[benchmark] fix compilation

benchmark/operator1x1x0.cpp

@@ -52,7 +52,7 @@ namespace
 void operator1x1x0(::benchmark::State &state)
 {
   for (auto _ : state) {
-    using kalman = fcarouge::kalman<float, 1, 1, 0>;
+    using kalman = fcarouge::kalman<float, float>;
     kalman k;
 
     ::benchmark::ClobberMemory();

benchmark/operator1x1x1.cpp

@@ -52,7 +52,7 @@ namespace
 void operator1x1x1(::benchmark::State &state)
 {
   for (auto _ : state) {
-    using kalman = fcarouge::kalman<float, 1, 1, 1>;
+    using kalman = fcarouge::kalman<float, float, float>;
     kalman k;
 
     ::benchmark::ClobberMemory();

benchmark/predict1x1x0.cpp

@@ -52,7 +52,7 @@ namespace
 void predict1x1x0(::benchmark::State &state)
 {
   for (auto _ : state) {
-    using kalman = fcarouge::kalman<float, 1, 1, 0>;
+    using kalman = fcarouge::kalman<float, float>;
     kalman k;
 
     ::benchmark::ClobberMemory();

benchmark/predict1x1x1.cpp

@@ -52,7 +52,7 @@ namespace
 void predict1x1x1(::benchmark::State &state)
 {
   for (auto _ : state) {
-    using kalman = fcarouge::kalman<float, 1, 1, 1>;
+    using kalman = fcarouge::kalman<float, float, float>;
     kalman k;
 
     ::benchmark::ClobberMemory();

benchmark/update1x1x0.cpp

@@ -52,7 +52,7 @@ namespace
 void update1x1x0(::benchmark::State &state)
 {
   for (auto _ : state) {
-    using kalman = fcarouge::kalman<float, 1, 1, 0>;
+    using kalman = fcarouge::kalman<float, float>;
     kalman k;
 
     ::benchmark::ClobberMemory();

benchmark/update1x1x1.cpp

@@ -52,7 +52,7 @@ namespace
 void update1x1x1(::benchmark::State &state)
 {
   for (auto _ : state) {
-    using kalman = fcarouge::kalman<float, 1, 1, 1>;
+    using kalman = fcarouge::kalman<float, float, float>;
     kalman k;
 
     const float input{ 0. };

include/fcarouge/kalman.hpp

@@ -789,51 +789,43 @@ class kalman
   //! @name Public Filtering Member Functions
   //! @{
 
-  //! @brief Runs a step of the filter.
-  //!
-  //! @details Predicts and updates the estimates per prediction arguments,
-  //! control input, and measurement output.
+  //! @brief Produces estimates of the state variables and uncertainties.
   //!
-  //! @tparam InputTypes The template parameter pack types passed in the
-  //! `arguments` parameters after the update arguments and before the output
-  //! arguments. `InputTypes` must be compatible with the `Input` type template
-  //! parameter of the control u.
+  //! @details Implements the total probability theorem.
   //!
-  //! @param arguments The prediction, update, input, and output parameters of
+  //! @param arguments The prediction and input parameters of
   //! the filter, in that order. The arguments need to be compatible with the
   //! filter types. The prediction parameters convertible to the
   //! `PredictionTypes` template pack types are passed through for computations
-  //! of prediction matrices. The update parameters convertible to the
-  //! `UpdateTypes` template pack types are passed through for computations of
-  //! update matrices. The control parameter pack types convertible to the
-  //! `Input` template type. The observation parameter pack types convertible to
-  //! the `Output` template type. The update and prediction types are explicitly
-  //! defined with the class definition and the observation parameter pack types
-  //! are always deduced per the greedy matching rule. However the control
-  //! parameter pack types must always be explicitly defined per the fair
-  //! matching rule.
-  //!
-  //! @note Called as `k(...);` with prediction values and output values when
-  //! the filter has no input parameters. The input type list is explicitly
-  //! empty. Otherwise can be called as `k.template operator()<input1_t,
-  //! input2_t, ...>(...);` with prediction values, input values, and output
-  //! values. The input type list being explicitly specified per the fair
-  //! matching rule. A lambda can come in handy to reduce the verbose call
-  //! `const auto kf{ [&k](const auto
-  //! &...args) { k.template operator()<input1_t, input2_t,
-  //! ...>(args...); } };` then called as `kf(...);`.
+  //! of prediction matrices. The control parameter pack types convertible to
+  //! the `Input` template type. The prediction types are explicitly defined
+  //! with the class definition.
   //!
+  //! @todo Consider whether this method needs to exist or if the operator() is
+  //! sufficient for all clients?
   //! @todo Consider if returning the state column vector X would be preferable?
   //! Or fluent interface? Would be compatible with an ES-EKF implementation?
-  //! @todo Understand why the implementation cannot be moved out of the class.
-  //! @todo What should be the order of the parameters? Update first?
-  template <typename... InputTypes>
-  inline constexpr void operator()(const auto &...arguments);
+  //! @todo Can the parameter pack of `PredictionTypes` be explicit in the
+  //! method declaration for user clarity?
+  inline constexpr void predict(const auto &...arguments);
+
+  //! @brief Returns the Nth prediction argument.
+  //!
+  //! @details Convenience access to the last used prediction arguments.
+  //!
+  //! @tparam The non-type template parameter index position of the prediction
+  //! argument types.
+  //!
+  //! @return The prediction argument corresponding to the Nth position of the
+  //! parameter pack of the tuple-like `PredictionTypes` class template type.
+  //!
+  //! @complexity Constant.
+  template <std::size_t Position> inline constexpr auto predict() const;
 
