Benchmarking and Comparing Encog, Neuroph and JOONE Neural Networks

import org.encog.util.logging.Logging;

public class Benchmark {

	public static final int INPUT_COUNT = 10;
	public static final int HIDDEN_COUNT = 20;
	public static final int OUTPUT_COUNT = 10;
	public static final int TRAINING_SIZE = 10000;

	public static final double LEARNING_RATE = 0.7;
	public static final double MOMENTUM = 0.7;
	public static final int ITERATIONS = 50;

	public static void performBenchmark(Benchmarkable benchmark)
	{
		GenerateData data = new GenerateData();
		data.generate(10000, Benchmark.INPUT_COUNT, Benchmark.OUTPUT_COUNT, -1, 1);
		double[][] input = data.getInput();
		double[][] ideal = data.getIdeal();
		benchmark.prepareBenchmark(input, ideal);
	}

	public static void main(String args[])
	{
		Logging.stopConsoleLogging();


		if( "encog".equalsIgnoreCase(args[0]))
		{
			Stopwatch.time("Encog", new BenchmarkEncog(0));
		}
		else if( "encog1".equalsIgnoreCase(args[0]))
		{
			Stopwatch.time("Encog", new BenchmarkEncog(1));
		}
		else if( "neuroph".equalsIgnoreCase(args[0]))
		{
			Stopwatch.time("Neuroph", new BenchmarkNeuroph());
		}
		else if( "joone".equalsIgnoreCase(args[0]))
		{
			Stopwatch.time("JOONE", new BenchmarkJOONE(false));
		}
		else if( "joone1".equalsIgnoreCase(args[0]))
		{
			Stopwatch.time("JOONE1", new BenchmarkJOONE(true));
		}

		System.exit(0);
	}
}