/// Implementation of the constraints computation.
/// Add noises to the computed constraint vector.
void noisy::compute_constraints_impl(constraint_vector &c, const decision_vector &x) const
{
//1 - Initialize a temporary constraint vector storing one trial result
//and we use it also to init the return value
constraint_vector tmp(c.size(),0.0);
c=tmp;
//2 - We set the seed
m_drng.seed(m_seed+m_decision_vector_hash(x));
//3 - We average upon multiple runs
for (unsigned int j=0; j< m_trials; ++j) {
m_original_problem->compute_constraints(tmp, x);
inject_noise_c(tmp);
for (constraint_vector::size_type i=0; i<c.size();++i) {
c[i] = c[i] + tmp[i] / (double)m_trials;
}
}
}
/// Implementation of the constraints computation.
/// Add noises to the decision vector before calling the actual constraint function.
void robust::compute_constraints_impl(constraint_vector &c, const decision_vector &x) const
{
// Temporary storage used for averaging
constraint_vector tmp(c.size(), 0.0);
c = tmp;
// Set the seed
m_drng.seed(m_seed);
// Perturb decision vector and evaluate
decision_vector x_perturbed(x);
for(unsigned int i = 0; i < m_trials; ++i){
inject_noise_x(x_perturbed);
m_original_problem->compute_constraints(tmp, x_perturbed);
for(constraint_vector::size_type j = 0; j < c.size(); ++j){
c[j] = tmp[j] / (double)m_trials;
}
}
}
/// Apply noise on a constraint vector
void noisy::inject_noise_c(constraint_vector& c) const
{
for(c_size_type i = 0; i < c.size(); i++){
if(m_noise_type == NORMAL){
c[i] += m_normal_dist(m_drng)*m_param_second+m_param_first;
}
else if(m_noise_type == UNIFORM){
c[i] += m_uniform_dist(m_drng)*(m_param_second-m_param_first)+m_param_first;
}
}
}
