Normal Distribution (Advanced)

Statistical distributions, such as the normal distribution, are useful for evaluating experimental or plant data. Although the cumulative distribution can be obtained by integrating numerically, it is more convenient to obtain it easily by steady-state calculation. Therefore, I created an example to calculate the normal distribution easily using Boost C++ library. The example contains a dll file to run the procedure in ACM. In addition, an example was created to compare with the results of the numerical integration. The Gear method was the most accurate integration method.

ACM example file: Boost_Procedure.zip (V12.1)

                          (Boost_Procedure.acmf , ACM_Boost.dll)

                           NormDist_Comparison.zip

                          (NormDist_Comparison.acmf)

How to develop the dll file: ACM_Boost Procedure Development.pdf

Visual C++ 2019 Source Code: ACM_Boost.zip

Eigen Values (Advanced)

ACM is not good at matrix eigenvalue problems because it cannot find multiple solutions simultaneously. Therefore, I created an example to calculate eigenvalues and eigenvectors in ACM using the C++ Eigen library. The example contains a dll file to run the procedure in ACM.

 ACM example file: Eigen_Procedure.zip (V12.1)

                           (Eigen_Procedure.acmf , ACM_Eigen.dll)

 How to develop the dll file: ACM_Eigen Procedure Development.pdf

 Visual C++ 2019 Source Code: ACM_Eigen.zip