Improve vectorized implementation.

- Remove almost all dependencies on Vc (Alignement directive remaining)
  by using vecCore directives.
- Allow code to switch ROOT::Double_v to other VecCore backends in the
  future.
- Refactor redundant code.

math/mathcore/inc/Fit/FitConfig.h

@@ -103,10 +103,34 @@ class FitConfig {
       set the parameter settings from a model function.
       Create always new parameter setting list from a given model function
    */
-   void CreateParamsSettings(const ROOT::Math::IParamMultiFunction & func);
-#ifdef R__HAS_VECCORE
-   void CreateParamsSettings(const ROOT::Math::IParamMultiFunctionTempl<ROOT::Double_v> & func);
+template<class T>
+void CreateParamsSettings(const ROOT::Math::IParamMultiFunctionTempl<T> & func) {
+   // initialize from model function
+   // set the parameters values from the function
+   unsigned int npar = func.NPar();
+   const double * begin = func.Parameters();
+   if (begin == 0) {
+      fSettings =  std::vector<ParameterSettings>(npar);
+      return;
+   }
+
+   fSettings.clear();
+   fSettings.reserve(npar);
+   const double * end =  begin+npar;
+   unsigned int i = 0;
+   for (const double * ipar = begin; ipar !=  end; ++ipar) {
+      double val = *ipar;
+      double step = 0.3 * std::fabs(val);   // step size is 30% of par value
+      //double step = 2.0*std::fabs(val);   // step size is 30% of par value
+      if (val ==  0) step  =  0.3;
+
+      fSettings.push_back( ParameterSettings(func.ParameterName(i), val, step ) );
+#ifdef DEBUG
+      std::cout << "FitConfig: add parameter " <<  func.ParameterName(i) << " val = " << val << std::endl;
 #endif
+      i++;
+   }
+}
 
    /**
       set the parameter settings from number of parameters and a vector of values and optionally step values. If there are not existing or number of parameters does not match existing one, create a new parameter setting list.

math/mathcore/inc/Fit/FitUtil.h

@@ -223,7 +223,7 @@ namespace FitUtil {
                 vecCore::Load<ROOT::Double_v>(xx, x);
                 const double *p0 = p;
                 auto res =  (*f)( &xx, (const double *)p0);
-                return res[0];
+                return vecCore::Get<ROOT::Double_v>(res, 0);
             }
 #endif
 
@@ -274,8 +274,10 @@ namespace FitUtil {
        return also nPoints as the effective number of used points in the LogL evaluation
    */
    void EvaluateLogLGradient(const IModelFunction & func, const UnBinData & data, const double * x, double * grad, unsigned int & nPoints);
+
 #ifdef R__HAS_VECCORE
-   void EvaluateLogLGradient(const IModelFunctionTempl<ROOT::Double_v> &, const UnBinData &, const double *, double *, unsigned int & ) ;
+   template<class NotCompileIfScalarBackend = std::enable_if<!(std::is_same<double, ROOT::Double_v>::value)>>
+   void EvaluateLogLGradient(const IModelFunctionTempl<ROOT::Double_v> &, const UnBinData &, const double *, double *, unsigned int & ) {}
 #endif
 
    /**
@@ -308,9 +310,20 @@ namespace FitUtil {
        is used
    */
    double EvaluatePdf(const IModelFunction & func, const UnBinData & data, const double * x, unsigned int ipoint, double * g = 0);
+
 #ifdef R__HAS_VECCORE
-   double EvaluatePdf(const IModelFunctionTempl<ROOT::Double_v> & func, const UnBinData & data, const double * p, unsigned int i, double *);
+   template<class NotCompileIfScalarBackend = std::enable_if<!(std::is_same<double, ROOT::Double_v>::value)>>
+   double EvaluatePdf(const IModelFunctionTempl<ROOT::Double_v> & func, const UnBinData & data, const double * p, unsigned int i, double *){
+       // evaluate the pdf contribution to the generic logl function in case of bin data
+       // return actually the log of the pdf and its derivatives
+       //func.SetParameters(p);
+       const auto x = vecCore::FromPtr<ROOT::Double_v>(data.GetCoordComponent(i,0));
+       auto fval = func (&x, p);
+       auto logPdf = ROOT::Math::Util::EvalLog(fval);
+       return vecCore::Get<ROOT::Double_v>(logPdf, 0);
+   }
 #endif
+
    /**
        evaluate the pdf contribution to the Poisson LogL given a model function and the BinPoint data.
        If the pointer g is not null evaluate also the gradient of the Poisson pdf.

math/mathcore/inc/Fit/Fitter.h

@@ -27,7 +27,7 @@ Classes used for fitting (regression analysis) and estimation of parameter value
 #include "Fit/FitConfig.h"
 #include "Fit/FitExecutionPolicy.h"
 #include "Fit/FitResult.h"
-#include "Math/IParamFunctionfwd.h"
+#include "Math/IParamFunction.h"
 #include <memory>
 
 namespace ROOT {
@@ -324,6 +324,7 @@ class Fitter {
        Set the fitted function (model function) from a vectorized parametric function interface
    */
 #ifdef R__HAS_VECCORE
+   template<class NotCompileIfScalarBackend = std::enable_if<!(std::is_same<double, ROOT::Double_v>::value)>>
    void  SetFunction(const IModelFunction_v & func);
 #endif
    /**
@@ -516,6 +517,21 @@ bool Fitter::GetDataFromFCN()  {
    }
 }
 
