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);
}
}
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);
}
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);
}
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];
Dictionary <LblT, int> lblToIdx = new Dictionary <LblT, int>(mLblCmp);
MultiSet <int> lblCount = new MultiSet <int>();
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;
val[i] = (float)vec.InnerDat[i]; // *** cast to float
}
int lbl;
if (!lblToIdx.TryGetValue(lblEx.Label, out lbl))
{
lblToIdx.Add(lblEx.Label, lbl = lblToIdx.Count);
mIdxToLbl.Add(lblEx.Label);
}
Utils.ThrowException(lbl == 2 ? new ArgumentValueException("dataset") : null);
trainSet[j++] = SvmLightLib.NewFeatureVector(idx.Length, idx, val, lbl == 0 ? 1 : -1);
lblCount.Add(lbl == 0 ? 1 : -1);
}
string costFactor = "";
if (mBiasedCostFunction)
{
costFactor = "-j " + ((double)lblCount.GetCount(-1) / (double)lblCount.GetCount(1));
}
mModelId = SvmLightLib.TrainModel(string.Format(CultureInfo.InvariantCulture, "-v {0} -c {1} -t {2} -g {3} -d {4} -s {5} -r {6} -b {7} -e {8} -# {9} {10} {11}",
(int)mVerbosityLevel, mC, (int)mKernelType, mKernelParamGamma, mKernelParamD, mKernelParamS, mKernelParamC, mBiasedHyperplane ? 1 : 0,
mEps, mMaxIter, mCustomParams, costFactor), trainSet.Length, trainSet);
// delete training vectors
foreach (int vecIdx in trainSet)
{
SvmLightLib.DeleteFeatureVector(vecIdx);
}
}
