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

 

 

目录

输出结果

核心代码

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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