Fix compilation error implicitly capturing variables from a structured binding.

Author: breyerml

bindings/Python/regression_report.cpp

@@ -50,7 +50,7 @@ void init_regression_report(py::module_ &m) {
                      throw py::value_error{ fmt::format(R"(The type of the correct labels "{}" differs from the type of the predicted labels "{}"!)", dtype_correct_label.attr("name").cast<std::string>(), dtype_predicted_label.attr("name").cast<std::string>()) };
                  }
 
-                 return std::visit([&](auto &&correct_label) {
+                 return std::visit([&predicted_label_variant, force_finite](auto &&correct_label) {
                      using vector_type = plssvm::detail::remove_cvref_t<decltype(correct_label)>;
                      return plssvm::regression_report{ correct_label, std::get<vector_type>(predicted_label_variant), plssvm::regression_report::force_finite = force_finite };
                  },

bindings/Python/sklearn_svc.cpp

@@ -613,7 +613,7 @@ void init_sklearn_svc(py::module_ &m) {
             self.feature_names_ = opt_feature_names;
 
             // create the data set to fit
-            std::visit([&](auto &&labels_vector) {
+            std::visit([&self, &data_matrix](auto &&labels_vector) {
                 // get the label type and possible data set types
                 using label_type = typename plssvm::detail::remove_cvref_t<decltype(labels_vector)>::value_type;
                 using possible_data_set_types = typename svc::possible_data_set_types;
@@ -635,7 +635,7 @@ void init_sklearn_svc(py::module_ &m) {
             // convert the data py::object to a plssvm::aos_matrix
             const auto &[data_matrix, opt_feature_names] = plssvm::bindings::python::util::pyobject_to_matrix(data);
 
-            return std::visit([&](auto &&model) {
+            return std::visit([&self, &data_matrix](auto &&model) {
                 // get the label type
                 using label_type = typename plssvm::detail::remove_cvref_t<decltype(model)>::label_type;
                 // create the data set to predict
@@ -661,7 +661,7 @@ void init_sklearn_svc(py::module_ &m) {
             const auto &[data_matrix, opt_feature_names] = plssvm::bindings::python::util::pyobject_to_matrix(data);
 
             // score the data
-            return std::visit([&](auto &&labels_vector) {
+            return std::visit([&self, &data_matrix, &dtype](auto &&labels_vector) {
                 // get the label types
                 using label_type = typename plssvm::detail::remove_cvref_t<decltype(labels_vector)>::value_type;
                 // create the data set to score

bindings/Python/sklearn_svr.cpp

@@ -343,7 +343,7 @@ void init_sklearn_svr(py::module_ &m) {
             self.feature_names_ = opt_feature_names;
 
             // create the data set to fit
-            std::visit([&](auto &&labels_vector) {
+            std::visit([&self, &data_matrix](auto &&labels_vector) {
                 // get the label type and possible data set types
                 using label_type = typename plssvm::detail::remove_cvref_t<decltype(labels_vector)>::value_type;
                 using possible_data_set_types = typename svr::possible_data_set_types;
@@ -365,7 +365,7 @@ void init_sklearn_svr(py::module_ &m) {
             // convert the data py::object to a plssvm::aos_matrix
             const auto &[data_matrix, opt_feature_names] = plssvm::bindings::python::util::pyobject_to_matrix(data);
 
-            return std::visit([&](auto &&model) {
+            return std::visit([&self, &data_matrix](auto &&model) {
                 // get the label type
                 using label_type = typename plssvm::detail::remove_cvref_t<decltype(model)>::label_type;
                 // create the data set to predict
@@ -388,7 +388,7 @@ void init_sklearn_svr(py::module_ &m) {
             const auto &[data_matrix, opt_feature_names] = plssvm::bindings::python::util::pyobject_to_matrix(data);
 
             // score the data
-            return std::visit([&](auto &&labels_vector) {
+            return std::visit([&self, &data_matrix, &dtype](auto &&labels_vector) {
                 // get the label types
                 using label_type = typename plssvm::detail::remove_cvref_t<decltype(labels_vector)>::value_type;
                 // create the data set to score