Updating the interface of nonlinear fitters and functions to adopt shared...

Updating the interface of nonlinear fitters and functions to adopt shared pointers for data. Note this is not a clean REV to updated to. ALTA won't comile.

sources/plugins/nonlinear_fitter_ceres/fitter.cpp

@@ -105,7 +105,7 @@ nonlinear_fitter_ceres::~nonlinear_fitter_ceres()
 {
 }
 
-bool nonlinear_fitter_ceres::fit_data(const data* d, function* fit, const arguments &args)
+bool nonlinear_fitter_ceres::fit_data(const ptr<data> d, function* fit, const arguments &args)
 {
     // I need to set the dimension of the resulting function to be equal
     // to the dimension of my fitting problem

sources/plugins/nonlinear_fitter_ceres/fitter.h

@@ -61,7 +61,7 @@ class nonlinear_fitter_ceres: public fitter
 
 		// Fitting a data object
 		//
-		virtual bool fit_data(const data* d, function* fit, const arguments& args) ;
+		virtual bool fit_data(const ptr<data> d, function* fit, const arguments& args) ;
 
 		// Provide user parameters to the fitter
 		//

sources/plugins/nonlinear_fitter_eigen/fitter.cpp

@@ -20,7 +20,7 @@ ALTA_DLL_EXPORT fitter* provide_fitter()
 
 struct EigenFunctor: Eigen::DenseFunctor<double>
 {
-	EigenFunctor(nonlinear_function* f, const data* d, bool use_cosine) :
+	EigenFunctor(nonlinear_function* f, const ptr<data> d, bool use_cosine) :
 		Eigen::DenseFunctor<double>(f->nbParameters(), d->dimY()*d->size()), _f(f), _d(d), _cosine(use_cosine)
 	{
 #ifndef DEBUG
@@ -130,7 +130,7 @@ struct EigenFunctor: Eigen::DenseFunctor<double>
 
 
 	nonlinear_function* _f;
-	const data* _d;
+	const ptr<data> _d;
 
 	// Flags
 	bool _cosine;
@@ -138,7 +138,7 @@ struct EigenFunctor: Eigen::DenseFunctor<double>
 
 struct CompoundFunctor: Eigen::DenseFunctor<double>
 {
-	CompoundFunctor(compound_function* f, int index, const data* d, bool use_cosine) :
+	CompoundFunctor(compound_function* f, int index, const ptr<data> d, bool use_cosine) :
 		Eigen::DenseFunctor<double>((*f)[index]->nbParameters(), d->dimY()*d->size()), _f(f), _index(index), _d(d), _cosine(use_cosine)
 	{
 #ifndef DEBUG
@@ -261,7 +261,7 @@ struct CompoundFunctor: Eigen::DenseFunctor<double>
 
 
 	compound_function* _f;
-	const data* _d;
+	const ptr<data> _d;
 
 	// Flags
 	bool _cosine;
@@ -275,7 +275,7 @@ nonlinear_fitter_eigen::~nonlinear_fitter_eigen()
 {
 }
 
-bool nonlinear_fitter_eigen::fit_data(const data* d, function* fit, const arguments &args)
+bool nonlinear_fitter_eigen::fit_data(const ptr<data> d, function* fit, const arguments &args)
 {
     // I need to set the dimension of the resulting function to be equal
     // to the dimension of my fitting problem

sources/plugins/nonlinear_fitter_eigen/fitter.h

@@ -45,7 +45,7 @@ class nonlinear_fitter_eigen: public fitter
 
 		// Fitting a data object
 		//
-		virtual bool fit_data(const data* d, function* fit, const arguments& args) ;
+		virtual bool fit_data(const ptr<data> d, function* fit, const arguments& args) ;
 
 		// Provide user parameters to the fitter
 		//

sources/plugins/nonlinear_fitter_ipopt/fitter.cpp

@@ -23,7 +23,7 @@ ALTA_DLL_EXPORT fitter* provide_fitter()
 class altaNLP : public Ipopt::TNLP
 {
 	public:
-		altaNLP(nonlinear_function* f, const data* d) : TNLP(), _f(f), _d(d)
+		altaNLP(nonlinear_function* f, const ptr<data> d) : TNLP(), _f(f), _d(d)
 		{
 		}
 
@@ -209,7 +209,7 @@ class altaNLP : public Ipopt::TNLP
 
 	protected:
 
