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

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)
     
    
  
 

相关文章
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

文章来源: yunyaniu.blog.csdn.net，作者：一个处女座的程序猿，版权归原作者所有，如需转载，请联系作者。

原文链接：yunyaniu.blog.csdn.net/article/details/80020220