Deep Markov

I'm playing with doing multiple layers of markov chains. ex: tokens get grouped into sentences with sentence features being the top k keywords, sentences get grouped into paragraphs the same way, and paragraphs into chapters. Each layer (token, sentence, paragraph, chapter) gets its own markov table composed of the features from the layer below. When doing generation, you pick a feature from the higher layer and use that to constrain the features you pick in the lower layer.

Though first I have to get some clean training data (which is what I'm working on now).

Here's my repo: https://github.com/rabidsnail/NaNoGenMo

The issue proposes hierarchical Markov-chain generation and says clean training data is still being prepared, but it names no repository files, tests, or entry points. Start by reviewing the repository and the training-data work, then define the implementation scope and a concrete condition for completion before coding.