Julia port of the BYU CS 478 machine learning toolkit
If you want to add a learner, create a new file in the project directory for the learner.
Create the learner type and have it extend the SupervisedLearner type (e.g. struct Perceptron <: SupervisedLearner ...).
Implement the train(learner::YourLearner, features::Matrix, labels::Matrix) function and the
predict(learner::YourLearner, features::Row) function for your learner. The predict function,
instead of modifying an array passed in (as the java toolkit does), simply returns the prediction.
Add include("YourFile.jl") in MLSystemManager.jl under all the rest of the includes at the top of the file,
and add a case to the getlearner function for your learner.
Any metaparameters for a learner should be defined as keyword arguments in the constructor (presumably with a default value).
They can then be passed in from the command line as extra options; see the BaselineLearner for an example.
When you run the MLSystemManager.jl file from the command line (non-interactively), it will run just as the java toolkit does.
It takes the same options as the java toolkit. If I were in the root directory of the project, and wanted to run the baseline
learner on a arff file in the training evaluation mode, I would do so with the following command:
./MLSystemManager.jl -L baseline -A data/iris.arff -E training
Julia indexing starts at 1 instead of 0. The Matrix class I created is 1-indexed, and most of the functions that involve
the Matrix class also use 1-indexing.