ML之Kmeans：利用自定义Kmeans函数实现对多个坐标点(自定义四个点)进行自动(最多迭代10次)分类

ML之Kmeans：利用自定义Kmeans函数实现对多个坐标点(自定义四个点)进行自动(最多迭代10次)分类

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输出结果

核心代码

#!/usr/bin/python
# -*- coding:utf-8 -*-

import numpy as np
#ML之Kmeans：利用自定义Kmeans函数实现对多个坐标点(自定义四个点)进行自动(最多迭代10次)分类

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 = dataSet[0:2, :]
    #Randomly assign labels to initial centorid给初始中心随机分配标签
    centroids[:, -1] = range(1, k +1)  

    iterations = 0
    oldCentroids = None  

    # Run the main k-means algorithm
    while not shouldStop(oldCentroids, centroids, iterations, maxIt):
        print ("iteration: \n", iterations)
        print ("dataSet: \n", dataSet)
        print ("centroids: \n", centroids)
        # Save old centroids for convergence test. Book keeping.
        oldCentroids = np.copy(centroids)
        iterations += 1                    

        # Assign labels to each datapoint based on centroids
        updateLabels(dataSet, centroids)    

        # Assign centroids based on datapoint labels
        centroids = getCentroids(dataSet, k) 

    # We can get the labels too by calling getLabels(dataSet, centroids)
    return dataSet
# Function: Should Stop
# -------------
# Returns True or False if k-means is done. K-means terminates either
# because it has run a maximum number of iterations OR the centroids
# stop changing.
def shouldStop(oldCentroids, centroids, iterations, maxIt):
    if iterations > maxIt:
        return True
    return np.array_equal(oldCentroids, centroids)
# Function: Get Labels
# -------------
# Update a label for each piece of data in the dataset.
def updateLabels(dataSet, centroids):
    # For each element in the dataset, chose the closest centroid.
    # Make that centroid the element's label.
    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

# Function: Get Centroids
# -------------
# Returns k random centroids, each of dimension n.
def getCentroids(dataSet, k):
    # Each centroid is the geometric mean of the points that
    # have that centroid's label. Important: If a centroid is empty (no points have
    # that centroid's label) you should randomly re-initialize it.
    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 

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)

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ML之Kmeans：利用自定义Kmeans函数实现对多个坐标点(自定义四个点)进行自动(最多迭代10次)分类