Remove sudden jump

DominikKamp · DominikKamp · commit b9ad815b945d · 2025-10-02T13:30:09.000+02:00
diff --git a/examples/bo_compare.cpp b/examples/bo_compare.cpp
@@ -27,9 +27,9 @@
 int main(int nargs, char *args[])
 {
   bayesopt::Parameters par;
+
   par.verbose_level = 0;
   par.noise = 1e-10;
-  par.force_jump = 30;
 
   std::ofstream log;
   std::clock_t start_t;
diff --git a/include/bayesopt/parameters.hpp b/include/bayesopt/parameters.hpp
@@ -97,12 +97,6 @@ namespace bayesopt {
         bool l_all;                 /**< Learn all hyperparameters or only kernel */
 
         double epsilon;             /**< For epsilon-greedy exploration */
-        size_t force_jump;          /**< If >0, and the difference between two 
-                                        consecutive observations is pure noise, 
-                                        for n consecutive steps, force a random 
-                                        jump. Avoid getting stuck if model is bad 
-                                        and there is few data, however, it might 
-                                        reduce the accuracy. */
 
         KernelParameters kernel;    /**< Kernel parameters */
         MeanParameters mean;        /**< Mean (parametric function) parameters */  
diff --git a/src/bayesoptbase.cpp b/src/bayesoptbase.cpp
@@ -111,30 +111,6 @@ namespace bayesopt
     // Find what is the next point.
     vectord xNext = nextPoint(); 
     double yNext = evaluateSampleInternal(xNext);
-
-    // If we are stuck in the same point for several iterations, try a random jump!
-    if (mParameters.force_jump)
-      {
-        if (std::pow(mYPrev - yNext,2) < mParameters.noise)
-          {
-            mCounterStuck++;
-            FILE_LOG(logDEBUG) << "Stuck for "<< mCounterStuck << " steps";
-          }
-        else
-          {
-            mCounterStuck = 0;
-          }
-        mYPrev = yNext;
-
-        if (mCounterStuck > mParameters.force_jump)
-          {
-            FILE_LOG(logINFO) << "Forced random query!";
-            xNext = samplePoint();
-            yNext = evaluateSampleInternal(xNext);
-            mCounterStuck = 0;
-          }
-      }
-
     double expectation = getPrediction(xNext)->getMean();
 
     mModel->addSample(xNext,yNext);
diff --git a/src/bopt_state.cpp b/src/bopt_state.cpp
@@ -91,9 +91,9 @@ namespace bayesopt
         }
         
         fp.readOrWrite("mParameters.l_all", mParameters.l_all);
+
         fp.readOrWrite("mParameters.epsilon", mParameters.epsilon);
-        fp.readOrWrite("mParameters.force_jump", mParameters.force_jump);
-        
+
         fp.readOrWrite("mParameters.kernel.name", mParameters.kernel.name);
         fp.readOrWrite("mParameters.kernel.hp_mean", mParameters.kernel.hp_mean);
         fp.readOrWrite("mParameters.kernel.hp_std", mParameters.kernel.hp_std);
diff --git a/src/parameters.cpp b/src/parameters.cpp
@@ -199,8 +199,7 @@ bopt_params initialize_parameters_to_default(void)
   params.sc_type = SC_MAP;
 
   params.epsilon = 0.0;
-  params.force_jump = 20;
-  
+
   params.crit_name = new char[128];
   strcpy(params.crit_name,CRIT_NAME.c_str());
   params.n_crit_params = 0;
@@ -274,10 +273,9 @@ namespace bayesopt {
         l_type = c_params.l_type;
         
         l_all = c_params.l_all;
-        
+
         epsilon = c_params.epsilon;
-        force_jump = c_params.force_jump;
-        
+
         kernel.name = std::string(c_params.kernel.name);
         kernel.hp_mean = bayesopt::utils::array2vector(c_params.kernel.hp_mean, 
 						       c_params.kernel.n_hp);
@@ -325,10 +323,9 @@ namespace bayesopt {
       c_params.l_type = l_type;
         
       c_params.l_all = l_all;
-        
+
       c_params.epsilon = epsilon;
-      c_params.force_jump = force_jump;
-        
+
       strcpy(c_params.kernel.name, kernel.name.c_str());
       //TODO (Javier): Should it be necessary to check size?
       for(size_t i=0; i<kernel.hp_mean.size(); i++){
@@ -404,10 +401,9 @@ namespace bayesopt {
         l_all = false;
         l_type = L_EMPIRICAL;
         sc_type = SC_MAP;
-        
+
         epsilon = 0.0;
-        force_jump = 20;
-        
+
         crit_name = CRIT_NAME;
     }
 }//namespace bayesopt
diff --git a/utils/param_loader.cpp b/utils/param_loader.cpp
@@ -74,9 +74,9 @@ namespace bayesopt
         }
         
         fp.readOrWrite("l_all", par.l_all);
+
         fp.readOrWrite("epsilon", par.epsilon);
-        fp.readOrWrite("force_jump", par.force_jump);
-        
+
         fp.readOrWrite("kernel.name", par.kernel.name);
         fp.readOrWrite("kernel.hp_mean", par.kernel.hp_mean);
         fp.readOrWrite("kernel.hp_std", par.kernel.hp_std);
