public virtual double Score <F>(IClassifier <L, F> classifier, GeneralDataset <L, F> data)
{
IList <L> guesses = new List <L>();
IList <L> labels = new List <L>();
for (int i = 0; i < data.Size(); i++)
{
IDatum <L, F> d = data.GetRVFDatum(i);
L guess = classifier.ClassOf(d);
guesses.Add(guess);
}
int[] labelsArr = data.GetLabelsArray();
labelIndex = data.labelIndex;
for (int i_1 = 0; i_1 < data.Size(); i_1++)
{
labels.Add(labelIndex.Get(labelsArr[i_1]));
}
labelIndex = new HashIndex <L>();
labelIndex.AddAll(data.LabelIndex().ObjectsList());
labelIndex.AddAll(classifier.Labels());
int numClasses = labelIndex.Size();
tpCount = new int[numClasses];
fpCount = new int[numClasses];
fnCount = new int[numClasses];
negIndex = labelIndex.IndexOf(negLabel);
for (int i_2 = 0; i_2 < guesses.Count; ++i_2)
{
L guess = guesses[i_2];
int guessIndex = labelIndex.IndexOf(guess);
L label = labels[i_2];
int trueIndex = labelIndex.IndexOf(label);
if (guessIndex == trueIndex)
{
if (guessIndex != negIndex)
{
tpCount[guessIndex]++;
}
}
else
{
if (guessIndex != negIndex)
{
fpCount[guessIndex]++;
}
if (trueIndex != negIndex)
{
fnCount[trueIndex]++;
}
}
}
return(GetFMeasure());
}
public override double Score <F>(IClassifier <L, F> classifier, GeneralDataset <L, F> data)
{
labelIndex = new HashIndex <L>();
labelIndex.AddAll(classifier.Labels());
labelIndex.AddAll(data.labelIndex.ObjectsList());
ClearCounts();
int[] labelsArr = data.GetLabelsArray();
for (int i = 0; i < data.Size(); i++)
{
IDatum <L, F> d = data.GetRVFDatum(i);
L guess = classifier.ClassOf(d);
AddGuess(guess, labelIndex.Get(labelsArr[i]));
}
FinalizeCounts();
return(GetFMeasure());
}
public BiasedLogConditionalObjectiveFunction(GeneralDataset dataset, double[][] confusionMatrix, LogPrior prior)
: this(dataset.NumFeatures(), dataset.NumClasses(), dataset.GetDataArray(), dataset.GetLabelsArray(), confusionMatrix, prior)
{
}