package com.core.perceptron;import java.util.Arrays;import org.neuroph.core.*;import org.neuroph.core.data.*;import org.neuroph.nnet.MultiLayerPerceptron;import org.neuroph.util.TransferFunctionType;public class XORDemo {    public static void main(String[] args){        DataSet trainingSet = new DataSet(2, 1);        trainingSet.addRow(new DataSetRow(new double[]{0, 0}, new double[] {0}));        trainingSet.addRow(new DataSetRow(new double[]{0, 1}, new double[] {1}));        trainingSet.addRow(new DataSetRow(new double[]{1, 0}, new double[] {1}));        trainingSet.addRow(new DataSetRow(new double[]{1, 1}, new double[] {0}));                MultiLayerPerceptron myMLP = new MultiLayerPerceptron(TransferFunctionType.TANH, 2 ,3 ,1);        System.out.println("Training network...");        myMLP.learn(trainingSet);        System.out.println("Testing network");        testNeuralNetwork(myMLP, trainingSet);    }        public static void testNeuralNetwork(NeuralNetwork nnet, DataSet tset) {                    for (DataSetRow dataRow : tset.getRows()) {                 nnet.setInput(dataRow.getInput());                 nnet.calculate();                 double[ ] networkOutput = nnet.getOutput();                 System.out.print("Input: " + Arrays.toString(dataRow.getInput()) );                 System.out.println(" Output: " + Arrays.toString(networkOutput) );               }      }  }