public static int getClassification(Matrix featureValues, AdaBoostRespons[] adaBoostResponses)
{
double sum = 0;
double alphaSum = 0;
foreach (AdaBoostRespons adaBoostRespons in adaBoostResponses)
{
sum += adaBoostRespons.alpha * getWeakClassification(featureValues[adaBoostRespons.featureIndex.j], adaBoostRespons.weakClassifier.parity, adaBoostRespons.weakClassifier.threshold);
alphaSum += adaBoostRespons.alpha;
}
return sum > (alphaSum / 2) ? 1 : 0;
}
public static AdaBoostRespons[] executeAdaBoost(Matrix[] integralImages, Feature[] allFeatures, Matrix isFaceList, int nrNegative, int nrWeakClassifiers)
{
FeatureType[] featureTypes =
(from feature in allFeatures
select feature.type).Distinct().ToArray();
InitiateFeatureValues(integralImages, allFeatures, featureTypes);
AdaBoostRespons[] adaBoostRespons = new AdaBoostRespons[nrWeakClassifiers];
Matrix weights = getInitializedWeights(isFaceList.nr_vals - nrNegative, nrNegative);
for (int t = 0; t < nrWeakClassifiers; t++)
{
weights = weights / weights.getSum();
FeatureIndex bestFeatureIndex = new FeatureIndex(FeatureType.type1, int.MinValue);
WeakClassifier bestClassifier = new WeakClassifier(int.MaxValue, double.MaxValue, double.MaxValue);
foreach (FeatureType featureType in featureTypes)
{
Matrix allFeatureValuesOfSameType = Matrix.DeserializeFromXML("FeatureValues_" + featureType.ToString() + ".xml");
for (int j = 1; j <= allFeatureValuesOfSameType.nr_rows; j++)
{
WeakClassifier weakClassifier = learnWeakClassifier(allFeatureValuesOfSameType, weights, isFaceList, j);
if (weakClassifier.error < bestClassifier.error)
{
bestClassifier = weakClassifier;
bestFeatureIndex.featureType = featureType;
bestFeatureIndex.j = j;
}
}
}
double beta = bestClassifier.error / (1 - bestClassifier.error);
double alpha = Math.Log(1 / beta);
Matrix featureValuesOfSameType = Matrix.DeserializeFromXML("FeatureValues_" + bestFeatureIndex.featureType.ToString() + ".xml");
for (int i = 1; i <= featureValuesOfSameType.nr_cols; i++)
{
int classification = getWeakClassification(featureValuesOfSameType[i, bestFeatureIndex.j], bestClassifier.parity, bestClassifier.threshold);
weights[i] = Math.Abs(classification - isFaceList[i]) == 0 ? weights[i] * beta : weights[i];
}
adaBoostRespons[t] = new AdaBoostRespons(bestClassifier, alpha, bestFeatureIndex);
}
return adaBoostRespons;
}