CPP/Eigen/LevenbergMarquardt - Revision history

Daviddoria at 22:44, 4 November 2011

2011-11-04T22:44:25Z

Daviddoria at 22:31, 4 November 2011

2011-11-04T22:31:16Z

Daviddoria at 21:23, 4 November 2011

2011-11-04T21:23:33Z

Daviddoria: Created page with '==ProjectPoints.cpp== #include #include #include struct MyFunctor { // typedef Eigen::…'

2011-11-04T20:19:02Z

Created page with '==ProjectPoints.cpp== <source lang="cpp"> #include <iostream> #include <Eigen/Dense> #include <unsupported/Eigen/NonLinearOptimization> struct MyFunctor { // typedef Eigen::…'

New page

==ProjectPoints.cpp==
<source lang="cpp">
#include <iostream>

#include <Eigen/Dense>

#include <unsupported/Eigen/NonLinearOptimization>

struct MyFunctor
{
// typedef Eigen::MatrixXf<float,InputsAtCompileTime,1> InputType;
// typedef Eigen::Matrix<Scalar,ValuesAtCompileTime,1> ValueType;
// typedef Eigen::Matrix<Scalar,ValuesAtCompileTime,InputsAtCompileTime> JacobianType;

//MyFunctor();

int operator()(const Eigen::VectorXd &x, Eigen::VectorXd &fvec) const
{
// Implement y = x^2
fvec(0) = x(0)*x(0);
return 0;
}

int df(const Eigen::VectorXd &x, Eigen::MatrixXd &fjac) const
{
// Implement dy/dx = 2*x
fjac(0) = 2.0f * x(0);
return 0;
}
};

int main(int argc, char *argv[])
{
Eigen::VectorXf x(1);
x(0) = 2;

// do the computation
MyFunctor functor;
Eigen::LevenbergMarquardt<MyFunctor> lm(functor);
int info = lm.lmder1(x);

// check x
VectorXd x_ref(n);

return 0;
}

</source>

==CMakeLists.txt==
<source lang="cmake">
cmake_minimum_required(VERSION 2.6)

PROJECT(LevenbergMarquardt)
include_directories(/media/portable/src/Eigen)
# FIND_PACKAGE(Eigen3 REQUIRED)
# INCLUDE(${Eigen_USE_FILE})

include_directories(${EIGEN3_INCLUDE_DIR} ${EIGEN3_INCLUDE_DIR}/unsupported/)
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DEIGEN_YES_I_KNOW_SPARSE_MODULE_IS_NOT_STABLE_YET")

ADD_EXECUTABLE(LevenbergMarquardt LevenbergMarquardt.cpp )

</source>

@@ Line 12: / Line 12: @@
+   {
      // Implement y = x^2
-     fvec(0) = x(0)*x(0);
+     fvec(0) = x(0)*x(0) - 5.0 * x(0);
      return 0;
+   }
@@ Line 24: / Line 24: @@
    int inputs() const { return 1; }
-   int values() const { return 0; }
+   int values() const { return 1; } // number of constraints
 };
@@ Line 34: / Line 34: @@
    std::cout << "x: " << x << std::endl;
    MyFunctor functor;
    Eigen::LevenbergMarquardt<MyFunctor, float> lm(functor);
    lm.minimize(x);
-   std::cout << "x: " << x << std::endl;
+   std::cout << "x that minimizes the function: " << x << std::endl;
-  std::cout << "lm.fvec: " << lm.fvec << std::endl;
    return 0;

@@ Line 9: / Line 9: @@
 struct MyFunctor
+{
-//   typedef Eigen::MatrixXf<float,InputsAtCompileTime,1> InputType;
+   int operator()(const Eigen::VectorXf &x, Eigen::VectorXf &fvec) const
-//   typedef Eigen::Matrix<Scalar,ValuesAtCompileTime,1> ValueType;
-//   typedef Eigen::Matrix<Scalar,ValuesAtCompileTime,InputsAtCompileTime> JacobianType;
-−
-  //MyFunctor();
-−
-   int operator()(const Eigen::VectorXd &x, Eigen::VectorXd &fvec) const
+   {
      // Implement y = x^2
@@ Line 22: / Line 16: @@
+   }
-   int df(const Eigen::VectorXd &x, Eigen::MatrixXd &fjac) const
+   int df(const Eigen::VectorXf &x, Eigen::MatrixXf &fjac) const
+   {
      // Implement dy/dx = 2*x
@@ Line 28: / Line 22: @@
      return 0;
+   }
 };
@@ Line 35: / Line 32: @@
    Eigen::VectorXf x(1);
    x(0) = 2;
    // do the computation
@@ Line 41: / Line 39: @@
    lm.minimize(x);
-   // check x
+   std::cout << "x: " << x << std::endl;
-   VectorXd x_ref(n);
+   std::cout << "lm.fvec: " << lm.fvec << std::endl;
-−
    return 0;
+}