KNN算法基础思想前面文章可以参考，这里主要讲解java和python的两种简单实现，也主要是理解简单的思想。

http://blog.csdn.net/u011067360/article/details/23941577

python版本：

这里实现一个手写识别算法，这里只简单识别0~9熟悉，在上篇文章中也展示了手写识别的应用，可以参考：机器学习与数据挖掘-logistic回归及手写识别实例的实现

输入：每个手写数字已经事先处理成32*32的二进制文本，存储为txt文件。0～9每个数字都有10个训练样本，5个测试样本。训练样本集如下图：左边是文件目录，右边是其中一个文件打开显示的结果，看着像1，这里有0~9，每个数字都有是个样本来作为训练集。

第一步：将每个txt文本转化为一个向量，即32*32的数组转化为1*1024的数组，这个1*1024的数组用机器学习的术语来说就是特征向量。

<span style="font-size:14px;">def img2vector(filename):      returnVect = zeros((1,1024))      fr = open(filename)      for i in range(32):          lineStr = fr.readline()          for j in range(32):              returnVect[0,32*i+j] = int(lineStr[j])      return returnVect</span>

第二步：训练样本中有10*10个图片，可以合并成一个100*1024的矩阵，每一行对应一个图片，也就是一个txt文档。

def handwritingClassTest():        hwLabels = []      trainingFileList = listdir('trainingDigits')        print trainingFileList              m = len(trainingFileList)      trainingMat = zeros((m,1024))      for i in range(m):          fileNameStr = trainingFileList[i]                    fileStr = fileNameStr.split('.')[0]          classNumStr = int(fileStr.split('_')[0])           hwLabels.append(classNumStr)          #print hwLabels          #print fileNameStr             trainingMat[i,:] = img2vector('trainingDigits/%s' % fileNameStr)          #print trainingMat[i,:]           #print len(trainingMat[i,:])             testFileList = listdir('testDigits')             errorCount = 0.0      mTest = len(testFileList)      for i in range(mTest):          fileNameStr = testFileList[i]          fileStr = fileNameStr.split('.')[0]               classNumStr = int(fileStr.split('_')[0])          vectorUnderTest = img2vector('testDigits/%s' % fileNameStr)          classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)          print "the classifier came back with: %d, the real answer is: %d" % (classifierResult, classNumStr)          if (classifierResult != classNumStr): errorCount += 1.0      print "\nthe total number of errors is: %d" % errorCount      print "\nthe total error rate is: %f" % (errorCount/float(mTest))

第三步：测试样本中有10*5个图片，同样的，对于测试图片，将其转化为1*1024的向量，然后计算它与训练样本中各个图片的“距离”（这里两个向量的距离采用欧式距离），然后对距离排序，选出较小的前k个，因为这k个样本来自训练集，是已知其代表的数字的，所以被测试图片所代表的数字就可以确定为这k个中出现次数最多的那个数字。

def classify0(inX, dataSet, labels, k):      dataSetSize = dataSet.shape[0]      #tile(A,(m,n))         print dataSet      print "----------------"      print tile(inX, (dataSetSize,1))      print "----------------"      diffMat = tile(inX, (dataSetSize,1)) - dataSet            print diffMat      sqDiffMat = diffMat**2      sqDistances = sqDiffMat.sum(axis=1)                        distances = sqDistances**0.5      sortedDistIndicies = distances.argsort()                  classCount={}                                            for i in range(k):          voteIlabel = labels[sortedDistIndicies[i]]          classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1      sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)      return sortedClassCount[0][0]

