Add DAG inference as a dicekit.learn submodule#16
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Introduce a Bayesian-network / DAG inference API (DAG, Node, Event, Evidence, Factor) in a new `dicekit.learn` submodule, queried via `p`. Core `p` becomes a `functools.singledispatch` generic that knows only about Dice; the DAG layer registers its own Node/Event handlers, so core stays unaware of the inference layer and there is no import cycle. Migrate the notebook build from nbs/build.py to `mobuild` (Makefile + dev dependency) and add a second export for nbs/learn.py -> dicekit/learn.py. Add tests/test_learn.py covering the submodule API. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
New feature release: adds the DAG inference submodule. Sync uv.lock. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Removed per review: the symbol move is covered elsewhere. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Introduces a Bayesian-network / DAG inference API (
DAG,Node,Event,Evidence,Factor) in a newdicekit.learnsubmodule, queried throughp. Corepbecomes afunctools.singledispatchgeneric that only knows aboutDice; the DAG layer registers its ownNode/Eventhandlers, keeping core unaware of the inference layer with no import cycle. Migrates the notebook build fromnbs/build.pytomobuild(Makefile + dev dependency), adding a second export fornbs/learn.py→dicekit/learn.py. Addstests/test_learn.pycovering the submodule API; all 45 tests pass andbasedpyrightreports zero errors.🤖 Generated with Claude Code