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;
val[i] = (float)example.InnerDat[i]; // *** cast to float
}
int vecId = SvmLightLib.NewFeatureVector(idx.Length, idx, val, 0);
SvmLightLib.Classify(mModelId, 1, new int[] { vecId });
double score = SvmLightLib.GetFeatureVectorClassifScore(vecId, 0);
LblT lbl = mIdxToLbl[score > 0 ? 0 : 1];
LblT otherLbl = mIdxToLbl[score > 0 ? 1 : 0];
result.Inner.Add(new KeyDat <double, LblT>(Math.Abs(score), lbl));
result.Inner.Add(new KeyDat <double, LblT>(-Math.Abs(score), otherLbl));
SvmLightLib.DeleteFeatureVector(vecId); // delete feature vector
return(result);
}