8.2 聚類(Clustering) K-means算法應用

1.python實現kmean 算法

import numpy as np

def kmeans(X,k,maxIt):
    numPoints,numDim=X.shape

    dataSet=np.zeros((numPoints,numDim+1))
    dataSet[:,:-1]=X
    centroids=dataSet[np.random.randint(numPoints,size=k),:]
    centroids[:,-1]=range(1,k+1)
    #print("centroids:",centroids)

    iterations=0;
    oldCentroids=None
    while not shouldStop(oldCentroids, centroids, iterations, maxIt):
        oldCentroids=np.copy(centroids)
        iterations+=1
        updateLabels(dataSet, centroids)
        centroids=getCentroids(dataSet, k)
    return dataSet
def shouldStop(oldCentroids,centroids,iterations,maxIt):
    if iterations>maxIt:
        return True
    return np.array_equal(oldCentroids, centroids)

def getLabelFromClosestCentroid(dataSetRow,centroids):
    label=centroids[0,-1]
    minDist=np.linalg.norm(dataSetRow-centroids[0,:-1])
    for i in range(1,centroids.shape[0]):
        dist=np.linalg.norm(dataSetRow-centroids[i,:-1])
        if dist<minDist:
            minDist=dist
            label=centroids[i,-1]
           # print "label:"+str(label)
    return label
def updateLabels(dataSet,centroids):
    numPoints,numDim=dataSet.shape
    for i in range(numPoints):
        dataSet[i,-1]=getLabelFromClosestCentroid(dataSet[i,:-1], centroids)

def getCentroids(dataSet,k):       
    result=np.zeros((k,dataSet.shape[1]))
    #print("resutl:",result)
    for i in range(1,k+1):
        oneCluster=dataSet[dataSet[:,-1]==i,:-1]
        #print("cluster:",oneCluster)
        result[i-1,:-1]=np.mean(oneCluster,axis=0)
        result[i-1,-1]=i
    #print("result:",result)
    return result

x1=np.array([1,1])        
x2=np.array([2,1]) 
x3=np.array([4,3]) 
x4=np.array([5,4])         
testX=np.vstack((x1,x2,x3,x4))    

result=kmeans(testX,2,10)
print('final result:',result)