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)