Use expected result

DominikKamp · DominikKamp · commit a6f5c11fd67e · 2025-09-06T22:52:00.000+02:00
diff --git a/include/bayesopt/bayesoptbase.hpp b/include/bayesopt/bayesoptbase.hpp
@@ -134,7 +134,7 @@ namespace bayesopt {
     /** Initialize the optimization process.  */
     void initializeOptimization();
     
-    /** Once the optimization has been perfomed, return the optimal point. */
+    /** Once the optimization has been performed, return the optimal point. */
     vectord getFinalResult();
 
     /** Saves the current state of the optimization process into a state class. */
diff --git a/include/dataset.hpp b/include/dataset.hpp
@@ -58,22 +58,19 @@ namespace bayesopt
     vectord getPointAtMinimum() const;
     double getValueAtMinimum() const;
     size_t getNSamples() const;
-    void updateMinMax( size_t i );
 
     vecOfvec mX;                                         ///< Data inputs
     vectord mY;                                          ///< Data values
-
-  private:
-    size_t mMinIndex, mMaxIndex;	
+    size_t mMinIndex, mMaxIndex;                         ///< Data optima
   };
 
 
   //// Inline methods
   
   inline void Dataset::addSample(const vectord &x, double y)
   {
-    mX.push_back(x); utils::append(mY,y);
-    updateMinMax(mY.size()-1);
+    mX.push_back(x);
+    utils::append(mY,y);
   }
 
   inline double Dataset::getSampleY(size_t index) const
@@ -92,11 +89,6 @@ namespace bayesopt
   inline vectord Dataset::getPointAtMinimum() const { return mX[mMinIndex]; };
   inline double Dataset::getValueAtMinimum() const { return mY(mMinIndex); };
   inline size_t Dataset::getNSamples() const { return mY.size(); };
-  inline void Dataset::updateMinMax( size_t i )
-  {
-    if ( mY(mMinIndex) > mY(i) )       mMinIndex = i;
-    else if ( mY(mMaxIndex) < mY(i) )  mMaxIndex = i;
-  };
 
   /**@}*/
   
diff --git a/include/posteriormodel.hpp b/include/posteriormodel.hpp
@@ -76,6 +76,7 @@ namespace bayesopt {
     void setSamples(const vectord &y);
     void setSample(const vectord &x, double y);
     void addSample(const vectord &x, double y);
+    void updateMinMax();
     double getValueAtMinimum();
     vectord getPointAtMinimum();
 
diff --git a/src/bayesoptbase.cpp b/src/bayesoptbase.cpp
@@ -149,6 +149,8 @@ namespace bayesopt
         mModel->updateSurrogateModel();
       } 
 
+    mModel->updateMinMax();
+
     plotStepData(mCurrentIter,xNext,yNext);
     mModel->updateCriteria(xNext);
     mCurrentIter++;
@@ -204,8 +206,10 @@ namespace bayesopt
     
     mModel->updateHyperParameters();
     mModel->fitSurrogateModel();
-    mCurrentIter = 0;
 
+    mModel->updateMinMax();
+
+    mCurrentIter = 0;
     mCounterStuck = 0;
     mYPrev = 0.0;
   }
@@ -215,7 +219,6 @@ namespace bayesopt
     return remapPoint(getPointAtMinimum());
   }
 
-
   // SAVE-RESTORE INTERFACE
   void BayesOptBase::saveOptimization(BOptState &state)
   {   
@@ -268,7 +271,9 @@ namespace bayesopt
     // Calculate the posterior model
     mModel->updateHyperParameters();
     mModel->fitSurrogateModel();
-    
+
+    mModel->updateMinMax();
+
     mCurrentIter = state.mCurrentIter;
     mCounterStuck = state.mCounterStuck;
     mYPrev = state.mYPrev;
diff --git a/src/dataset.cpp b/src/dataset.cpp
@@ -42,28 +42,23 @@ namespace bayesopt
     // WARNING: It assumes mX is empty
     mY = y;
     for (size_t i=0; i<x.size1(); ++i)
-      {
-	mX.push_back(row(x,i));
-	updateMinMax(i);
-      } 
+    {
+      mX.push_back(row(x,i));
+    }
   };
 
   void Dataset::setSamples(const vectord &y)
   {
     mY = y;
-    for (size_t i=0; i<y.size(); ++i)
-      {
-	updateMinMax(i);
-      } 
   };
 
 
   void Dataset::setSamples(const matrixd &x)
   {
     for (size_t i=0; i<x.size1(); ++i)
-      {
-	mX.push_back(row(x,i));
-      } 
+    {
+      mX.push_back(row(x,i));
+    }
   };
 
   void Dataset::plotData(TLogLevel level)
diff --git a/src/posteriormodel.cpp b/src/posteriormodel.cpp
@@ -81,6 +81,30 @@ namespace bayesopt
   void PosteriorModel::addSample(const vectord &x, double y)
   {  mData.addSample(x,y); mMean.addNewPoint(x);  };
 
+  void PosteriorModel::updateMinMax()
+  {
+    double minmean = std::numeric_limits<double>::infinity();
+    double maxmean = -std::numeric_limits<double>::infinity();
+
+    mData.mMinIndex = 0;
+    mData.mMaxIndex = 0;
+
+    for( size_t index = 0; index < mData.mX.size(); ++index )
+    {
+      const double mean = getPrediction(mData.mX[index])->getMean();
 
+      if( minmean >= mean )
+      {
+        minmean = mean;
+        mData.mMinIndex = index;
+      }
+
+      if( maxmean <= mean )
+      {
+        maxmean = mean;
+        mData.mMaxIndex = index;
+      }
+    }
+  }
 } //namespace bayesopt
 
