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)
  
 

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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

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