底湘西网站制作,openshift wordpress 访问,php网站虚拟机,平面设计的范围63.1 AdaBoosting基本算法#xff1a;先从初始训练集训练一个弱学习器#xff0c;在根据弱学习器的表现对训练样本进行权重调整#xff0c;经过若干轮之后#xff0c;将得到一组分类器#xff0c;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 WeightedInstances63.3 结果部分
