function [model, ss] = bay_initlssvm(model)% Initialize the hyperparameters [$ \gamma$]  and [$ \sigma^2$]  before optimization with bay_optimize% % >> [gam, sig2] = bay_initlssvm({X,Y,type,[],[]})% >> model       = bay_initlssvm(model)% % % A starting value for sig2 is only given if the model has kernel type 'RBF_kernel'.% %% Full syntax% %     1. Using the functional interface:% % >> [gam, sig2] = bay_initlssvm({X,Y,type,[],[],kernel})% %       Outputs    %         gam  : Proposed initial regularization parameter%         sig2 : Proposed initial 'RBF_kernel' parameter%       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')%         kernel(*) : Kernel type (by default 'RBF_kernel')%%     2. Using the object oriented interface:% % >> model = bay_initlssvm(model)% %       Outputs    %         model : Object oriented representation of the LS-SVM model with initial hyperparameters%       Inputs    %         model : Object oriented representation of the LS-SVM model% % See also:%    bay_lssvm, bay_optimize% -disclaimerif iscell(model),  iscell_model = 1;  model = initlssvm(model{:});else  iscell_model = 0;end% start sig2% sig2 as the std of the fitting Gaussianif strcmp(model.kernel_type,'RBF_kernel'),  model.kernel_pars = sum(range(model.xtrain))./1.96.*ones(1,model.y_dim);else  warning('Only usefull for ''RBF_kernel''...');  if iscell_model,    ss = model.kernel_pars;    model = model.gam;  end  returnend% set starting valueif prod(size(model.gam))~=1, model.gam = 1; end% start gammafor i=1:10,  gam(i,1) = exp(i-5);  model.gam = gam(i,1);  [A,B,C,bay] = bay_lssvm(model,2,'svd');  gam(i,2) = bay.Geff;end[v,index] = sort(abs(gam(:,1)-gam(:,2).*2));model.gam = gam(index(1),1);if iscell_model,  ss = model.kernel_pars;  model = model.gam;end