set_mu_to_optimize.m

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function [model] = set_mu_to_optimize(model,x,varargin)
%function [model] = change_x(model,x,t,d)
%Funcion sets the parameters that are to be optimized to the values given
%by x
%Also usable for optimization: function sets the parameters to x+t*d if t
%and d are given
%
%Required fields of model:
% model.mu_names: field with the names of the paramters that are to be
%changed
% model.??? all the fields that are given in model.mu_names and are to be
%optimized
% model.optimization.params_to_optimize: which parameters are to be changed?
%
% x : the current setting of the parameters (should have the same length as there
% are ones in model.optimization.params_to_optimize, i.e. the number of parameters that are to be optimized)
%optional:
% t : is stepsize
% d : the search direction (i.e. the gradient in gradient search)
%
% Oliver Zeeb 07.06.10

if length(varargin) > 0
    t=varargin{1};
    d=varargin{2};
    %error('more implementation needed here')
else
    t=0;
    d=zeros(size(x));
end

if length(x)~=length(get_mu(model))
    error('implement exception for params to optimize here!')
else
    %check if x+t*d is inside model bounds
    mu_next = x+t.*d;
    mu_ranges = model.mu_ranges;
    if(length(mu_next) == length(mu_ranges))
        for i=1:length(mu_ranges)
            if mu_next(i)<mu_ranges{i}(1)
                mu_next(i) = mu_ranges{i}(1);
            elseif mu_next(i)>mu_ranges{i}(2)
                mu_next(i) = mu_ranges{i}(2);
            end
        end
    end
    model = set_mu(model,mu_next);
end
% l=1;
% for k=1:length(model.optimization.params_to_optimize)
%     if model.optimization.params_to_optimize(k) == 1
%         model.(model.mu_names{k}) = x(l)+t*d(l);
%         l=l+1;
%     end
% end