double ConfusionMatrix::averageClassAccuracy(bool includeVoid) const {
if (!normalized) {
ConfusionMatrix normalizedConfusionMatrix(*this);
normalizedConfusionMatrix.normalize();
assert(normalizedConfusionMatrix.isNormalized());
return normalizedConfusionMatrix.averageClassAccuracy(includeVoid);
}
utils::Average averageClassAccuracy;
for (unsigned int label = 0; label < getNumClasses(); label++) {
double classAccuracy = data(label, label);
assertProbability(classAccuracy);
bool ignore = false;
if (!includeVoid && !ignoredLabels.empty())
for (LabelType ID: ignoredLabels)
if (ID == label)
{
ignore = true;
break;
}
if (ignore)
continue;
else
averageClassAccuracy.addValue(classAccuracy);
}
return averageClassAccuracy.getAverage();
}
double ConfusionMatrix::averageClassAccuracy(bool includeVoid) const {
if (!normalized) {
ConfusionMatrix normalizedConfusionMatrix(*this);
normalizedConfusionMatrix.normalize();
assert(normalizedConfusionMatrix.isNormalized());
return normalizedConfusionMatrix.averageClassAccuracy(includeVoid);
}
utils::Average averageClassAccuracy;
for (unsigned int label = 0; label < getNumClasses(); label++) {
double classAccuracy = data(label, label);
assertProbability(classAccuracy);
if (includeVoid || label > 0) {
averageClassAccuracy.addValue(classAccuracy);
}
}
return averageClassAccuracy.getAverage();
}