public void Train(ILabeledExampleCollection <LblT, ExT> dataset)
{
Preconditions.CheckNotNull(dataset);
var trainDataset = new LabeledDataset <LblT, ExT>(dataset);
for (int i = 0; i < mInnerModels.Length; i++)
{
if (mInnerModels[i] == null)
{
mInnerModels[i] = CreateModel(i);
}
mInnerModels[i].Train(GetTrainSet(i, mInnerModels[i], trainDataset));
}
foreach (LabeledExample <LblT, ExT> le in trainDataset)
{
LabeledExample <LblT, ExT> le_ = le;
double[] scores = GetPredictionScores(mInnerModels.Select(m => m.Predict(le_.Example)).ToArray()).ToArray();
mTagDistrTable.AddCount(le.Label, scores);
}
mTagDistrTable.Calculate();
IsTrained = true;
}
private void UpdateDistrTable()
{
if (mExampleScores == null || mBinWidth == 0)
{
mTagDistrTable = null;
return;
}
mTagDistrTable = new EnumTagDistrTable <SentimentLabel>(2, mBinWidth, -5, 5, SentimentLabel.Exclude)
{
CalcDistrFunc = (tagCounts, values, tag) => ((double)tagCounts[tag] + 1) / (tagCounts.Values.Sum() + tagCounts.Count)
};
foreach (ExampleScore es in mExampleScores)
{
mTagDistrTable.AddCount(es.Label, es.PosScore, es.NegScore);
}
mTagDistrTable.Calculate();
}
public override void Train(ILabeledExampleCollection <SentimentLabel, SparseVector <double> > dataset)
{
Preconditions.CheckNotNull(dataset);
Preconditions.CheckArgumentRange(TagDistrTable == null || TagDistrTable.NumOfDimensions == 2);
mBinModel = CreateModel();
mBinModel.Train(new LabeledDataset <SentimentLabel, SparseVector <double> >(dataset.Where(le => le.Label != SentimentLabel.Neutral)));
TagDistrTable = new EnumTagDistrTable <SentimentLabel>(1, BinWidth, -5, 5, SentimentLabel.Exclude)
{
CalcDistrFunc = (tagCounts, values, tag) => ((double)tagCounts[tag] + 1) / (tagCounts.Values.Sum() + tagCounts.Count) // use Laplace formula
};
foreach (LabeledExample <SentimentLabel, SparseVector <double> > le in dataset)
{
Prediction <SentimentLabel> prediction = mBinModel.Predict(le.Example);
TagDistrTable.AddCount(le.Label, prediction.BestClassLabel == SentimentLabel.Positive ? prediction.BestScore : -prediction.BestScore);
}
TagDistrTable.Calculate();
IsTrained = true;
}
