/// <summary>
/// Returns a counter for the log probability of each of the classes
/// looking at the the sum of e^v for each count v, should be 1
/// Note: Uses SloppyMath.logSum which isn't exact but isn't as
/// offensively slow as doing a series of exponentials
/// </summary>
public override ICounter <L> LogProbabilityOf(IDatum <L, F> example)
{
if (platt == null)
{
throw new NotSupportedException("If you want to ask for the probability, you must train a Platt model!");
}
ICounter <L> scores = ScoresOf(example);
scores.IncrementCount(null);
ICounter <L> probs = platt.LogProbabilityOf(new RVFDatum <L, L>(scores));
//System.out.println(scores+" "+probs);
return(probs);
}
private void EnsureProbs(int word, bool subtractTagScore)
{
if (word == lastWord)
{
return;
}
lastWord = word;
if (functionWordTags.Contains(wordIndex.Get(word)))
{
logProbs = new ClassicCounter <string>();
string trueTag = functionWordTags[wordIndex.Get(word)];
foreach (string tag in tagIndex.ObjectsList())
{
if (ctlp.BasicCategory(tag).Equals(trueTag))
{
logProbs.SetCount(tag, 0);
}
else
{
logProbs.SetCount(tag, double.NegativeInfinity);
}
}
return;
}
IDatum datum = new BasicDatum(featExtractor.MakeFeatures(wordIndex.Get(word)));
logProbs = scorer.LogProbabilityOf(datum);
if (subtractTagScore)
{
ICollection <string> tagSet = logProbs.KeySet();
foreach (string tag in tagSet)
{
logProbs.IncrementCount(tag, -Math.Log(tagDist.ProbabilityOf(tag)));
}
}
}