public override ClassificationModel Train(BaseVector[] x, int[][] y, int ngroups, Parameters param, int nthreads,
Action <double> reportProgress)
{
string err = CheckInput(x, y, ngroups);
if (err != null)
{
throw new Exception(err);
}
ParameterWithSubParams <int> kernelParam = param.GetParamWithSubParams <int>("Kernel");
SvmParameter sp = new SvmParameter {
kernelFunction = KernelFunctions.GetKernelFunction(kernelParam.Value, kernelParam.GetSubParameters()),
svmType = SvmType.CSvc,
c = param.GetParam <double>("C").Value
};
bool[] invert;
SvmProblem[] problems = CreateProblems(x, y, ngroups, out invert);
SvmModel[] models = new SvmModel[problems.Length];
ThreadDistributor td = new ThreadDistributor(nthreads, models.Length,
i => { models[i] = SvmMain.SvmTrain(problems[i], sp); }, fractionDone => { reportProgress?.Invoke(fractionDone); });
td.Start();
return(new SvmClassificationModel(models, invert));
}
private static int[] RankBinary(SvmProblem prob, SvmParameter param, double redfactor)
{
int nfeatures = prob.x[0].Length;
int[] result = new int[nfeatures];
int[] survivingFeatures = ArrayUtils.ConsecutiveInts(nfeatures);
SvmProblem problem = prob.Copy();
int p = nfeatures - 1;
int[] indices = ArrayUtils.ConsecutiveInts(nfeatures);
while (survivingFeatures.Length > 0)
{
problem = problem.ExtractFeatures(survivingFeatures);
indices = ArrayUtils.SubArray(indices, survivingFeatures);
int nfeatures2 = survivingFeatures.Length;
double[] criteria = ComputeRankingCriteria(SvmMain.SvmTrain(problem, param)
.ComputeBinaryClassifierWeights(nfeatures2));
int[] order = ArrayUtils.Order(criteria);
int numFeaturesToRemove = Math.Max((int)Math.Round(nfeatures2 / redfactor), 1);
for (int i = 0; i < numFeaturesToRemove; ++i)
{
result[p--] = indices[order[i]];
}
survivingFeatures = ArrayUtils.SubArray(order, numFeaturesToRemove, nfeatures2);
}
return(result);
}
private static int[] RankBinary(SvmProblem prob, SvmParameter param)
{
int nfeatures = prob.x[0].Length;
double[] criteria = ComputeRankingCriteria(SvmMain.SvmTrain(prob, param).ComputeBinaryClassifierWeights(nfeatures));
int[] order = ArrayUtils.Order(criteria);
Array.Reverse(order);
return(order);
}
public RegressionModel Train(BaseVector[] x, float[] y, Parameters param, int nthreads)
{
ParameterWithSubParams <int> kernelParam = param.GetParamWithSubParams <int>("Kernel");
SvmParameter sp = new SvmParameter {
kernelFunction = KernelFunctions.GetKernelFunction(kernelParam.Value, kernelParam.GetSubParameters()),
svmType = SvmType.EpsilonSvr,
c = param.GetParam <double>("C").Value
};
SvmModel model = SvmMain.SvmTrain(new SvmProblem(x, y), sp);
return(new SvmRegressionModel(model));
}
public override RegressionModel Train(BaseVector[] x, int[] nominal, double[] y, Parameters param, int nthreads, Action <double> reportProgress)
{
x = ClassificationMethod.ToOneHotEncoding(x, nominal);
ParameterWithSubParams <int> kernelParam = param.GetParamWithSubParams <int>("Kernel");
SvmParameter sp = new SvmParameter {
kernelFunction = KernelFunctions.GetKernelFunction(kernelParam.Value, kernelParam.GetSubParameters()),
svmType = SvmType.EpsilonSvr,
c = param.GetParam <double>("C").Value
};
SvmModel model = SvmMain.SvmTrain(new SvmProblem(x, y), sp);
return(new SvmRegressionModel(model));
}
public override double[] PredictStrength(BaseVector x)
{
if (models.Length == 1)
{
double[] result = new double[2];
double[] decVal = new double[1];
SvmMain.SvmPredictValues(models[0], x, decVal);
result[0] = invert[0] ? -(float)decVal[0] : (float)decVal[0];
result[1] = -result[0];
return(result);
}
double[] result1 = new double[models.Length];
for (int i = 0; i < result1.Length; i++)
{
double[] decVal = new double[1];
SvmMain.SvmPredictValues(models[i], x, decVal);
result1[i] = invert[i] ? -(float)decVal[0] : (float)decVal[0];
}
return(result1);
}
public override float Predict(BaseVector x)
{
return(SvmMain.SvmPredict(model, x));
}