Changing the min/max functions in parametrized class

sources/core/data.h

@@ -155,21 +155,9 @@ class data_params : public data
 			return _data.size();
 		}
 
-		// Get min and max input space values
-		virtual vec min() const
-		{
-			return _min;
-		}
-		virtual vec max() const
-		{
-			return _max;
-		}
-
 	protected: // data
 
 		data_params::clustrering _clustering_method;
 
 		std::vector<vec> _data;
-
-		vec _min, _max;
 };

sources/core/params.cpp

@@ -373,11 +373,11 @@ void parametrized::setMax(const vec& max)
 #endif
     _max = max ;
 }
-vec parametrized::getMin() const
+vec parametrized::min() const
 {
     return _min ;
 }
-vec parametrized::getMax() const
+vec parametrized::max() const
 {
     return _max ;
 }

sources/core/params.h

@@ -360,10 +360,10 @@ class parametrized
 		virtual void setMax(const vec& max) ;
 
 		//! \brief Get the minimum value the input can take
-		virtual vec getMin() const ;
+		virtual vec min() const ;
 
 		//! \brief Get the maximum value the input can take
-		virtual vec getMax() const ;
+		virtual vec max() const ;
 
 
 	protected:

sources/core/rational_function.cpp

@@ -328,8 +328,8 @@ rational_function_1d* rational_function::get(int i)
 			// Test if the input domain is not empty. If one dimension of
 			// the input domain is a point, I manually inflate this dimension
 			// to avoid numerical issues.
-			vec _min = getMin();
-			vec _max = getMax();
+			vec _min = min();
+			vec _max = max();
 			for(int k=0; k<dimX(); ++k)
 			{
 				if(_min[k] == _max[k]) 

sources/core/vertical_segment.cpp

@@ -235,13 +235,3 @@ int vertical_segment::size() const
 {
 	return _data.size() ;
 }
-
-vec vertical_segment::min() const 
-{
-	return _min ;
-}
-
-vec vertical_segment::max() const 
-{
-	return _max ;
-}

sources/core/vertical_segment.h

@@ -57,19 +57,10 @@ class vertical_segment : public data
 		// Get data size
 		virtual int size() const ;
 
-
-		// Get min and max input parameters
-		virtual vec min() const ;
-		virtual vec max() const ; 
-
 	private: // data
 
 		// Store for each point of data, the upper
 		// and lower value
 		std::vector<vec> _data ;
-
-		// Store the min and max value on the input
-		// domain
-		vec _min, _max ;
 } ;
 

sources/plugins/data_interpolant/data.cpp

@@ -26,8 +26,8 @@ void data_interpolant::load(const std::string& filename)
 	// Copy the informations
 	setDimX(_data->dimX());
 	setDimY(_data->dimY());
-	setMin(_data->getMin());
-	setMax(_data->getMax());
+	setMin(_data->min());
+	setMax(_data->max());
 
 	// Update the KDtreee by inserting all points
 	for(int i=0; i<_data->size(); ++i)

sources/plugins/nonlinear_fitter_ceres/fitter.cpp

@@ -176,8 +176,8 @@ bool nonlinear_fitter_ceres::fit_data(const data* d, function* fit, const argume
     // to the dimension of my fitting problem
     fit->setDimX(d->dimX()) ;
     fit->setDimY(d->dimY()) ;
-    fit->setMin(d->getMin()) ;
-    fit->setMax(d->getMax()) ;
+    fit->setMin(d->min()) ;
+    fit->setMax(d->max()) ;
 
 	 // Convert the function and bootstrap it with the data
     if(dynamic_cast<nonlinear_function*>(fit) == NULL)

sources/plugins/nonlinear_fitter_eigen/fitter.cpp

@@ -273,8 +273,8 @@ bool nonlinear_fitter_eigen::fit_data(const data* d, function* fit, const argume
     // to the dimension of my fitting problem
     fit->setDimX(d->dimX()) ;
     fit->setDimY(d->dimY()) ;
-    fit->setMin(d->getMin()) ;
-    fit->setMax(d->getMax()) ;
+    fit->setMin(d->min()) ;
+    fit->setMax(d->max()) ;
 
 	 // Convert the function and bootstrap it with the data
     if(dynamic_cast<nonlinear_function*>(fit) == NULL)

sources/plugins/nonlinear_fitter_ipopt/fitter.cpp

@@ -218,8 +218,8 @@ bool nonlinear_fitter_ipopt::fit_data(const data* d, function* fit, const argume
 	// to the dimension of my fitting problem
 	fit->setDimX(d->dimX()) ;
 	fit->setDimY(d->dimY()) ;
-	fit->setMin(d->getMin()) ;
-	fit->setMax(d->getMax()) ;
+	fit->setMin(d->min()) ;
+	fit->setMax(d->max()) ;
 
 	// Convert the function and bootstrap it with the data
 	if(dynamic_cast<nonlinear_function*>(fit) == NULL)

sources/plugins/nonlinear_fitter_nlopt/fitter.cpp

@@ -122,8 +122,8 @@ bool nonlinear_fitter_nlopt::fit_data(const data* d, function* fit, const argume
 	// to the dimension of my fitting problem
 	fit->setDimX(d->dimX()) ;
 	fit->setDimY(d->dimY()) ;
-	fit->setMin(d->getMin()) ;
-	fit->setMax(d->getMax()) ;
+	fit->setMin(d->min()) ;
+	fit->setMax(d->max()) ;
 
 	// Convert the function and bootstrap it with the data
 	if(dynamic_cast<nonlinear_function*>(fit) == NULL)

sources/plugins/rational_function_chebychev/rational_function.cpp

@@ -99,8 +99,8 @@ rational_function_1d* rational_function_chebychev::get(int i)
 			// Test if the input domain is not empty. If one dimension of
 			// the input domain is a point, I manually inflate this dimension
 			// to avoid numerical issues.
-			vec _min = getMin();
-			vec _max = getMax();
+			vec _min = min();
+			vec _max = max();
 			for(int k=0; k<dimX(); ++k)
 			{
 				if(_min[k] == _max[k]) 

sources/plugins/rational_function_legendre/rational_function.h

@@ -54,8 +54,8 @@ class rational_function_legendre : public rational_function
 					// Test if the input domain is not empty. If one dimension of
 					// the input domain is a point, I manually inflate this dimension
 					// to avoid numerical issues.
-					vec _min = getMin();
-					vec _max = getMax();
+					vec _min = min();
+					vec _max = max();
 					for(int k=0; k<dimX(); ++k)
 					{
 						if(_min[k] == _max[k])