63.1 AdaBoosting基本算法：先从初始训练集训练一个弱学习器，在根据弱学习器的表现对训练样本进行权重调整，经过若干轮之后，将得到一组分类器，将数据输入这组分类器后会得到一个综合且准确的的分类结果。“三个臭皮匠，顶个诸葛亮”，多个这样的弱分类器相互补充，最后会变成一个强分类器。

63.2 代码：

package dl;import java.io.FileReader;
import java.util.Arrays;import weka.core.Instances;/*** Weighted instances.*/
public class WeightedInstances extends Instances {/*** Just the requirement of some classes, any number is ok.*/private static final long serialVersionUID = 110;/*** Weights.*/private double[] weights;/********************* The first constructor.** @param paraFileReader*            The given reader to read data from file.*******************/public WeightedInstances(FileReader paraFileReader) throws Exception {super(paraFileReader);setClassIndex(numAttributes() - 1);// Initialize weightsweights = new double[numInstances()];double tempAverage = 1.0 / numInstances();for (int i = 0; i < weights.length; i++) {weights[i] = tempAverage;} // Of for iSystem.out.println("Instances weights are: " + Arrays.toString(weights));} // Of the first constructor/********************* The second constructor.** @param paraInstances*            The given instance.*******************/public WeightedInstances(Instances paraInstances) {super(paraInstances);setClassIndex(numAttributes() - 1);// Initialize weightsweights = new double[numInstances()];double tempAverage = 1.0 / numInstances();for (int i = 0; i < weights.length; i++) {weights[i] = tempAverage;} // Of for iSystem.out.println("Instances weights are: " + Arrays.toString(weights));} // Of the second constructor/********************* Getter.** @param paraIndex*            The given index.* @return The weight of the given index.*******************/public double getWeight(int paraIndex) {return weights[paraIndex];} // Of getWeight/********************* Adjust the weights.** @param paraCorrectArray*            Indicate which instances have been correctly classified.* @param paraAlpha*            The weight of the last classifier.*******************/public void adjustWeights(boolean[] paraCorrectArray, double paraAlpha) {// Step 1. Calculate alpha.double tempIncrease = Math.exp(paraAlpha);// Step 2. Adjust.double tempWeightsSum = 0; // For normalization.for (int i = 0; i < weights.length; i++) {if (paraCorrectArray[i]) {weights[i] /= tempIncrease;} else {weights[i] *= tempIncrease;} // Of iftempWeightsSum += weights[i];} // Of for i// Step 3. Normalize.for (int i = 0; i < weights.length; i++) {weights[i] /= tempWeightsSum;} // Of for iSystem.out.println("After adjusting, instances weights are: " + Arrays.toString(weights));} // Of adjustWeights/********************* Test the method.*******************/public void adjustWeightsTest() {boolean[] tempCorrectArray = new boolean[numInstances()];for (int i = 0; i < tempCorrectArray.length / 2; i++) {tempCorrectArray[i] = true;} // Of for idouble tempWeightedError = 0.3;adjustWeights(tempCorrectArray, tempWeightedError);System.out.println("After adjusting");System.out.println(toString());} // Of adjustWeightsTest/********************* For display.*******************/public String toString() {String resultString = "I am a weighted Instances object.\r\n" + "I have " + numInstances() + " instances and "+ (numAttributes() - 1) + " conditional attributes.\r\n" + "My weights are: " + Arrays.toString(weights)+ "\r\n" + "My data are: \r\n" + super.toString();return resultString;} // Of toString/********************* For unit test.** @param args*            Not provided.*******************/public static void main(String args[]) {WeightedInstances tempWeightedInstances = null;String tempFilename = "C:\\Users\\86183\\IdeaProjects\\deepLearning\\src\\main\\java\\resources\\iris.arff";try {FileReader tempFileReader = new FileReader(tempFilename);tempWeightedInstances = new WeightedInstances(tempFileReader);tempFileReader.close();} catch (Exception exception1) {System.out.println("Cannot read the file: " + tempFilename + "\r\n" + exception1);System.exit(0);} // Of trySystem.out.println(tempWeightedInstances.toString());tempWeightedInstances.adjustWeightsTest();} // Of main} // Of class WeightedInstances

63.3 结果（部分）