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README.md

Encog Machine Learning Framework

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 C# XOR Example in Encog

using System;
using ConsoleExamples.Examples;
using Encog.Engine.Network.Activation;
using Encog.ML.Data;
using Encog.ML.Data.Basic;
using Encog.ML.Train;
using Encog.Neural.Networks;
using Encog.Neural.Networks.Layers;
using Encog.Neural.Networks.Training.Propagation.Back;
using Encog.Neural.Networks.Training.Propagation.Resilient;

namespace Encog.Examples.XOR
{
    public class XORHelloWorld : IExample
    {
        /// <summary>
        /// Input for the XOR function.
        /// </summary>
        public static double[][] XORInput = {
            new[] {0.0, 0.0},
            new[] {1.0, 0.0},
            new[] {0.0, 1.0},
            new[] {1.0, 1.0}
        };

        /// <summary>
        /// Ideal output for the XOR function.
        /// </summary>
        public static double[][] XORIdeal = {
            new[] {0.0},
            new[] {1.0},
            new[] {1.0},
            new[] {0.0}
        };

        public static ExampleInfo Info
        {
            get
            {
                var info = new ExampleInfo(
                    typeof(XORHelloWorld),
                    "xor",
                    "Simple XOR with backprop, no factories or helper functions.",
                    "This example shows how to train an XOR with no factories or helper functions.");
                return info;
            }
        }

        #region IExample Members

        /// <summary>
        /// Program entry point.
        /// </summary>
        /// <param name="app">Holds arguments and other info.</param>
        public void Execute(IExampleInterface app)
        {
            // create a neural network, without using a factory
            var network = new BasicNetwork();
            network.AddLayer(new BasicLayer(null, true, 2));
            network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 3));
            network.AddLayer(new BasicLayer(new ActivationSigmoid(), false, 1));
            network.Structure.FinalizeStructure();
            network.Reset();

            // create training data
            IMLDataSet trainingSet = new BasicMLDataSet(XORInput, XORIdeal);

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

            int epoch = 1;

            do
            {
                train.Iteration();
                Console.WriteLine(@"Epoch #" + epoch + @" Error:" + train.Error);
                epoch++;
            } while (train.Error > 0.01);

            // test the neural network
            Console.WriteLine(@"Neural Network Results:");
            foreach (IMLDataPair pair in trainingSet)
            {
                IMLData output = network.Compute(pair.Input);
                Console.WriteLine(pair.Input[0] + @"," + pair.Input[1]
                                  + @", actual=" + output[0] + @",ideal=" + pair.Ideal[0]);
            }
        }

        #endregion
    }
}