hand_writing.zip_Java识别

package finalWork; /** * Description: Use tri-training to exploit unlabeled data. * * Reference: Z.-H. Zhou, Y. Jiang, M. Li. Tri-Training: exploiting unlabeled data using three classifiers. * IEEE Transactions on Knowledge and Data Engineering. in press. * * ATTN: This package is free for academic usage. You can run it at your own risk. * For other purposes, please contact Prof. Zhi-Hua Zhou (zhouzh@nju.edu.cn). * * Requirement: To use this package, the whole WEKA environment (ver 3.4) must be available. * refer: I.H. Witten and E. Frank. Data Mining: Practical Machine Learning * Tools and Techniques with Java Implementations. Morgan Kaufmann, * San Francisco, CA, 2000. * * Data format: Both the input and output formats are the same as those used by WEKA. * * ATTN2: This package was developed by Mr. Ming Li (lim@lamda.nju.edu.cn). There * is a ReadMe file provided for roughly explaining the codes. But for any * problem concerning the code, please feel free to contact with Mr. Li. * */ import java.io.*; import java.util.*; import weka.core.*; import weka.classifiers.Classifier; import weka.classifiers.bayes.NaiveBayes; import weka.classifiers.functions.LibSVM; import weka.classifiers.functions.SMO; import weka.classifiers.pmml.consumer.NeuralNetwork; import weka.classifiers.trees.*; public class test { /** base classifiers */ private Classifier m_baseClassifier = null; /** classifiers */ private Classifier[] m_classifiers; private Random m_rand = new Random(1); /** * The constructor */ public test(Classifier c1,Classifier c2,Classifier c3) { m_classifiers = new Classifier[3]; m_classifiers[0] = c1; m_classifiers[1] = c2; m_classifiers[2] = c3; } /** * Sets the base classifier * * @param c Classifier The base classifier */ public void setClassifier(Classifier c) { m_baseClassifier = c; } public void setRandomObject(Random random) { m_rand = random; } /** * Builds classifier via tri-training * * @param labeled Instances The labeled set * @param unlabeled Instances The unlabeled set * @throws Exception Some exception */ public void buildClassifier(Instances labeled, Instances unlabeled) throws Exception { double[] err = new double[3]; // e_i double[] err_prime = new double[3]; // e'_i double[] s_prime = new double[3]; // l'_i /*if (m_baseClassifier == null) throw new Exception("Base classifier should be set before the building process");*/ if (!labeled.classAttribute().isNominal()) throw new Exception("The class value should be nominal"); //m_classifiers = Classifier.makeCopies(m_baseClassifier, 3); Instances[] labeleds = new Instances[3]; for(int i = 0; i < 3; i++) { labeleds[i] = new Instances(labeled.resampleWithWeights(m_rand)); //L_i <-- Bootstrap(L) m_classifiers[i].buildClassifier(labeleds[i]); //h_i <-- Learn(L_i) err_prime[i] = 0.5; //e'_i <-- .5 s_prime[i] = 0; //l'_i <-- 0 } boolean bChanged = true; /** repeat until none of h_i ( i \in {1...3} ) changes */ while(bChanged) { bChanged = false; boolean[] bUpdate = new boolean[m_classifiers.length]; Instances[] Li = new Instances[3]; /** for i \in {1...3} do */ for(int i = 0; i < 3; i++) { Li[i] = new Instances(labeled, 0); //L_i <-- \phi err[i] = measureError(labeled, i); //e_i <-- MeasureError(h_j & h_k) (j, k \ne i) /** if (e_i < e'_i) */ if(err[i] < err_prime[i]) { /** for every x \in U do */ for(int j = 0; j < unlabeled.numInstances(); j++) { Instance curInst = new Instance(unlabeled.instance(j)); curInst.setDataset(Li[i]); double classval = m_classifiers[(i+1)%3].classifyInstance(curInst); /** if h_j(x) = h_k(x) (j,k \ne i) */ if(classval == m_classifiers[(i+2)%3].classifyInstance(curInst)) { curInst.setClassValue(classval); Li[i].add(curInst); //L_i <-- L_i \cup {(x, h_j(x))} } }// end of for j /** if (l'_i == 0 ) */ if(s_prime[i] == 0) s_prime[i] = Math.floor(err[i] / (err_prime[i] - err[i]) + 1); //l'_i <-- floor(e_i/(e'_i-e_i) +1) /** if (l'_i < |L_i| ) */ if(s_prime[i] < Li[i].numInstances()) { /** if ( e_i * |L_i| < e'_i * l'_i) */ if(err[i] * Li[i].numInstances() < err_prime[i] * s_prime[i]) bUpdate[i] = true; // update_i <-- TURE /** else if (l'_i > (e_i / (e'_i - e_i))) */ else if (s_prime[i] > (err[i] / (err_prime[i] - err[i]))) { int numInstAfterSubsample = (int) Math.ceil(err_prime[i] * s_prime[i] / err[i] - 1); Li[i].randomize(m_rand); Li[i] = new Instances(Li[i], 0, numInstAfterSubsample); //L_i <-- Subsample(L_i, ceilling(e'_i*l'_i/e_i-1) bUpdate[i] = true; //update_i <-- TRUE } } } }//end for i = 1...3 //update for(int i = 0; i < 3; i++) { /** if update_i = TRUE */ if(bUpdate[i]) { int size = Li[i].numInstances(); bChanged = true; m_classifiers[i].buildClassifier(combine(labeled, Li[i])); //h_i <-- Learn(L \cup L_i) err_prime[i] = err[i]; //e'_i <-- e_i s_prime[i] = size; //l'_i <-- |L_i| } }// end fo for } //end of repeat } /** * Returns the probability label of a given instance * * @param inst Instance The instance * @return double[] The probability label * @throws Exception Some exception */ public double[] distributionForInstance(Instance inst) throws Exception { double[] res = new double[inst.numClasses()]; for(int i = 0; i < m_classifiers.length; i++) { double[] distr = m_classifiers[i].distributionForInstance(inst); for(int j = 0; j < res.length; j++) res[j] += distr[j]; } Utils.normalize(res); return res; } /** * Classifies a given instance * * @param inst Instance The instance * @return double The class value * @throws Exception Some Exception */ public double classifyInstance(Instance inst) throws Exception { double[] distr = distributionForInstance(inst); return Utils.maxIndex(distr); } /** * Adds the instances in initial training set L to the newly labeled set Li * * @param L Instances The initial training set * @param Li Instances The newly labeled set * @return Instances The combined data set */ private Instances combine(Instances L, Instances Li) { for(int i = 0; i < L.numInstances(); i++) Li.add(L.instance(i)); return Li; } /** * Measure combined error excluded the classifier 'id' on the given data set * * @param data Instances The data set * @param id int The id of classifier to be excluded * @return double The error * @throws Exception Some Exception */ protected double measureError(Instances data, int id) throws Exception { Classifier c1 = m_classifiers[(id+1)%3]; Classifier c2 = m_classifiers[(id+2)%3]; double err = 0; int count =