forwsel_main.m

Feature Selection using matlab

            HYelLines(FeatureToInclude)=plot([0 NPatterns+2],...
                (FeatureToInclude-0.5)*ones(1,2),'y');
        else
            HYelLines(IndexFeature)= plot((FeatureToInclude-0.5)...
                *ones(1,2),[0 NPatterns+2],'y');
        end
        set(gca,'Visible','off');
        drawnow
        set(findobj(gcf,'Tag','ListSelFeats'), 'String', ...
            sort(SelectedFeatPool));
        axes(handles.FeatSelCurve);
    end
    HCCRCurve(1) = plot( ResultMat(:,2)          , 'b.-');
    legend(HCCRCurve(1), 'CCR');  
    hold on
    if strcmp(MahalInfoLossMethod , 'on')
        HCCRCurve(2) = plot( LowLimitMahalInfLoss, 'r.-');
        legend(HCCRCurve, 'CCR', 'Lower Limit of CCR',...
                                                'Location','Best');
    end
    title([DatasetToUse ' ' ErrorEstMethod ' ' FSMethod]);
    drawnow
    %--------------------------------------------------------------
       

    %--------------- Report ---------------------------------------
    fprintf(1, ['\n\n' StrFeatSelected StrLine '\n']);
    for pre_i=1:size(ResultMat,1)
        fprintf(1,FormatToPrintExterStep, ResultMat(pre_i,1), ...
                      ResultMat(pre_i,2), ResultMat(pre_i,3), ...
                      ResultMat(pre_i,4), ResultMat(pre_i,5),...
                      ResultMat(pre_i,6));
    end
    fprintf(1,[StrLine '\n\n\n']);

    if ConfMatSwitch == 1
        ShowConfMat(ConfMatOpt);
    end
    %---------------End Report ------------------------------------

    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    %-------Conditional Exclusion ---
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

    % Now keep removing features until the criterion gets worse.
    CritvalMaxCurrBack = CritvalMaxCurrForw;
    FlagBackwSteps=1;

    if NSelectedFeat == KFeatures
        ContinueFlag = 0;
    end

    while (NSelectedFeat > 2) && FlagBackwSteps == 1 && ...
                       ContinueFlag == 1 && strcmp(FSMethod,'SFFS')
        

        if LogViewOfIntStep == 1                   
          fprintf(1, [StrExclusionStep StrLine '\n']);
        end
        
        for FeatSerialIndx = 1:NSelectedFeat
            CandidateFeatSet = SelectedFeatPool;
            % Remove one feature from the selected ones.
            CandidateFeatSet(FeatSerialIndx) = [];
            HISTORYtable(end+1,1) =...
                -SelectedFeatPool(FeatSerialIndx);

            [Critval(FeatSerialIndx),  ConfMat, lowCL, upCL] = ...
             BayesClassMVGaussPDFs(Patterns(:,CandidateFeatSet),...
                   Targets, PercTest, ErrorEstMethod, NRepThres,...
                    GammaParam, CritvalMaxCurrBack, ConfMatSwitch);
                     
            HISTORYtable(end, 2)  =  Critval(FeatSerialIndx);
            % If removing this feature gives the best result so far
            % (or the same result using less features), and we have
            % not yet removed all NFeatToSelect features, store it.

            if strcmp(ErrorEstMethod,'ProposedAB')
                CritvalThres = CritvalMaxCurrBack + GammaParam;
            else
                CritvalThres = CritvalMaxCurrBack;
            end

            if Critval(FeatSerialIndx) > CritvalThres
                CritvalMaxCurrBack       = Critval(FeatSerialIndx);
                FeatSerialIndxCritvalMaxCurr = FeatSerialIndx;
                ConfMatOptBack           = ConfMat;
                lowCLoptBack             = lowCL;
                upCLoptBack              = upCL;
                HISTORYtable(end, 4)     = Critval(FeatSerialIndx);
            end

            
           if LogViewOfIntStep == 1
              fprintf(1, FormatToPrintInterStep, ...
                   HISTORYtable(end,1), HISTORYtable(end,2), ...
                          HISTORYtable(end,3), CritvalMaxCurrBack);
           end
        end    % end checking all selected features

        % If this subset is better than any found before, store
        % and report it. Otherwise, stop removing features.

        if strcmp(ErrorEstMethod,'ProposedAB')
            CritvalThres = CritvalMaxCurrForw + GammaParam;
        else
            CritvalThres = CritvalMaxCurrForw;
        end

