public virtual Pair <Clusterer.CandidateAction, Clusterer.CandidateAction> GetActions(Clusterer.ClustererClassifier classifier)
{
ICounter <string> mergeFeatures = GetFeatures(doc, c1, c2, globalFeatures[currentIndex]);
double mergeScore = Math.Exp(classifier.WeightFeatureProduct(mergeFeatures));
hashedScores[new Clusterer.MergeKey(c1, c2, currentIndex)] = mergeScore > 0.5;
Clusterer.State merge = new Clusterer.State(this);
merge.DoAction(true);
double mergeB3 = merge.GetFinalCost(classifier);
Clusterer.State noMerge = new Clusterer.State(this);
noMerge.DoAction(false);
double noMergeB3 = noMerge.GetFinalCost(classifier);
double weight = doc.mentions.Count / 100.0;
double maxB3 = Math.Max(mergeB3, noMergeB3);
return(new Pair <Clusterer.CandidateAction, Clusterer.CandidateAction>(new Clusterer.CandidateAction(mergeFeatures, weight * (maxB3 - mergeB3)), new Clusterer.CandidateAction(new ClassicCounter <string>(), weight * (maxB3 - noMergeB3))));
}
public virtual bool DoBestAction(Clusterer.ClustererClassifier classifier)
{
bool doMerge = hashedScores[new Clusterer.MergeKey(c1, c2, currentIndex)];
if (doMerge == null)
{
ICounter <string> features = GetFeatures(doc, c1, c2, globalFeatures[currentIndex]);
doMerge = classifier.WeightFeatureProduct(features) > 0;
hashedScores[new Clusterer.MergeKey(c1, c2, currentIndex)] = doMerge;
sMisses += isTraining;
}
else
{
sHits += isTraining;
}
DoAction(doMerge);
return(doMerge);
}
