Kmeans：利用Kmeans实现对多个点进行自动分类—Jason niu

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)  
    iterations = 0      
    oldCentroids = None 
    

    while not shouldStop(oldCentroids, centroids, iterations, maxIt):  
        print ("iteration: \n", iterations) 
        print ("dataSet: \n", dataSet)       
        print ("centroids: \n", centroids)  
       
        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 updateLabels(dataSet, centroids): 
    
    numPoints, numDim = dataSet.shape  
    for i in range(0, numPoints):      
        dataSet[i, -1] = getLabelFromClosestCentroid(dataSet[i, :-1], 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 ("minDist:", minDist)
    return label
    
def getCentroids(dataSet, k):   
    result = np.zeros((k, dataSet.shape[1]))  
    for i in range(1, k + 1):
        oneCluster = dataSet[dataSet[:, -1] == i, :-1]  )
        result[i - 1, :-1] = np.mean(oneCluster, axis = 0) 
        result[i - 1, -1] = i  
    
    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:")
print (result)