Comparing Neural Networks in Neuroph, Encog and JOONE

import org.encog.neural.activation.ActivationSigmoid;
import org.encog.neural.activation.ActivationTANH;
import org.encog.neural.data.NeuralData;
import org.encog.neural.data.NeuralDataPair;
import org.encog.neural.data.NeuralDataSet;
import org.encog.neural.data.basic.BasicNeuralDataSet;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.BasicLayer;
import org.encog.neural.networks.logic.FeedforwardLogic;
import org.encog.neural.networks.training.Train;
import org.encog.neural.networks.training.lma.LevenbergMarquardtTraining;
import org.encog.neural.networks.training.propagation.back.Backpropagation;
import org.encog.neural.networks.training.strategy.RequiredImprovementStrategy;
import org.encog.util.logging.Logging;

public class EncogXOR {

	public static double XOR_INPUT[][] = { { 0.0, 0.0 }, { 1.0, 0.0 },
			{ 0.0, 1.0 }, { 1.0, 1.0 } };

	public static double XOR_IDEAL[][] = { { 0.0 }, { 1.0 }, { 1.0 }, { 0.0 } };

	public static void main(final String args[]) {
		
		Logging.stopConsoleLogging();
		
		BasicNetwork network = new BasicNetwork();
		network.addLayer(new BasicLayer(new ActivationSigmoid(),true,2));
		network.addLayer(new BasicLayer(new ActivationSigmoid(),true,3));
		network.addLayer(new BasicLayer(new ActivationSigmoid(),true,1));
		network.setLogic(new FeedforwardLogic());
		network.getStructure().finalizeStructure();
		network.reset();

		NeuralDataSet trainingSet = new BasicNeuralDataSet(XOR_INPUT, XOR_IDEAL);
		
		// train the neural network
		final Train train = new LevenbergMarquardtTraining(network, trainingSet);
		
		// reset if improve is less than 1% over 5 cycles
		train.addStrategy(new RequiredImprovementStrategy(5));

		int epoch = 1;

		do {
			train.iteration();
			System.out
					.println("Epoch #" + epoch + " Error:" + train.getError());
			epoch++;
		} while(train.getError() > 0.01);

		// test the neural network
		System.out.println("Neural Network Results:");
		for(NeuralDataPair pair: trainingSet ) {
			final NeuralData output = network.compute(pair.getInput());
			System.out.println(pair.getInput().getData(0) + "," + pair.getInput().getData(1)
					+ ", actual=" + output.getData(0) + ",ideal=" + pair.getIdeal().getData(0));
		}
	}
}