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); Console.WriteLine(result1 + " result 2 :"+result2); 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 ); }