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 );
}
public void TrainTest1()
{
const int LearnCount = 100;
const int TestCount = 1000;
const int Length = 300;
const double PositiveRatio = 0.1;
// create samples
List <(float[] x, bool y, float weight)> samples = SupportVectorMachineTest.GenerateSamples(LearnCount + TestCount, Length, PositiveRatio)
.Select(x => (x.x.Select(w => (float)w).ToArray(), x.y, (float)x.weight))
.ToList();
// learn
SequentualMinimalOptimization smo = new SequentualMinimalOptimization(new ChiSquare())
{
Algorithm = SMOAlgorithm.LibSVM,
Tolerance = 0.01f,
};
SupportVectorMachine machine = SupportVectorMachine.Learn(
smo,
samples.Take(LearnCount).Select(x => x.x).ToList(),
samples.Take(LearnCount).Select(x => x.y).ToList(),
samples.Take(LearnCount).Select(x => x.weight).ToList(),
CancellationToken.None);
// test
List <ClassificationResult <bool?> > results = samples
.Skip(LearnCount)
.Select(x => new ClassificationResult <bool?>(null, machine.Classify(x.x) > 0.5f, x.y, 1.0f, true))
.ToList();
ClassificationReport <bool?> report = new ClassificationReport <bool?>(results);
}
