public virtual void InitMC <F>(IProbabilisticClassifier <L, F> classifier, GeneralDataset <L, F> data)
{
//if (!(gData instanceof Dataset)) {
// throw new UnsupportedOperationException("Can only handle Datasets, not "+gData.getClass().getName());
//}
//
//Dataset data = (Dataset)gData;
IPriorityQueue <Pair <int, Pair <double, bool> > > q = new BinaryHeapPriorityQueue <Pair <int, Pair <double, bool> > >();
total = 0;
correct = 0;
logLikelihood = 0.0;
for (int i = 0; i < data.Size(); i++)
{
IDatum <L, F> d = data.GetRVFDatum(i);
ICounter <L> scores = classifier.LogProbabilityOf(d);
L guess = Counters.Argmax(scores);
L correctLab = d.Label();
double guessScore = scores.GetCount(guess);
double correctScore = scores.GetCount(correctLab);
int guessInd = data.LabelIndex().IndexOf(guess);
int correctInd = data.LabelIndex().IndexOf(correctLab);
total++;
if (guessInd == correctInd)
{
correct++;
}
logLikelihood += correctScore;
q.Add(new Pair <int, Pair <double, bool> >(int.Parse(i), new Pair <double, bool>(guessScore, bool.ValueOf(guessInd == correctInd))), -guessScore);
}
accuracy = (double)correct / (double)total;
IList <Pair <int, Pair <double, bool> > > sorted = q.ToSortedList();
scores = new double[sorted.Count];
isCorrect = new bool[sorted.Count];
for (int i_1 = 0; i_1 < sorted.Count; i_1++)
{
Pair <double, bool> next = sorted[i_1].Second();
scores[i_1] = next.First();
isCorrect[i_1] = next.Second();
}
}
public virtual double Score <F>(IProbabilisticClassifier <L, F> classifier, GeneralDataset <L, F> data)
{
List <Pair <double, int> > dataScores = new List <Pair <double, int> >();
for (int i = 0; i < data.Size(); i++)
{
IDatum <L, F> d = data.GetRVFDatum(i);
ICounter <L> scores = classifier.LogProbabilityOf(d);
int labelD = d.Label().Equals(posLabel) ? 1 : 0;
dataScores.Add(new Pair <double, int>(Math.Exp(scores.GetCount(posLabel)), labelD));
}
PRCurve prc = new PRCurve(dataScores);
confWeightedAccuracy = prc.Cwa();
accuracy = prc.Accuracy();
optAccuracy = prc.OptimalAccuracy();
optConfWeightedAccuracy = prc.OptimalCwa();
logLikelihood = prc.LogLikelihood();
accrecall = prc.CwaArray();
optaccrecall = prc.OptimalCwaArray();
return(accuracy);
}
public virtual double Score <F>(IProbabilisticClassifier <L, F> classifier, GeneralDataset <L, F> data)
{
InitMC(classifier, data);
return(Score());
}
public MultiClassAccuracyStats(IProbabilisticClassifier <L, F> classifier, GeneralDataset <L, F> data, string file, int scoreType)
{
saveFile = file;
this.scoreType = scoreType;
InitMC(classifier, data);
}
public MultiClassAccuracyStats(IProbabilisticClassifier <L, F> classifier, GeneralDataset <L, F> data, string file)
: this(classifier, data, file, UseAccuracy)
{
}
public AccuracyStats(IProbabilisticClassifier <L, F> classifier, GeneralDataset <L, F> data, L posLabel)
{
// = null;
this.posLabel = posLabel;
Score(classifier, data);
}
public virtual double Score <F>(IProbabilisticClassifier <L, F> classifier, GeneralDataset <L, F> data)
{
return(Score((IClassifier <L, F>)classifier, data));
}
