private static SupportVectorMachine <Gaussian> TrainSVMModel(IDataView trainingData)
{
AccordIO data = IDataViewToAccord(trainingData);
Console.WriteLine("Creating and training Poly kernel SVM");
var smo = new SequentialMinimalOptimization <Gaussian>();
smo.UseKernelEstimation = true;
smo.UseComplexityHeuristic = true;
smo.Epsilon = 1.0e-3;
smo.Tolerance = 1.0e-2;
Console.WriteLine("Starting training");
var svm = smo.Learn(data.inputs, data.labels);
Console.WriteLine("Training complete");
//double[][] sVectors = svm.SupportVectors;
//for (int i = 0; i < sVectors.Length; ++i)
//{
// for (int j = 0; j < sVectors[i].Length; ++j)
// {
// svmExporter.Write(sVectors[i][j].ToString("F1") + " ");
// }
// svmExporter.WriteLine("");
//}
//}AccordIO data = IDataViewToAccord(trainingData);
//Console.WriteLine("Creating and training Poly kernel SVM");
//var smo = new SequentialMinimalOptimization<Polynomial>();
////smo.UseKernelEstimation = true;
//smo.UseComplexityHeuristic = true;
//smo.Kernel = new Polynomial(20, 0.0);
////smo.Complexity = 2.0;
//smo.Epsilon = 1.0e-3;
//smo.Tolerance = 1.0e-2;
//Console.WriteLine("Starting training");
//var svm = smo.Learn(data.inputs, data.labels);
//Console.WriteLine("Training complete");
//Console.WriteLine("Model support vectors: ");
//double[][] sVectors = svm.SupportVectors;
//for (int i = 0; i < sVectors.Length; ++i)
//{
// for (int j = 0; j < sVectors[i].Length; ++j)
// {
// Console.Write(sVectors[i][j].ToString("F1") + " ");
// }
// Console.WriteLine("");
//}
return(svm);
}
private static LogisticRegression TrainAccordModel(IDataView trainingData)
{
AccordIO data = IDataViewToAccord(trainingData);
var trainer = new IterativeReweightedLeastSquares <LogisticRegression>()
{
MaxIterations = 1000,
Regularization = 1e-6
};
// Use the teacher algorithm to learn the regression:
LogisticRegression lr = trainer.Learn(data.inputs, data.labels);
return(lr);
// Classify the samples using the model
//bool[] answers = lr.Decide(inputs);
}
