由Java编写的极限学习机毕业设计源代码.zip_极限学习机_源代码

/* * This library is free software; * The original version is a matlab programmer,I rewrote it in Java * The original Authors: MR QIN-YU ZHU AND DR GUANG-BIN HUANG, * The original WEBSITE: http://www.ntu.edu.sg/eee/icis/cv/egbhuang.htm * */ import java.io.BufferedReader; import java.io.BufferedWriter; import java.io.File; import java.io.FileReader; import java.io.FileWriter; import java.io.IOException; import no.uib.cipr.matrix.DenseMatrix; import no.uib.cipr.matrix.DenseVector; import no.uib.cipr.matrix.Matrices; import no.uib.cipr.matrix.NotConvergedException; public class elm { private DenseMatrix train_set; private DenseMatrix test_set; private int numTrainData; private int numTestData; private DenseMatrix InputWeight; private float TrainingTime; private float TestingTime; private double TrainingAccuracy, TestingAccuracy; private int Elm_Type; private int NumberofHiddenNeurons; private int NumberofOutputNeurons; //also the number of classes private int NumberofInputNeurons; //also the number of attribution private String func; private int []label; //this class label employ a lazy and easy method,any class must written in 0,1,2...so the preprocessing is required //the blow variables in both train() and test() private DenseMatrix BiasofHiddenNeurons; private DenseMatrix OutputWeight; private DenseMatrix testP; private DenseMatrix testT; private DenseMatrix Y; private DenseMatrix T; /** * Construct an ELM * @param * elm_type - 0 for regression; 1 for (both binary and multi-classes) classification * @param * numberofHiddenNeurons - Number of hidden neurons assigned to the ELM * @param * ActivationFunction - Type of activation function: * 'sig' for Sigmoidal function * 'sin' for Sine function * 'hardlim' for Hardlim function * 'tribas' for Triangular basis function * 'radbas' for Radial basis function (for additive type of SLFNs instead of RBF type of SLFNs) * @throws NotConvergedException */ public elm(int elm_type, int numberofHiddenNeurons, String ActivationFunction){ Elm_Type = elm_type; NumberofHiddenNeurons = numberofHiddenNeurons; func = ActivationFunction; TrainingTime = 0; TestingTime = 0; TrainingAccuracy= 0; TestingAccuracy = 0; NumberofOutputNeurons = 1; } public elm(){ } //the first line of dataset file must be the number of rows and columns,and number of classes if neccessary //the first column is the norminal class value 0,1,2... //if the class value is 1,2...,number of classes should plus 1 public DenseMatrix loadmatrix(String filename) throws IOException{ BufferedReader reader = new BufferedReader(new FileReader(new File(filename))); //FileInputStream String firstlineString = reader.readLine(); String []strings = firstlineString.split(" "); int m = Integer.parseInt(strings[0]); int n = Integer.parseInt(strings[1]); if(strings.length > 2) NumberofOutputNeurons = Integer.parseInt(strings[2]); DenseMatrix matrix = new DenseMatrix(m, n); firstlineString = reader.readLine(); int i = 0; while (i<m) { String []datatrings = firstlineString.split(" "); for (int j = 0; j < n; j++) { matrix.set(i, j, Double.parseDouble(datatrings[j])); } i++; firstlineString = reader.readLine(); } /* for(int ii = 0; ii<m; ii++) matrix.add(ii, 0, -1); */ return matrix; } public void train(String TrainingData_File) throws NotConvergedException{ try { train_set = loadmatrix(TrainingData_File); } catch (IOException e) { // TODO Auto-generated catch block e.printStackTrace(); } train(); } public void train(double [][]traindata) throws