function [model,cost,O3] = tunelssvm(model,startvalues, varargin);% Tune the hyperparameters of the model with respect to the given performance measure%% 1. Using the functional interface:%%% >> [gam, sig2, cost] = tunelssvm({X,Y,type,igam,isig2,kernel,preprocess})% >> [gam, sig2, cost] = tunelssvm({X,Y,type,igam,isig2,kernel,preprocess}, StartingValues)% >> [gam, sig2, cost] = tunelssvm({X,Y,type,igam,isig2,kernel,preprocess},...%                                          StartingValues, optfun, optargs)% >> [gam, sig2, cost] = tunelssvm({X,Y,type,igam,isig2,kernel,preprocess},...%                                          StartingValues, optfun, optargs, costfun, costargs)%%      Outputs    %        gam     : Optimal regularization parameter%        sig2    : Optimal kernel parameter(s)%        cost(*) : Estimated cost of the optimal hyperparameters%      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')%        igam    : Starting value of the regularization parameter%        isig2   : Starting value of the kernel parameter(s) (bandwidth in the case of the 'RBF_kernel')%        kernel(*) : Kernel type (by default 'RBF_kernel')%        preprocess(*) : 'preprocess'(*) or 'original'%        StartingValues(*) : Starting values of the optimization routine (or '[]')%        optfun(*) : Optimization function (by default 'gridsearch')%        optargs(*) : Cell with extra optimization function arguments%        costfun(*) : Function estimating the cost-criterion (by default 'crossvalidate')%        costargs(*) : Cell with extra cost function arguments%% 2. Using the object oriented interface:%% >> [model, cost] = tunelssvm(model)% >> [model, cost] = tunelssvm(model, StartingValues)% >> [model, cost] = tunelssvm(model, StartingValues, optfun, optargs)% >> [model, cost] = tunelssvm(model, StartingValues, optfun, optargs, costfun, costargs)%%      Outputs    %        model            : Object oriented representation of the LS-SVM model with optimal hyperparameters%        cost(*)          : Estimated cost of the optimal hyperparameters%      Inputs    %        model            : Object oriented representation of the LS-SVM model with initial hyperparameters%        StartingValues(*): Starting values of the optimization routine (or '[]')%        optfun(*)        : Optimization function (by default 'gridsearch')%        optfun(*)        : Cell with extra optimization function arguments%        costfun(*)       : Function estimating the cost-criterion (by default 'crossvalidate')%        optfun(*)        : Cell with extra cost function arguments%%  See also:%    trainlssvm, crossvalidate, validate, gridsearch, linesearch  % Copyright (c) 2002,  KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlab    %% starting values%try,  if ~isnumeric(startvalues),    warning('Startingvalues has to be in a matrix');  endcatchend  %% initiate variables%if iscell(model),  model = initlssvm(model{:});  func=1;else  func=0;endmodel = trainlssvm(model);%% defaults%if length(varargin)>=1, optfun  = varargin{1}; else optfun='gridsearch';endif length(varargin)>=2, optargs = varargin{2}; else optargs = {}; endif length(varargin)>=3,   costfun = varargin{3};   if length(varargin)>=4, costargs = varargin{4};   else     costargs ={};     if strcmp(costfun,'crossvalidate') | strcmp(costfun,'rcrossvalidate') |strcmp(costfun,'leaveoneout'),      eval('[X,Y]=postlssvm(model,model.xtrain, codelssvm(model,model.ytrain));costargs = {X,Y};',...           '[X,Y]=postlssvm(model,model.xtrain, model.ytrain);costargs = {X,Y};')    end  endelse   costfun= 'crossvalidate';   eval('[X,Y]=postlssvm(model,model.xtrain, codelssvm(model,model.ytrain));costargs = {X,Y};',...       '[X,Y]=postlssvm(model,model.xtrain, model.ytrain);costargs = {X,Y};')end%% multiple outputsif (model.y_dim>1)% & (size(model.kernel_pars,1)==model.y_dim |size(model.gam,1)==model.y_dim |prod(size(model.kernel_type,1))==model.y_dim))  disp(' -->> tune individual outputs');  for d=1:model.y_dim,    fprintf(['\n -> dim ' num2str(d) ': \n']);    eval('gam = model.gam(:,d);','gam = model.gam;');    eval('sig2 = model.kernel_pars(:,d);','sig2 = model.kernel_pars;');    eval('kernel = model.kernel_typejspbqcd;','kernel=model.kernel_type;');    [g,s,c] = ...        tunelssvm({model.xtrain,model.ytrain(:,d),...                   model.type,gam,sig2,kernel,'original'},...                  [],varargin{:});    gamt(:,d) = g;    eval('kernel_part(:,d) = s;','kernel_part = []; ');    costs(d) = c;  end  model.gam = gamt;  model.kernel_pars = kernel_part;  if func,    O3 = costs;    cost = model.kernel_pars;    model = model.gam;  end  return  endif length(model.gam)>1,   error('Only one gamma per output allowed'); end%% depending on kernel%%% lineare kernel%if strcmp(model.kernel_type,'lin_kernel'),  optfun = 'linesearch';  disp(' TUNELSSVM: chosen specifications:');  disp([' 1. optimization routine:           ' optfun]);  disp(['    cost function:                  ' costfun]);  disp(' ');  eval('startvalues = log(startvalues);','startvalues = [];');  if isempty(startvalues),    startvalues = log(model.gam)+[-5;10];  end    tic;  c = costofmodel1(startvalues(1),model,costfun,costargs);  et = toc;   disp([' 2. starting values:                   ' num2str(exp(startvalues(1,:)))]);  disp(['    cost of starting values:           ' num2str(c)]);  disp(['    time needed for 1 evaluation (sec):' num2str(et)]);  disp(['    limits of the grid:   [gam]         ' num2str(exp(startvalues(:,1))')]);  disp(' ');  disp('OPTIMIZATION IN LOG SCALE...');    %  % major call  [gs, cost,evals, fig] = feval(optfun, @costofmodel1,startvalues,{model, costfun,costargs},optargs{:});  figure(fig);  xlabel('log(gamma)');  ylabel(costfun);    gamma = exp(gs(1));  kernel_pars = [];  disp(['Obtained hyper-parameters: [gamma]: ' num2str([gamma])]);%% RBF kernel%elseif strcmp(model.kernel_type,'RBF_kernel'),      disp(' TUNELSSVM: chosen specifications:');  disp([' 1. optimization routine:           ' optfun]);  disp(['    cost function:                  ' costfun]);  disp(' ');    eval('startvalues = log(startvalues);','startvalues = [];');  if isempty(startvalues),    startvalues = [log(model.gam)+[-3;5] log(model.kernel_pars)+[-2.5;2.5]];  end    tic;  c = costofmodel2(startvalues(1,:),model,costfun,costargs);  et = toc;   disp([' 2. starting values:                   ' num2str(exp(startvalues(1,:)))]);  disp(['    cost of starting values:           ' num2str(c)]);  disp(['    time needed for 1 evaluation (sec):' num2str(et)]);  disp(['    limits of the grid:   [gam]         ' num2str(exp(startvalues(:,1))')]);  disp(['                          [sig2]        ' num2str(exp(startvalues(:,2))')]);  disp(' ');  disp('OPTIMIZATION IN LOG SCALE...');  [gs, cost, evals, fig] = feval(optfun,@costofmodel2,startvalues,{model, costfun,costargs},optargs{:});    figure(fig);  xlabel('log(\gamma)');  ylabel('log(\sigma^2)');  zlabel(costfun);  gamma = exp(gs(1));  kernel_pars = exp(gs(2:end))';        disp(['Obtained hyper-parameters: [gamma sig2]: ' num2str([gamma kernel_pars])]);    %% polynoom kernel%elseif strcmp(model.kernel_type,'poly_kernel'),  dg = model.kernel_pars(2);  disp(' TUNELSSVM: chosen specifications:');  disp([' 1. optimization routine:           ' optfun]);  disp(['    cost function:                  ' costfun]);  disp(' ');    eval('startvalues = log(startvalues);','startvalues = [];');  if isempty(startvalues),    startvalues = [log(model.gam)+[-3;5] log(model.kernel_pars(1))+[-2.5;2.5]];  end    tic;  c = costofmodel3(startvalues(1,:),dg,model,costfun,costargs);  et = toc;   disp([' 2. starting values:                   ' num2str([exp(startvalues(1,:)) dg])]);  disp(['    cost of starting values:           ' num2str(c)]);  disp(['    time needed for 1 evaluation (sec):' num2str(et)]);  disp(['    limits of the grid:   [gam]         ' num2str(exp(startvalues(:,1))')]);  disp(['                          [t]           ' num2str(exp(startvalues(:,2))')]);  disp(['                          [degree]      ' num2str(dg)]);  disp('OPTIMIZATION IN LOG SCALE...');  [gs, cost, evals, fig] = feval(optfun,@costofmodel3,startvalues,{dg,model, costfun,costargs},optargs{:});  figure(fig);  xlabel('log(\gamma)');  ylabel('log(t)');  zlabel(costfun);    gamma = exp(gs(1));  kernel_pars = [exp(gs(2:end));dg];    disp(['Obtained hyper-parameters: [gamma t degree]: ' num2str([gamma kernel_pars'])]);else  warning('Tuning for other kernels is not actively supported,  see ''gridsearch'' and ''linesearch''.')endmodel.cga_startvalues = [];if func,  O3 = cost;  eval('cost = [kernel_pars;degree];','cost = kernel_pars;');  model = gamma;else  model = changelssvm(changelssvm(model,'gam',exp(gs(1))),'kernel_pars',exp(gs(2:end)));endfunction cost =  costofmodel1(gs, model,costfun,costargs)  gam = exp(min(max(gs(1),-50),50));  modelf = changelssvm(model,'gam',gam);  cost = feval(costfun,modelf,costargs{:});  function cost =  costofmodel2(gs, model,costfun,costargs)  gam = exp(min(max(gs(1),-50),50));  sig2 = zeros(length(gs)-1,1);  for i=1:length(gs)-1, sig2(i,1) = exp(min(max(gs(1+i),-50),50)); end  modelf = changelssvm(changelssvm(model,'gam',gam),'kernel_pars',sig2);  cost = feval(costfun,modelf,costargs{:});  function cost =  costofmodel3(gs,d, model,costfun,costargs)  gam = exp(min(max(gs(1),-50),50));  sig2 = exp(min(max(gs(2),-50),50));  modelf = changelssvm(changelssvm(model,'gam',gam),'kernel_pars',[sig2;d]);  cost = feval(costfun,modelf,costargs{:});