/*************************************
* Function: update_feature_weight
* -------------------------------
* Given TrainingData and a WeakClassifier that has been weighted in
* get_best_classifier(), we recalculate the weights of all the features
*
* td: training data (set of features)
* wc: (weighted) weak classifier
*
* returns true if successful, false otherwise
*/
bool AdaBooster::update_feature_weight(TrainingData &td, WeakClassifier &wc){
// check that WeakClassifier has actually been weighted
if (wc.weight() < 0){
printf("Error in update_feature_weight: WeakClassifier has invalid weight\n");
return false;
}
// traverse features in feature set and adjust their weights
for (unsigned int i=0; i<num_features; i++){
FeatureVector* fv = td.feature(i);
// either 1 or -1 (used in weight below)
int is_correct = is_classifier_correct(wc, *fv) ? 1 : -1;
// calculate and update weight
// note M_E := 2.71828
float weight = pow(M_E, (double) -1 * wc.weight() * is_correct);
td.setWeight(i, td.weight(i)*weight);
}
// calculate normalization factor
float norm = 0;
for (unsigned int i=0; i<num_features; i++)
norm += td.weight(i);
// normalize feature weights
for (unsigned int i=0; i<num_features; i++)
td.setWeight(i, td.weight(i)/norm);
return true; // successful
}
float StrongClassifier::evaluate(const std::vector<float> &features) const { //feature
float decision = 0;
for (int i=0; i<m_weakClassifiers.size(); i++) {
WeakClassifier weak = m_weakClassifiers.at(i);
int sign;
if ( (weak.threshold() > features[weak.dimension()] && !weak.isFlipped()) ||
(weak.threshold() < features[weak.dimension()] && weak.isFlipped()) )
sign = 1;
else
sign = -1;
decision += weak.weight() * sign;
}
return decision;
}