hp_gen_model_elliptic.m

function hp_model = hp_gen_model_elliptic ( model,hp_params )
%function hp_model = hp_gen_model_elliptic ( model,hp_params )
%
% generates the model for elliptic hp-approach

model.h_part=1;
model.RB_generation_mode = 'greedy_fixed';

hp_model.base_model = model;

hp_model.mu_names = model.mu_names;
hp_model.mu_ranges = model.mu_ranges;



hp_model.error_tol=model.RB_stop_epsilon;

hp_model.tree = leaf();


hp_model.gen_model_data = @(model) ...
    model.base_model.gen_model_data(model.base_model);


hp_model.error_distance_extionsion = 0;



if isfield(hp_params,'trainingset_generation_mode')
    hp_model.trainingset_generation_mode = hp_params.trainingset_generation_mode;
else
    hp_model.trainingset_generation_mode = 'boundary_box'; % 'mu_ranges';
end

if isfield(hp_params,'boundarybox_factor')
    hp_model.boundarybox_factor = hp_params.boundarybox_factor;
else
   hp_model.boundarybox_factor = 0.5;
end

if isfield(hp_params,'boundarybox_minimum')
    hp_model.boundarybox_minimum = hp_params.boundarybox_minimum;
else
    hp_model.boundarybox_minimum = 0.3;
end


if isfield(hp_params,'distance_function')
    hp_model.distance_function = hp_params.distance_function;
else
    hp_model.distance_function = @euclidean_distance;
end



hp_model.set_mu=@set_mu_in_model_and_base_model;
hp_model.get_mu = @get_mu_default;
hp_model.plot_sim_data = @(hp_model,model_data,sim_data,plot_params) ...
hp_model.base_model.plot_sim_data(hp_model.base_model,model_data,sim_data,plot_params);

end