prtFeatSelExhaustive   Exhaustive feature selection
 
     FEATSEL = prtFeatSelExhaustive returns an exhaustive feature selection
     object.
 
     FEATSEL = prtFeatSelExhaustive(PROPERTY1, VALUE1, ...) constructs a
     prttFeatSelExhaustive object FEATSEL with properties as specified by
     PROPERTY/VALUE pair
 
     A prtFeatSelExhaustive object has the following properties:
 
     nFeatures             - The number of features to be selected
     showProgressBar       - Flag indicating whether or not to show the
                             progress bar during feature selection.
     evaluationMetric      - The metric to be used to determine which
                             features are selected. evaluationMetric must
                             be a function handle. The function handle must
                             be in the form:
                             @(dataSet)prtEval(prtClass, dataSet, varargin)
                             where prtEval is a prtEval function, prtClass
                             is a prt classifier object, and varargin 
                             represents optional input arguments to a 
                             prtEval function.
     Peformance            - The performance obtained by the using the
                             features selected.
     selectedFeatures      - The indices of the features selected that gave
                             the best performance.
 
    A prtFeatSelExhaustive object inherits the TRAIN and RUN methods from prtClass.
 
    Example:
  
    dataSet = prtDataGenFeatureSelection;      % Generate a data set
    featSel = prtFeatSelExhaustive;   % Create a feature selction object
    featSel.nFeatures = 3;            % Select only one feature of the data
    featSel = featSel.train(dataSet); % Train the feature selection object
    outDataSet = featSel.run(dataSet);% Extract the data set with only the
                                      % selected features
 
    %   Change the scoring function to prtScorePdAtPf, and change the
    %   classification method to prtClassMAP
 
    featSel.evaluationMetric = @(DS)prtEvalPdAtPf( prtClassMap, DS, .9);
 
    featSel = featSel.train(dataSet); 
    outDataSet = featSel.run(dataSet);