🚀 Add AR(p) model example

Author: bstaber

crates/CMakeLists.txt

@@ -1,2 +1,3 @@
 add_subdirectory("kf_linear")
-add_subdirectory("simple_optimizers")
+add_subdirectory("simple_optimizers")
+add_subdirectory("ar_models")

crates/ar_models/CMakeLists.txt

@@ -0,0 +1,19 @@
+# Header-only target
+add_library(ar_models INTERFACE)
+
+# Public includes (exports the include/ dir to consumers)
+target_include_directories(ar_models INTERFACE
+  ${CMAKE_CURRENT_SOURCE_DIR}/include
+)
+
+# Link Eigen and set language level
+target_link_libraries(ar_models INTERFACE Eigen3::Eigen)
+target_compile_features(ar_models INTERFACE cxx_std_17)
+
+# Demo executable
+add_executable(ar_demo src/main.cpp)
+target_link_libraries(ar_demo PRIVATE ar_models)
+
+# Tests
+enable_testing()
+add_subdirectory(tests)

crates/ar_models/include/ar.hpp

@@ -0,0 +1,24 @@
+#pragma once
+
+#include <Eigen/Dense>
+
+namespace cppx::ar_models {
+
+template <int order> class ARModel {
+  public:
+		using Vector = Eigen::Vector<double, order>;
+
+    ARModel() = default;
+    ARModel(double mean, double noise_variance) : c_(mean), sigma2_(noise_variance){};
+
+    [[nodiscard]] double mean() const noexcept { return c_; }
+    [[nodiscard]] double noise() const noexcept { return sigma2_; }
+    [[nodiscard]] const Vector &coefficients() const noexcept { return phi_; }
+
+  private:
+    Vector phi_;
+    double c_ = 0.0;
+    double sigma2_ = 1.0;
+};
+
+} // namespace cppx::ar_models

crates/ar_models/src/main.cpp

@@ -0,0 +1,6 @@
+#include <iostream>
+
+int main() {
+    std::cout << "Hello, world!" << "\n";
+    return 0;
+}

crates/ar_models/tests/CMakeLists.txt

@@ -0,0 +1,7 @@
+# crates/simple_optimizers/tests/CMakeLists.txt
+
+add_executable(ar_models_test test_ar_models.cpp)
+
+target_link_libraries(ar_models_test PRIVATE ar_models)
+
+add_test(NAME ar_models_test COMMAND ar_models_test)

crates/ar_models/tests/test_ar_models.cpp

@@ -0,0 +1,16 @@
+#include "ar.hpp"
+#include <cassert>
+
+using namespace cppx::ar_models;
+
+int main() {
+
+    {
+        ARModel<1> model;
+        assert(model.mean() == 0.0);
+        assert(model.noise() == 1.0);
+        assert(model.coefficients().size() == 1);
+    }
+
+    return 0;
+};

crates/kf_linear/include/kf_linear.hpp

@@ -6,6 +6,8 @@
 #include <stdexcept>
 #include <vector>
 
+namespace cppx::kf_linear {
+
 /**
  * @brief Generic linear Kalman filter (templated, no control term).
  *
@@ -121,3 +123,5 @@ template <int Nx, int Ny> class KFLinear {
     StateVec x_; ///< State mean
     StateMat P_; ///< State covariance
 };
+
+} // namespace cppx::kf_linear

crates/kf_linear/tests/test_kf_linear.cpp

@@ -8,6 +8,7 @@
 #include "kf_linear.hpp"
 
 using Catch::Approx;
+using namespace cppx::kf_linear;
 
 TEST_CASE("Predict-only step leaves state at A*x and P -> A P A^T + Q") {
     using KF = KFLinear<2, 1>;

crates/simple_optimizers/tests/test_simple_optimizers.cpp

@@ -55,4 +55,4 @@ int main() {
     }
 
     return 0;
-}
+};