全部实现代码：

#-*-coding:utf-8-*-  from numpy import *  import operator  from os import listdir    def classify0(inX, dataSet, labels, k):      dataSetSize = dataSet.shape[0]      #tile(A,(m,n))         print dataSet      print "----------------"      print tile(inX, (dataSetSize,1))      print "----------------"      diffMat = tile(inX, (dataSetSize,1)) - dataSet            print diffMat      sqDiffMat = diffMat**2      sqDistances = sqDiffMat.sum(axis=1)                        distances = sqDistances**0.5      sortedDistIndicies = distances.argsort()                  classCount={}                                            for i in range(k):          voteIlabel = labels[sortedDistIndicies[i]]          classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1      sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)      return sortedClassCount[0][0]    def img2vector(filename):      returnVect = zeros((1,1024))      fr = open(filename)      for i in range(32):          lineStr = fr.readline()          for j in range(32):              returnVect[0,32*i+j] = int(lineStr[j])      return returnVect    def handwritingClassTest():        hwLabels = []      trainingFileList = listdir('trainingDigits')        print trainingFileList              m = len(trainingFileList)      trainingMat = zeros((m,1024))      for i in range(m):          fileNameStr = trainingFileList[i]                    fileStr = fileNameStr.split('.')[0]          classNumStr = int(fileStr.split('_')[0])           hwLabels.append(classNumStr)          #print hwLabels          #print fileNameStr             trainingMat[i,:] = img2vector('trainingDigits/%s' % fileNameStr)          #print trainingMat[i,:]           #print len(trainingMat[i,:])             testFileList = listdir('testDigits')             errorCount = 0.0      mTest = len(testFileList)      for i in range(mTest):          fileNameStr = testFileList[i]          fileStr = fileNameStr.split('.')[0]               classNumStr = int(fileStr.split('_')[0])          vectorUnderTest = img2vector('testDigits/%s' % fileNameStr)          classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)          print "the classifier came back with: %d, the real answer is: %d" % (classifierResult, classNumStr)          if (classifierResult != classNumStr): errorCount += 1.0      print "\nthe total number of errors is: %d" % errorCount      print "\nthe total error rate is: %f" % (errorCount/float(mTest))        handwritingClassTest()

运行结果：源码文章尾可下载

java版本

先看看训练集和测试集：

训练集：

测试集：

训练集最后一列代表分类（0或者1）

代码实现：

 KNN算法主体类：

package Marchinglearning.knn2;    import java.util.ArrayList;  import java.util.Comparator;  import java.util.HashMap;  import java.util.List;  import java.util.Map;  import java.util.PriorityQueue;    /**   * KNN算法主体类   */  public class KNN {   /**    * 设置优先级队列的比较函数，距离越大，优先级越高    */   private Comparator<KNNNode> comparator = new Comparator<KNNNode>() {    public int compare(KNNNode o1, KNNNode o2) {     if (o1.getDistance() >= o2.getDistance()) {      return 1;     } else {      return 0;     }    }   };   /**    * 获取K个不同的随机数    * @param k 随机数的个数    * @param max 随机数最大的范围    * @return 生成的随机数数组    */   public List<Integer> getRandKNum(int k, int max) {    List<Integer> rand = new ArrayList<Integer>(k);    for (int i = 0; i < k; i++) {     int temp = (int) (Math.random() * max);     if (!rand.contains(temp)) {      rand.add(temp);     } else {      i--;     }    }    return rand;   }   /**    * 计算测试元组与训练元组之前的距离    * @param d1 测试元组    * @param d2 训练元组    * @return 距离值    */   public double calDistance(List<Double> d1, List<Double> d2) {    System.out.println("d1:"+d1+",d2"+d2);    double distance = 0.00;    for (int i = 0; i < d1.size(); i++) {     distance += (d1.get(i) - d2.get(i)) * (d1.get(i) - d2.get(i));    }    return distance;   }   /**    * 执行KNN算法，获取测试元组的类别    * @param datas 训练数据集    * @param testData 测试元组    * @param k 设定的K值    * @return 测试元组的类别    */   public String knn(List<List<Double>> datas, List<Double> testData, int k) {    PriorityQueue<KNNNode> pq = new PriorityQueue<KNNNode>(k, comparator);    List<Integer> randNum = getRandKNum(k, datas.size());    System.out.println("randNum:"+randNum.toString());    for (int i = 0; i < k; i++) {     int index = randNum.get(i);     List<Double> currData = datas.get(index);     String c = currData.get(currData.size() - 1).toString();     System.out.println("currData:"+currData+",c:"+c+",testData"+testData);     //计算测试元组与训练元组之前的距离     KNNNode node = new KNNNode(index, calDistance(testData, currData), c);     pq.add(node);    }    for (int i = 0; i < datas.size(); i++) {     List<Double> t = datas.get(i);     System.out.println("testData:"+testData);     System.out.println("t:"+t);     double distance = calDistance(testData, t);     System.out.println("distance:"+distance);     KNNNode top = pq.peek();     if (top.getDistance() > distance) {      pq.remove();      pq.add(new KNNNode(i, distance, t.get(t.size() - 1).toString()));     }    }        return getMostClass(pq);   }   /**    * 获取所得到的k个最近邻元组的多数类    * @param pq 存储k个最近近邻元组的优先级队列    * @return 多数类的名称    */   private String getMostClass(PriorityQueue<KNNNode> pq) {    Map<String, Integer> classCount = new HashMap<String, Integer>();    for (int i = 0; i < pq.size(); i++) {     KNNNode node = pq.remove();     String c = node.getC();     if (classCount.containsKey(c)) {      classCount.put(c, classCount.get(c) + 1);     } else {      classCount.put(c, 1);     }    }    int maxIndex = -1;    int maxCount = 0;    Object[] classes = classCount.keySet().toArray();    for (int i = 0; i < classes.length; i++) {     if (classCount.get(classes[i]) > maxCount) {      maxIndex = i;      maxCount = classCount.get(classes[i]);     }    }    return classes[maxIndex].toString();   }  }

