These codes train and test a Self-Organizing Internal Model Architecture (SOIMA); after being trained, weights may be saved as .txt

To plot the SOM, this script search for the winner nodes for each element from the training element (normalized in range 0:1), then it determines which type of data such node stands for by the most common element (label) identified with such node. Thus, plotting still works with testing data. hebbianian learning only works with squared modal and amodal soms*