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Add lean source and binary files (part 63)
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import OAI.Computability.RandomMean.ControlCorrectness
namespace OAI
section
namespace MeanPayoff.TreeDevice.Certified
open BinaryData Code
local infixl:80 " ≫ₚ " => Code.comp
namespace Word
end Word
namespace Array
end Array
end MeanPayoff.TreeDevice.Certified
end
section
namespace MeanPayoff.TreeDevice.Certified
open BinaryData Code
namespace Array
end Array
namespace RawBudget
end RawBudget
namespace RawBox
end RawBox
end MeanPayoff.TreeDevice.Certified
end
section
namespace MeanPayoff.TreeDevice.Certified
open BinaryData Code
namespace RawBasic
theorem fold_digits {g : Arena} {b : RawBox.Data} {E M : ℕ} (hg : ArenaBounded g E)
(ha : Array.Bounded b.2.1 M) (hb : Array.Bounded b.2.2 M) (xs : List W) (cs : Count)
(hx : Array.Bounded xs M) : Array.Bounded (Fold.fold advance.eval ((g,b),xs) cs).2 (M+cs.length*(E+1)) := by
induction cs generalizing M xs with
| nil => simpa only [Fold.fold,List.foldl_nil,List.length_nil,Nat.zero_mul,Nat.add_zero] using hx
| cons u cs ih =>
have hn : Array.Bounded (next ((g,b),xs)) (M+(E+1)) :=
clip_bound (ha.mono (by omega)) (hb.mono (by omega)) (by change Array.Bounded (graph (xs,g)) (M+(E+1)); simpa only [Nat.add_assoc] using graph_bound hx hg)
have h := ih (ha.mono (by omega : M≤M+(E+1))) (hb.mono (by omega : M≤M+(E+1))) (next ((g,b),xs)) hn
change Array.Bounded (Fold.fold advance.eval ((g,b),next ((g,b),xs)) cs).2 _
convert h using 1 ; simp only [List.length_cons,Nat.add_mul,Nat.one_mul] ; omega
theorem solve_small {g : Arena} {b : RawBox.Data} {n E M : ℕ} (hg : ArenaBounded g E)
(hb : RawBox.Small n M b) (cs : Count) : Array.Small n (M+cs.length*(E+1)) (solve ((g,b),cs)) := by
refine ⟨?_,fold_digits hg hb.lower.digits hb.upper.digits b.2.1 cs hb.lower.digits⟩
change (Fold.fold advance.eval ((g,b),b.2.1) cs).2.length≤n
rw [(fold_layout (g,b) b.2.1 cs).2]
split <;> first | exact hb.lower.length | exact hb.upper.length
end RawBasic
namespace Array
theorem Small.foldMin {n M : ℕ} {xs : List W} {xss : List (List W)} (hx : Small n M xs) (hs : ∀ ys∈xss, Small n M ys) :
Small n M (foldMin (xs,xss)) :=
⟨(fold_select_bound Word.min EdgeScan.low_mem hx.digits (fun ys hy => (hs ys hy).digits)).2.le.trans hx.length,
(fold_select_bound Word.min EdgeScan.low_mem hx.digits (fun ys hy => (hs ys hy).digits)).1⟩
theorem Small.foldMax {n M : ℕ} {xs : List W} {xss : List (List W)} (hx : Small n M xs) (hs : ∀ ys∈xss, Small n M ys) :
Small n M (foldMax (xs,xss)) :=
⟨(fold_select_bound Word.max EdgeScan.high_mem hx.digits (fun ys hy => (hs ys hy).digits)).2.le.trans hx.length,
(fold_select_bound Word.max EdgeScan.high_mem hx.digits (fun ys hy => (hs ys hy).digits)).1⟩
end Array
namespace RawBox
theorem Small.smallVector {n M : ℕ} {b : Data} (hb : Small n M b) {p : RawBudget.Data} (hp : RawBudget.Small n M p) :
Array.Small n M (smallVector (p,b)) := by
apply Array.choose_small hb.upper hb.lower
change (Zip.zip _ _ _ _).length≤n
rw [Array.zip_length]
change (p.2.2.2.map _).length≤n
simpa only [List.length_map] using hp.beta.length
end RawBox
end MeanPayoff.TreeDevice.Certified
end
section
namespace MeanPayoff.TreeDevice.Certified
