Merge 719e115 into f4d1e00

README.md

@@ -202,9 +202,9 @@ class kalman
 | `gain` | x by z | Type of the gain matrix `k`. | *K*, *L* |
 | `innovation_uncertainty` | z by z | Type of the innovation uncertainty matrix `s`. | *S* |
 | `innovation` | z by 1 | Type of the innovation column vector `y`. | *Y* |
-| `input_control` | x by u | Type of the control transition matrix `g`. | *G*, *B* |
-| `input` | u by 1 | Type of the control column vector `u`. | *U* |
-| `output_model` | z by x | Type of the observation transition matrix `h`. | *H*, *C* |
+| `input_control` | x by u | Type of the control transition matrix `g`. This member type is defined only if the filter supports input control. | *G*, *B* |
+| `input` | u by 1 | Type of the control column vector `u`. This member type is defined only if the filter supports input. | *U* |
+| `output_model` | z by x | Type of the observation transition matrix `h`. This member type is defined only if the filter supports output model. | *H*, *C* |
 | `output_uncertainty` | z by z | Type of the observation, measurement noise covariance matrix `r`. | *R* |
 | `output` | z by 1 | Type of the observation column vector `z`. | *Z*, *Y*, *O* |
 | `process_uncertainty` | x by x | Type of the process noise covariance matrix `q`. | *Q* |
@@ -224,14 +224,14 @@ class kalman
 | Characteristic | Definition |
 | --- | --- |
 | `f` | Manages the state transition matrix *F*. Gets the value. Initializes and sets the value. Configures the callable object of expression `state_transition(const state &, const input &, const PredictionTypes &...)` to compute the value. The default value is the identity matrix. |
-| `g` | Manages the control transition matrix *G*. Gets the value. Initializes and sets the value. Configures the callable object of expression `input_control(const PredictionTypes &...)` to compute the value. The default value is the identity matrix. |
-| `h` | Manages the observation transition matrix *H*. Gets the value. Initializes and sets the value. Configures the callable object of expression `output_model(const state &, const UpdateTypes &...)` to compute the value. The default value is the identity matrix. |
+| `g` | Manages the control transition matrix *G*. Gets the value. Initializes and sets the value. Configures the callable object of expression `input_control(const PredictionTypes &...)` to compute the value. The default value is the identity matrix. This member function is defined only if the filter supports input control. |
+| `h` | Manages the observation transition matrix *H*. Gets the value. Initializes and sets the value. Configures the callable object of expression `output_model(const state &, const UpdateTypes &...)` to compute the value. The default value is the identity matrix. This member function is defined only if the filter supports output model. |
 | `k` | Manages the gain matrix *K*. Gets the value last computed during the update. The default value is the identity matrix. |
 | `p` | Manages the estimated covariance matrix *P*. Gets the value. Initializes and sets the value. The default value is the identity matrix. |
 | `q` | Manages the process noise covariance matrix *Q* from the process noise *w* expected value *E[wwᵀ]* and its variance *σ²* found by measuring, tuning, educated guesses of the noise. Gets the value. Initializes and sets the value. Configures the callable object of expression `process_uncertainty(const state &, const PredictionTypes &...)` to compute the value. The default value is the null matrix. |
 | `r` | Manages the observation, measurement noise covariance matrix *R* from the measurement noise *v* expected value *E[vvᵀ]* and its variance *σ²* found by measuring, tuning, educated guesses of the noise. Gets the value. Initializes and sets the value. Configures the callable object of expression `output_uncertainty(const state &, const output &, const UpdateTypes &...)` to compute the value. The default value is the null matrix. |
 | `s` | Manages the innovation uncertainty matrix *S*. Gets the value last computed during the update. The default value is the identity matrix. |
-| `u` | Manages the control column vector *U*. Gets the value last used in prediction. |
+| `u` | Manages the control column vector *U*. Gets the value last used in prediction. This member function is defined only if the filter supports input. |
 | `x` | Manages the state estimate column vector *X*. Gets the value. Initializes and sets the value. The default value is the null column vector. |
 | `y` | Manages the innovation column vector *Y*. Gets the value last computed during the update. The default value is the null column vector. |
 | `z` | Manages the observation column vector *Z*. Gets the value last used during the update. The default value is the null column vector. |

include/fcarouge/internal/kalman.tpp

@@ -233,8 +233,13 @@ kalman<State, Output, Input, UpdateTypes, PredictionTypes>::h(
     const auto &value, const auto &...values)
   requires(has_output_model<implementation>)
 {
-  if constexpr (std::is_convertible_v<decltype(value), output_model>) {
-    filter.h = std::move(output_model{value, values...});
+  if constexpr (std::is_convertible_v<
+                    decltype(value),
+                    typename kalman<State, Output, Input, UpdateTypes,
+                                    PredictionTypes>::output_model>) {
+    filter.h = std::move(
+        typename kalman<State, Output, Input, UpdateTypes,
+                        PredictionTypes>::output_model{value, values...});
   } else {
     using observation_state_function = decltype(filter.observation_state_h);
     filter.observation_state_h =

include/fcarouge/internal/utility.hpp

@@ -84,6 +84,44 @@ template <typename Filter>
 concept has_output_model =
     has_output_model_member<Filter> || has_output_model_method<Filter>;
 