-		const data* _d;
+		const ptr<data> _d;
 		nonlinear_function* _f;
 };
 
@@ -220,7 +220,7 @@ nonlinear_fitter_ipopt::~nonlinear_fitter_ipopt()
 {
 }
 
-bool nonlinear_fitter_ipopt::fit_data(const data* d, function* fit, const arguments &args)
+bool nonlinear_fitter_ipopt::fit_data(const ptr<data> d, function* fit, const arguments &args)
 {
 	// I need to set the dimension of the resulting function to be equal
 	// to the dimension of my fitting problem

sources/plugins/nonlinear_fitter_ipopt/fitter.h

@@ -54,7 +54,7 @@ class nonlinear_fitter_ipopt: public fitter
 
 		// Fitting a data object
 		//
-		virtual bool fit_data(const data* d, function* fit, const arguments& args) ;
+		virtual bool fit_data(const ptr<data> d, function* fit, const arguments& args) ;
 
 		// Provide user parameters to the fitter
 		//

sources/plugins/nonlinear_fitter_nlopt/fitter.cpp

@@ -34,7 +34,7 @@ void print_nlopt_error(nlopt_result res, const std::string& string)
 
 // The parameter of the function _f should be set prior to this function
 // call. If not it will produce undesirable results.
-void df(double* fjac, const nonlinear_function* f, const data* d)
+void df(double* fjac, const nonlinear_function* f, const ptr<data> d)
 {
 	// Clean memory
 	memset(fjac, 0.0, f->nbParameters()*sizeof(double));
@@ -77,7 +77,7 @@ void df(double* fjac, const nonlinear_function* f, const data* d)
 double f(unsigned n, const double* x, double* dy, void* dat)
 {
 	nonlinear_function* _f = (nonlinear_function*)(((void**)dat)[0]);
-	const data* _d = (const data*)(((void**)dat)[1]);
+	const ptr<data> _d = *(const ptr<data>*)(((void**)dat)[1]);
 
 	// Update the parameters vector
 	vec _p(_f->nbParameters());
@@ -125,7 +125,7 @@ nonlinear_fitter_nlopt::~nonlinear_fitter_nlopt()
 {
 }
 
-bool nonlinear_fitter_nlopt::fit_data(const data* d, function* fit, const arguments &args)
+bool nonlinear_fitter_nlopt::fit_data(const ptr<data> d, function* fit, const arguments &args)
 {
 	// I need to set the dimension of the resulting function to be equal
 	// to the dimension of my fitting problem
@@ -234,7 +234,7 @@ bool nonlinear_fitter_nlopt::fit_data(const data* d, function* fit, const argume
 	// Create the problem
 	void* dat[2];
 	dat[0] = (void*)nf;
-	dat[1] = (void*)d;
+	dat[1] = (void*)&d;
 	res = nlopt_set_min_objective(opt, f, dat);
 	if(res < 0)
 	{

sources/plugins/nonlinear_fitter_nlopt/fitter.h

@@ -46,7 +46,7 @@ class nonlinear_fitter_nlopt: public fitter
 
 		// Fitting a data object
 		//
-		virtual bool fit_data(const data* d, function* fit, const arguments& args) ;
+		virtual bool fit_data(const ptr<data> d, function* fit, const arguments& args) ;
 
 		// Provide user parameters to the fitter
 		//

sources/plugins/nonlinear_fresnel_normalized_schlick/function.cpp

@@ -186,7 +186,7 @@ vec schlick::parametersJacobian(const vec& x) const
 }
 
 
-void schlick::bootstrap(const data* d, const arguments& args)
+void schlick::bootstrap(const ptr<data> d, const arguments& args)
 {
     for(int i=0; i<dimY(); ++i) { R[i] = 0.5; }
 }

sources/plugins/nonlinear_fresnel_normalized_schlick/function.h

@@ -49,7 +49,7 @@ class schlick : public nonlinear_function
 		virtual vec parametersJacobian(const vec& x) const ;
 
 		//! \brief Boostrap the function by defining the diffuse term
-		virtual void bootstrap(const data* d, const arguments& args);
+		virtual void bootstrap(const ptr<data> d, const arguments& args);
 
