public void Merge(NGram <TSymbol> toBeMerged)
{
if (_n != toBeMerged.GetN())
{
return;
}
_vocabulary.UnionWith(toBeMerged._vocabulary);
rootNode.Merge(toBeMerged.rootNode);
}
/**
* <summary>Wrapper function to set the N-gram probabilities with interpolated smoothing.</summary>
* <param name="nGram">N-Gram for which the probabilities will be set.</param>
* <param name="level">Level for which N-Gram probabilities will be set. Probabilities for different levels of the</param>
* N-gram can be set with this function. If level = 1, N-Gram is treated as UniGram, if level = 2,
* N-Gram is treated as Bigram, etc.
*
*/
public override void SetProbabilities(NGram <TSymbol> nGram, int level)
{
for (var j = 2; j <= nGram.GetN(); j++)
{
nGram.CalculateNGramProbabilities(_simpleSmoothing, j);
}
nGram.CalculateNGramProbabilities(_simpleSmoothing, 1);
switch (nGram.GetN())
{
case 2:
nGram.SetLambda(_lambda1);
break;
case 3:
nGram.SetLambda(_lambda1, _lambda2);
break;
}
}
/**
* <summary>Wrapper function to learn parameters of the smoothing method and set the N-gram probabilities.</summary>
*
* <param name="corpus">Train corpus used to optimize parameters of the smoothing method.</param>
* <param name="nGram">N-Gram for which the probabilities will be set.</param>
*/
public void Train(List <List <TSymbol> > corpus, NGram <TSymbol> nGram)
{
LearnParameters(corpus, nGram.GetN());
SetProbabilities(nGram);
}