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;
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);
mIdxToLbl.Add(lblEx.Label);
}
trainSet[j++] = SvmLightLib.NewFeatureVector(idx.Length, idx, val, lbl == 2 ? -1 : 1);
}
mModelId = SvmLightLib.TrainModel(string.Format(""), trainSet.Length, trainSet);
// delete training vectors
foreach (int vecIdx in trainSet)
{
SvmLightLib.DeleteFeatureVector(vecIdx);
}
}
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);
}
}
