backsel_main.m

Feature Selection using matlab

    if NSelectedFeat == 1
        ContinueFlag = 0;
    end
    %------ Curse-of-dim limits For Exclusion ---------------------
    if strcmp(MahalInfoLossMethod, 'on')
       LowLimitMahalInfLoss(NSteps) =  ...
            MahalaInfoLoss(NSelectedFeat, ResultMat(end,2),...
                     NPatterns/CClasses, CClasses, ErrorEstMethod);

        ResultMat(end, 6) = LowLimitMahalInfLoss(NSteps);
    else
        LowLimitMahalInfLoss(NSteps) = 0;
        ResultMat(end, 6) = LowLimitMahalInfLoss(NSteps);
    end

    %-------------------------------------------------------------- 

    %------------- Plot Module for Exclusion ----------------------
    if ~isempty(handles)
        axes(handles.FeatSelCurve);
        plot( ResultMat(:,2)      , 'b.-');
        hold on
        plot( LowLimitMahalInfLoss, 'r.-');
        
        axes(handles.YelLinesAxes);           % Delete lines of  
        delete(HYelLines(-ResultMat(end,3))); % discarded features
        set(findobj(gcf,'Tag','ListSelFeats'), 'String', ...
                                                 SelectedFeatPool);
    else
        plot( ResultMat(:,2)          , 'b.-');
        hold on
        plot( LowLimitMahalInfLoss, 'r.-');
        drawnow
        title([DatasetToUse ' ' ErrorEstMethod ' Backward']);
    end
    drawnow
    %----------------End Plot -------------------------------------

    
    
    %--------------- Report for Exclusion -------------------------
    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 Inclusion ---
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

    % Now keep adding features until the criterion gets worse.
    CritvalMaxCurrForw = CritvalMaxCurrBack;
    FlagForwSteps = 1;

    if NSelectedFeat == KFeatures
        ContinueFlag = 0;
    end

    while (NSelectedFeat<KFeatures - 2) && FlagForwSteps == 1 &&...
                       ContinueFlag == 1 && strcmp(FSMethod,'SFBS')

        if LogViewOfIntStep == 1                   
          fprintf(1, [StrInclusionStep StrLine '\n']);
        end

        for FeatSerialIndx = 1:length(PoolRemainFeat)
            % Add one feature to the already selected ones.
            CandFeatureToAdd = PoolRemainFeat(FeatSerialIndx);
            CandidateFeatSet = [SelectedFeatPool CandFeatureToAdd];
            HISTORYtable(end+1,1) = CandFeatureToAdd;

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

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

            if Critval(FeatSerialIndx)  >= CritvalThres
                CritvalMaxCurrForw       = Critval(FeatSerialIndx);
                FeatSerialIndxCritvalMaxCurr = FeatSerialIndx;
                ConfMatOptForw           = ConfMat;
                lowCLoptForw             = lowCL;
                upCLoptForw              = upCL;
                HISTORYtable(end, 4)     = Critval(FeatSerialIndx);
            end

            
           if LogViewOfIntStep == 1
              fprintf(1, FormatToPrintInterStep, ...
                   HISTORYtable(end,1), HISTORYtable(end,2), ...
                          HISTORYtable(end,3), CritvalMaxCurrForw);
           end
        end    % End internal step of inclusion

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

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

        if CritvalMaxCurrForw > CritvalThres
            FeatureToInclude         = ...
                      PoolRemainFeat(FeatSerialIndxCritvalMaxCurr);
            NSelectedFeat            = NSelectedFeat + 1;
            CritvalOpt(NSelectedFeat)= CritvalMaxCurrForw ;
            ConfMatOpt               = ConfMatOptForw;
            lowCLopt                 = lowCLoptForw;
            upCLopt                  = upCLoptForw;
            PoolRemainFeat(FeatSerialIndxCritvalMaxCurr) = [];
            PoolRemainFeat           = sort(PoolRemainFeat);
            ResultMat(end+1,1:5)     = [KFeatures-NSelectedFeat ...
             CritvalMaxCurrForw FeatureToInclude lowCLopt upCLopt];
            NSteps = size(ResultMat,1);
            SelectedFeatPool = [SelectedFeatPool FeatureToInclude];
            HISTORYtable(end, 4)       = CritvalMaxCurrForw;
            CritvalMaxCurrBack         = CritvalMaxCurrForw;
            %----- 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 inclusion ------------
            if ~isempty(handles)
                axes(handles.YelLinesAxes);
                axis([0 NPatterns 0 KFeatures]); axis manual
                hold on
                if (NPatterns > KFeatures)
                    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
                
                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
            FlagForwSteps = 0;   % continue with backward 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')
    [OptimumError, OptimumStepIndx] = max( ResultMat(:,6));
else
    [OptimumError, OptimumStepIndx] = max( ResultMat(:,4));
end

OptimumFeatureSet= FindOptFeatSet(InitialSelectedFeatPool,...
                                  ResultMat(1:OptimumStepIndx, 3));    

%--------- Calculate Execution Time -------------------------------
TimeStampEnd =  clock;
Tlapse       =  etime(TimeStampEnd, TimeStampStart);
                              
                              
%---------- Plot modulo -------------------------------------------
if ~isempty(handles)
    axes(handles.YelLinesAxes);
    delete(HYelLines(SelectedFeatPool)); 
    axis([0 NPatterns 0 KFeatures]); axis manual
    hold on
    for IndexFeatures = 1:length(OptimumFeatureSet)
    if (NPatterns > KFeatures)
        HYelLines(IndexFeatures) = plot([0 NPatterns+2], ...
            (OptimumFeatureSet(IndexFeatures)-.5)*ones(1,2),'y');
    else
        HYelLines(IndexFeatures) = plot(...
                        (OptimumFeatureSet(IndexFeatures)-.5)*...
                                    ones(1,2),[0 NPatterns+2],'y');
    end
    set(gca,'Visible','off');
    drawnow
    end
    set(findobj(gcf,'Tag','ListSelFeats'), 'String', ...
                                       num2str(OptimumFeatureSet));
    axes(handles.FeatSelCurve);
end
drawnow


hold on
plot(OptimumStepIndx*ones(2,1), [0 OptimumError], 'r');
xlabel(['Time Lapse: ' num2str(Tlapse) ' secs']);
%------------------------------------------------------------------
StopByUser = 0;
return

%------------------------------------------------------------------
%--------------- Best Set after Removals --------------------------
%------------------------------------------------------------------
function OptimumFeatureSet =FindOptFeatSet(OptimumFeatureSet,... 
                                                    PathOfFeatures)
                                       
for IndexStep=1:length(PathOfFeatures),
    if PathOfFeatures(IndexStep) < 0
      OptimumFeatureSet(-PathOfFeatures(IndexStep)) = 0;
    elseif PathOfFeatures(IndexStep) > 0
      OptimumFeatureSet(PathOfFeatures(IndexStep)) = ...
                                         PathOfFeatures(IndexStep);
    end
end

OptimumFeatureSet = OptimumFeatureSet(find(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