NotConvergedException{ //classification require a the number of class train_set = new DenseMatrix(traindata); int m = train_set.numRows(); if(Elm_Type == 1){ double maxtag = traindata[0][0]; for (int i = 0; i < m; i++) { if(traindata[i][0] > maxtag) maxtag = traindata[i][0]; } NumberofOutputNeurons = (int)maxtag+1; } train(); } private void train() throws NotConvergedException{ numTrainData = train_set.numRows(); NumberofInputNeurons = train_set.numColumns() - 1; InputWeight = (DenseMatrix) Matrices.random(NumberofHiddenNeurons, NumberofInputNeurons); DenseMatrix transT = new DenseMatrix(numTrainData, 1); DenseMatrix transP = new DenseMatrix(numTrainData, NumberofInputNeurons); for (int i = 0; i < numTrainData; i++) { transT.set(i, 0, train_set.get(i, 0)); for (int j = 1; j <= NumberofInputNeurons; j++) transP.set(i, j-1, train_set.get(i, j)); } T = new DenseMatrix(1,numTrainData); DenseMatrix P = new DenseMatrix(NumberofInputNeurons,numTrainData); transT.transpose(T); transP.transpose(P); if(Elm_Type != 0) //CLASSIFIER { label = new int[NumberofOutputNeurons]; for (int i = 0; i < NumberofOutputNeurons; i++) { label[i] = i; //class label starts form 0 } DenseMatrix tempT = new DenseMatrix(NumberofOutputNeurons,numTrainData); tempT.zero(); for (int i = 0; i < numTrainData; i++){ int j = 0; for (j = 0; j < NumberofOutputNeurons; j++){ if (label[j] == T.get(0, i)) break; } tempT.set(j, i, 1); } T = new DenseMatrix(NumberofOutputNeurons,numTrainData); // T=temp_T*2-1; for (int i = 0; i < NumberofOutputNeurons; i++){ for (int j = 0; j < numTrainData; j++) T.set(i, j, tempT.get(i, j)*2-1); } transT = new DenseMatrix(numTrainData,NumberofOutputNeurons); T.transpose(transT); } //end if CLASSIFIER long start_time_train = System.currentTimeMillis(); // Random generate input weights InputWeight (w_i) and biases BiasofHiddenNeurons (b_i) of hidden neurons // InputWeight=rand(NumberofHiddenNeurons,NumberofInputNeurons)*2-1; BiasofHiddenNeurons = (DenseMatrix) Matrices.random(NumberofHiddenNeurons, 1); DenseMatrix tempH = new DenseMatrix(NumberofHiddenNeurons, numTrainData); InputWeight.mult(P, tempH); //DenseMatrix ind = new DenseMatrix(1, numTrainData); DenseMatrix BiasMatrix = new DenseMatrix(NumberofHiddenNeurons, numTrainData); for (int j = 0; j < numTrainData; j++) { for (int i = 0; i < NumberofHiddenNeurons; i++) { BiasMatrix.set(i, j, BiasofHiddenNeurons.get(i, 0)); } } tempH.add(BiasMatrix); DenseMatrix H = new DenseMatrix(NumberofHiddenNeurons, numTrainData); if(func.startsWith("sig")){ for (int j = 0; j < NumberofHiddenNeurons; j++) { for (int i = 0; i < numTrainData; i++) { double temp = tempH.get(j, i); temp = 1.0f/ (1 + Math.exp(-temp)); H.set(j, i, temp); } } } else if(func.startsWith("sin")){ for (int j = 0; j < NumberofHiddenNeurons; j++) { for (int i = 0; i < numTrainData; i++) { double temp = tempH.get(j, i); temp = Math.sin(temp); H.set(j, i, temp); } } } else if(func.startsWith("hardlim")){ //If you need it ,you can absolutely complete it yourself } else if(func.startsWith("tribas")){ //If you need it ,you can absolutely complete it yourself } else if(func.startsWith("radbas")){ //If you need it ,you can absolutely complete it yourself } DenseMatrix Ht = new DenseMatrix(numTrainData,NumberofHiddenNeurons); H.transpose(Ht); Inverse invers = new Inverse(Ht); DenseMatrix pinvHt = invers.getMPInverse(); //NumberofHiddenNeurons*numTrainData //DenseMatrix pinvHt = invers.getMPInverse(0.000001); //fast method, PLEASE CITE in your pap