Example usage for weka.classifiers.functions MultilayerPerceptron toString

Introduction

In this page you can find the example usage for weka.classifiers.functions MultilayerPerceptron toString.

Prototype

@Override
public String toString() 

Source Link

Usage

From source file:cyber009.main.UDALNeuralNetwork.java

public static void main(String[] args) {
    UDALNeuralNetwork udal = new UDALNeuralNetwork(0.014013);
    Statistics statis = new Statistics(udal.v);
    long timeStart = 0, timeEnd = 0;
    for (int f = 2; f <= 2; f++) {
        udal.initUDAL(4, 5000);//  w w  w . ja v a  2 s.c  o m
        udal.activeLearning(0, 5000);
        udal.arraytoInstances();
        udal.ann.weightReset();
        timeStart = System.currentTimeMillis();
        MultilayerPerceptron wekaNN = new MultilayerPerceptron();
        wekaNN.setAutoBuild(true);
        //wekaNN.setGUI(true);
        try {
            wekaNN.buildClassifier(udal.dataSet);
            Evaluation eval = new Evaluation(udal.dataSet);
            System.out.println(wekaNN.toString());
            eval.crossValidateModel(wekaNN, udal.dataSet, 4999, new Random(System.currentTimeMillis()));
            System.out.println(wekaNN.toString());
            System.out.println(eval.toClassDetailsString());

            //            udal.ann.gradientDescent(10000L, 3, 100);
            //            for (Double target : udal.v.CLASSES) {
            //                statis.calMVMuSigma(target);
            //                System.out.println(udal.v.N_DATA_IN_CLASS.get(target));
            //                System.out.println(statis.mu.get(target));
            //                System.out.println(statis.sigma.get(target));
            //            }
            //            for(int d=0; d<udal.v.D; d++) {
            //                if(udal.v.LABEL[d] == false) {
            //                    double [][] val = new double[udal.v.N-1][1];
            //                    for(int n=1; n<udal.v.N; n++) {
            //                        val[n-1][0] = udal.v.X[d][n];
            ////                        System.out.print(udal.v.X[d][n] + "   ");
            ////                        System.out.println(val[n-1][0]);
            //                    }
            //                    Matrix mVal = new Matrix(val);
            //                    double pp = 0.0D;
            //                    for (Double target : udal.v.CLASSES) {
            //                        //System.out.println("-----------------------\nClass:"+ target);
            //                        pp += statis.posteriorDistribution(target, mVal);
            //                        System.out.println("conditional: Entropy: "+ 
            //                                statis.conditionalEntropy(target, mVal, d));
            //                    }
            //                    System.out.print("Sum posterior:"+ pp+ " for "+new Matrix(val).transpose());
            //                    
            //                }
            //            }
            //            System.out.println("-----------------------");
            //            timeEnd = System.currentTimeMillis();
            //            System.out.println("feature #:"+udal.v.N+" time:("+ (timeEnd - timeStart) +")");
            //            udal.v.showResult();
            //            
        } catch (Exception ex) {
            Logger.getLogger(UDALNeuralNetwork.class.getName()).log(Level.SEVERE, null, ex);
        }

    }
}

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