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));
}
private ART1 Create()
{
var network = new ART1(6, 3);
network.WeightsF1ToF2[1, 1] = 2.0;
network.WeightsF2ToF1[2, 2] = 3.0;
return(network);
}
public void TestPersistSerial()
{
ART1 network = Create();
SerializeObject.Save(SERIAL_FILENAME.ToString(), network);
var network2 = (ART1)SerializeObject.Load(SERIAL_FILENAME.ToString());
Validate(network2);
}
public void TestPersistEG()
{
ART1 network = Create();
EncogDirectoryPersistence.SaveObject(EG_FILENAME, network);
var network2 = (ART1)EncogDirectoryPersistence.LoadObject(EG_FILENAME);
Validate(network2);
}
/// <summary>
/// Generate the neural network.
/// </summary>
///
/// <returns>The generated neural network.</returns>
public IMLMethod Generate()
{
var art = new ART1(_inputNeurons, _outputNeurons)
{
A1 = _a1,
B1 = _b1,
C1 = _c1,
D1 = _d1,
L = _l,
Vigilance = _vigilance
};
return(art);
}
private void Validate(ART1 network)
{
Assert.AreEqual(6, network.F1Count);
Assert.AreEqual(3, network.F2Count);
Assert.AreEqual(18, network.WeightsF1ToF2.Size);
Assert.AreEqual(18, network.WeightsF2ToF1.Size);
Assert.AreEqual(2.0, network.WeightsF1ToF2[1, 1]);
Assert.AreEqual(3.0, network.WeightsF2ToF1[2, 2]);
Assert.AreEqual(1.0, network.A1);
Assert.AreEqual(1.5, network.B1);
Assert.AreEqual(5.0, network.C1);
Assert.AreEqual(0.9, network.D1);
Assert.AreEqual(0.9, network.Vigilance);
}
