/// <summary>
/// Converts <see cref="SupportVectorMachineLearningConfigurationFunction"/>
/// into a lambda function that can be passed to the <see cref=" OneVsOneLearning{TInput, TBinary, TModel}.Learner"/>
/// property of a <see cref="MulticlassSupportVectorLearning"/> learning algorithm.
/// </summary>
///
public Func <InnerParameters, InnerLearning> Convert(SupportVectorMachineLearningConfigurationFunction conf)
{
return(delegate(InnerParameters parameters)
{
int[] y = Classes.ToMinusOnePlusOne(parameters.Outputs);
var machine = (KernelSupportVectorMachine)parameters.Model;
ISupportVectorMachineLearning r = conf(machine, parameters.Inputs, y, parameters.Pair.Class1, parameters.Pair.Class2);
var c = r as ISupervisedLearning <SupportVectorMachine <IKernel <double[]> >, double[], bool>;
if (c != null)
{
return c;
}
// TODO: The following checks exist only to provide support to previous way of using
// the library and should be removed after a few releases.
var svc = r as ISupportVectorMachineLearning;
if (svc != null)
{
svc.Run();
return null;
}
throw new Exception();
});
}
public double TrainClassifierWithParameters(
ClassificationData trainingData,
IKernel kernel = null,
SupportVectorMachineLearningConfigurationFunction algorithm = null)
{
double classifierError = 0;
// Use default parameters if no valid new
// values are provided.
if (kernel == null)
{
kernel = new Linear();
}
if (algorithm == null)
{
algorithm = (SVM, inputData, outputData, i, j) =>
new SequentialMinimalOptimization(SVM, inputData, outputData)
{
// Avoid OutOfMemory exception.
CacheSize = 1000
}
}
;
// Create a new SVM classifier.
SVMachine = new MulticlassSupportVectorMachine(
trainingData.InputAttributeNumber,
kernel,
trainingData.OutputPossibleValues);
// Create an algorithm to be learned by the SVM.
SVMachineLearning = new MulticlassSupportVectorLearning(
SVMachine,
trainingData.InputData,
trainingData.OutputData);
SVMachineLearning.Algorithm = algorithm;
// Run the learning algorithm.
classifierError = SVMachineLearning.Run(true);
return(classifierError);
}
