[filter] more optional internals

include/fcarouge/internal/kalman.tpp

@@ -113,28 +113,37 @@ kalman<State, Output, Input, UpdateTypes, PredictionTypes>::p(
 template <typename State, typename Output, typename Input, typename UpdateTypes,
           typename PredictionTypes>
 [[nodiscard("The returned process noise covariance matrix Q is unexpectedly "
-            "discarded.")]] inline constexpr auto
+            "discarded.")]] inline constexpr const auto &
 kalman<State, Output, Input, UpdateTypes, PredictionTypes>::q() const
-    -> const process_uncertainty & {
+  requires(has_process_uncertainty<implementation>)
+{
   return filter.q;
 }
 
 template <typename State, typename Output, typename Input, typename UpdateTypes,
           typename PredictionTypes>
 [[nodiscard("The returned process noise covariance matrix Q is unexpectedly "
-            "discarded.")]] inline constexpr auto
+            "discarded.")]] inline constexpr auto &
 kalman<State, Output, Input, UpdateTypes, PredictionTypes>::q()
-    -> process_uncertainty & {
+  requires(has_process_uncertainty<implementation>)
+{
   return filter.q;
 }
 
 template <typename State, typename Output, typename Input, typename UpdateTypes,
           typename PredictionTypes>
 inline constexpr void
 kalman<State, Output, Input, UpdateTypes, PredictionTypes>::q(
-    const auto &value, const auto &...values) {
-  if constexpr (std::is_convertible_v<decltype(value), process_uncertainty>) {
-    filter.q = std::move(process_uncertainty{value, values...});
+    const auto &value, const auto &...values)
+  requires(has_process_uncertainty<implementation>)
+{
+  if constexpr (std::is_convertible_v<
+                    decltype(value),
+                    typename kalman<State, Output, Input, UpdateTypes,
+                                    PredictionTypes>::process_uncertainty>) {
+    filter.q = std::move(typename kalman<State, Output, Input, UpdateTypes,
+                                         PredictionTypes>::process_uncertainty{
+        value, values...});
   } else {
     using noise_process_function = decltype(filter.noise_process_q);
     filter.noise_process_q =
@@ -145,28 +154,37 @@ kalman<State, Output, Input, UpdateTypes, PredictionTypes>::q(
 template <typename State, typename Output, typename Input, typename UpdateTypes,
           typename PredictionTypes>
 [[nodiscard("The returned observation noise covariance matrix R is "
-            "unexpectedly discarded.")]] inline constexpr auto
+            "unexpectedly discarded.")]] inline constexpr const auto &
 kalman<State, Output, Input, UpdateTypes, PredictionTypes>::r() const
-    -> const output_uncertainty & {
+  requires(has_output_uncertainty<implementation>)
+{
   return filter.r;
 }
 
 template <typename State, typename Output, typename Input, typename UpdateTypes,
           typename PredictionTypes>
 [[nodiscard("The returned observation noise covariance matrix R is "
-            "unexpectedly discarded.")]] inline constexpr auto
+            "unexpectedly discarded.")]] inline constexpr auto &
 kalman<State, Output, Input, UpdateTypes, PredictionTypes>::r()
-    -> output_uncertainty & {
+  requires(has_output_uncertainty<implementation>)
+{
   return filter.r;
 }
 