+#ifdef R__HAS_VECCORE
+template<class NotCompileIfScalarBackend>
+void Fitter::SetFunction(const IModelFunction_v & func)
+{
+   //  set the fit model function (clone the given one and keep a copy )
+   //std::cout << "set a non-grad function" << std::endl;
+   fUseGradient = false;
+   fFunc_v = std::shared_ptr<IModelFunction_v>(dynamic_cast<IModelFunction_v *>(func.Clone() ) );
+   assert(fFunc_v);
+
+   // creates the parameter  settings
+   fConfig.CreateParamsSettings(*fFunc_v);
+   fFunc.reset();
+}
+#endif
 
    } // end namespace Fit
 

math/mathcore/src/FitConfig.cxx

@@ -171,67 +171,6 @@ void FitConfig::SetParamsSettings(unsigned int npar, const double *params, const
    }
 }
 
-void FitConfig::CreateParamsSettings(const ROOT::Math::IParamMultiFunction & func) {
-   // initialize from model function
-   // set the parameters values from the function
-   unsigned int npar = func.NPar();
-   const double * begin = func.Parameters();
-   if (begin == 0) {
-      fSettings =  std::vector<ParameterSettings>(npar);
-      return;
-   }
-
-   fSettings.clear();
-   fSettings.reserve(npar);
-   const double * end =  begin+npar;
-   unsigned int i = 0;
-   for (const double * ipar = begin; ipar !=  end; ++ipar) {
-      double val = *ipar;
-      double step = 0.3*std::fabs(val);   // step size is 30% of par value
-      //double step = 2.0*std::fabs(val);   // step size is 30% of par value
-      if (val ==  0) step  =  0.3;
-
-      fSettings.push_back( ParameterSettings(func.ParameterName(i), val, step ) );
-#ifdef DEBUG
-      std::cout << "FitConfig: add parameter " <<  func.ParameterName(i) << " val = " << val << std::endl;
-#endif
-      i++;
-   }
-
-}
-
-#ifdef R__HAS_VECCORE
-void FitConfig::CreateParamsSettings(const ROOT::Math::IParamMultiFunctionTempl<ROOT::Double_v> & func){
-
-   // initialize from model function
-   // set the parameters values from the function
-   unsigned int npar = func.NPar();
-   const double * begin = func.Parameters();
-   if (begin == 0) {
-      fSettings =  std::vector<ParameterSettings>(npar);
-      return;
-   }
-
-   fSettings.clear();
-   fSettings.reserve(npar);
-   const double * end =  begin+npar;
-   unsigned int i = 0;
-   for (const double * ipar = begin; ipar !=  end; ++ipar) {
-      double val = *ipar;
-      double step = 0.3*std::fabs(val);   // step size is 30% of par value
-      //double step = 2.0*std::fabs(val);   // step size is 30% of par value
-      if (val ==  0) step  =  0.3;
-
-      fSettings.push_back( ParameterSettings(func.ParameterName(i), val, step ) );
-#ifdef DEBUG
-      std::cout << "FitConfig: add parameter " <<  func.ParameterName(i) << " val = " << val << std::endl;
-#endif
-      i++;
-   }
-
-}
-#endif
-
 ROOT::Math::Minimizer * FitConfig::CreateMinimizer() {
    // create minimizer according to the chosen configuration using the
    // plug-in manager

math/mathcore/src/FitUtil.cxx

@@ -754,21 +754,6 @@ void FitUtil::EvaluateChi2Gradient(const IModelFunction & f, const BinData & dat
 // utility function used by the likelihoods
 
 // for LogLikelihood functions
-#ifdef R__HAS_VECCORE
-double FitUtil::EvaluatePdf(const IModelFunctionTempl<ROOT::Double_v> & func, const UnBinData & data, const double * p, unsigned int i, double *) {
-   // evaluate the pdf contribution to the generic logl function in case of bin data
-   // return actually the log of the pdf and its derivatives
-
-
-   //func.SetParameters(p);
-
-
-   const auto x = vecCore::FromPtr<ROOT::Double_v>(data.GetCoordComponent(i,0));
-   auto fval = func (&x, p);
-   auto logPdf = ROOT::Math::Util::EvalLog(fval);
-   return logPdf[0];
-}
-#endif
 
 double FitUtil::EvaluatePdf(const IModelFunction & func, const UnBinData & data, const double * p, unsigned int i, double * g) {
    // evaluate the pdf contribution to the generic logl function in case of bin data
@@ -1000,11 +985,6 @@ nPoints = 0;
    return -logl;
 }
 
-#ifdef R__HAS_VECCORE
-void FitUtil::EvaluateLogLGradient(const IModelFunctionTempl<ROOT::Double_v> &, const UnBinData &, const double * , double *, unsigned int & ) {
-}
-#endif
-
 void FitUtil::EvaluateLogLGradient(const IModelFunction & f, const UnBinData & data, const double * p, double * grad, unsigned int & ) {
    // evaluate the gradient of the log likelihood function
 

math/mathcore/src/Fitter.cxx

@@ -128,21 +128,6 @@ void Fitter::SetFunction(const IModelFunction & func, bool useGradient)
    fFunc_v.reset();
 }
 
-#ifdef R__HAS_VECCORE
-void Fitter::SetFunction(const IModelFunction_v & func)
-{
-   //  set the fit model function (clone the given one and keep a copy )
-   //std::cout << "set a non-grad function" << std::endl;
-   fUseGradient = false;
-   fFunc_v = std::shared_ptr<IModelFunction_v>(dynamic_cast<IModelFunction_v *>(func.Clone() ) );
-   assert(fFunc_v);
-
-   // creates the parameter  settings
-   fConfig.CreateParamsSettings(*fFunc_v);
-   fFunc.reset();
-}
-#endif
-
 void Fitter::SetFunction(const IModel1DFunction & func, bool useGradient)
 {
    fUseGradient = useGradient;