A Go implementation of Compact Prediction Tree. A blog post related is available.
To install:
git clone https://github.com/made2591/go-cpt cd go-cpt go run main.go Assuming you already installed UniK correctly, then with a daemon running in a terminal open another shell and run:
unik build --name go-cpt-image --path ./ --base rump --language go --provider virtualbox --force unik run --instanceName go-cpt-instance --imageName go-cpt-image To retrieve the running instances:
unik instances You can see IP assigned to instances in the last column of the output
To see the logs of the running instances run:
unik logs --instance go-cpt-instance What this image does is actually expose the different endpoint to initialize training and make prediction by rest api - it's only a draft:
A sample file are already uploaded into the upload folder: you can modify the main.go root of the project to avoid cutting the training and testing set. Otherwise, to see the run you can both execute the code locally or
curl http://<YOUR_RUNNING_INSTANCES>:8080/initcpt You should see predictions for the first 10 sequences :-) 
- Matteo Madeddu - here is my github personal page -