 template <typename State, typename Output, typename Input, typename UpdateTypes,
           typename PredictionTypes>
 inline constexpr void
 kalman<State, Output, Input, UpdateTypes, PredictionTypes>::r(
-    const auto &value, const auto &...values) {
-  if constexpr (std::is_convertible_v<decltype(value), output_uncertainty>) {
-    filter.r = std::move(output_uncertainty{value, values...});
+    const auto &value, const auto &...values)
+  requires(has_output_uncertainty<implementation>)
+{
+  if constexpr (std::is_convertible_v<
+                    decltype(value),
+                    typename kalman<State, Output, Input, UpdateTypes,
+                                    PredictionTypes>::output_uncertainty>) {
+    filter.r = std::move(
+        typename kalman<State, Output, Input, UpdateTypes,
+                        PredictionTypes>::output_uncertainty{value, values...});
   } else {
     using noise_observation_function = decltype(filter.noise_observation_r);
     filter.noise_observation_r =
@@ -177,28 +195,37 @@ kalman<State, Output, Input, UpdateTypes, PredictionTypes>::r(
 template <typename State, typename Output, typename Input, typename UpdateTypes,
           typename PredictionTypes>
 [[nodiscard("The returned state transition matrix F is unexpectedly "
-            "discarded.")]] inline constexpr auto
+            "discarded.")]] inline constexpr const auto &
 kalman<State, Output, Input, UpdateTypes, PredictionTypes>::f() const
-    -> const state_transition & {
+  requires(has_state_transition<implementation>)
+{
   return filter.f;
 }
 
 template <typename State, typename Output, typename Input, typename UpdateTypes,
           typename PredictionTypes>
 [[nodiscard("The returned state transition matrix F is unexpectedly "
-            "discarded.")]] inline constexpr auto
+            "discarded.")]] inline constexpr auto &
 kalman<State, Output, Input, UpdateTypes, PredictionTypes>::f()
-    -> state_transition & {
+  requires(has_state_transition<implementation>)
+{
   return filter.f;
 }
 
 template <typename State, typename Output, typename Input, typename UpdateTypes,
           typename PredictionTypes>
 inline constexpr void
 kalman<State, Output, Input, UpdateTypes, PredictionTypes>::f(
-    const auto &value, const auto &...values) {
-  if constexpr (std::is_convertible_v<decltype(value), state_transition>) {
-    filter.f = std::move(state_transition{value, values...});
+    const auto &value, const auto &...values)
+  requires(has_state_transition<implementation>)
+{
+  if constexpr (std::is_convertible_v<
+                    decltype(value),
+                    typename kalman<State, Output, Input, UpdateTypes,
+                                    PredictionTypes>::state_transition>) {
+    filter.f = std::move(
+        typename kalman<State, Output, Input, UpdateTypes,
+                        PredictionTypes>::state_transition{value, values...});
   } else {
     using transition_state_function = decltype(filter.transition_state_f);
     filter.transition_state_f =

include/fcarouge/internal/utility.hpp

@@ -55,13 +55,39 @@ concept eigen = requires { typename Type::PlainMatrix; };
 template <typename Filter>
 concept has_input_member = requires(Filter filter) { filter.u; };
 
+//! @todo Is the _method concept extraneous or incorrect? Explain the
+//! shortcoming?
+
 template <typename Filter>
 concept has_input_method = requires(Filter filter) { filter.u(); };
 
 //! @todo Shorten when MSVC has better if-constexpr-requires support.
 template <typename Filter>
 concept has_input = has_input_member<Filter> || has_input_method<Filter>;
 
+template <typename Filter>
+concept has_process_uncertainty_member = requires(Filter filter) { filter.q; };
+
+template <typename Filter>
+concept has_process_uncertainty_method =
+    requires(Filter filter) { filter.q(); };
+
+//! @todo Shorten when MSVC has better if-constexpr-requires support.
+template <typename Filter>
+concept has_process_uncertainty = has_process_uncertainty_member<Filter> ||
+                                  has_process_uncertainty_method<Filter>;
+
+template <typename Filter>
+concept has_output_uncertainty_member = requires(Filter filter) { filter.r; };
+
+template <typename Filter>
+concept has_output_uncertainty_method = requires(Filter filter) { filter.r(); };
+
+//! @todo Shorten when MSVC has better if-constexpr-requires support.
+template <typename Filter>
+concept has_output_uncertainty = has_output_uncertainty_member<Filter> ||
+                                 has_output_uncertainty_method<Filter>;
+
 template <typename Filter>
 concept has_input_control_member = requires(Filter filter) { filter.g; };
 