+// There is only one known way to do conditional member types: partial
+// specialization of class templates.
+template <typename Filter> struct conditional_input {};
+
+template <has_input Filter> struct conditional_input<Filter> {
+  //! @brief Type of the control column vector U.
+  //!
+  //! @details This member type is conditionally present. The presence of the
+  //! member depends on the filter capabilities.
+  using input = Filter::input;
+};
+
+template <typename Filter> struct conditional_input_control {};
+
+template <has_input_control Filter> struct conditional_input_control<Filter> {
+  //! @brief Type of the control transition matrix G.
+  //!
+  //! @details Also known as B. This member type is conditionally present. The
+  //! presence of the member depends on the filter capabilities.
+  using input_control = Filter::input_control;
+};
+
+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.
+  using output_model = Filter::output_model;
+};
+
+// 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> {};
+
 struct empty {
   inline constexpr explicit empty([[maybe_unused]] auto &&...any) noexcept {
     // Constructs from anything for all initializations compatibility.

include/fcarouge/kalman.hpp

@@ -157,14 +157,16 @@ namespace fcarouge {
 template <typename State = double, typename Output = double,
           typename Input = void, typename UpdateTypes = empty_pack,
           typename PredictionTypes = empty_pack>
-class kalman final {
+class kalman final : public internal::conditional_member_types<internal::kalman<
+                         State, Output, Input, internal::repack_t<UpdateTypes>,
+                         internal::repack_t<PredictionTypes>>> {
 private:
   //! @name Private Member Types
   //! @{
   //! @brief Implementation details of the filter.
   //!
-  //! @brief The internal implementation unpacks the parameter packs from
-  //! tuple-like types which allows for multiple parameter pack deductions.
+  //! @details The internal implementation, filtering strategies, and presence
+  //! of members vary based on the configured filter.
   using implementation =
       internal::kalman<State, Output, Input, internal::repack_t<UpdateTypes>,
                        internal::repack_t<PredictionTypes>>;
@@ -187,11 +189,6 @@ class kalman final {
   //! @details Also known as Y or O.
   using output = implementation::output;
 
-  //! @brief Type of the control column vector U.
-  //!
-  //! @todo Conditionally remove this member type when no input is present.
-  using input = implementation::input;
-
   //! @brief Type of the estimated correlated variance matrix P.
   //!
   //! @details Also known as Σ.
@@ -208,18 +205,6 @@ class kalman final {
   //! @details Also known as the fundamental matrix, propagation, Φ, or A.
   using state_transition = implementation::state_transition;
 
-  //! @brief Type of the observation transition matrix H.
-  //!
-  //! @details Also known as the measurement transition matrix or C.
-  using output_model = implementation::output_model;
-
-  //! @brief Type of the control transition matrix G.
-  //!
-  //! @details Also known as B.
-  //!
-  //! @todo Conditionally remove this member type when no input is present.
-  using input_control = implementation::input_control;
-
   //! @brief Type of the gain matrix K.
   using gain = implementation::gain;
 
@@ -447,6 +432,9 @@ class kalman final {
 
   //! @brief Returns the observation transition matrix H.
   //!
+  //! @details This member function is not present when the filter has no output
+  //! model.
+  //!
   //! @return The observation, measurement transition matrix H.
   //!
   //! @complexity Constant.
@@ -462,7 +450,8 @@ class kalman final {
   //! &...)`. For non-linear system, or extended filter, H is the Jacobian of
   //! the state observation function: `H = ∂h/∂X = ∂hj/∂xi` that is each row i
   //! contains the derivatives of the state observation function for every
-  //! element j in the state column vector X.
+  //! element j in the state column vector X. This member function is not
+  //! present when the filter has no output model.
   //!
   //! @param value The first copied initializer used to set the copied
   //! observation transition matrix H or the copied target Callable object
@@ -479,7 +468,8 @@ class kalman final {
 
   //! @brief Returns the control transition matrix G.
   //!
-  //! @details This member function is not present when the filter has no input.
+  //! @details This member function is not present when the filter has no input
+  //! control.
   //!
   //! @return The control transition matrix G.
   //!
@@ -493,7 +483,7 @@ class kalman final {
   //!
   //! @details The control transition matrix G is of type `input_control` and
   //! the function is of the form `input_control(const PredictionTypes &...)`.
-  //! This member function is not present when the filter has no input.
+  //! This member function is not present when the filter has no input control.
   //!
   //! @param value The first copied initializer used to set the copied control
   //! transition matrix G or the copied target Callable object (function object,