public void TestSOM()
{
// create the training set
IMLDataSet training = new BasicMLDataSet(
SOMInput, null);
// Create the neural network.
var network = new SOMNetwork(4, 2) {Weights = new Matrix(MatrixArray)};
var train = new BasicTrainSOM(network, 0.4,
training, new NeighborhoodSingle()) {ForceWinner = true};
int iteration = 0;
for (iteration = 0; iteration <= 100; iteration++)
{
train.Iteration();
}
IMLData data1 = new BasicMLData(
SOMInput[0]);
IMLData data2 = new BasicMLData(
SOMInput[1]);
int result1 = network.Classify(data1);
int result2 = network.Classify(data2);
Assert.IsTrue(result1 != result2);
}
public void TestSOM2()
{
// create the training set
IMLDataSet training = new BasicMLDataSet(
SOMInput2, null);
// Create the neural network.
var network = new SOMNetwork(4,4);
var train = new BasicTrainSOM(network, 0.01,
training, new NeighborhoodSingle()) { ForceWinner = true };
int iteration = 0;
for (iteration = 0; iteration <= 1000; iteration++)
{
train.Iteration();
}
IMLData data1 = new BasicMLData(
SOMInput2[2]);
IMLData data2 = new BasicMLData(
SOMInput2[0]);
IMLData data3 = new BasicMLData(
SOMInput2[1]);
IMLData data4 = new BasicMLData(
SOMInput2[3]);
int result1 = network.Classify(data1);
int result2 = network.Classify(data2);
int result3 = network.Classify(data3);
int result4 = network.Classify(data4);
Console.WriteLine("Winner in someinput 2 "+network.Winner(new BasicMLData(SOMInput2[0])));
Console.WriteLine("First :" +result1);
Console.WriteLine("Second "+result2);
Console.WriteLine("Third :" + result3);
Console.WriteLine("Fourth " + result4);
Assert.IsTrue(result1 != result2);
train.TrainPattern(new BasicMLData(SOMInput2[2]));
Console.WriteLine("After training pattern: " + network.Winner(new BasicMLData(SOMInput2[1])));
var result = new SupportVectorMachine(4, SVMType.SupportVectorClassification, KernelType.Sigmoid);
training = new BasicMLDataSet(
SOMInput2, SOMInput2);
SVMTrain trainsvm = new SVMTrain(result, training);
trainsvm.Iteration(50);
result1 = result.Classify(data1);
result2 = result.Classify(data2);
result3 = result.Classify(data3);
result4 = result.Classify(data4);
Console.WriteLine("SVM classification : EURUSD 1 :"+result1 + " GBPUSD:"+result2 + " EURCHF :"+result3+ " EURJPY:"+result4 );
}
/// <summary>
/// Uczy siec Kohonena z podanymi parametrami.
/// </summary>
/// <param name="learningRate">Początkowy współczynnik nauki.</param>
/// <param name="learningChangeRate">Współczynnik zmiany współczynnika nauki</param>
/// <param name="neighbourhoodRate">Początkowy współczynnik sąsiedztwa.</param>
/// <param name="neighbourhoodChangeRate">Współczynnik zmiany współczynnika sąsiedztwa.</param>
/// <param name="trainIterations">Na ilu przykladach przebiega nauka.</param>
/// <param name="learningSet">Zbiór danych uczących.</param>
public void Train(double learningRate, double learningChangeRate,
double neighbourhoodRate, double neighbourhoodChangeRate,
int trainIterations, InputDataSet learningSet)
{
var basicMlDataSet = new BasicMLDataSet(learningSet.InputSet, null);
INeighborhoodFunction neighborhoodFunc =
new KohonenNeighbourhoodFunction(neighbourhoodRate, rows, columns);
var train = new BasicTrainSOM(network, learningRate, basicMlDataSet, neighborhoodFunc);
IStrategy strategy = new KohonenTrainStrategy(learningChangeRate, neighbourhoodChangeRate);
strategy.Init(train);
train.Strategies.Add(strategy);
train.Iteration(trainIterations);
train.FinishTraining();
}