@@ -73,6 +99,17 @@ template <typename Filter>
 concept has_input_control =
     has_input_control_member<Filter> || has_input_control_method<Filter>;
 
+template <typename Filter>
+concept has_state_transition_member = requires(Filter filter) { filter.f; };
+
+template <typename Filter>
+concept has_state_transition_method = requires(Filter filter) { filter.f(); };
+
+//! @todo Shorten when MSVC has better if-constexpr-requires support.
+template <typename Filter>
+concept has_state_transition =
+    has_state_transition_member<Filter> || has_state_transition_method<Filter>;
+
 template <typename Filter>
 concept has_output_model_member = requires(Filter filter) { filter.h; };
 
@@ -111,16 +148,35 @@ template <typename Filter> struct conditional_output_model {};
 template <has_output_model Filter> struct conditional_output_model<Filter> {
   //! @brief Type of the observation transition matrix H.
   //!
-  //! @details Also known as the measurement transition matrix or C.
+  //! @details Also known as the measurement transition matrix or C. The
+  //! presence of the member depends on the filter capabilities.
   using output_model = Filter::output_model;
 };
 
+template <typename Filter> struct conditional_process_uncertainty {};
+
+template <has_process_uncertainty Filter>
+struct conditional_process_uncertainty<Filter> {
+  //! @brief Type of the process noise correlated variance matrix Q.
+  using process_uncertainty = Filter::process_uncertainty;
+};
+
+template <typename Filter> struct conditional_output_uncertainty {};
+
+template <has_output_uncertainty Filter>
+struct conditional_output_uncertainty<Filter> {
+  //! @brief Type of the observation noise correlated variance matrix R.
+  using output_uncertainty = Filter::output_uncertainty;
+};
+
 // The only way to have a conditional member type is to inherit from a template
 // specialization on the member type.
 template <typename Filter>
 struct conditional_member_types : public conditional_input<Filter>,
                                   conditional_input_control<Filter>,
-                                  conditional_output_model<Filter> {};
+                                  conditional_output_model<Filter>,
+                                  conditional_process_uncertainty<Filter>,
+                                  conditional_output_uncertainty<Filter> {};
 
 struct empty {
   inline constexpr explicit empty([[maybe_unused]] auto &&...any) noexcept {

include/fcarouge/kalman.hpp

@@ -167,7 +167,8 @@ class kalman final : public internal::conditional_member_types<internal::kalman<
   //! @brief Implementation details of the filter.
   //!
   //! @details The internal implementation, filtering strategies, and presence
-  //! of members vary based on the configured filter.
+  //! of members vary based on the constructed, configured, declared, deduced
+  //! filter.
   using implementation =
       internal::kalman<State, Output, Input, internal::repack_t<UpdateTypes>,
                        internal::repack_t<PredictionTypes>>;
@@ -177,6 +178,9 @@ class kalman final : public internal::conditional_member_types<internal::kalman<
   //! @{
 
   //! @brief Encapsulates the implementation details of the filter.
+  //!
+  //! @details Optionally exposes a variety of members and methods according to
+  //! the selected implementation.
   implementation filter;
   //! @}
 
@@ -197,12 +201,6 @@ class kalman final : public internal::conditional_member_types<internal::kalman<
   //! @details Also known as Σ.
   using estimate_uncertainty = implementation::estimate_uncertainty;
 
-  //! @brief Type of the process noise correlated variance matrix Q.
-  using process_uncertainty = implementation::process_uncertainty;
-
-  //! @brief Type of the observation noise correlated variance matrix R.
-  using output_uncertainty = implementation::output_uncertainty;
-
   //! @brief Type of the state transition matrix F.
   //!
   //! @details Also known as the fundamental matrix, propagation, Φ, or A.
@@ -360,8 +358,10 @@ class kalman final : public internal::conditional_member_types<internal::kalman<
   //! @return The process noise correlated variance matrix Q.
   //!
   //! @complexity Constant.
-  inline constexpr auto q() const -> const process_uncertainty &;
-  inline constexpr auto q() -> process_uncertainty &;
+  inline constexpr const auto &q() const
+    requires(has_process_uncertainty<implementation>);
+  inline constexpr auto &q()
+    requires(has_process_uncertainty<implementation>);
 
