protected ProbabilitySupportVectorMachine TrainSVM(double C, double gamma, List <ISample> trainingSamples, Func <ISample, double> idealFunction)
{
// duplicate the training dataset for better cross validation by LIBSVM probability generator (see LIBSVM documentation)
List <double[]> inputSamples = trainingSamples.Select(sample => sample.GetDimensions()).ToList();
inputSamples.AddRange(trainingSamples.Select(sample => sample.GetDimensions()));
// account for imposter samples (identifier not in identifierMap)
List <double[]> outputSamples = trainingSamples.Select(sample => new double[] { idealFunction.Invoke(sample) }).ToList();
outputSamples.AddRange(trainingSamples.Select(sample => new double[] { idealFunction.Invoke(sample) }));
double[][] INPUT = inputSamples.ToArray();
double[][] IDEAL = outputSamples.ToArray();
// train the SVM classifier with the provided data
IMLDataSet trainingData = new BasicMLDataSet(INPUT, IDEAL);
ProbabilitySupportVectorMachine svmNetwork = new ProbabilitySupportVectorMachine(trainingSamples[0].GetDimensionCount(), false, 0.00000001);
// train the SVM classifier with the provided C and gamma
SVMTrain trainedSVM = new SVMTrain(svmNetwork, trainingData)
{
Fold = 0,
Gamma = gamma,
C = C
};
trainedSVM.Iteration();
Console.WriteLine("SVM training error: " + trainedSVM.Error);
return(svmNetwork);
}
SVMClassifier(Dictionary <string, int> identifiersMap, svm_model model)
{
/* for serialization */
svmNetwork = new ProbabilitySupportVectorMachine(model);
this.identifiersMap = identifiersMap;
this.model = model;
}
protected void Train(double C, double gamma, List <ISample> trainingSamples, string[] trainingIdentifiers)
{
// generate a numeric mapping of our string identifiers to unique numeric values
identifiersMap = trainingIdentifiers.Select((identifier, index) => new { identifier, index }).ToDictionary(id => id.identifier, id => id.index);
// don't include imposter samples
svmNetwork = TrainSVM(C, gamma, trainingSamples, (sample) => identifiersMap[sample.GetIdentifier()]);
model = svmNetwork.Model;
}
