/**************************************
* Function: getStrongError
* ------------------------
* calculates error rates at each "level" of the strong classifier; i.e. at
* each weak classifier
*
* td: Training data to check strong classifier against
* strong: strong classifier (i.e. ordered set of weak classifiers)
*
* returns: a list of the errors at each level
*/
vector<vector <double> > AdaBooster::getStrongError(TrainingData &td, const WeakClassifierList &strong){
unsigned int true_pos, false_pos, true_neg, false_neg;
float precision, recall;
vector< vector<double> > strong_err;
vector<double> stats;
// clear false_indices
false_indices.clear();
// set dimensions and number of features
dimensions = td.dimensions();
num_features = td.size();
// initialize vector of num_ftrs to zero
vector<double> classify;
for (unsigned int i=0; i<num_features; i++)
classify.push_back(0.0);
int sign;
// traverse all weak classifiers
for (unsigned int i=0; i<strong.size(); i++){
true_pos = false_pos = true_neg = false_neg = precision = recall = 0;
// traverse all features
for (unsigned int j=0; j<num_features; j++){
// check what the classifier guessed. If weak classifier decided
// the feature was POS, sign = 1, otherwise sign = -1
if ( (strong[i].threshold() > td.at(j,strong[i].dimension()) && !strong[i].isFlipped()) ||
(strong[i].threshold() < td.at(j,strong[i].dimension()) && strong[i].isFlipped()) )
sign = 1;
else
sign = -1;
// calculate classify so far
classify[j] += strong[i].weight() * sign;
// check classification against reality
if (classify.at(j) >= strong_err_threshold && td.val(j) == POS)
true_pos++;
else if (classify.at(j) >= strong_err_threshold && td.val(j) == NEG){
false_pos++;
// if we're at the last weak classifier and we still can't classify this point
if (i == strong.size()-1)
false_indices.push_back(j); // add index to false indices vector
}
else if (classify.at(j) < strong_err_threshold && td.val(j) == POS){
false_neg++;
// similarly, we can't classify the point
if (i == strong.size()-1)
false_indices.push_back(j);
}
else
true_neg++;
}
// calculate some stats and push into strong_err
stats.clear();
stats.push_back((double)(false_pos + false_neg)/num_features); // flat error percentage
stats.push_back((double)(true_pos)/(true_pos+false_pos)); // precision
stats.push_back((double)(true_pos)/(true_pos+false_neg)); // recall
stats.push_back((double)true_pos); // true positives
stats.push_back((double)true_neg); // true negatives
stats.push_back((double)false_pos); // false positives
stats.push_back((double)false_neg); // false negatives
strong_err.push_back(stats);
}
return strong_err;
}
