public Prediction <LblT> Predict(SparseVector <double> example)
{
Utils.ThrowException(mModelId == -1 ? new InvalidOperationException() : null);
Utils.ThrowException(example == null ? new ArgumentNullException("example") : null);
Prediction <LblT> result = new Prediction <LblT>();
int[] idx = new int[example.Count];
float[] val = new float[example.Count];
for (int i = 0; i < example.Count; i++)
{
idx[i] = example.InnerIdx[i] + 1; // *** indices are 1-based in SvmLightLib
val[i] = (float)example.InnerDat[i]; // *** loss of precision (double -> float)
}
int vecId = SvmLightLib.NewFeatureVector(idx.Length, idx, val, 0);
SvmLightLib.MulticlassClassify(mModelId, 1, new int[] { vecId });
int n = SvmLightLib.GetFeatureVectorClassifScoreCount(vecId);
for (int i = 0; i < n; i++)
{
double score = SvmLightLib.GetFeatureVectorClassifScore(vecId, i);
LblT lbl = mIdxToLbl[i];
result.Inner.Add(new KeyDat <double, LblT>(score, lbl));
}
result.Inner.Sort(DescSort <KeyDat <double, LblT> > .Instance);
result.Trim();
SvmLightLib.DeleteFeatureVector(vecId); // delete feature vector
return(result);
}
