mixle 0.6.1
Highlights since 0.6.0.
Neural densities as composable distributions. Normalizing flows (coupling and masked-autoregressive), a VAE, mixture density networks, autoregressive-categorical densities over discrete vectors, and an energy-based model — each a mixle Distribution that fits jointly with classical families by EM and drops into the mixle.ppl surface (Flow, MAF, VAE, MDN, EBM, CondFlow, ...). The mixle.models neural leaves were also renamed off the ...Leaf suffix (back-compat aliases kept).
Automatic dependency-structure learning. optimize(data) / fit(data) now discovers the cross-field dependency graph by default (BIC-gated, never worse than independence). Heterogeneous Bayesian networks with regression edges, mixtures of DAGs, and counterfactual queries on the learned graph.
Cross-modal reasoning (mixle.reason). Product-of-experts and hybrid attention/structured fusion, a transfer-safe adapter over frozen encoders, iterated-EKF assimilation of nonlinear forward models, and retrieval by what the model believes.
Task distillation (mixle.task). solve() for regression, multi-label, and structured routines with conformal calibration; a generative-text student; agent-trace harvesting; grammar-constrained plan generation; and live-traffic health monitoring.
Inference & tooling. Closed-form Fisher-weighted parameter merge, closed-form variational projections (exact GMM collapse + Runnalls reduction), exact multi-prime NTT convolution for count-DP ranking, and a refreshed documentation site.
- Install:
pip install mixle - Docs: https://gmboquet.github.io/mixle/
- Notebooks: https://github.com/gmboquet/mixle-notebooks