function [model,Yt] = postlssvm(model,Xt,Yt)% Postprocessing of the LS-SVM%% These functions should only be called by trainlssvm or by% simlssvm. At first the preprocessing assigns a label to each in-% and output component (c for continuous, a for categorical or b% for binary variables). According to this label each dimension is rescaled:% %     * continuous: zero mean and unit variance%     * categorical: no preprocessing%     * binary: labels -1 and +1% % Full syntax (only using the object oriented interface):% % >> model   = postssvm(model)% >> Xp = postlssvm(model, Xt)% >> [empty, Yp] = postlssvm(model, [], Yt)% >> [Xp, Yp] = postlssvm(model, Xt, Yt)% %       Outputs    %         model : Preprocessed object oriented representation of the LS-SVM model%         Xt    : Nt x d matrix with the inputs of the test data to preprocess%         Yt    : Nt x d matrix with the outputs of the test data to preprocess%       Inputs    %         model : Object oriented representation of the LS-SVM model%         Xp    : Nt x d matrix with the preprocessed inputs of the test data%         Yp    : Nt x d matrix with the preprocessed outputs of the test data% % % See also:%   prelssvm, trainlssvm% Copyright (c) 2002,  KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlab%% test of postprocessing needed, and if model is properly coded before% decoding %if model.preprocess(1)~='p',  if nargin>=2,    % no postprocessing nor coding needed    model=Xt;   end  returnend%% postprocess the LS-SVM%if nargin==1,           %    % execute rescaling as defined     %    if (model.prestatus(1)=='o' & model.preprocess(1)=='p') | ... % 'preprocess' &'ok'       (model.prestatus(1)=='c' & model.preprocess(1)=='o'),      % 'original' &'changed'      model=postmodel(model);         model.preprocess = 'original';     end    model.prestatus='ok';           %    % rescaling of the to simulate inputs    %else  eval('Yt;','Yt=[];');  [model,Yt] = postmodel(model,Xt,Yt);endfunction [model,Yt] = postmodel(model,Xt,Yt)%% ' [Xt,Yt] = postmodel(model,Xt,Yt)'% ' [model] = postmodel(model)'%if nargin==1,    for i=1:model.x_dim,    % CONTINU VARIABLE:     if model.pre_xscheme(i)=='c',      model.xtrain(:,i) = post_zmuv(model.xtrain(:,i),model.pre_xmean(i),model.pre_xstd(i));    % CATHEGORICAL VARIBALE:     elseif model.pre_xscheme(i)=='a',      model.xtrain(:,i) = post_cat(model.xtrain(:,i),model.pre_xmean(i),model.pre_xstd(i));    % BINARY VARIBALE:     elseif model.pre_xscheme(i)=='b',      model.xtrain(:,i) = post_bin(model.xtrain(:,i),model.pre_xmean(i),model.pre_xstd(i));    end  end    for i=1:model.y_dim,    % CONTINU VARIABLE:     if model.pre_yscheme(i)=='c',      model.ytrain(:,i) = post_zmuv(model.ytrain(:,i),model.pre_ymean(i),model.pre_ystd(i));    % CATHEGORICAL VARIBALE:     elseif model.pre_yscheme(i)=='a',            model.ytrain(:,i) = post_cat(model.ytrain(:,i),model.pre_ymean(i),model.pre_ystd(i));    % BINARY VARIBALE:     elseif model.pre_yscheme(i)=='b',      model.ytrain(:,i) = post_bin(model.ytrain(:,i),model.pre_ymean(i),model.pre_ystd(i));    end    endelse    if nargin>1, % testdayta Xt,    if ~isempty(Xt),      if size(Xt,2)~=model.x_dim, warning('dimensions of Xt not compatible with dimensions of supprt vectors...');end      for i=1:model.x_dim,	% CONTINU VARIABLE: 	if model.pre_xscheme(i)=='c',	  Xt(:,i) = post_zmuv(Xt(:,i),model.pre_xmean(i),model.pre_xstd(i));	  % CATHEGORICAL VARIBALE: 	elseif model.pre_xscheme(i)=='a',	  Xt(:,i) = post_cat(Xt(:,i),model.pre_xmean(i),model.pre_xstd(i));	% BINARY VARIBALE: 	elseif model.pre_xscheme(i)=='b',	  Xt(:,i) = post_bin(Xt(:,i),model.pre_xmean(i),model.pre_xstd(i));	end      end    end        if nargin>2 & ~isempty(Yt),      if size(Yt,2)~=model.y_dim, warning('dimensions of Yt not compatible with dimensions of supprt vectors...');end      for i=1:model.y_dim,	% CONTINU VARIABLE: 	if model.pre_yscheme(i)=='c',	  Yt(:,i) = post_zmuv(Yt(:,i),model.pre_ymean(i), model.pre_ystd(i));	  % CATHEGORICAL VARIBALE: 	elseif model.pre_yscheme(i)=='a',      	  Yt(:,i) = post_cat(Yt(:,i),model.pre_ymean(i),model.pre_ystd(i));	% BINARY VARIBALE: 	elseif model.pre_yscheme(i)=='b',	  Yt(:,i) = post_bin(Yt(:,i),model.pre_ymean(i),model.pre_ystd(i));	end      end    end    model = Xt;  endendfunction X = post_zmuv(X,mean,var)%% postprocessing a continu signal; rescaling to zero mean and unit% variance % 'c'%X = X.*var+mean;function X = post_cat(X,mean,range)%% postprocessing a cathegorical signal, rescaling to -1:1;% 'a'%X = X;function X = post_bin(X,min,max)%% postprocessing a binary signal, rescaling to -1:1;% 'a'%X = min.*(X<=0)+max.*(X>0);