public static void Run(IRegressionProblemData problemData, IEnumerable <string> allowedInputVariables,
string svmType, string kernelType, double cost, double nu, double gamma, double epsilon, int degree,
out ISupportVectorMachineModel model, out int nSv)
{
var dataset = problemData.Dataset;
string targetVariable = problemData.TargetVariable;
IEnumerable <int> rows = problemData.TrainingIndices;
svm_parameter parameter = new svm_parameter {
svm_type = GetSvmType(svmType),
kernel_type = GetKernelType(kernelType),
C = cost,
nu = nu,
gamma = gamma,
p = epsilon,
cache_size = 500,
probability = 0,
eps = 0.001,
degree = degree,
shrinking = 1,
coef0 = 0
};
svm_problem problem = SupportVectorMachineUtil.CreateSvmProblem(dataset, targetVariable, allowedInputVariables, rows);
RangeTransform rangeTransform = RangeTransform.Compute(problem);
svm_problem scaledProblem = rangeTransform.Scale(problem);
var svmModel = svm.svm_train(scaledProblem, parameter);
nSv = svmModel.SV.Length;
model = new SupportVectorMachineModel(svmModel, rangeTransform, targetVariable, allowedInputVariables);
}
private SupportVectorMachineModel(SupportVectorMachineModel original, Cloner cloner)
: base(original, cloner)
{
// only using a shallow copy here! (gkronber)
this.model = original.model;
this.rangeTransform = original.rangeTransform;
this.allowedInputVariables = (string[])original.allowedInputVariables.Clone();
if (original.classValues != null)
{
this.classValues = (double[])original.classValues.Clone();
}
}
public static void Run(IClassificationProblemData problemData, IEnumerable <string> allowedInputVariables,
int svmType, int kernelType, double cost, double nu, double gamma, int degree,
out ISupportVectorMachineModel model, out int nSv)
{
var dataset = problemData.Dataset;
string targetVariable = problemData.TargetVariable;
IEnumerable <int> rows = problemData.TrainingIndices;
svm_parameter parameter = new svm_parameter {
svm_type = svmType,
kernel_type = kernelType,
C = cost,
nu = nu,
gamma = gamma,
cache_size = 500,
probability = 0,
eps = 0.001,
degree = degree,
shrinking = 1,
coef0 = 0
};
var weightLabels = new List <int>();
var weights = new List <double>();
foreach (double c in problemData.ClassValues)
{
double wSum = 0.0;
foreach (double otherClass in problemData.ClassValues)
{
if (!c.IsAlmost(otherClass))
{
wSum += problemData.GetClassificationPenalty(c, otherClass);
}
}
weightLabels.Add((int)c);
weights.Add(wSum);
}
parameter.weight_label = weightLabels.ToArray();
parameter.weight = weights.ToArray();
svm_problem problem = SupportVectorMachineUtil.CreateSvmProblem(dataset, targetVariable, allowedInputVariables, rows);
RangeTransform rangeTransform = RangeTransform.Compute(problem);
svm_problem scaledProblem = rangeTransform.Scale(problem);
var svmModel = svm.svm_train(scaledProblem, parameter);
nSv = svmModel.SV.Length;
model = new SupportVectorMachineModel(svmModel, rangeTransform, targetVariable, allowedInputVariables, problemData.ClassValues);
}
public SupportVectorClassificationSolution(SupportVectorMachineModel model, IClassificationProblemData problemData)
: base(model, problemData)
{
}
public SupportVectorRegressionSolution(SupportVectorMachineModel model, IRegressionProblemData problemData)
: base(model, problemData)
{
}
public SupportVectorRegressionSolution(SupportVectorMachineModel model, IRegressionProblemData problemData)
: base(model, problemData) {
}
public SupportVectorClassificationSolution(SupportVectorMachineModel model, IClassificationProblemData problemData)
: base(model, problemData) {
}
private SupportVectorMachineModel(SupportVectorMachineModel original, Cloner cloner)
: base(original, cloner) {
// only using a shallow copy here! (gkronber)
this.model = original.model;
this.rangeTransform = original.rangeTransform;
this.allowedInputVariables = (string[])original.allowedInputVariables.Clone();
if (original.classValues != null)
this.classValues = (double[])original.classValues.Clone();
}
