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;
		
	}
}