// BackwardsCompatibility3.4
#region Backwards compatible code, remove with 3.5
public static SupportVectorClassificationSolution CreateSupportVectorClassificationSolution(IClassificationProblemData problemData, IEnumerable <string> allowedInputVariables,
int svmType, int kernelType, double cost, double nu, double gamma, int degree, out double trainingAccuracy, out double testAccuracy, out int nSv)
{
ISupportVectorMachineModel model;
Run(problemData, allowedInputVariables, svmType, kernelType, cost, nu, gamma, degree, out model, out nSv);
var solution = new SupportVectorClassificationSolution((SupportVectorMachineModel)model, (IClassificationProblemData)problemData.Clone());
trainingAccuracy = solution.TrainingAccuracy;
testAccuracy = solution.TestAccuracy;
return(solution);
}
protected override void Run()
{
IClassificationProblemData problemData = Problem.ProblemData;
IEnumerable <string> selectedInputVariables = problemData.AllowedInputVariables;
int nSv;
ISupportVectorMachineModel model;
Run(problemData, selectedInputVariables, GetSvmType(SvmType.Value), GetKernelType(KernelType.Value), Cost.Value, Nu.Value, Gamma.Value, Degree.Value, out model, out nSv);
if (CreateSolution)
{
var solution = new SupportVectorClassificationSolution((SupportVectorMachineModel)model, (IClassificationProblemData)problemData.Clone());
Results.Add(new Result("Support vector classification solution", "The support vector classification solution.",
solution));
}
{
// calculate classification metrics
// calculate regression model metrics
var ds = problemData.Dataset;
var trainRows = problemData.TrainingIndices;
var testRows = problemData.TestIndices;
var yTrain = ds.GetDoubleValues(problemData.TargetVariable, trainRows);
var yTest = ds.GetDoubleValues(problemData.TargetVariable, testRows);
var yPredTrain = model.GetEstimatedClassValues(ds, trainRows);
var yPredTest = model.GetEstimatedClassValues(ds, testRows);
OnlineCalculatorError error;
var trainAccuracy = OnlineAccuracyCalculator.Calculate(yPredTrain, yTrain, out error);
if (error != OnlineCalculatorError.None)
{
trainAccuracy = double.MaxValue;
}
var testAccuracy = OnlineAccuracyCalculator.Calculate(yPredTest, yTest, out error);
if (error != OnlineCalculatorError.None)
{
testAccuracy = double.MaxValue;
}
Results.Add(new Result("Accuracy (training)", "The mean of squared errors of the SVR solution on the training partition.", new DoubleValue(trainAccuracy)));
Results.Add(new Result("Accuracy (test)", "The mean of squared errors of the SVR solution on the test partition.", new DoubleValue(testAccuracy)));
Results.Add(new Result("Number of support vectors", "The number of support vectors of the SVR solution.",
new IntValue(nSv)));
}
}
private SupportVectorClassificationSolution(SupportVectorClassificationSolution original, Cloner cloner)
: base(original, cloner)
{
}
private SupportVectorClassificationSolution(SupportVectorClassificationSolution original, Cloner cloner)
: base(original, cloner) {
}