 KNN结点类，用来存储最近邻的k个元组相关的信息

package Marchinglearning.knn2;  /**   * KNN结点类，用来存储最近邻的k个元组相关的信息   */  public class KNNNode {   private int index; // 元组标号   private double distance; // 与测试元组的距离   private String c; // 所属类别   public KNNNode(int index, double distance, String c) {    super();    this.index = index;    this.distance = distance;    this.c = c;   }         public int getIndex() {    return index;   }   public void setIndex(int index) {    this.index = index;   }   public double getDistance() {    return distance;   }   public void setDistance(double distance) {    this.distance = distance;   }   public String getC() {    return c;   }   public void setC(String c) {    this.c = c;   }  }

KNN算法测试类

package Marchinglearning.knn2;  import java.io.BufferedReader;  import java.io.File;  import java.io.FileReader;  import java.util.ArrayList;  import java.util.List;  /**   * KNN算法测试类   */  public class TestKNN {      /**    * 从数据文件中读取数据    * @param datas 存储数据的集合对象    * @param path 数据文件的路径    */   public void read(List<List<Double>> datas, String path){    try {     BufferedReader br = new BufferedReader(new FileReader(new File(path)));     String data = br.readLine();     List<Double> l = null;     while (data != null) {      String t[] = data.split(" ");      l = new ArrayList<Double>();      for (int i = 0; i < t.length; i++) {       l.add(Double.parseDouble(t[i]));      }      datas.add(l);      data = br.readLine();     }    } catch (Exception e) {     e.printStackTrace();    }   }      /**    * 程序执行入口    * @param args    */   public static void main(String[] args) {    TestKNN t = new TestKNN();    String datafile = new File("").getAbsolutePath() + File.separator +"knndata2"+File.separator + "datafile.data";    String testfile = new File("").getAbsolutePath() + File.separator +"knndata2"+File.separator +"testfile.data";    System.out.println("datafile:"+datafile);    System.out.println("testfile:"+testfile);    try {     List<List<Double>> datas = new ArrayList<List<Double>>();     List<List<Double>> testDatas = new ArrayList<List<Double>>();     t.read(datas, datafile);     t.read(testDatas, testfile);     KNN knn = new KNN();     for (int i = 0; i < testDatas.size(); i++) {      List<Double> test = testDatas.get(i);      System.out.print("测试元组: ");      for (int j = 0; j < test.size(); j++) {       System.out.print(test.get(j) + " ");      }      System.out.print("类别为: ");      System.out.println(Math.round(Float.parseFloat((knn.knn(datas, test, 3)))));     }    } catch (Exception e) {     e.printStackTrace();    }   }  }

运行结果为：

资源下载：

python版本下载

java版本下载

来自： http://blog.csdn.net//u011067360/article/details/45937327