da_coeff_med_filter.m

主成分分析和偏最小二乘SquaresPrincipal成分分析（ PCA ）和偏最小二乘（ PLS ）

function da=da_coeff_med_filter(da,coeff_names,aperture);
% DA_COEFF_MED_FILTER provide median filtration of coefficients with the
% da - discriminant model
% names in coeff_names array
% aperture - aperture of median filter
% example: da_coeff_med_filter(da,{'c0 (-discr. fun.)' 'c1' 'c3' 'c22'},5);
%
% last modified 7.02.05


str=da.coefficients_txt;
str=[{'c0 (-discr. fun.)'} str];

if nargin<2
	[selection,ok] = listdlg('PromptString','Select coefficients:',...
                    'SelectionMode','multiple','ListString',str);
else    
    ok=0;
    selection=[];
    for i=1:length(coeff_names)
        for j=1:length(str)
            if strcmp(str{j},coeff_names{i})
                ok=1;
                selection=[selection j];
            end
        end
    end
end

if ok
    if nargin<3
        prompt = {'Enter filter aperture '};
		dlg_title = 'Median filter';
		num_lines= 1;
		def     = {'5'};
		answer  = inputdlg(prompt,dlg_title,num_lines,def);
    else
        answer={num2str(aperture)};
    end
	if ~isempty(answer)
        aperture=str2num(answer{1});
        Data=[-da.discriminant_func da.coeff_data];
        for i=1:length(selection)
            Data(:,selection(i))=med_filt(Data(:,selection(i)),aperture);
            waitbar_handle=waitbar(i/length(selection));
        end
        delete(waitbar_handle);
        if selection(1)==1
            da.discriminant_func=-Data(:,1);
        end
        for i=1:length(selection)
            if selection(i)>1
                da.coeff_data(:,selection(i)-1)=Data(:,selection(i));
            end
        end
    end
end


function Y=med_filt(X,ap);
Y=X;
L=length(X);
if mod(ap,2)
	for i=((ap-1)/2+1):(L-(ap-1)/2)
        Y(i)=median(X((i-(ap-1)/2):(i+(ap-1)/2)));
    end
else
    for i=ap/2:(L-ap/2)
        Y(i)=median(X((1+i-ap/2):(i+ap/2)));
    end
end