int GP_Hedge::init(NonParametricProcess *proc,
const std::vector<Criteria*>& list)
{
mProc = proc;
mCriteriaList = list;
size_t n = mCriteriaList.size();
loss_ = zvectord(n);
gain_ = zvectord(n);
prob_ = zvectord(n);
cumprob_ = zvectord(n);
return 0;
};
void GP_Hedge::init(NonParametricProcess *proc)
{
mProc = proc;
size_t n = mCriteriaList.size();
if (!n)
{
throw std::logic_error("Criteria list should be created (pushed)"
" before initializing combined criterion.");
}
loss_ = zvectord(n);
gain_ = zvectord(n);
prob_ = zvectord(n);
cumprob_ = zvectord(n);
};
NLOPT_Optimization::NLOPT_Optimization(RGBOptimizable* rgbo, size_t dim):
mDown(dim),mUp(dim)
{
rbobj = NULL; rgbobj = new RGBOptimizableWrapper(rgbo);
alg = DIRECT; maxEvals = MAX_INNER_EVALUATIONS;
setLimits(zvectord(dim),svectord(dim,1.0));
};
int main()
{
randEngine reng;
Posterior post;
bayesopt::MCMCSampler sampler(&post,2,reng);
vectord x = zvectord(2);
sampler.run(x);
sampler.printParticles();
return 0;
}
double NLOPT_Optimization::evaluate_nlopt_grad (unsigned int n, const double *x,
double *grad, void *my_func_data)
{
vectord vx(n);
std::copy(x,x+n,vx.begin());
void *objPointer = my_func_data;
RGBOptimizableWrapper* OPTIMIZER = static_cast<RGBOptimizableWrapper*>(objPointer);
vectord vgrad = zvectord(n);
double f = OPTIMIZER->evaluate(vx,vgrad);
if (grad && n) std::copy(vgrad.begin(),vgrad.end(),grad);
return f;
} /* evaluate_criteria_nlopt */
