ErrorStruct StrongClassifier::errorForFeatures(const TrainingData &features, bool printStats) const {
ErrorStruct e;
for (int i=0; i<features.size(); i++) {
FeatureVector feature = *(features.feature(i));
if (decide(feature)) {
feature.val() == POS ? e.true_pos++ : e.false_pos++;//it is really positive
} else {
feature.val() == NEG ? e.true_neg++ : e.false_neg++;//it is really negative
}
}
// if all 10 samples, 3 is misclassified, error is 3/10
e.error = (e.false_pos + e.false_neg) / ((float)features.size());
if (printStats) {
std::cout << e.true_pos << " true positives" << std::endl;
std::cout << e.false_pos << " false positives" << std::endl;
std::cout << e.true_neg << " true negatives" << std::endl;
std::cout << e.false_neg << " false negatives" << std::endl;
std::cout << e.error * 100 << "% error" << std::endl;
std::cout << std::endl;
}
return e;
}
/**************************************
* Fucntion: is_classifier_correct
* -------------------------------
* returns true if weak classifier (wc) correctly identified the
* feature vector (fv), false otherwise.
*/
bool AdaBooster::is_classifier_correct(WeakClassifier &wc, FeatureVector &fv){
// check if threshold is greater than (or equal to) feature
bool guess = ( wc.threshold() >= fv.at(wc.dimension()) );
// if classifier is flipped, negate guess
guess = wc.isFlipped() ? !guess : guess;
// find actual value of point
bool real = ( fv.val() == POS );
// return if guess and real agree
return ( real == guess );
}
