source: anac2020/BlingBling/src/main/java/geniusweb/blingbling/Ranknet4j/Test1.java

package geniusweb.blingbling.Ranknet4j;

import java.util.ArrayList;
import java.util.List;
import java.util.Random;

import org.neuroph.core.Layer;
import org.neuroph.core.NeuralNetwork;
import org.neuroph.core.data.DataSet;
import org.neuroph.core.data.DataSetRow;
import org.neuroph.core.learning.LearningRule;
import org.neuroph.util.TransferFunctionType;

public class Test1 {

        public static void main(String[] args) {
                // TODO Auto-generated method stub
                int inputsCount = 10;
                int samplenum = 10;
                NeuralNetwork ann = new Ranknet(TransferFunctionType.SIGMOID, inputsCount, 10, 1);

                LearningRule lr = ann.getLearningRule();
                
                System.out.print(lr.toString());
                List<double[]> datalist = getData(inputsCount, samplenum);
                for (double[] input: datalist) {
                        ann.setInput(input);
                        ann.calculate();
                        System.out.println(ann.getOutput()[0]);
                }
                
                DataSet dataset = getDataset(datalist, inputsCount, samplenum);
                dataset.shuffle();
                ann.learn(dataset);
                System.out.println("-----------------------------");
                for (double[] input: datalist) {
                        ann.setInput(input);
                        ann.calculate();
//                      System.out.print(input);
                        System.out.println(ann.getOutput()[0]);
                }

        }

        public static List<double[]> getData(int inputsize, int sample) {

                Random rand = new Random();

                List<double[]> l = new ArrayList<double[]>();
                for (int num = 0; num<sample; num++) {
                        double[] d = new double[inputsize];
                        for (int i=0; i<inputsize; i++) {
                                if (i==num) {
                                        d[i]=1.0;
                                }else {
                                        d[i]=0.0;
                                }
//                              d[i] = rand.nextDouble();
                        }
                        l.add(d);
                }
                

                return l;
        }
        
        public static DataSet getDataset(List<double[]> l,int inputsize, int sample) {
                DataSet ds = new DataSet(inputsize*2, 1);
                double[] output = new double[1];
                output[0] = 1.0;
                for (int i =0; i <sample; i++) {
                        for (int j=i+1; j<sample; j++) {
                                double[] data = new double[inputsize*2];
                                for (int ind=0; ind<inputsize*2; ind++) {
                                        if (ind<inputsize) {
                                                data[ind] = l.get(i)[ind];
                                        }else {
                                                data[ind] = l.get(j)[ind-inputsize];
                                        }
                                }
                                ds.add(data, output);
                        }
                }
                ds.shuffle();
                return ds.split(10)[0];
//              return ds;
                
        }
}