sg_add refactor on GPL branch

* Replaced the SG_ADD macro calls with the new ParameterProperties enum class
* this was done in the same way as in PR shogun-toolbox/shogun#4417

shogun/classifier/FeatureBlockLogisticRegression.cpp

@@ -57,13 +57,13 @@ CFeatureBlockLogisticRegression::~CFeatureBlockLogisticRegression()
 
 void CFeatureBlockLogisticRegression::register_parameters()
 {
-	SG_ADD((CSGObject**)&m_feature_relation, "feature_relation", "feature relation", MS_NOT_AVAILABLE);
-	SG_ADD(&m_z, "z", "regularization coefficient", MS_AVAILABLE);
-	SG_ADD(&m_q, "q", "q of L1/Lq", MS_AVAILABLE);
-	SG_ADD(&m_termination, "termination", "termination", MS_NOT_AVAILABLE);
-	SG_ADD(&m_regularization, "regularization", "regularization", MS_NOT_AVAILABLE);
-	SG_ADD(&m_tolerance, "tolerance", "tolerance", MS_NOT_AVAILABLE);
-	SG_ADD(&m_max_iter, "max_iter", "maximum number of iterations", MS_NOT_AVAILABLE);
+	SG_ADD((CSGObject**)&m_feature_relation, "feature_relation", "feature relation");
+	SG_ADD(&m_z, "z", "regularization coefficient", ParameterProperties::HYPER);
+	SG_ADD(&m_q, "q", "q of L1/Lq", ParameterProperties::HYPER);
+	SG_ADD(&m_termination, "termination", "termination");
+	SG_ADD(&m_regularization, "regularization", "regularization");
+	SG_ADD(&m_tolerance, "tolerance", "tolerance");
+	SG_ADD(&m_max_iter, "max_iter", "maximum number of iterations");
 }
 
 CIndexBlockRelation* CFeatureBlockLogisticRegression::get_feature_relation() const

shogun/classifier/svm/SVMLin.cpp

@@ -43,12 +43,12 @@ CSVMLin::~CSVMLin()
 void CSVMLin::init()
 {
 	SG_ADD(
-	    &use_bias, "use_bias", "Indicates if bias is used.", MS_NOT_AVAILABLE);
+	    &use_bias, "use_bias", "Indicates if bias is used.");
 	SG_ADD(
-	    &C1, "C1", "C constant for negatively labeled examples.", MS_AVAILABLE);
+	    &C1, "C1", "C constant for negatively labeled examples.", ParameterProperties::HYPER);
 	SG_ADD(
-	    &C2, "C2", "C constant for positively labeled examples.", MS_AVAILABLE);
-	SG_ADD(&epsilon, "epsilon", "Convergence precision.", MS_NOT_AVAILABLE);
+	    &C2, "C2", "C constant for positively labeled examples.", ParameterProperties::HYPER);
+	SG_ADD(&epsilon, "epsilon", "Convergence precision.");
 }
 
 bool CSVMLin::train_machine(CFeatures* data)

shogun/classifier/svm/SVMSGD.cpp

@@ -213,16 +213,16 @@ void CSVMSGD::init()
 	loss=new CHingeLoss();
 	SG_REF(loss);
 
-	SG_ADD(&C1, "C1", "Cost constant 1.", MS_AVAILABLE);
-	SG_ADD(&C2, "C2", "Cost constant 2.", MS_AVAILABLE);
-	SG_ADD(&wscale, "wscale", "W scale", MS_NOT_AVAILABLE);
-	SG_ADD(&bscale, "bscale", "b scale", MS_NOT_AVAILABLE);
-	SG_ADD(&epochs, "epochs", "epochs", MS_NOT_AVAILABLE);
-	SG_ADD(&skip, "skip", "skip", MS_NOT_AVAILABLE);
-	SG_ADD(&count, "count", "count", MS_NOT_AVAILABLE);
+	SG_ADD(&C1, "C1", "Cost constant 1.", ParameterProperties::HYPER);
+	SG_ADD(&C2, "C2", "Cost constant 2.", ParameterProperties::HYPER);
+	SG_ADD(&wscale, "wscale", "W scale");
+	SG_ADD(&bscale, "bscale", "b scale");
+	SG_ADD(&epochs, "epochs", "epochs");
+	SG_ADD(&skip, "skip", "skip");
+	SG_ADD(&count, "count", "count");
 	SG_ADD(
-	    &use_bias, "use_bias", "Indicates if bias is used.", MS_NOT_AVAILABLE);
+	    &use_bias, "use_bias", "Indicates if bias is used.");
 	SG_ADD(
 	    &use_regularized_bias, "use_regularized_bias",
-	    "Indicates if bias is regularized.", MS_NOT_AVAILABLE);
+	    "Indicates if bias is regularized.");
 }

