$ git clone https://github.com/wilderlopes/OpenGA.git
$ docker pull wilderlopes/openga
$ docker load < openga.tar
$ docker imagesYou should expect an output similar to
REPOSITORY TAG IMAGE ID CREATED SIZE wilderlopes/openga latest beb957393d05 About an hour ago 2.45GB
$ cd OpenGAYou will find the file "start.sh". Run it like
$ ./start.shThis file runs the Docker image, creating a container. You will find yourself inside the container, in the directory '/home/openga' (notice that the prompt changes, showing the container ID -- it will be a different one for you). A welcome message is printed:
root@2095a07347a0:/home/openga# Welcome to OpenGA! Please have a look on the README file or access www.openga.org.
root@2095a07347a0:/home/openga# cd scripts/GAAFs_standard/python/There you find the python script 'gaafs.py', which you can run as
root@2095a07347a0:/home/openga/scripts/GAAFs_standard/python# python gaafs.pyIt will run 100 realizations, each with 1000 iterations, of the GA-LMS (standard) in a system identification task. The estimated and optimal multivectors are printed on the terminal, and MSE and EMSE learning curves are saved in the file 'learningCurvesGA-LMS.pdf' inside the working path. At the moment, the only way to change the coefficients of the plant to be identified (optimal weights) is modifying the source codes and rebuilding the binaries.
$ cd OpenGA/scripts/GAAFs_standard/pythonThere you will find the same 'learningCurvesGA-LMS.pdf' which you can open with your pdf reader.