Merge pull request #5165 from ye-luo/remove-unused

Remove unused code and write code more compact

src/QMCDrivers/WFOpt/QMCCostFunctionBatched.cpp

@@ -287,9 +287,7 @@ void QMCCostFunctionBatched::checkConfigurations(EngineHandle& handle)
 
     for (int inb = 0; inb < num_batches; inb++)
     {
-      int current_batch_size = walkers_per_crowd[crowd_id];
-      if (inb == num_batches - 1)
-        current_batch_size = final_batch_size;
+      const int current_batch_size = inb == num_batches - 1 ? final_batch_size : walkers_per_crowd[crowd_id];
 
       const int base_sample_index = inb * walkers_per_crowd[crowd_id] + samples_per_crowd_offsets[crowd_id];
 
@@ -487,9 +485,7 @@ void QMCCostFunctionBatched::checkConfigurationsSR(EngineHandle& handle)
 
     for (int inb = 0; inb < num_batches; inb++)
     {
-      int current_batch_size = walkers_per_crowd[crowd_id];
-      if (inb == num_batches - 1)
-        current_batch_size = final_batch_size;
+      const int current_batch_size = inb == num_batches - 1 ? final_batch_size : walkers_per_crowd[crowd_id];
 
       const int base_sample_index = inb * walkers_per_crowd[crowd_id] + samples_per_crowd_offsets[crowd_id];
 
@@ -660,8 +656,7 @@ QMCCostFunctionBatched::EffectiveWeight QMCCostFunctionBatched::correlatedSampli
   }
 
   //Return_rt wgt_node = 0.0, wgt_node2 = 0.0;
-  Return_rt wgt_tot  = 0.0;
-  Return_rt wgt_tot2 = 0.0;
+  Return_rt wgt_tot = 0.0;
 
   // Ensure number of samples did not change after getConfiguration
   assert(rank_local_num_samples_ == samples_.getNumSamples());
@@ -698,11 +693,7 @@ QMCCostFunctionBatched::EffectiveWeight QMCCostFunctionBatched::correlatedSampli
 
         for (int inb = 0; inb < num_batches; inb++)
         {
-          int current_batch_size = walkers_per_crowd[crowd_id];
-          if (inb == num_batches - 1)
-          {
-            current_batch_size = final_batch_size;
-          }
+          const int current_batch_size = inb == num_batches - 1 ? final_batch_size : walkers_per_crowd[crowd_id];
 
           const int base_sample_index = inb * walkers_per_crowd[crowd_id] + samples_per_crowd_offsets[crowd_id];
 
@@ -820,17 +811,12 @@ QMCCostFunctionBatched::EffectiveWeight QMCCostFunctionBatched::correlatedSampli
               compute_all_from_scratch, vmc_or_dmc, needGrad);
   // Sum weights over crowds
   for (int i = 0; i < opt_eval.size(); i++)
-  {
     wgt_tot += opt_eval[i]->get_wgt();
-    wgt_tot2 += opt_eval[i]->get_wgt2();
-  }
 
   //this is MPI barrier
   OHMMS::Controller->barrier();
   //collect the total weight for normalization and apply maximum weight
   myComm->allreduce(wgt_tot);
-  myComm->allreduce(wgt_tot2);
-  //    app_log()<<"Before Purge"<<wgt_tot<<" "<<wgt_tot2<< std::endl;
   Return_rt wgtnorm = (wgt_tot == 0) ? 0 : wgt_tot;
   wgt_tot           = 0.0;
   {
@@ -843,7 +829,7 @@ QMCCostFunctionBatched::EffectiveWeight QMCCostFunctionBatched::correlatedSampli
     }
   }
   myComm->allreduce(wgt_tot);
-  //    app_log()<<"During Purge"<<wgt_tot<<" "<< std::endl;
+
   wgtnorm = (wgt_tot == 0) ? 1 : 1.0 / wgt_tot;
   wgt_tot = 0.0;
   {
@@ -1038,8 +1024,7 @@ QMCCostFunctionBatched::Return_rt QMCCostFunctionBatched::fillHamVec(std::vector
   return 1.0;
 }
 
-void QMCCostFunctionBatched::calcOvlParmVec(const std::vector<Return_rt>& parm,
-                                            std::vector<Return_rt>& ovlParmVec)
+void QMCCostFunctionBatched::calcOvlParmVec(const std::vector<Return_rt>& parm, std::vector<Return_rt>& ovlParmVec)
 {
   ScopedTimer tmp_timer(fill_timer_);
 
@@ -1066,7 +1051,7 @@ void QMCCostFunctionBatched::calcOvlParmVec(const std::vector<Return_rt>& parm,
     Return_rt weight                = saved[REWEIGHT] * wgtinv;
     const Return_t* Dsaved          = DerivRecords_[iw];
     for (int pm = 0; pm < getNumParams(); pm++)
-      prod[iw] += (Dsaved[pm] - D_avg[pm])  * parm[pm];
+      prod[iw] += (Dsaved[pm] - D_avg[pm]) * parm[pm];
   }