shogun/kernel/string/DistantSegmentsKernel.cpp

@@ -43,8 +43,8 @@ bool CDistantSegmentsKernel::init(CFeatures* l, CFeatures* r)
 
 void CDistantSegmentsKernel::init()
 {
-	SG_ADD(&m_delta, "delta", "Delta parameter of the DS-Kernel", MS_AVAILABLE);
-	SG_ADD(&m_theta, "theta", "Theta parameter of the DS-Kernel", MS_AVAILABLE);
+	SG_ADD(&m_delta, "delta", "Delta parameter of the DS-Kernel", ParameterProperties::HYPER);
+	SG_ADD(&m_theta, "theta", "Theta parameter of the DS-Kernel", ParameterProperties::HYPER);
 }
 
 float64_t CDistantSegmentsKernel::compute(int32_t idx_a, int32_t idx_b)

shogun/mathematics/SparseInverseCovariance.cpp

@@ -31,12 +31,11 @@ CSparseInverseCovariance::~CSparseInverseCovariance()
 void CSparseInverseCovariance::register_parameters()
 {
 	SG_ADD(&m_lasso_max_iter,"lasso_max_iter",
-	       "maximum iteration of LASSO step",MS_NOT_AVAILABLE);
-	SG_ADD(&m_max_iter,"max_iter","maximum total iteration",
-	       MS_NOT_AVAILABLE);
-	SG_ADD(&m_f_gap,"f_gap","f gap",MS_NOT_AVAILABLE);
-	SG_ADD(&m_x_gap,"x_gap","x gap",MS_NOT_AVAILABLE);
-	SG_ADD(&m_xtol,"xtol","xtol",MS_NOT_AVAILABLE);
+	       "maximum iteration of LASSO step");
+	SG_ADD(&m_max_iter,"max_iter","maximum total iteration");
+	SG_ADD(&m_f_gap,"f_gap","f gap");
+	SG_ADD(&m_x_gap,"x_gap","x gap");
+	SG_ADD(&m_xtol,"xtol","xtol");
 }
 
 SGMatrix<float64_t> CSparseInverseCovariance::estimate(SGMatrix<float64_t> S, float64_t lambda_c)

shogun/multiclass/MulticlassLogisticRegression.cpp

@@ -42,9 +42,9 @@ void CMulticlassLogisticRegression::init_defaults()
 
 void CMulticlassLogisticRegression::register_parameters()
 {
-	SG_ADD(&m_z, "m_z", "regularization constant",MS_AVAILABLE);
-	SG_ADD(&m_epsilon, "m_epsilon", "tolerance epsilon",MS_NOT_AVAILABLE);
-	SG_ADD(&m_max_iter, "m_max_iter", "max number of iterations",MS_NOT_AVAILABLE);
+	SG_ADD(&m_z, "m_z", "regularization constant", ParameterProperties::HYPER);
+	SG_ADD(&m_epsilon, "m_epsilon", "tolerance epsilon");
+	SG_ADD(&m_max_iter, "m_max_iter", "max number of iterations");
 }
 
 CMulticlassLogisticRegression::~CMulticlassLogisticRegression()

shogun/multiclass/MulticlassTreeGuidedLogisticRegression.cpp

@@ -41,9 +41,9 @@ void CMulticlassTreeGuidedLogisticRegression::init_defaults()
 
 void CMulticlassTreeGuidedLogisticRegression::register_parameters()
 {
-	SG_ADD(&m_z, "m_z", "regularization constant",MS_AVAILABLE);
-	SG_ADD(&m_epsilon, "m_epsilon", "tolerance epsilon",MS_NOT_AVAILABLE);
-	SG_ADD(&m_max_iter, "m_max_iter", "max number of iterations",MS_NOT_AVAILABLE);
+	SG_ADD(&m_z, "m_z", "regularization constant", ParameterProperties::HYPER);
+	SG_ADD(&m_epsilon, "m_epsilon", "tolerance epsilon");
+	SG_ADD(&m_max_iter, "m_max_iter", "max number of iterations");
 }
 
 CMulticlassTreeGuidedLogisticRegression::~CMulticlassTreeGuidedLogisticRegression()

shogun/optimization/NLOPTMinimizer.cpp

@@ -57,13 +57,13 @@ void CNLOPTMinimizer::init()
 	m_target_variable=SGVector<float64_t>();
 	set_nlopt_parameters();
 	SG_ADD(&m_max_iterations, "CNLOPTMinimizer__m_max_iterations",
-		"max_iterations in CNLOPTMinimizer", MS_NOT_AVAILABLE);
+		"max_iterations in CNLOPTMinimizer");
 	SG_ADD(&m_variable_tolerance, "CNLOPTMinimizer__m_variable_tolerance",
-		"variable_tolerance in CNLOPTMinimizer", MS_NOT_AVAILABLE);
+		"variable_tolerance in CNLOPTMinimizer");
 	SG_ADD(&m_function_tolerance, "CNLOPTMinimizer__m_function_tolerance",
-		"function_tolerance in CNLOPTMinimizer", MS_NOT_AVAILABLE);
+		"function_tolerance in CNLOPTMinimizer");
 	SG_ADD(&m_nlopt_algorithm_id, "CNLOPTMinimizer__m_nlopt_algorithm_id",
-		"nlopt_algorithm_id in CNLOPTMinimizer", MS_NOT_AVAILABLE);
+		"nlopt_algorithm_id in CNLOPTMinimizer");
 #endif
 }
 

shogun/structure/DualLibQPBMSOSVM.cpp

@@ -55,21 +55,17 @@ CDualLibQPBMSOSVM::~CDualLibQPBMSOSVM()
 
 void CDualLibQPBMSOSVM::init()
 {
-	SG_ADD(&m_TolRel, "m_TolRel", "Relative tolerance", MS_AVAILABLE);
-	SG_ADD(&m_TolAbs, "m_TolAbs", "Absolute tolerance", MS_AVAILABLE);
-	SG_ADD(&m_BufSize, "m_BuffSize", "Size of CP Buffer", MS_AVAILABLE);
-	SG_ADD(&m_lambda, "m_lambda", "Regularization constant lambda",
-			MS_AVAILABLE);
-	SG_ADD(&m_cleanICP, "m_cleanICP", "Inactive cutting plane removal flag",
-			MS_AVAILABLE);
+	SG_ADD(&m_TolRel, "m_TolRel", "Relative tolerance", ParameterProperties::HYPER);
+	SG_ADD(&m_TolAbs, "m_TolAbs", "Absolute tolerance", ParameterProperties::HYPER);
+	SG_ADD(&m_BufSize, "m_BuffSize", "Size of CP Buffer", ParameterProperties::HYPER);
+	SG_ADD(&m_lambda, "m_lambda", "Regularization constant lambda");
+	SG_ADD(&m_cleanICP, "m_cleanICP", "Inactive cutting plane removal flag");
 	SG_ADD(&m_cleanAfter,
 			"m_cleanAfter",
-			"Number of inactive iterations after which ICP will be removed",
-			MS_AVAILABLE);
-	SG_ADD(&m_K, "m_K", "Parameter K", MS_NOT_AVAILABLE);
-	SG_ADD(&m_Tmax, "m_Tmax", "Parameter Tmax", MS_AVAILABLE);
-	SG_ADD(&m_cp_models, "m_cp_models", "Number of cutting plane models",
-			MS_AVAILABLE);
+			"Number of inactive iterations after which ICP will be removed");
+	SG_ADD(&m_K, "m_K", "Parameter K");
+	SG_ADD(&m_Tmax, "m_Tmax", "Parameter Tmax", ParameterProperties::HYPER);
+	SG_ADD(&m_cp_models, "m_cp_models", "Number of cutting plane models");
 
 	set_TolRel(0.001);
 	set_TolAbs(0.0);

shogun/transfer/multitask/MultitaskL12LogisticRegression.cpp

@@ -49,8 +49,8 @@ CMultitaskL12LogisticRegression::CMultitaskL12LogisticRegression(
 
 void CMultitaskL12LogisticRegression::init()
 {
-	SG_ADD(&self->m_rho1,"rho1","rho L1/L2 regularization parameter",MS_AVAILABLE);
-	SG_ADD(&self->m_rho2,"rho2","rho L2 regularization parameter",MS_AVAILABLE);
+	SG_ADD(&self->m_rho1,"rho1","rho L1/L2 regularization parameter", ParameterProperties::HYPER);
+	SG_ADD(&self->m_rho2,"rho2","rho L2 regularization parameter", ParameterProperties::HYPER);
 }
 
