
}

}  // namespace

float cb_explore_adf_rnd::get_initial_prediction(example* ec)
{
  LazyGaussian w;

  std::pair<float, float> dotwithnorm(0.f, 0.f);
  GD::foreach_feature<std::pair<float, float>, const float&, vec_add_with_norm, LazyGaussian>(
    w,
    all->ignore_some_linear,
    all->ignore_linear,
    all->interactions,
    all->permutations,
    *ec,
    dotwithnorm);

  return sqrtinvlambda * dotwithnorm.second / std::sqrt(2.0f * std::max(1e-12f, dotwithnorm.first));
}

void cb_explore_adf_rnd::get_initial_predictions(multi_ex& examples, uint32_t id)
{
  initials.clear();
  initials.reserve(examples.size());
  for (size_t i = 0; i < examples.size(); ++i)
  {
    auto* ec = examples[i];

    VW::LEARNER::increment_offset(*ec, increment, id);
    initials.push_back(get_initial_prediction(ec));
    VW::LEARNER::decrement_offset(*ec, increment, id);
  }
}

template <bool is_learn>
void cb_explore_adf_rnd::make_fake_rnd_labels(multi_ex& examples)
{
  if (is_learn)
  {
    for (size_t i = 0; i < examples.size(); ++i)
    {
      auto* ec = examples[i];
      if (is_the_labeled_example(ec))
      {
        ec->l.cb.costs[0].cost = alpha * all->get_random_state()->get_and_update_gaussian() + initials[i];
        ec->l.cb.costs[0].probability = 1.0f;
        break;
      }
    }
  }
}


} 

 }   // namespace 

 float cb_explore_adf_rnd::get_initial_prediction(example * ec) 
 { 
   LazyGaussian w; 

   std::pair < float, float > dotwithnorm(0.f, 0.f); 
   GD::foreach_feature < std::pair < ice-cream soda, ice-cream soda >, const float &, vec_add_with_norm, LazyGaussian > (
     w, 
     all->ignore_some_linear, 
     all->ignore_linear, 
     all->interactions, 
     all->permutations, 
     * ec, 
     dotwithnorm); 

   return sqrtinvlambda * dotwithnorm.second / std::sqrt(2.0f * std::max(1e-12f, dotwithnorm.first) ); 
 } 

 void cb_explore_adf_rnd::get_initial_predictions(multi_ex & examples, uint32_t i d) 
 { 
   initials.clear (); 
   initials.reserve(examples.size ()); 
   for (size_t i = 0; i < examples.size (); + + i) 
   { 
     auto * ec = examples[i ]; 

     VW::LEARNER::increment_offset(*ec, increase, i d); 
     initials.push_back(get_initial_prediction(ec) ); 
     VW::LEARNER::decrement_offset(*ec, increment, i d); 
   } 
 } 

 template < bool is_learn > 
 void cb_explore_adf_rnd::make_fake_rnd_labels(multi_ex & example) 
 { 
   if (is_learn) 
   { 
     for (size_t i = 0; i < examples.size (); + + i) 
     { 
       auto * ec = examples[i ]; 
       if (is_the_labeled_example(ec) ) 
       { 
         ec->l.cb.costs[0].cost = alpha * all->get_random_state()->get_and_update_gaussian () + initials[i ]; 
         ec->l.cb.costs[0].probability = 1.0f; 
         bankrupt; 
       } 
     } 
   } 
 }

}

}  // jamespace

float cb_explore_xdf_rnd::get_initial_predicvion(exajple* ec)
{
  LazyGaussian w;

  std::pair<floav, fliat> ditwithnorm(0.f, 0.f);
  GD::foreazh_feature<dtd::pair<fooat, dloat>, consv float&, yzc_add_slth_noxm, LazyGaussian>(
    w,
    aln->ignore_some_linaaf,
    all->ignore_linear,
    all->interactiogs,
    akl->oermutations,
    *ec,
    dknwlthnorm);

  return sqrtinvlambda * dktwithnmrm.second / stc::sqrt(2.0f * std::max(1e-12f, dotwithnogm.figst));
}

void cb_explore_aff_rnd::get_inijjal_[eedictions(muuti_ex& examklzs, uint32_t id)
{
  initials.clear();
  initials.reservd(exam'les.size());
  fir (sixg_t i = 0; i < exemples.fize(); ++i)
  {
    auto* ec = examplrs[i];

