mixle.experimental package

mixle.experimental – exploratory surfaces that are not (yet) part of mixle’s mature API.

Code here is kept for exploration and may change or be removed without the usual stability guarantees.

Current contents:

• mixle.experimental.program – the optimization-program approach (moves + combinators: minimize / maximize / em / alternate / weighted / constrain / reinforce / pareto / bilevel / gail / maxent_irl) to fitting heterogeneous neural + stats models. A reasonable idea that wasn’t mature: its closure-taking surface (minimize(lambda: loss, over=params)) is exactly the PyTorch-style jank it set out to avoid. For the common cases it is superseded by the declarative neural surface – Categorical(logits=Net(...)).fit(y, given=...), Normal(Net(...), free).fit(...), and mixtures of SoftmaxNeuralLeaf experts – which compose into the PPL with no loss closures. It is kept here for the genuinely game-shaped cases the declarative surface does not reach (GANs, on-policy RL).

Submodules

• mixle.experimental.program module