During the seventh week I have been primarily working on implementation of different parts of the hyper-parameter tuning module. I have implemented the cross-validation wrapper that I mentioned previously, as well as grid-search based optimization. The grid-search optimizer follows the same interface as other mlpack optimizers, so it is possible to use it in other mlpack methods too.
Now I'm working on a hyper-parameter tuner itself. It should be able to accept different types of information for hyper-parameters - e.g. that some hyper-parameters should be bound to particular values, while others be chosen from sets of values.
I haven't sent any of the implemented parts for review yet since my latest PR is still under review (and the new code depends on this one). Once my latest PR is merged and the hyper-parameter tuner is ready, I will send the whole hyper-parameter tuning module as a PR.