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 */