我已经将kNN算法应用于手写数字的分类。这些数字最初是以8*8的向量格式存储，并被拉伸成1*64的向量。
目前，我的代码仅使用k=1来应用kNN算法。我尝试了几次更改k值，但总是遇到错误。如果有人能指导我正确的方向，我将非常感激。训练数据集可以在这里找到，验证集可以在这里找到。
ImageMatrix.java
import java.util.*;public class ImageMatrix { private int[] data; private int classCode; private int curData;public ImageMatrix(int[] data, int classCode) { assert data.length == 64; //maximum array length of 64 this.data = data; this.classCode = classCode;} public String toString() { return "Class Code: " + classCode + " Data :" + Arrays.toString(data) + "\n"; //outputs readable } public int[] getData() { return data; } public int getClassCode() { return classCode; } public int getCurData() { return curData; }}ImageMatrixDB.java
import java.util.*;import java.io.*;import java.util.ArrayList;public class ImageMatrixDB implements Iterable<ImageMatrix> { private List<ImageMatrix> list = new ArrayList<ImageMatrix>(); public ImageMatrixDB load(String f) throws IOException { try ( FileReader fr = new FileReader(f); BufferedReader br = new BufferedReader(fr)) { String line = null; while((line = br.readLine()) != null) { int lastComma = line.lastIndexOf(','); int classCode = Integer.parseInt(line.substring(1 + lastComma)); int[] data = Arrays.stream(line.substring(0, lastComma).split(",")) .mapToInt(Integer::parseInt) .toArray(); ImageMatrix matrix = new ImageMatrix(data, classCode); // Classcode->100% when 0 -> 0% when 1 - 9.. list.add(matrix); } } return this; } public void printResults(){ //output results for(ImageMatrix matrix: list){ System.out.println(matrix); } } public Iterator<ImageMatrix> iterator() { return this.list.iterator(); } /// kNN implementation /// public static int distance(int[] a, int[] b) { int sum = 0; for(int i = 0; i < a.length; i++) { sum += (a[i] - b[i]) * (a[i] - b[i]); } return (int)Math.sqrt(sum); } public static int classify(ImageMatrixDB trainingSet, int[] curData) { int label = 0, bestDistance = Integer.MAX_VALUE; for(ImageMatrix matrix: trainingSet) { int dist = distance(matrix.getData(), curData); if(dist < bestDistance) { bestDistance = dist; label = matrix.getClassCode(); } } return label; } public int size() { return list.size(); //returns size of the list } public static void main(String[] argv) throws IOException { ImageMatrixDB trainingSet = new ImageMatrixDB(); ImageMatrixDB validationSet = new ImageMatrixDB(); trainingSet.load("cw2DataSet1.csv"); validationSet.load("cw2DataSet2.csv"); int numCorrect = 0; for(ImageMatrix matrix:validationSet) { if(classify(trainingSet, matrix.getData()) == matrix.getClassCode()) numCorrect++; } //285 correct System.out.println("Accuracy: " + (double)numCorrect / validationSet.size() * 100 + "%"); System.out.println(); }}回答：
在classify方法的for循环中，你试图找到与测试点最接近的训练样本。你需要将其改为寻找与测试数据最接近的K个训练点。然后，你应该对这K个点调用getClassCode方法，并找出其中最常见的类别代码（即出现频率最高的）。classify方法将返回你找到的主要类别代码。
你可以根据需要以任何方式处理平局情况（即有两个或多个最常见的类别代码分配给相同数量的训练数据）。
我对Java的经验非常有限，但通过查阅语言参考，我提出了下面的实现方案。
public static int classify(ImageMatrixDB trainingSet, int[] curData, int k) { int label = 0, bestDistance = Integer.MAX_VALUE; int[][] distances = new int[trainingSet.size()][2]; int i=0; // Place distances in an array to be sorted for(ImageMatrix matrix: trainingSet) { distances[i][0] = distance(matrix.getData(), curData); distances[i][1] = matrix.getClassCode(); i++; } Arrays.sort(distances, (int[] lhs, int[] rhs) -> lhs[0]-rhs[0]); // Find frequencies of each class code i = 0; Map<Integer,Integer> majorityMap; majorityMap = new HashMap<Integer,Integer>(); while(i < k) { if( majorityMap.containsKey( distances[i][1] ) ) { int currentValue = majorityMap.get(distances[i][1]); majorityMap.put(distances[i][1], currentValue + 1); } else { majorityMap.put(distances[i][1], 1); } ++i; } // Find the class code with the highest frequency int maxVal = -1; for (Entry<Integer, Integer> entry: majorityMap.entrySet()) { int entryVal = entry.getValue(); if(entryVal > maxVal) { maxVal = entryVal; label = entry.getKey(); } } return label;}你只需将K作为参数添加进去即可。不过，请注意，上述代码并未特别处理平局情况。
