function model = codelssvm(model,Yt,Ytt)% This function is only for intern LS-SVMlab use. For extern coding of the classes, use the functions 'code'.%% Function to decode & encode the responses of the classification model% before applying the LS-SVM.%% Firstly, the appropriate functions has to be set:%   >> model = changelssvm(model,'codetype','code_MOC');%   >> model = changelssvm(model,'codedist_fct','codedist_bay');% The corresponding encoding is invoked by%   >> model = codelssvm(model)% % The 2nd argument is decoded, %%   Y = codelssvm(model,Y)%% The 3th argument is encoded, % %   Y = codelssvm(model,[],Ytt)'%% By default, a one dimensional categorical coding of the% (multiclass) labels is assumed. % % ENCODE OPTIONS in the model:%           codetype: used coding for multiclass classification;%               code: status of the coding {'original','encoded'};%           codetype: used coding for multiclass classification or 'none';%       codedist_fct: function used to calculate to which class a%                     coded result belongs;%      codetype_args: arguments of the codetype function;%      codedist_args: arguments of the codedist function;%          codebook2: codebook of the new coding%          codebook1: codebook of the original coding%% see also:%      code, trainlssvm, simlssvm, code_OneVsAll, code_OneVsOne,%      code_cat, codedist_hamming % (c) SCD-KULeuven, rights & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlab% default argseval('model.codedist_args;','model.codedist_args = {};');eval('model.codetype_args;','model.codetype_args = {};');eval('model.codedist_fct;','model.codedist_fct = ''codedist_hamming'';');%% encode model% a new rescaling is looked up in case of preprocessing%%if nargin==1,  % CLASSIFICATION %  if model.type(1)=='c' & model.code(1)~='o', % not 'original' 			    if model.code(1)=='c', % 'changed'				                        % check dimension      eval('if model.y_dim~=size(model.codebook2,1), warning(''Y different dimension than original code;'');  end',...	   'if model.y_dim~=1, warning(''Uncoded Y needs to be dimension 1;'');  end');      % back to original coding      eval('ys = code(model.ytrain, model.codebook1,{}, model.codebook2, model.codedist_fct,model.codedist_args{:});','ys = model.ytrain;');      if ~strcmpi(model.codetype,'none'),	% convert into new coding	eval(['[ys, model.codebook2, model.codebook1,pre_yscheme] = ' ...	      'code(ys, model.codetype, model.codetype_args, model.codebook1, model.codedist_fct, model.codedist_args{:});'],  ...	     ['[ys, model.codebook2, model.codebook1,pre_yscheme] = ' ...	      'code(ys, model.codetype, model.codetype_args, [],model.codedist_fct,model.codedist_args{:});']);      end      % postprocess - set code - preprocess      prepro = model.preprocess; model = postlssvm(model);      model.pre_yscheme = pre_yscheme;      model.ytrain = ys;      model.y_dim = size(ys,2);      model.code = 'encoded';        model = changelssvm(model,'preprocess',prepro); model = prelssvm(model);    end;  end  %% decode signal%elseif nargin==2,      % CLASSIFICATION       %  if model.type(1)=='c' & model.code(1)~='o', % not 'original'         eval('model.codebook1; model.codebook2;','model = trainlssvm(model);');    if ~strcmpi(model.codetype,'none'),      eval('model = code(Yt,model.codebook1, {}, model.codebook2, model.codedist_fct, model.codedist_args);',...	   'model = code(Yt,model.codebook1, {}, model.codebook2);');    else      model=Yt;    end  else    model=Yt;  end    %% encode signal%elseif nargin==3,    % CLASSIFICATION       %  if model.type(1)=='c' & model.code(1)~='o', % not 'original'     eval('model.codebook1; model.codebook2;','model = trainlssvm(model);');    if ~strcmpi(model.codetype,'none'),       eval('model = code(Ytt,model.codebook2, {}, model.codebook1, model.codedist_fct, model.codedist_args);',...	    'model = code(Ytt,model.codebook2, {}, model.codebook1);');     else       model=Ytt;     end  else    model=Ytt;  endend