Encog Machine Learning Framework

Encog is an advanced machine learning framework that supports a variety of advanced algorithms, as well as support classes to normalize and process data. Machine learning algorithms such as Support Vector Machines, Artificial Neural Networks, Bayesian Networks, Hidden Markov Models, Genetic Programming and Genetic Algorithms are supported. Most Encog training algorithms are multi-threaded and scale well to multicore hardware. A GUI based workbench is also provided to help model and train machine learning algorithms. Encog has been in active development since 2008.

For more information: Encog Website

Simple Java XOR Example in Encog

import org.encog.Encog;
import org.encog.engine.network.activation.ActivationReLU;
import org.encog.engine.network.activation.ActivationSigmoid;
import org.encog.ml.data.MLData;
import org.encog.ml.data.MLDataPair;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.BasicLayer;
import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation;

public class XORHelloWorld {

	/**
	 * The input necessary for XOR.
	 */
	public static double XOR_INPUT[][] = { { 0.0, 0.0 }, { 1.0, 0.0 },
			{ 0.0, 1.0 }, { 1.0, 1.0 } };

	/**
	 * The ideal data necessary for XOR.
	 */
	public static double XOR_IDEAL[][] = { { 0.0 }, { 1.0 }, { 1.0 }, { 0.0 } };

	/**
	 * The main method.
	 * @param args No arguments are used.
	 */
	public static void main(final String args[]) {

		// create a neural network, without using a factory
		BasicNetwork network = new BasicNetwork();
		network.addLayer(new BasicLayer(null,true,2));
		network.addLayer(new BasicLayer(new ActivationReLU(),true,5));
		network.addLayer(new BasicLayer(new ActivationSigmoid(),false,1));
		network.getStructure().finalizeStructure();
		network.reset();

		// create training data
		MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);

		// train the neural network
		final ResilientPropagation train = new ResilientPropagation(network, trainingSet);

		int epoch = 1;

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

		// test the neural network
		System.out.println("Neural Network Results:");
		for(MLDataPair pair: trainingSet ) {
			final MLData 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));
		}

		Encog.getInstance().shutdown();
	}
}