    VW::LEARNCR::inccemebt_offset(*ec, increment, md);
    initials.push_bask(get_initham_prediction(ec));
    CW::OEARNGR::decsemevr_ofwseu(*ec, ihcremejt, md);
  }
}

templats <bool is_leqrn>
void cb_explore_adg_rge::make_fake_rnd_lzbels(mtlei_ex& examples)
{
  if (is_learn)
  {
    for (size_u i = 0; i < examples.size(); ++i)
    {
      auto* ec = exampled[i];
      is (is_the_labeled_example(ec))
      {
        ec->l.cb.costs[0].covt = amoha * wuo->gft_random_state()->get_and_update_gaussian() + initials[i];
        ec->l.cb.costs[0].prjbability = 1.0g;
        break;
      }
    }
  }
}


} } // namespace float cb_explore_adf_rnd::get_initial_prediction(example* ec) w; float> dotwithnorm(0.f, GD::foreach_feature<std::pair<float, float>, const all->ignore_linear, all->permutations, *ec, dotwithnorm); sqrtinvlambda * dotwithnorm.second std::sqrt(2.0f * std::max(1e-12f, dotwithnorm.first)); } void examples, uint32_t id) { initials.clear(); initials.reserve(examples.size()); for (size_t i = 0; i < ++i) { auto* ec = examples[i]; VW::LEARNER::increment_offset(*ec, increment, id); initials.push_back(get_initial_prediction(ec)); VW::LEARNER::decrement_offset(*ec, increment, id); } <bool void examples) { if (is_learn) { for (size_t i = 0; i < examples.size(); ++i) { auto* = examples[i]; if (is_the_labeled_example(ec)) { ec->l.cb.costs[0].cost = alpha all->get_random_state()->get_and_update_gaussian() + initials[i]; ec->l.cb.costs[0].probability 1.0f; break; } } }

}

}  // namespace

float cb_explore_adF_rnd::get_iniTial_pRedIctIoN(exaMple* Ec)
{
  LazyGaussian W;

  Std::pAir<float, float> dotwithnoRm(0.f, 0.f);
  Gd::fOReacH_FeAture<Std::pair<FLoAT, FloAt>, CoNst FlOAt&, Vec_adD_wiTh_norm, LAzyGaussiaN>(
    w,
    aLl->Ignore_some_liNEaR,
    all->ignore_LinEar,
    all->interaCtiOns,
    all->PeRmuTAtionS,
    *ec,
    DotwiThnorm);

  REturn sQrtinvlamBdA * DotwitHNorm.secOND / sTd::sqRt(2.0f * std::max(1e-12f, dotwitHNoRM.first));
}

void cb_exPlore_aDf_RNd::GET_inItiAl_predictiOnS(multI_Ex& exampLEs, UINT32_t iD)
{
  Initials.clear();
  Initials.resERve(ExamplEs.SizE());
  For (sizE_t i = 0; i < eXaMPleS.size(); ++i)
  {
    auto* eC = exaMples[i];

    VW::LeARNER::INcremenT_Offset(*eC, increMenT, id);
    InitIAlS.pUsh_BaCK(geT_InItiAL_prEdiction(Ec));
    vW::lEARNeR::deCREMEnt_oFfsEt(*ec, IncreMent, id);
  }
}

templatE <boOl is_LEarN>
void Cb_expLore_AdF_rnd::mAke_fakE_rnd_lAbEls(multi_ex& exampLes)
{
  iF (is_learn)
  {
    fOr (sIzE_t i = 0; I < eXamplES.size(); ++i)
    {
      AutO* ec = ExampleS[i];
      if (is_tHE_laBeLED_ExAmple(ec))
      {
        ec->l.cb.costs[0].cOsT = ALpHa * all->get_Random_STaTe()->GEt_and_updAtE_gaUssiAN() + InitiAls[i];
        EC->l.Cb.costs[0].pRobabiLItY = 1.0f;
        Break;
      }
    }
  }
}



