openAI-math-dataset / lean /OAI /NumberTheory /TwoPoint /Circuits /CircuitCanonicalTree.lean
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import OAI.NumberTheory.TwoPoint.Circuits.CircuitPartialAssignment
import OAI.NumberTheory.TwoPoint.Circuits.CircuitDecisionTree
namespace OAI
/-! The canonical tree queries the unset variables of the first compatible
term, then continues through the remaining terms. This concrete tree is the
one encoded in the switching argument. -/
namespace TwoPointCorrelations
open Finset
open scoped Classical
namespace BooleanDecisionTree
def queryVariables {n : ℕ} : List (Fin n) →
(PartialAssignment n → BooleanDecisionTree n) → PartialAssignment n → BooleanDecisionTree n
| [], k, ρ => k ρ
| i :: is, k, ρ => .query i
(queryVariables is k (Function.update ρ i (some false)))
(queryVariables is k (Function.update ρ i (some true)))
lemma queryVariables_eval {n : ℕ} (is : List (Fin n))
(k : PartialAssignment n → BooleanDecisionTree n) (ρ : PartialAssignment n)
(x : BooleanCube n) :
(queryVariables is k ρ).eval x = (k (ρ.assign is.toFinset x)).eval x := by
induction is generalizing ρ with
| nil => simp [queryVariables]
| cons i is ih =>
simp only [queryVariables, eval]
cases hx : x i <;> simp only [Bool.false_eq_true, ite_false, ite_true]
· rw [ih]
have ha := PartialAssignment.assign_insert ρ is.toFinset x i
rw [hx] at ha
simp only [List.toFinset_cons, ha]
· rw [ih]
have ha := PartialAssignment.assign_insert ρ is.toFinset x i
rw [hx] at ha
simp only [List.toFinset_cons, ha]
lemma queryVariables_depth_le {n d : ℕ} (is : List (Fin n))
(k : PartialAssignment n → BooleanDecisionTree n)
(hk : ∀ ρ, (k ρ).depth ≤ d) (ρ : PartialAssignment n) :
(queryVariables is k ρ).depth ≤ is.length + d := by
induction is generalizing ρ with
| nil => simpa only [queryVariables, List.length_nil, Nat.zero_add] using hk ρ
| cons i is ih =>
change max _ _ + 1 ≤ _
have hl := ih (Function.update ρ i (some false))
have hh := ih (Function.update ρ i (some true))
have hm := max_le hl hh
simpa only [List.length_cons, Nat.add_right_comm] using Nat.add_le_add_right hm 1
end BooleanDecisionTree
def dnfEval {n : ℕ} (F : List (CubeTerm n)) (x : BooleanCube n) : Bool :=
F.any (fun C => C.eval x)
noncomputable def canonicalDNFTree {n : ℕ} :
List (CubeTerm n) → PartialAssignment n → BooleanDecisionTree n
| [], _ => .leaf false
| C :: F, ρ =>
if C.Compatible ρ then
BooleanDecisionTree.queryVariables ((C.live ρ).sort (· ≤ ·))
(fun τ => if C.Satisfied τ then .leaf true else canonicalDNFTree F τ) ρ
else canonicalDNFTree F ρ
theorem canonicalDNFTree_eval {n : ℕ} (F : List (CubeTerm n))
(ρ : PartialAssignment n) (x : BooleanCube n) :
(canonicalDNFTree F ρ).eval x = dnfEval F (ρ.apply x) := by
induction F generalizing ρ with
| nil => simp [canonicalDNFTree, dnfEval, BooleanDecisionTree.eval]
| cons C F ih =>
by_cases hC : C.Compatible ρ
· rw [canonicalDNFTree, ite_eq_left hC, BooleanDecisionTree.queryVariables_eval]
simp only [Finset.sort_toFinset]
have happly := ρ.apply_assign_free (C.live ρ) (C.live_subset_free ρ) x
have hs := C.satisfied_assign_live_iff ρ hC x
by_cases hsat : C.Satisfied (ρ.assign (C.live ρ) x)
· have he := hs.mp hsat
simp [hsat, BooleanDecisionTree.eval, dnfEval, he]
· have he : C.eval (ρ.apply x) = false := Bool.eq_false_iff.mpr (mt hs.mpr hsat)
simp only [hsat, ite_false, ih, happly, dnfEval, List.any_cons, he,
Bool.false_or]
· have he := C.eval_apply_of_incompatible ρ hC x
simp only [canonicalDNFTree, hC, ite_false, ih, dnfEval, List.any_cons, he,
Bool.false_or]
end TwoPointCorrelations
end OAI