        if CritvalMaxCurrBack > CritvalThres
            FeatureToExclude         = ...
                    SelectedFeatPool(FeatSerialIndxCritvalMaxCurr);
            NSelectedFeat            = NSelectedFeat - 1;
            CritvalOpt(NSelectedFeat)= CritvalMaxCurrBack ;
            ConfMatOpt               = ConfMatOptBack;
            lowCLopt                 = lowCLoptBack;
            upCLopt                  = upCLoptBack;
            PoolRemainFeat      =[PoolRemainFeat FeatureToExclude];
            PoolRemainFeat           = sort(PoolRemainFeat);
            ResultMat(end+1,1:5)     = [NSelectedFeat ...
                                         CritvalMaxCurrBack ...
                               -FeatureToExclude lowCLopt upCLopt];
            NSteps = size(ResultMat,1);
            SelectedFeatPool(FeatSerialIndxCritvalMaxCurr ) = [];
            HISTORYtable(end, 4)       = CritvalMaxCurrBack;
            CritvalMaxCurrForw         = CritvalMaxCurrBack;
            %----- Curse-of-dim limits For Exclusion --------------
            if strcmp(MahalInfoLossMethod, 'on')
                LowLimitMahalInfLoss(NSteps) =  ...
                 MahalaInfoLoss(NSelectedFeat, ResultMat(end,2),...
                     NDc, CClasses, ErrorEstMethod);
               
                ResultMat(NSteps, 6)= LowLimitMahalInfLoss(NSteps);
            end
            %------------------------------------------------------
            
            %--------------- Plot module for exclusion ------------
            if ~isempty(handles)
                delete(HYelLines(FeatureToExclude));
                axes(handles.FeatSelCurve);
                plot( ResultMat(:,2),  'b.-');
                hold on
                if strcmp(MahalInfoLossMethod, 'on')
                    plot( LowLimitMahalInfLoss, 'r.-');
                end
                drawnow
                set(findobj(gcf,'Tag','ListSelFeats'),'String',...
                                           sort(SelectedFeatPool));
                pause(0.1);
            end
            %------------------------------------------------------
        else
            FlagBackwSteps = 0;   % continue with forward steps
        end
        %-------------------- Report Log --------------------------
        fprintf(1, ['\n\n' StrFeatSelected StrLine '\n']);
        for pre_i=1:size(ResultMat,1)
            fprintf(1,FormatToPrintExterStep, ResultMat(pre_i,1),...
                ResultMat(pre_i,2), ResultMat(pre_i,3),...
                ResultMat(pre_i,4), ResultMat(pre_i,5),...
                ResultMat(pre_i,6));
        end
        fprintf(1,[StrLine '\n\n\n\n']);
        %----------------------------------------------------------
    end % end backwards loop

    %----------- Plot Hypergeo Upper Lower Limits -----------------
    if 0
        plot(   ResultMat(:,4) ,'b.-' )
        hold on
        plot(   ResultMat(:,5), 'r.-')
        drawnow
    end
    %--------------------------------------------------------------
end %====================== END FINAL =============================

if ConfMatSwitch == 1
    ShowConfMat(ConfMatOpt);
end

%--------- Find Optimum Feature Set -------------------------------
if strcmp(MahalInfoLossMethod, 'on')
    [OptimumCCR, OptimumStepIndx] = max( ResultMat(:,6));
else
    [OptimumCCR, OptimumStepIndx] = max( ResultMat(:,4));
end
OptimumFeatureSet= FindOptFeatSet(ResultMat(1:OptimumStepIndx, 3));    
OptimumFeatureSet= sort(OptimumFeatureSet);

%--------- Calculate Execution Time -------------------------------
TimeStampEnd =  clock;
Tlapse       =  etime(TimeStampEnd, TimeStampStart);

%----------- Plot module ------------------------------------------
LinesToDeleteFeatInd = setxor(SelectedFeatPool, OptimumFeatureSet);

if ~isempty(handles)
    delete( HYelLines(LinesToDeleteFeatInd));
    set(findobj(gcf,'Tag','ListSelFeats'), 'String', ...
        OptimumFeatureSet);

    axes(handles.FeatSelCurve);
end
hold on
plot(OptimumStepIndx*ones(2,1), [0 OptimumCCR], 'r');
title(['Optimum FeatSet: ' num2str(OptimumFeatureSet')]);
xlabel(['Time Lapse: ' num2str(Tlapse) ' secs']);



StopByUser = 0;
return

%------------------------------------------------------------------
%--------------- Best Set after Removals --------------------------
%------------------------------------------------------------------
function OptimumFeatureSet = FindOptFeatSet(PathOfFeaturesSelected)

OptimumFeatureSet=PathOfFeaturesSelected;

for IndexStep=1:length(OptimumFeatureSet),
    if OptimumFeatureSet(IndexStep)<0,
        remov = OptimumFeatureSet(1:IndexStep) ...
                                 == -OptimumFeatureSet(IndexStep);
        OptimumFeatureSet(remov==1) =0;
        OptimumFeatureSet(IndexStep)=0;
    end
end

OptimumFeatureSet = OptimumFeatureSet( OptimumFeatureSet>0);
return

%------------------------------------------------------------------
%-------------- Mahalanobis Info Loss Limits ----------------------
%------------------------------------------------------------------
function LowLimitMahalInfLoss = MahalaInfoLoss(D, CCR, NDc, ...
                                          CClasses, ErrorEstMethod) 
%Calculate Info Loss 
InfoLoss = CalcInfoLoss(D, floor(NDc), ErrorEstMethod);
LowLimitMahalInfLoss = CCR - InfoLoss*(CCR-1/CClasses);
return