 void CMultitaskL12LogisticRegression::set_rho1(float64_t rho1)

shogun/transfer/multitask/MultitaskLeastSquaresRegression.cpp

@@ -41,12 +41,12 @@ CMultitaskLeastSquaresRegression::~CMultitaskLeastSquaresRegression()
 
 void CMultitaskLeastSquaresRegression::register_parameters()
 {
-	SG_ADD(&m_z, "z", "regularization coefficient", MS_AVAILABLE);
-	SG_ADD(&m_q, "q", "q of L1/Lq", MS_AVAILABLE);
-	SG_ADD(&m_termination, "termination", "termination", MS_NOT_AVAILABLE);
-	SG_ADD(&m_regularization, "regularization", "regularization", MS_NOT_AVAILABLE);
-	SG_ADD(&m_tolerance, "tolerance", "tolerance", MS_NOT_AVAILABLE);
-	SG_ADD(&m_max_iter, "max_iter", "maximum number of iterations", MS_NOT_AVAILABLE);
+	SG_ADD(&m_z, "z", "regularization coefficient", ParameterProperties::HYPER);
+	SG_ADD(&m_q, "q", "q of L1/Lq", ParameterProperties::HYPER);
+	SG_ADD(&m_termination, "termination", "termination");
+	SG_ADD(&m_regularization, "regularization", "regularization");
+	SG_ADD(&m_tolerance, "tolerance", "tolerance");
+	SG_ADD(&m_max_iter, "max_iter", "maximum number of iterations");
 }
 
 void CMultitaskLeastSquaresRegression::initialize_parameters()

shogun/transfer/multitask/MultitaskLinearMachine.cpp

@@ -46,7 +46,7 @@ CMultitaskLinearMachine::~CMultitaskLinearMachine()
 
 void CMultitaskLinearMachine::register_parameters()
 {
-	SG_ADD((CSGObject**)&m_task_relation, "task_relation", "task relation", MS_NOT_AVAILABLE);
+	SG_ADD((CSGObject**)&m_task_relation, "task_relation", "task relation");
 }
 
 int32_t CMultitaskLinearMachine::get_current_task() const

shogun/transfer/multitask/MultitaskLogisticRegression.cpp

@@ -40,12 +40,12 @@ CMultitaskLogisticRegression::~CMultitaskLogisticRegression()
 
 void CMultitaskLogisticRegression::register_parameters()
 {
-	SG_ADD(&m_z, "z", "regularization coefficient", MS_AVAILABLE);
-	SG_ADD(&m_q, "q", "q of L1/Lq", MS_AVAILABLE);
-	SG_ADD(&m_termination, "termination", "termination", MS_NOT_AVAILABLE);
-	SG_ADD(&m_regularization, "regularization", "regularization", MS_NOT_AVAILABLE);
-	SG_ADD(&m_tolerance, "tolerance", "tolerance", MS_NOT_AVAILABLE);
-	SG_ADD(&m_max_iter, "max_iter", "maximum number of iterations", MS_NOT_AVAILABLE);
+	SG_ADD(&m_z, "z", "regularization coefficient", ParameterProperties::HYPER);
+	SG_ADD(&m_q, "q", "q of L1/Lq", ParameterProperties::HYPER);
+	SG_ADD(&m_termination, "termination", "termination");
+	SG_ADD(&m_regularization, "regularization", "regularization");
+	SG_ADD(&m_tolerance, "tolerance", "tolerance");
+	SG_ADD(&m_max_iter, "max_iter", "maximum number of iterations");
 }
 
 void CMultitaskLogisticRegression::initialize_parameters()

shogun/transfer/multitask/MultitaskTraceLogisticRegression.cpp

@@ -36,7 +36,7 @@ CMultitaskTraceLogisticRegression::CMultitaskTraceLogisticRegression(
 
 void CMultitaskTraceLogisticRegression::init()
 {
-	SG_ADD(&m_rho,"rho","rho",MS_AVAILABLE);
+	SG_ADD(&m_rho,"rho","rho", ParameterProperties::HYPER);
 }
 
 void CMultitaskTraceLogisticRegression::set_rho(float64_t rho)