 		//! \brief resize the parameter vector
 		virtual void setDimY(int nY)

sources/plugins/nonlinear_fresnel_retroschlick/function.cpp

@@ -98,7 +98,7 @@ vec retro_schlick::getParametersMax() const
 	return M;
 }
 
-void retro_schlick::bootstrap(const data* d, const arguments& args)
+void retro_schlick::bootstrap(const ptr<data> d, const arguments& args)
 {
 	for(int i=0; i<dimY(); ++i) {R[i] = 0.5; }
 }

sources/plugins/nonlinear_fresnel_retroschlick/function.h

@@ -49,7 +49,7 @@ class retro_schlick : public nonlinear_function
 		virtual vec parametersJacobian(const vec& x) const ;
 
 		//! \brief Boostrap the function by defining the diffuse term
-		virtual void bootstrap(const data* d, const arguments& args);
+		virtual void bootstrap(const ptr<data> d, const arguments& args);
 
 		//! \brief resize the parameter vector
 		virtual void setDimY(int nY)

sources/plugins/nonlinear_fresnel_schlick/function.cpp

@@ -179,7 +179,7 @@ vec schlick_fresnel::parametersJacobian(const vec& x) const
 }
 
 
-void schlick_fresnel::bootstrap(const data* d, const arguments& args)
+void schlick_fresnel::bootstrap(const ptr<data> d, const arguments& args)
 {
     for(int i=0; i<dimY(); ++i) { R[i] = 0.5; }
 }

sources/plugins/nonlinear_fresnel_schlick/function.h

@@ -58,7 +58,7 @@ class schlick_fresnel : public nonlinear_function
 		virtual vec parametersJacobian(const vec& x) const ;
 
 		//! \brief Boostrap the function by defining the diffuse term
-		virtual void bootstrap(const data* d, const arguments& args);
+		virtual void bootstrap(const ptr<data> d, const arguments& args);
 
 		//! \brief resize the parameter vector
 		virtual void setDimY(int nY)

sources/plugins/nonlinear_function_abc/function.cpp

@@ -142,7 +142,7 @@ vec abc_function::parametersJacobian(const vec& x) const
     return jac;
 }
 		
-void abc_function::bootstrap(const data* d, const arguments& args)
+void abc_function::bootstrap(const ptr<data> d, const arguments& args)
 {
 	for(int i=0; i<dimY(); ++i)
 	{

sources/plugins/nonlinear_function_abc/function.h

@@ -45,7 +45,7 @@ class abc_function : public nonlinear_function
 		//! \brief Boostrap the function by defining the diffuse term
 		//!
 		//! \details
-		virtual void bootstrap(const data* d, const arguments& args);
+		virtual void bootstrap(const ptr<data> d, const arguments& args);
 
 		//! \brief Number of parameters to this non-linear function
 		virtual int nbParameters() const ;

sources/plugins/nonlinear_function_beckmann/function.cpp

@@ -161,7 +161,7 @@ vec beckmann_function::parametersJacobian(const vec& x) const
     return jac;
 }
 		
-void beckmann_function::bootstrap(const data* d, const arguments& args)
+void beckmann_function::bootstrap(const ptr<data> d, const arguments& args)
 {
 	for(int i=0; i<dimY(); ++i)
 	{

sources/plugins/nonlinear_function_beckmann/function.h

@@ -48,7 +48,7 @@ class beckmann_function : public nonlinear_function
 		//! \brief Boostrap the function by defining the diffuse term
 		//!
 		//! \details
-		virtual void bootstrap(const data* d, const arguments& args);
+		virtual void bootstrap(const ptr<data> d, const arguments& args);
 
 		//! \brief Number of parameters to this non-linear function
 		virtual int nbParameters() const ;

sources/plugins/nonlinear_function_blinn/function.cpp

@@ -154,7 +154,7 @@ vec blinn_function::parametersJacobian(const vec& x) const
 }
 
 
-void blinn_function::bootstrap(const data* d, const arguments& args)
+void blinn_function::bootstrap(const ptr<data> d, const arguments& args)
 {
     for(int i=0; i<dimY(); ++i)
     {

sources/plugins/nonlinear_function_blinn/function.h

@@ -31,7 +31,7 @@ class blinn_function : public nonlinear_function
 
 
 		//! \brief Boostrap the function by defining the diffuse term
-		virtual void bootstrap(const data* d, const arguments& args);
+		virtual void bootstrap(const ptr<data> d, const arguments& args);
 
 		//! \brief Load function specific files
         virtual bool load(std::istream& filename) ;