/// <summary>
/// Runs this sample
/// </summary>
public static void Main(string[] args)
{
// create training set (H and T letter in 3x3 grid)
DataSet trainingSet = new DataSet(9);
trainingSet.addRow(new DataSetRow(new double[] { 1, 0, 1, 1, 1, 1, 1, 0, 1 })); // H letter
trainingSet.addRow(new DataSetRow(new double[] { 1, 1, 1, 0, 1, 0, 0, 1, 0 })); // T letter
// create hopfield network
Hopfield myHopfield = new Hopfield(9);
// learn the training set
myHopfield.learn(trainingSet);
// test hopfield network
Console.WriteLine("Testing network");
// add one more 'incomplete' H pattern for testing - it will be recognized as H
trainingSet.addRow(new DataSetRow(new double[] { 1, 0, 0, 1, 0, 1, 1, 0, 1 }));
// print network output for the each element from the specified training set.
foreach (DataSetRow trainingSetRow in trainingSet.Rows)
{
myHopfield.Input = trainingSetRow.Input;
myHopfield.calculate();
myHopfield.calculate();
double[] networkOutput = myHopfield.Output;
Console.Write("Input: " + Arrays.ToString(trainingSetRow.Input));
Console.WriteLine(" Output: " + Arrays.ToString(networkOutput));
}
}
public ActionResult TrainMany(InputOutputViewModel vm)
{
vm.HopfieldOutput = new MatrixViewModel(64);
vm.ARTOutput = new MatrixViewModel(64);
vm.patterns = patterns;
// trenowanie Hopfielda
hp = new Hopfield();
hp.TrainByPseudoInverse(patterns);
//trenowanie ART
art = new ART1();
art.Train(teachingSet);
//art.Train(patterns);
art2 = new ART();
art2.Train(teachingSet);
int[,] randomPatterns = new int[10, 64];
int[] numbers = new int[9];
foreach (int i in numbers)
{
var rnd = new Random();
numbers[i] = rnd.Next(0, 9);
}
ViewBag.Message = "Neural networks were trained by patterns.";
return(View("Index", vm));
}
public Form1()
{
InitializeComponent();
PerceptronInputs = new List <Input>();
myDrawing = new MyDrawing();
// Transfer function 0 == binary, 1 == sigmoid
_neuralNetwork = new NeuralNetwork.NeuralNetwork(2, 2, 1);
_perceptron = new Perceptron(ParseData("AND.txt"));
//_perceptron.PerceptronNeuron.TrainUntil(_perceptron.TrainingSet, 0.2);
myDrawing.DrawPerceptron(_perceptron, perceptronPictureBox);
InitControls();
refreshNetwork();
errorChart.Series.Clear();
//Hopfield
_hopfield = new Hopfield(5, 7, 50);
_hopImage = new List <double>();
}
/// <summary>
/// Creates and returns a new instance of Hopfield network </summary>
/// <param name="neuronsCount"> number of neurons in Hopfield network </param>
/// <returns> instance of Hopfield network </returns>
public static Hopfield createHopfield(int neuronsCount)
{
Hopfield nnet = new Hopfield(neuronsCount);
return(nnet);
}
