slop prototype

Author: David Ledvinka

README.md

@@ -1 +1,49 @@
-# Random-do notation
+# Random-do notation
+
+Write a probability program once using `rdo`, then interpret it in different measurable-space
+monads. `Measure` gives its distribution; `RandomM Ω P` samples from a probability source while
+preserving fresh source state. `SampleM Ω P` uses an infinite stream of independent `P` draws.
+
+To relate the two interpretations, add `rdo_program` to a polymorphic definition:
+
+```lean
+import RandomDo
+
+open MeasureTheory
+universe v
+
+def sumDraws {m : (α : Type) → [MeasurableSpace α] → Type v}
+    [MeasurableSpaceMonad m] (coin : m Bool) : ℕ → m ℕ
+  | 0 => rdo return 0
+  | n + 1 => rdo
+    let b ← coin
+    let s ← sumDraws coin n
+    return b.toNat + s
+
+attribute [rdo_program] sumDraws
+
+example {Ω : Type*} [MeasurableSpace Ω] {P : Measure Ω}
+    [IsProbabilityMeasure P] (coin : RandomM Ω P Bool) (n : ℕ) :
+    (sumDraws coin n).law = sumDraws (m := Measure) coin.law n :=
+  sumDraws.law coin n
+```
+
+The attribute leaves the original definition unchanged. It generates a recorded program
+(`.program`), a certificate (`.valid` and `.certified`), bridges to the original interpretations
+(`.sample_bridge` and `.measure_bridge`), and the resulting `.law` theorem. The proof uses
+`RDo.Program.Certified.law`, which holds for every certified program. The `rdo_valid` tactic can
+also construct certificates directly, leaving any unresolved measurability conditions as goals.
+
+The current automation supports returns, binds, measurable conditionals, sampler arguments,
+and one-argument sampler families. A family argument adds a joint-measurability hypothesis to
+the generated certificate and law theorem. `SampleM.ofKernel` provides this property for Markov
+kernels; `SampleM.ofMeasure` supplies independent draws from probability measures on standard
+Borel spaces. Both constructors use a stream of uniform draws from the unit interval.
+
+The attribute currently requires leading `{m} [MeasurableSpaceMonad m]` parameters and an
+independent universe parameter for the monad's output, as above. It attempts induction on the
+last explicit `Nat` argument. General recursion and loops need further support. Certification
+fails if a proof obligation remains; no global measurable-evaluation assumption is required.
+
+See `Test/Program.lean` for continuous and kernel examples, and `Test/SampleM.lean` for independent
+draws and the proof that a sum of `n` Bernoulli samples has the binomial distribution.

RandomDo.lean

@@ -6,6 +6,8 @@ public import RandomDo.Monad.ForInInstances
 public import RandomDo.Monad.Instances
 public import RandomDo.Monad.MeasurableSpace
 public import RandomDo.Monad.Notation
+public import RandomDo.Monad.Program
+public import RandomDo.Monad.Sample
 public import RandomDo.NumLean.Distributions
 public import RandomDo.NumLean.PCG64
 public import RandomDo.NumLean.SeedSequence
@@ -16,3 +18,4 @@ public import RandomDo.Tactic.Elab
 public import RandomDo.Tactic.ForInStep
 public import RandomDo.Tactic.IsMarkov
 public import RandomDo.Tactic.Lemmas
+public import RandomDo.Tactic.Program