AND M-Adeline example with my C# AI framework

---------- Very simple neural network in 'AND' configuration ------------------

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;

using aneuro32.Nn.Structure.FeedForward;
using aneuro32.Functions;
using aneuro32.Learning.Supervised.Propagation;
using aneuro32.Misc;

namespace M_Adeline_testApp
{
    class Program
    {
        static void Main(string[] args)
        {
            double[][] input =
            {
                new double[] {0,0},
                new double[] {0,1},
                new double[] {1,0},
                new double[] {1,1}
            };
            double[][] target =
            {
                new double[] {0},
                new double[] {0},
                new double[] {0},
                new double[] {1}
            };

            // Build M-Adeline network, a very simple neural network configuration

            MAdeline MyNet = new MAdeline(2);

            // Add neuron threshold, important where input values are zero

            MyNet.AddBiasWeight();

            // Connects neurons, initilize all

            MyNet.Build();

            // Instantius training algorithm

            WindrowHoff trainingAlg = new WindrowHoff(
                ref MyNet,
                input,
                target,
                activation_mode.sigmoid,
                0.001,
                0.5
                );
            bool retVal;

            // Training

            do
            {
                retVal = trainingAlg.iteration();
                if (retVal != true)
                {
                    Console.WriteLine("Training error: " + trainingAlg.NetworkError);
                    break;
                }
            } while (!trainingAlg.isTrained);

            // Testing

            int j = 0;
            Console.WriteLine("Net test:");
            foreach (double[] _in in input)
            {
                MyNet.SetInput(input[j]);
                MyNet.Exec();
                Console.WriteLine("\nPattern n° "+j);
                Console.WriteLine("Input: " + input[j][0] + " " + input[j][1]);
                Console.WriteLine("Output: " + MyNet.output.OutputValue.ToString("0.00") + " Ideal: " + target[j][0]);
                j++;
            }
            Console.WriteLine("\nNet final error: "+trainingAlg.CurrentMeanSquareError.ToString("0.######"));
            Console.WriteLine("Total epoch: "+trainingAlg.CurrentEpoch);
            List<double> weights = MyNet.SaveWeights();

            // Display weights:

            Console.WriteLine("\nNet final weights: ");
            foreach (double d in weights)
            {
                Console.WriteLine(d.ToString("0.######"));
            }
            Console.ReadLine();
        }
    }
}