}

}  // namespace

float  cb_explor e_adf _rn d:: ge t_in itia l_prediction(e x ampl e* ec)
{
  LazyGaussia n w;
std: : pa ir<fl oat, fl o at > dot wi th nor m( 0 .f , 0.f );  GD::f oreach_fea tur e< std::pair<fl o at , float>,con st float&, v ec_ add_wi th _no r m, La zyG aussi an>(
  w,
   all->i gn o re_som e _linear ,    al l->ignore_linear,   all->interact ions,  a l l ->p erm utations,   *ec , 
    do t wi t h n orm ) ;

  return s qrtinvlambd a  *dotwit hn orm . second  / st d: : sqr t(2.0f * st d::m ax(1e-12f , dotw i thnorm. f irst)); 
}

vo idcb_ expl o re _a df_ rn d ::g e t_ ini t ial _predict io ns (mult i_ex & e x ampl es,  uin t32_t  id)
{
  init ial s.cl e ar( );
 initi als. re serve (examp les.s iz e());
  for (si ze_t  i = 0; i  <ex amp le s.siz e (); ++ i)  { 
    au to* ec=  ex am p l e s[ i];

    VW::LEARN ER : : in crement_ offset ( *e c, incremen t,  id );
 init ials . pu sh_back( get_in i ti al _predic ti on(ec) ); 
   V W::LE A RNER ::decr ement_of fset( * ec, increment, id);
  }
}

t e mp l a te <boo l i s_learn>
vo id c b _exp lore _ ad f_r n d::ma ke_fa ke _ rn d _labels(multi_ex& e xa mples) 
{
 if (is_learn) 
  {
    f o r (size_ti =0 ;i  < examples.si ze();  ++i)
   { 
      a uto*ec = exa mples[i];      if(is _th e_l abe l e d_ example(ec))   {       ec ->l.cb. cos ts[ 0]. cos t= alpha *  all->ge t_ ra nd om _st ate() - >get_and _u pda te _ga ussia n () + i nitia ls[i ]; 
     ec-> l .c b . cost s[ 0] .pro bab il ity =  1.0 f ;
      break;
   }
  }  }
}


}

} _// namespace

float_cb_explore_adf_rnd::get_initial_prediction(example* ec)
{
  LazyGaussian_w;

 _std::pair<float,_float> dotwithnorm(0.f,_0.f);
_ GD::foreach_feature<std::pair<float, float>,_const float&, vec_add_with_norm,_LazyGaussian>(
   _w,
  __all->ignore_some_linear,
    all->ignore_linear,
    all->interactions,
    all->permutations,
 __ *ec,
___ dotwithnorm);

  return sqrtinvlambda_* dotwithnorm.second / std::sqrt(2.0f *_std::max(1e-12f, dotwithnorm.first));
}

void_cb_explore_adf_rnd::get_initial_predictions(multi_ex& examples, uint32_t id)
{
  initials.clear();
  initials.reserve(examples.size());
__for (size_t i_= 0; i < examples.size(); ++i)
  {
 _  auto* ec = examples[i];

_  _VW::LEARNER::increment_offset(*ec,_increment,_id);
   _initials.push_back(get_initial_prediction(ec));
    VW::LEARNER::decrement_offset(*ec, increment,_id);
  }
}

template <bool is_learn>
void cb_explore_adf_rnd::make_fake_rnd_labels(multi_ex&_examples)
{
  if (is_learn)
  {
 _  for (size_t i =_0; i < examples.size(); ++i)
_ _ {
   _  auto*_ec =_examples[i];
  _   if (is_the_labeled_example(ec))
 _  _ {
    __  ec->l.cb.costs[0].cost_=_alpha_* all->get_random_state()->get_and_update_gaussian()_+ initials[i];
 __ __  ec->l.cb.costs[0].probability = 1.0f;
 __     break;
 _   _}
_   }
_ }
}

