public override void TrainNetwork()
{
IMLTrain train = new SVMSearchTrain(Svm, TrainingSet);
var iteration = 1;
var errors = new List<double[]>();
do
{
train.Iteration();
errors.Add(new[] { -1, iteration, train.Error });
Console.WriteLine(@"Iteration #" + iteration++ + @" Training error:" + train.Error);
} while ((iteration < Parameters.IterationsCount) && (train.Error > Parameters.AcceptedError));
train.FinishTraining();
ErrorSet = errors.ToArray();
}
public static SupportVectorMachine SVMSearch(SupportVectorMachine anetwork, IMLDataSet training)
{
SVMSearchTrain bestsearch = new SVMSearchTrain(anetwork, training);
StopTrainingStrategy stop = new StopTrainingStrategy(0.00000000001, 1);
bestsearch.AddStrategy(stop);
while (bestsearch.IterationNumber < 30 && !stop.ShouldStop())
{
bestsearch.Iteration();
Console.WriteLine("Iteration #" + bestsearch.IterationNumber + " Error :" + bestsearch.Error);
}
bestsearch.FinishTraining();
return anetwork;
}
public void Execute(IExampleInterface app)
{
// create a neural network, without using a factory
var svm = new SupportVectorMachine(1,true); // 1 input, & true for regression
// create training data
IMLDataSet trainingSet = new BasicMLDataSet(RegressionInput, RegressionIdeal);
// train the SVM
IMLTrain train = new SVMSearchTrain(svm, trainingSet);
int epoch = 1;
do
{
train.Iteration();
Console.WriteLine(@"Epoch #" + epoch + @" Error:" + train.Error);
epoch++;
} while (train.Error > 0.01);
// test the SVM
Console.WriteLine(@"SVM Results:");
foreach (IMLDataPair pair in trainingSet)
{
IMLData output = svm.Compute(pair.Input);
Console.WriteLine(pair.Input[0]
+ @", actual=" + output[0] + @",ideal=" + pair.Ideal[0]);
}
}
