fixed hiding 'teach' function

regression/gpregression/RegGaussianProcess.cpp

@@ -84,7 +84,7 @@ RegGaussianProcess::~RegGaussianProcess()
 
 }
 
-void RegGaussianProcess::teachKernel ( KernelData *kernelData, const NICE::Vector & y )
+void RegGaussianProcess::teach ( KernelData *kernelData, const NICE::Vector & y )
 {
 	if ( optimizeParameters ) 
 	{

regression/gpregression/RegGaussianProcess.h

@@ -16,6 +16,7 @@
 #include "vislearning/regression/regressionbase/TeachWithInverseKernelMatrix.h"
 
 #include "vislearning/regression/gpregression/modelselcrit/genericGPModelSelection.h"
+#include "../../../../nice/vislearning/regression/regressionbase/RegressionAlgorithmKernel.h"
 
 namespace OBJREC {
   
@@ -58,10 +59,12 @@ class RegGaussianProcess : public RegressionAlgorithmKernel
 		/** simple destructor */
 		virtual ~RegGaussianProcess();
 		 
+    using RegressionAlgorithmKernel::teach;   // <-- un-hides teach function
+    
 		/** learn parameters/models/whatever with a kernel matrix of a set
 		 *  of vectors and the corresponding function values \c y 
 		 */
-		void teachKernel ( KernelData *kernelData, const NICE::Vector & y );
+		void teach ( KernelData *kernelData, const NICE::Vector & y );
 
 		/** predict the function value for a vector by using its kernel values with
 		 * the used training set, be careful with the order in \c kernelVector

regression/regressionbase/RegressionAlgorithmKernel.cpp

@@ -49,7 +49,7 @@ void RegressionAlgorithmKernel::teach ( const VVector & X, const NICE::Vector &
 	kernelFunction->calcKernelData ( this->X, kernelData );
 	kernelData->updateCholeskyFactorization();
 
-	teachKernel ( kernelData, this->y );
+	teach ( kernelData, this->y );
 }
 
 double RegressionAlgorithmKernel::predict ( const NICE::Vector & x )

regression/regressionbase/RegressionAlgorithmKernel.h

@@ -41,7 +41,7 @@ class RegressionAlgorithmKernel : public RegressionAlgorithm
      *  of a set
      *  of vectors and the corresponding function values \c y
      */
-    virtual void teachKernel ( KernelData *kernelData, const NICE::Vector & y ) = 0;
+    virtual void teach ( KernelData *kernelData, const NICE::Vector & y ) = 0;
 
     /** predict the function value for a vector by using its kernel values with
      * the used training set, be careful with the order in \c kernelVector