if clusterAssment.iloc[i,0] != minIndex:                clusterChanged = True            clusterAssment.iloc[i,:] = minIndex,minDist**2        #print centroids        centroids = data.groupby(clusterAssment.clusterName).mean()    return centroids,clusterAssment#print kMeans(data,3)def biKmeans(data,k):    m = data.shape[0]    clusterAssment = DataFrame(np.zeros((m,2)),columns=['clusterName','dist'])    centroid0 = list(data.mean(0))    centList = [centroid0]    while len(centList)<k:        lowestSSE = np.inf        for i in range(len(centList)):            splitCluster = data.iloc[clusterAssment[clusterAssment.clusterName==i].index,:]            splitCentroids,splitClusterAssement = kMeans(splitCluster,2)            splitSSE = splitClusterAssement.dist.sum()            nosplitSSE = clusterAssment[clusterAssment.clusterName!=i].dist.sum()            if (splitSSE+nosplitSSE)<lowestSSE:                besttosplit = i                newCentroids = splitCentroids.copy()                newAssement = splitClusterAssement.copy()                lowestSSE = splitSSE+nosplitSSE        centList[besttosplit] = list(newCentroids.iloc[0,:])        centList.append(list(newCentroids.iloc[1,:]))        newAssement.clusterName[newAssement.clusterName==0] = besttosplit        newAssement.clusterName[newAssement.clusterName==1] = len(centList)-1        clusterAssment=clusterAssment[clusterAssment.clusterName!=besttosplit]        clusterAssment = pd.concat([clusterAssment,newAssement],axis=0)    return centList,clusterAssmentprint biKmeans(data,4)