Example of Kohonen working as a Self-Organizing Map.

The implementation of Kohonen is a modified version of MiniSom. The difference is an exposed method to apply the training in steps to be able to hook up to that and visualize the process.

Create a virtual environment and install all dependencies:

virtualenv .env
source .env/bin/activate
pip install -r requirements.txt

To run the method, the command is:

python main.py -n KOHONEN_SIZE -it ITERATIONS -nd SPACE_POINTS -fig grid|circle|triangle|random-circle -line

• -n --> Size of the network (n x n)
• -it --> Number of iterations to use
• -nd --> Total points to attempt to adjust to (should be perfect square, ie, 25, 36, 49, etc) within the range [-1, 1]
• -fig --> Point distribution to use.
• -line --> Enables or disables drawing lines between Kohonen points.

Example call:

python main.py -n 5 -it 3000 -nd 25 -fig grid 

To run the method, the command is:

python image_to_gif.py -f FOLDER_PATH -s STEPS_IN_GIF

• -f --> Path to folder with images
• -s --> Frames to skip in every iteration, it's the step

Example call:

python image_to_gif.py -f results/5-3000-25 -s 10

To run the method, the command is:

python image_to_video.py -f FOLDER_PATH -s STEPS_IN_GIF -fps VIDEO_FPS

• -f --> Path to folder with images
• -s --> Frames to skip in every iteration, it's the step
• -fps --> FPS for the video

Example call:

python image_to_video.py -f results/5-3000-25 -s 1 -fps 60