RealAdaBooster

 function [ab] = RealAdaBooster(cl, nStages)
    constructor of the realAdaBooster class that inherits from the classifier
    class. the realAdaBooster implements the RealAdaBoost boosting algorithm to
    build a strong (boosted) classifier from several weak classifiers.

    Inputs:
       cl: the weak classifier that needs to be boosted or another
           realAdaBooster that was trained with a specific number of stages
           and we need to increase them (default [])

0001 function [rab] = RealAdaBooster(cl)
0002 % function [ab] = RealAdaBooster(cl, nStages)
0003 %    constructor of the realAdaBooster class that inherits from the classifier
0004 %    class. the realAdaBooster implements the RealAdaBoost boosting algorithm to
0005 %    build a strong (boosted) classifier from several weak classifiers.
0006 %
0007 %    Inputs:
0008 %       cl: the weak classifier that needs to be boosted or another
0009 %           realAdaBooster that was trained with a specific number of stages
0010 %           and we need to increase them (default [])
0011 
0012 % parameters needed for training
0013 % the error bound after reaching which the classifier stops learning,
0014 % used only when the nStages argument to learn is Inf
0015 rab.errBound = 0.001;
0016 
0017 % paramters needed to define the classifier
0018 if nargin == 0
0019     rab.weakCl = [];
0020     numClasses = 2;
0021 else
0022     rab.weakCl = cl;
0023     numClasses = getNumClasses(cl);
0024 end
0025 
0026 % parameters to be learned by the classifier
0027 % 1. a cell array of trained instances of the weak classifier
0028 rab.trndCls = {};
0029 
0030 % 2. Number of stages
0031 rab.nStages = 0;
0032 
0033 % 3. example weights of the last iteration of realAdaBoost learning
0034 % algorithm. this is saved after training the classifier to be used
0035 % later if we want to increase the number of stages later on
0036 rab.lastExWeights = [];
0037 
0038 % 4. Weak classifiers weights
0039 rab.clsWeights = [];
0040 
0041 % 5. the threshold whereby the classifier can distinguish between the
0042 % positive and negative examples
0043 rab.thresh = NaN;
0044 
0045 % 6. detection rate after training
0046 rab.detectionRate = NaN;
0047 
0048 rab = class(rab, 'RealAdaBooster', Classifier(numClasses));