机器学习与数据挖掘-K最近邻(KNN)算法的实现（java和python版）
jopen
8年前
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]
#-*-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：
来自： http://blog.csdn.net//u011067360/article/details/45937327