import java.util.Arrays;
import org.neuroph.core.*;
import org.neuroph.core.events.*;
import org.neuroph.core.data.*;
import org.neuroph.core.learning.*;
import org.neuroph.nnet.*;
import org.neuroph.nnet.learning.*;
import org.neuroph.util.*;

/**
 * This sample shows how to create, train, save and load simple Multi Layer Perceptron for the XOR problem.
 */
public class neural_net_MLP {

    public static void main(String[] args) {
    
        // create training set (logical XOR function)
        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}));

        // create multi layer perceptron
        MultiLayerPerceptron myMlPerceptron = new MultiLayerPerceptron(TransferFunctionType.TANH, 2, 3, 1);

        // enable batch if using MomentumBackpropagation
        if( myMlPerceptron.getLearningRule() instanceof MomentumBackpropagation )
        	((MomentumBackpropagation)myMlPerceptron.getLearningRule()).setBatchMode(true);

        LearningRule learningRule = myMlPerceptron.getLearningRule();
        
        // learn the training set
        System.out.println("Training neural network...");
        myMlPerceptron.learn(trainingSet);

        // test perceptron
        System.out.println("Testing trained neural network");
        testNeuralNetwork(myMlPerceptron, trainingSet);

        // save trained neural network
        myMlPerceptron.save("myMlPerceptron.nnet");

        // load saved neural network
        NeuralNetwork loadedMlPerceptron = NeuralNetwork.load("myMlPerceptron.nnet");

        // test loaded neural network
        System.out.println("Testing loaded neural network");
        testNeuralNetwork(loadedMlPerceptron, trainingSet);
    }

    /**
     * Prints network output for the each element from the specified training set.
     * @param neuralNet neural network
     * @param trainingSet training set
     */
    public static void testNeuralNetwork(NeuralNetwork neuralNet, DataSet testSet) {

        for(DataSetRow testSetRow : testSet.getRows()) {
            neuralNet.setInput(testSetRow.getInput());
            neuralNet.calculate();
            double[] networkOutput = neuralNet.getOutput();

            System.out.print("Input: " + Arrays.toString( testSetRow.getInput() ) );
            System.out.println(" Output: " + Arrays.toString( networkOutput) );
        }
    }
    
}