   //! @brief Updates the estimates with the outcome of a measurement.
   //!
-  //! @details Implements the Bayes' theorem. Combine one measurement and the
-  //! prior estimate.
+  //! @details Also known as the observation or correction step. Implements the
+  //! Bayes' theorem. Combine one measurement and the prior estimate.
   //!
   //! @param arguments The update and output parameters of
   //! the filter, in that order. The arguments need to be compatible with the
@@ -864,38 +856,46 @@ class kalman
   //! @complexity Constant.
   template <std::size_t Position> inline constexpr auto update() const;
 
-  //! @brief Produces estimates of the state variables and uncertainties.
+  //! @brief Runs a full step of the filter.
   //!
-  //! @details Implements the total probability theorem.
+  //! @details Predicts and updates the estimates per arguments,
+  //! control input, and measurement output.
   //!
-  //! @param arguments The prediction and input parameters of
+  //! @tparam InputTypes The template parameter pack types passed in the
+  //! `arguments` parameters after the update arguments and before the output
+  //! arguments. `InputTypes` must be compatible with the `Input` type template
+  //! parameter of the control u.
+  //!
+  //! @param arguments The prediction, update, input, and output parameters of
   //! the filter, in that order. The arguments need to be compatible with the
   //! filter types. The prediction parameters convertible to the
   //! `PredictionTypes` template pack types are passed through for computations
-  //! of prediction matrices. The control parameter pack types convertible to
-  //! the `Input` template type. The prediction types are explicitly defined
-  //! with the class definition.
+  //! of prediction matrices. The update parameters convertible to the
+  //! `UpdateTypes` template pack types are passed through for computations of
+  //! update matrices. The control parameter pack types convertible to the
+  //! `Input` template type. The observation parameter pack types convertible to
+  //! the `Output` template type. The update and prediction types are explicitly
+  //! defined with the class definition and the observation parameter pack types
+  //! are always deduced per the greedy matching rule. However the control
+  //! parameter pack types must always be explicitly defined per the fair
+  //! matching rule.
+  //!
+  //! @note Called as `k(...);` with prediction values and output values when
+  //! the filter has no input parameters. The input type list is explicitly
+  //! empty. Otherwise can be called as `k.template operator()<input1_t,
+  //! input2_t, ...>(...);` with prediction values, input values, and output
+  //! values. The input type list being explicitly specified per the fair
+  //! matching rule. A lambda can come in handy to reduce the verbose call
+  //! `const auto kf{ [&k](const auto
+  //! &...args) { k.template operator()<input1_t, input2_t,
+  //! ...>(args...); } };` then called as `kf(...);`.
   //!
-  //! @todo Consider whether this method needs to exist or if the operator() is
-  //! sufficient for all clients?
   //! @todo Consider if returning the state column vector X would be preferable?
   //! Or fluent interface? Would be compatible with an ES-EKF implementation?
-  //! @todo Can the parameter pack of `PredictionTypes` be explicit in the
-  //! method declaration for user clarity?
-  inline constexpr void predict(const auto &...arguments);
-
-  //! @brief Returns the Nth prediction argument.
-  //!
-  //! @details Convenience access to the last used prediction arguments.
-  //!
-  //! @tparam The non-type template parameter index position of the prediction
-  //! argument types.
-  //!
-  //! @return The prediction argument corresponding to the Nth position of the
-  //! parameter pack of the tuple-like `PredictionTypes` class template type.
-  //!
-  //! @complexity Constant.
-  template <std::size_t Position> inline constexpr auto predict() const;
+  //! @todo Understand why the implementation cannot be moved out of the class.
+  //! @todo What should be the order of the parameters? Update first?
+  template <typename... InputTypes>
+  inline constexpr void operator()(const auto &...arguments);
 
   //! @}
 };