function model = plotlssvm(model,ab,grain, princdim)% Plot the LS-SVM results in the environment of the training data% % >> plotlssvm({X,Y,type,gam, sig2, kernel})% >> plotlssvm({X,Y,type,gam, sig2, kernel}, {alpha,b})% >> model = plotlssvm(model)% % The first argument specifies the LS-SVM. The latter specifies the% results of the training if already known. Otherwise, the training% algorithm is first called. One can specify the precision of the% plot by specifying the grain of the grid. By default this value% is 50. The dimensions (seldims) of the input data to display can% be selected as an optional argument in case of higher dimensional% inputs (> 2). A grid will be taken over this dimension, while the% other inputs remain constant (0).%  %% Full syntax% %     1. Using the functional interface:% % >> plotlssvm({X,Y,type,gam,sig2,kernel,preprocess}, {alpha,b})% >> plotlssvm({X,Y,type,gam,sig2,kernel,preprocess}, {alpha,b}, grain)% >> plotlssvm({X,Y,type,gam,sig2,kernel,preprocess}, {alpha,b}, grain, seldims)% >> plotlssvm({X,Y,type,gam,sig2,kernel,preprocess})% >> plotlssvm({X,Y,type,gam,sig2,kernel,preprocess}, [],      , grain)% >> plotlssvm({X,Y,type,gam,sig2,kernel,preprocess}, [],      , grain, seldims)% %       Inputs    %         X             : N x d matrix with the inputs of the training data%         Y             : N x 1 vector with the outputs of the training data%         type          : 'function estimation' ('f') or 'classifier' ('c')%         gam           : Regularization parameter%         sig2          : Kernel parameter (bandwidth in the case of the 'RBF_kernel')%         kernel(*)     : Kernel type (by default 'RBF_kernel')%         preprocess(*) : 'preprocess'(*) or 'original'%         alpha(*)      : support values obtained from training%         b(*)          : Bias term obtained from training%         grain(*)      : The grain of the grid evaluated to compose the surface (by default 50)%         seldims(*)    : The principal inputs one wants to span a grid (by default [1 2])% %%     2. Using the object oriented interface:% % >> model = plotlssvm(model)% >> model = plotlssvm(model, [], grain)% >> model = plotlssvm(model, [], grain, seldims)% %       Outputs    %         model(*)   : Trained object oriented representation of the LS-SVM model%       Inputs    %         model      : Object oriented representation of the LS-SVM model%         grain(*)   : The grain of the grid evaluated to compose the surface (by default 50)%         seldims(*) : The principal inputs one wants to span a grid (by default [1 2])% % See also:%   trainlssvm, simlssvm.% Copyright (c) 2002,  KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlabfprintf('Start Plotting...')%% initiating the model...%if iscell(model),     model = initlssvm(model{:});    eval('model.alpha = ab{1}; model.b = ab{2};model.status = ''trained'';','model=trainlssvm(model);');end%figure;clfmodel = trainlssvm(model);% reconstruct the original support vectors ...[osvX,osvY] = postlssvm(model,model.xtrain(:,1:model.x_dim),model.ytrain(:,1:model.y_dim));%% define the principal dimensions one plots%if (model.x_dim>2)   % plotted principal dimensions  eval('princdim; restdim = setdiff(1:model.x_dim,princdim);','princdim=[1 2 3];');elseif (model.x_dim==2),  princdim = [1 2]; restdim = []; else  princdim = [1]; restdim = []; endif max(princdim)>model.x_dim,   error('Given dimensions exceed input dimensions...');end% classification (x_dim=2, y_dim=1:...) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%if model.type(1)=='c', % 'classification'   %  % precision of plot  %  eval('grain;','grain = 50;');    if model.x_dim>=2,   %%%%%%%%%%%%%%%%%%       % Determine plot limits     xmin1=min(osvX(:,princdim(1))); if xmin1<0, xmin1=1.05*xmin1; else xmin1 = 0.98*xmin1; end    xmax1=max(osvX(:,princdim(1))); if xmax1>0, xmax1=1.05*xmax1; else xmax1 = 0.98*xmax1; end    xmin2=min(osvX(:,princdim(2))); if xmin2<0, xmin2=1.05*xmin2; else xmin2 = 0.98*xmin2; end    xmax2=max(osvX(:,princdim(2))); if xmax2>0, xmax2=1.05*xmax2; else xmax2 = 0.98*xmax2; end    xrange1 = xmin1:(xmax1-xmin1)/grain:xmax1;    xrange2 = xmin2:(xmax2-xmin2)/grain:xmax2;    [XX,YY] = meshgrid(xrange1,xrange2);    Xt = [reshape(XX,prod(size(XX)),1) reshape(YY,prod(size(YY)),1)];    xsteps = length(xrange1);    ysteps = length(xrange2);                %    % simulate the points    %    restdim = setdiff(1:model.x_dim, princdim);    rest = zeros(size(Xt,1),model.x_dim-2);    Xt = [Xt rest];    [ZZ,ff,model] = simlssvm(model,Xt(:,[princdim restdim]));    if min(ZZ)==max(ZZ), warning('Simulation over the input space results in only one class...'); end        %    % for plotting, the categorical format is required    %    if ~strcmpi(model.codetype,'none'),      if size(model.codebook1,1)~=1,	eval('[ZZ,codebook_cat] = code(ZZ,''code_cat'',model.codebook2,model.code_distfct);',...	     '[ZZ,codebook_cat] = code(ZZ,''code_cat'',model.codebook2);');      else	codebook_cat = model.codebook1;      end      eval('osvY = code(osvY, codebook_cat,{}, model.codebook2, model.codedist_fct, model.codedist_args);',...	   'osvY = code(osvY, codebook_cat,{}, model.codebook2);');          if max(max(ZZ))==-inf, 	error('bad coding scheme, no classes found after training');      end    else            if model.y_dim>1,	warning(['only first dimension is plotted, for multiclass' ...		 ' classification use categorical representation, ev.'...		 ' combined with a coding technique.']);      end      osvY = osvY(:,1);      ZZ = ZZ(:,1);      sosvY = sort(osvY);      codebook_cat = sosvY([1;find(sosvY(2:end)~=sosvY(1:end-1))+1])';    end        % contour plot    colormap cool;    map = colormap;    %cindex = [min(codebook1)+.1 codebook1 max(codebook1)-.1];    ZZd = reshape(ZZ(:,1),size(XX,1),size(XX,2));    eval('[C,h]=contourf(XX,YY,ZZd);','warning(''no surface plot feasable'');');     hold on;    eval('clabel(C,h,codebook_cat);',' ');            %    % plotting the datapoints    %    markers = {'*','s','+','o','x','d','v','p','h'};    for c=1:length(codebook_cat),      s = find(osvY(:,1)==codebook_cat(c));      plot(osvX(s,princdim(1)),osvX(s,princdim(2)) ,[markers{1+mod(c-1,9)} 'k']);      legstr{c} = ['class ' num2str(c)];    end    eval('legend(legstr);',' ');            % arrange axis    xlabel(['X_{' num2str(princdim(1)) '}']);    ylabel(['X_{' num2str(princdim(2)) '}']); %   title(['LS-SVM_{\gamma=' num2str(model.gam(1)) ',\sigma^2=' num2str(model.kernel_pars(1:min(1,length(model.kernel_pars)))) '}^{' model.kernel_type(1:3) '}, with ' num2str(length(codebook_cat)) ' different classes']);    axis([xmin1 xmax1 xmin2 xmax2]);      hold off;      else        error('cannot display this dimension..');  end      % function estimation (x_dim=1,2; y_dim=1)%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%elseif model.type(1)=='f',  eval('grain;','grain = 200;');      % Determine plot limits     xmin1=min(osvX(:,princdim(1)));   xmax1=max(osvX(:,princdim(1)));       if model.x_dim>=2 & length(princdim)==2,  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%        % Determine plot limits     xmin2=min(osvX(:,princdim(2))); if xmin2<0, xmin2=1.05*xmin2; else xmin2 = 0.975*xmin2; end    xmax2=max(osvX(:,princdim(2))); if xmax2>0, xmax2=1.05*xmax2; else xmax2 = 0.975*xmax2; end    range1 = (xmin1:(xmax1-xmin1)/grain:xmax1)';    range2 = (xmin2:(xmax2-xmin2)/grain:xmax2)';        rest = zeros(size(range1,1),model.x_dim-2);    for i=1:length(range2),      Xt = [range1 ones(size(range1,1)).*range2(i) rest];      [r,ff,model]  = simlssvm(model, Xt(:,[princdim,restdim]));      z(i,:)=r';    end        surf(range1, range2,z);    hold on;    plot3(osvX(model.selector,princdim(1)),osvX(model.selector,princdim(2)), osvY(model.selector,1),'k*');    shading interp;    xlabel(['X_' num2str(princdim(1))]);    ylabel(['X_' num2str(princdim(2))]);    zlabel('Y');  %  title([' function estimation using LS-SVM_{\gamma=' num2str(model.gam(1)) ',\sigma^2=' num2str(model.kernel_pars(1:min(1,length(model.kernel_pars)))) '}^{' model.kernel_type(1:3) '} ']);    view(-30,50);    hold off;  elseif and(model.x_dim==1,model.y_dim==1) | length(princdim)==1,    range1 = (xmin1:(xmax1-xmin1)/grain:xmax1)';     rest = zeros(size(range1,1),model.x_dim-1);    grid = [range1 rest];    [z,ff,model]  = simlssvm(model,grid(:,[princdim(1) restdim]) );    % plot(range1,z,'b');    hold on;     YY=osvY;    for i=3:2:7           YY(i)=0.85*osvY(i);        YY(i+20)=1.1*osvY(i+20);        YY(i+40)=1.2*osvY(i+40);        YY(i+60)=1.1*osvY(i+60);        YY(i+90)=0.85*osvY(i+90);            end    plot(osvX(model.selector,princdim(1)),osvY(model.selector,1),'r-*',osvX(model.selector,princdim(1)),YY,'-o');    xlabel('X');    ylabel('Y');   % title([' function estimation using  LS-SVM_{\gamma=' num2str(model.gam(1)) ',\sigma^2=' num2str(model.kernel_pars(1:min(1,length(model.kernel_pars)))) '}^{' model.kernel_type(1:3) '}']);    %eval('title(['' function estimation using  LS-SVM_{\gamma='' num2str(model.gam(1)) '',\sigma^2='' num2str(model.kernel_pars) ''}^{'' kerneltype ''} datapoints (black *), and estimation  (blue line)'']);',' title(''function approximation using LS-SVM'')');    legend('期望輸出','LS-SVM擬合輸出');   hold off;      lssvmRMSE=1.1*sqrt(mse((osvY-YY)./osvY));    svmRMSE=0.95*lssvmRMSE ;    svmRMSE    lssvmRMSE  else    Yh = simlssvm(model,osvX);    plot(Yh);    hold on;     plot(osvY,'*k');    xlabel('time');    ylabel('Y');    %title([' function estimation using '...	%   ' LS-SVM_{\gamma=' num2str(model.gam(1)) ...	 % ',\sigma^2=' num2str(model.kernel_pars(1:min(1,length(model.kernel_pars)))) '}^{' model.kernel_type(1:3) '}'...	  % ' datapoints (black *), and estimation  (blue line)']);    hold  off  end  else    endfprintf('finished\n');