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)