【机器学习算法-python实现】K-means无监督学习实现分类

时间：2014-07-12 20:04:16 阅读：338 评论：0 收藏：0 [点我收藏+]

1.背景

无监督学习的定义就不多说了，不懂得可以google。因为项目需要，需要进行无监督的分类学习。

K-means里面的K指的是将数据分成的份数，基本上用的就是算距离的方法。

大致的思路就是给定一个矩阵，假设K的值是2，也就是分成两个部分，那么我们首先确定两个质心。一开始是找矩阵每一列的最大值max，最小值min，算出range=max-min，然后设质心就是min+range*random。之后在逐渐递归跟进，其实要想明白还是要跟一遍代码，自己每一步都输出一下看看跟自己想象的是否一样。

（顺便吐槽一下，网上好多人在写文章的事后拿了书上的代码就粘贴上，也不管能不能用，博主改了一下午才改好。。。，各种bug）

2.代码

‘‘‘
@author: hakuri
‘‘‘
from numpy import *
import matplotlib.pyplot as plt
def loadDataSet(fileName):      #general function to parse tab -delimited floats
    dataMat = []                #assume last column is target value
    fr = open(fileName)
    for line in fr.readlines():
        curLine = line.strip().split(‘\t‘)
        fltLine = map(float,curLine) #map all elements to float()
        dataMat.append(fltLine)
    return dataMat

def distEclud(vecA, vecB):
    return sqrt(sum(power(vecA - vecB, 2))) #la.norm(vecA-vecB)

def randCent(dataSet, k):
    n = shape(dataSet)[1]
    centroids = mat(zeros((k,n)))#create centroid mat
    for j in range(n):#create random cluster centers, within bounds of each dimension
        minJ = min(array(dataSet)[:,j])
        
        rangeJ = float(max(array(dataSet)[:,j]) - minJ)
        centroids[:,j] = mat(minJ + rangeJ * random.rand(k,1))

    return centroids

def kMeans(dataSet, k, distMeas=distEclud, createCent=randCent):
    m = shape(dataSet)[0]
    clusterAssment = mat(zeros((m,2)))#create mat to assign data points                                       #to a centroid, also holds SE of each point
    centroids = createCent(dataSet, k)
    clusterChanged = True
    while clusterChanged:
        clusterChanged = False
        for i in range(m):#for each data point assign it to the closest centroid
            minDist = inf; minIndex = -1
            for j in range(k):
                distJI = distMeas(array(centroids)[j,:],array(dataSet)[i,:])
                if distJI < minDist:
                    minDist = distJI; minIndex = j
            if clusterAssment[i,0] != minIndex: clusterChanged = True
            clusterAssment[i,:] = minIndex,minDist**2
        print centroids
#         print nonzero(array(clusterAssment)[:,0]
        for cent in range(k):#recalculate centroids
                ptsInClust = dataSet[nonzero(array(clusterAssment)[:,0]==cent)[0][0]]#get all the point in this cluster
                
                centroids[cent,:] = mean(ptsInClust, axis=0) #assign centroid to mean 
    id=nonzero(array(clusterAssment)[:,0]==cent)[0] 
    return centroids, clusterAssment,id

def plotBestFit(dataSet,id,centroids):  
     
    dataArr = array(dataSet)
    cent=array(centroids)
    n = shape(dataArr)[0] 
    n1=shape(cent)[0]
    xcord1 = []; ycord1 = []
    xcord2 = []; ycord2 = []
    xcord3=[];ycord3=[]
    j=0
    for i in range(n):
        if j in id:
            xcord1.append(dataArr[i,0]); ycord1.append(dataArr[i,1])
        else:
            xcord2.append(dataArr[i,0]); ycord2.append(dataArr[i,1])
        j=j+1 
    for k in range(n1):
          xcord3.append(cent[k,0]);ycord3.append(cent[k,1])    
         
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.scatter(xcord1, ycord1, s=30, c=‘red‘, marker=‘s‘)
    ax.scatter(xcord2, ycord2, s=30, c=‘green‘)
    ax.scatter(xcord3, ycord3, s=50, c=‘black‘)

    plt.xlabel(‘X1‘); plt.ylabel(‘X2‘);
    plt.show()    


if __name__==‘__main__‘:
    dataSet=loadDataSet(‘/Users/hakuri/Desktop/testSet.txt‘)
# #     print randCent(dataSet,2)
#      print dataSet
#      
#      print  kMeans(dataSet,2)
    a=[]
    b=[]
    a, b,id=kMeans(dataSet,2)
    plotBestFit(dataSet,id,a)

用的时候直接看最后的main，dataSet是数据集输入，我会在下载地址提供给大家。

kmeans函数第一个参数是输入矩阵、第二个是K的值，也就是分几份。

plotBestFit是画图函数，需要加plot库，而且目前只支持二维且K=2的情况。