Benchmarking and Comparing Encog, Neuroph and JOONE Neural Networks

import org.encog.neural.activation.ActivationSigmoid;
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.propagation.back.Backpropagation;
import org.encog.util.logging.Logging;

public class BenchmarkEncog implements Benchmarkable {

	private BasicNetwork network;
	private NeuralDataSet trainingSet;
	private Backpropagation train;
	private int threadCount;

	public BenchmarkEncog(int threadCount)
	{
		this.threadCount = threadCount;
	}


	@Override
	public double benchmark(int iterations) {
		// train the neural network

		int epoch = 1;

		do {
			train.iteration();
			epoch++;
		} while (epoch <= Benchmark.ITERATIONS );

		return 0;
	}

	@Override
	public void prepareBenchmark(double[][] input, double[][] ideal) {
		// construct a network
		this.network = new BasicNetwork();
		network.addLayer(new BasicLayer(new ActivationSigmoid(),true,Benchmark.INPUT_COUNT));
		network.addLayer(new BasicLayer(new ActivationSigmoid(),true,Benchmark.HIDDEN_COUNT));
		network.addLayer(new BasicLayer(new ActivationSigmoid(),true,Benchmark.OUTPUT_COUNT));
		network.setLogic(new FeedforwardLogic());
		network.getStructure().finalizeStructure();
		network.reset();

		// construct training data
		this.trainingSet = new BasicNeuralDataSet(input, ideal);

		this.train = new Backpropagation(this.network, this.trainingSet,
				Benchmark.LEARNING_RATE, Benchmark.MOMENTUM);
		this.train.setNumThreads(this.threadCount);
	}
}