   //! @brief Sets the process noise covariance matrix Q.
   //!
@@ -377,7 +377,8 @@ class kalman final : public internal::conditional_member_types<internal::kalman<
   //! process noise covariance matrix Q.
   //!
   //! @complexity Constant.
-  inline constexpr void q(const auto &value, const auto &...values);
+  inline constexpr void q(const auto &value, const auto &...values)
+    requires(has_process_uncertainty<implementation>);
 
   //! @brief Returns the observation noise covariance
   //! matrix R.
@@ -387,8 +388,10 @@ class kalman final : public internal::conditional_member_types<internal::kalman<
   //! @return The observation noise correlated variance matrix R.
   //!
   //! @complexity Constant.
-  inline constexpr auto r() const -> const output_uncertainty &;
-  inline constexpr auto r() -> output_uncertainty &;
+  inline constexpr const auto &r() const
+    requires(has_output_uncertainty<implementation>);
+  inline constexpr auto &r()
+    requires(has_output_uncertainty<implementation>);
 
   //! @brief Sets the observation noise covariance matrix R.
   //!
@@ -404,15 +407,18 @@ class kalman final : public internal::conditional_member_types<internal::kalman<
   //! observation noise covariance matrix R.
   //!
   //! @complexity Constant.
-  inline constexpr void r(const auto &value, const auto &...values);
+  inline constexpr void r(const auto &value, const auto &...values)
+    requires(has_output_uncertainty<implementation>);
 
   //! @brief Returns the state transition matrix F.
   //!
   //! @return The state transition matrix F.
   //!
   //! @complexity Constant.
-  inline constexpr auto f() const -> const state_transition &;
-  inline constexpr auto f() -> state_transition &;
+  inline constexpr const auto &f() const
+    requires(has_state_transition<implementation>);
+  inline constexpr auto &f()
+    requires(has_state_transition<implementation>);
 
   //! @brief Sets the state transition matrix F.
   //!
@@ -433,7 +439,8 @@ class kalman final : public internal::conditional_member_types<internal::kalman<
   //! state transition matrix F.
   //!
   //! @complexity Constant.
-  inline constexpr void f(const auto &value, const auto &...values);
+  inline constexpr void f(const auto &value, const auto &...values)
+    requires(has_state_transition<implementation>);
 
   //! @brief Returns the observation transition matrix H.
   //!

include/fcarouge/utility.hpp

@@ -78,13 +78,34 @@ concept eigen = internal::eigen<Type>;
 template <typename Filter>
 concept has_input = internal::has_input<Filter>;
 
+//! @brief Filter process uncertainty support concept.
+//!
+//! @details The filter supports the process uncertainty related functionality:
+//! `process_uncertainty` type member and `q()` method.
+template <typename Filter>
+concept has_process_uncertainty = internal::has_process_uncertainty<Filter>;
+
+//! @brief Filter output uncertainty support concept.
+//!
+//! @details The filter supports the output uncertainty related functionality:
+//! `output_uncertainty` type member and `r()` method.
+template <typename Filter>
+concept has_output_uncertainty = internal::has_output_uncertainty<Filter>;
+
 //! @brief Filter input control support concept.
 //!
 //! @details The filter supports the input control related functionality:
 //! `input_control` type member and `g()` method.
 template <typename Filter>
 concept has_input_control = internal::has_input_control<Filter>;
 
+//! @brief Filter state transition support concept.
+//!
+//! @details The filter supports the state transition related functionality:
+//! `state_transition` type member and `f()` method.
+template <typename Filter>
+concept has_state_transition = internal::has_state_transition<Filter>;
+
 //! @brief Filter output model support concept.
 //!
 //! @details The filter supports the output model related functionality: