public void Train(ILabeledExampleCollection <LblT, SparseVector <double> > dataset)
{
Utils.ThrowException(dataset == null ? new ArgumentNullException("dataset") : null);
Utils.ThrowException(dataset.Count == 0 ? new ArgumentValueException("dataset") : null);
Dispose();
int[] trainSet = new int[dataset.Count];
int[] labels = new int[dataset.Count];
int j = 0;
foreach (LabeledExample <LblT, SparseVector <double> > lblEx in dataset)
{
SparseVector <double> vec = lblEx.Example;
int[] idx = new int[vec.Count];
float[] val = new float[vec.Count];
for (int i = 0; i < vec.Count; i++)
{
idx[i] = vec.InnerIdx[i] + 1; // *** indices are 1-based in SvmLightLib
val[i] = (float)vec.InnerDat[i]; // *** loss of precision (double -> float)
}
int lbl;
if (!mLblToId.TryGetValue(lblEx.Label, out lbl))
{
mLblToId.Add(lblEx.Label, lbl = mLblToId.Count + 1); // *** labels start with 1 in SvmLightLib
mIdxToLbl.Add(lblEx.Label);
}
trainSet[j++] = SvmLightLib.NewFeatureVector(idx.Length, idx, val, lbl);
}
mModelId = SvmLightLib.TrainMulticlassModel(string.Format("-c {0} -e {1}", mC.ToString(CultureInfo.InvariantCulture), mEps.ToString(CultureInfo.InvariantCulture)),
trainSet.Length, trainSet);
// delete training vectors
foreach (int vecIdx in trainSet)
{
SvmLightLib.DeleteFeatureVector(vecIdx);
}
}
