Merge branch 'regression' into spack

Author: vancraar

bindings/Python/sklearn_svc.cpp

@@ -166,15 +166,15 @@ void parse_provided_kwargs(svc &self, const py::kwargs &args) {
         plssvm::kernel_function_type kernel{};
         if (kernel_str == "linear") {
             kernel = plssvm::kernel_function_type::linear;
-        } else if (kernel_str == "poly") {
+        } else if (kernel_str == "poly" || kernel_str == "polynomial") {
             kernel = plssvm::kernel_function_type::polynomial;
         } else if (kernel_str == "rbf") {
             kernel = plssvm::kernel_function_type::rbf;
         } else if (kernel_str == "sigmoid") {
             kernel = plssvm::kernel_function_type::sigmoid;
         } else if (kernel_str == "laplacian") {
             kernel = plssvm::kernel_function_type::laplacian;
-        } else if (kernel_str == "chi_squared") {
+        } else if (kernel_str == "chi_squared" || kernel_str == "chi-squared") {
             kernel = plssvm::kernel_function_type::chi_squared;
         } else if (kernel_str == "precomputed") {
             throw py::value_error{ R"(The "kernel = 'precomputed'" parameter for the 'SVC' is not implemented yet!)" };

bindings/Python/sklearn_svr.cpp

@@ -122,15 +122,15 @@ void parse_provided_kwargs(svr &self, const py::kwargs &args) {
         plssvm::kernel_function_type kernel{};
         if (kernel_str == "linear") {
             kernel = plssvm::kernel_function_type::linear;
-        } else if (kernel_str == "poly") {
+        } else if (kernel_str == "poly" || kernel_str == "polynomial") {
             kernel = plssvm::kernel_function_type::polynomial;
         } else if (kernel_str == "rbf") {
             kernel = plssvm::kernel_function_type::rbf;
         } else if (kernel_str == "sigmoid") {
             kernel = plssvm::kernel_function_type::sigmoid;
         } else if (kernel_str == "laplacian") {
             kernel = plssvm::kernel_function_type::laplacian;
-        } else if (kernel_str == "chi_squared") {
+        } else if (kernel_str == "chi_squared" || kernel_str == "chi-squared") {
             kernel = plssvm::kernel_function_type::chi_squared;
         } else if (kernel_str == "precomputed") {
             throw py::value_error{ R"(The "kernel = 'precomputed'" parameter for the 'SVR' is not implemented yet!)" };

bindings/Python/type_caster/matrix_type_caster.hpp

@@ -110,7 +110,7 @@ struct type_caster<plssvm::matrix<T, layout>> {
         const T *ptr = static_cast<T *>(buffer.ptr);
 
         // check the memory layout of the Python Numpy array
-        if constexpr (Flags & py::array::c_style) {
+        if constexpr (static_cast<bool>(Flags & py::array::c_style)) {
             // the provided Python Numpy array has C style layout
             if constexpr (layout == plssvm::layout_type::aos) {
                 // memory layout of Python Numpy array and PLSSVM matrix are the same -> can use memcpy to convert
@@ -130,7 +130,7 @@ struct type_caster<plssvm::matrix<T, layout>> {
                 // unsupported PLSSVM matrix memory layout
                 return false;
             }
-        } else if constexpr (Flags & py::array::f_style) {
+        } else if constexpr (static_cast<bool>(Flags & py::array::f_style)) {
             if constexpr (layout == plssvm::layout_type::aos) {
                 // the memory layouts don't match -> must use loops to convert layouts
 #pragma omp parallel for collapse(2)