open BinaryData Code
namespace Array
theorem Bounded.nth {M : ℕ} {xs : List W} (h : Bounded xs M) (i : ℕ) : (xs.getD i zero).digits.length≤M := by
induction xs generalizing i with
| nil => exact Nat.zero_le _
| cons x xs ih => cases i with
| zero => exact h x (by simp)
| succ i => exact ih (fun y hy => h y (by simp [hy])) i
end Array
namespace RawMedian
theorem scalar_bound {M : ℕ} {xs : List W} (h : Array.Bounded xs M) : (scalar xs).digits.length≤M := by
apply Array.Bounded.headD
intro w hw
exact h w ((Sort.sort_perm xs).mem_iff.mp (List.mem_of_mem_drop hw))
theorem column_bound {M : ℕ} {xs : List (List W)} (h : ∀ ys∈xs, Array.Bounded ys M) (c : Count) :
(column (xs,c)).digits.length≤M := by
apply scalar_bound
intro w hw
obtain ⟨ys,hy,rfl⟩ := List.mem_map.mp hw
change (Code.nth Array.zero (ys,c)).digits.length≤M
rw [Code.nth_eval]
exact (h ys hy).nth _
theorem coordinates_small {M n : ℕ} {xs : List (List W)} (h : ∀ ys∈xs, Array.Bounded ys M) (c : Count) (hc : c.length≤n) :
Array.Small n M (coordinates (c,xs)) := by
refine ⟨?_,?_⟩
· change ((Counter.enum c).map _).length≤n
rw [List.length_map,Counter.enum_eq,List.length_map,List.length_range]
exact hc
· intro w hw
obtain ⟨i,hi,rfl⟩ := List.mem_map.mp hw
exact column_bound h i
theorem boxed_small {n M : ℕ} {b : RawBox.Data} (hb : RawBox.Small n M b) {xs : List (List W)} (h : ∀ ys∈xs, Array.Small n M ys) :
Array.Small n M (boxed (b,xs)) := by
simp only [boxed,branch_eval,comp_eval,pair_eval,fst_eval,snd_eval,empty_eval]
split
· exact hb.lower
· apply coordinates_small (fun ys hy => (h ys hy).digits)
change (b.2.1.map (fun _ => ())).length≤n
simpa only [List.length_map] using hb.lower.length
end RawMedian
namespace RawOrientation
def Small (n : ℕ) (o : Data) : Prop := ∀ hs∈o.2, hs.length≤n
theorem positives_length (b : RawBox.Data) (s : W) (xs : List W) : (positives (b,s,xs)).length=xs.length := by
change ((_ : List W).map _).length=xs.length
rw [List.length_map]
change ((_ : List W).map _).length=xs.length
rw [List.length_map]
exact Array.zip_length _ _ _ _
theorem negatives_length (b : RawBox.Data) (s : W) (xs : List W) : (negatives (b,s,xs)).length=xs.length := by
change ((_ : List W).map _).length=xs.length
rw [List.length_map]
change ((_ : List W).map _).length=xs.length
rw [List.length_map]
exact Array.zip_length _ _ _ _
theorem trueComplement_small {n : ℕ} {bs : List Bool} (h : bs.length≤n) : Small n (trueComplement bs) := by
intro cs hc
change cs∈[bs.map Bool.not] at hc
rcases List.mem_singleton.mp hc with rfl
simpa only [List.length_map] using h
theorem falseComplement_small {n : ℕ} {bs : List Bool} (h : bs.length≤n) : Small n (falseComplement bs) := by
intro cs hc
change cs∈[bs.map Bool.not] at hc
rcases List.mem_singleton.mp hc with rfl
simpa only [List.length_map] using h
theorem base_small {n M : ℕ} (p : RawBudget.Data) (b : RawBox.Data) {xs : List W} (h : Array.Small n M xs) : Small n (base (p,b,xs)) := by
have hl : (baseMask (b,xs)).length≤n := by
change ((_ : List W).map _).length≤n
rw [List.length_map]
change ((_ : List W).map _).length≤n
rw [List.length_map]
change (Array.sub (xs,b.2.1)).length≤n
exact (Array.zip_length _ _ _ _).le.trans h.length
change Small n (if RawBudget.measureLarge (p,baseMask (b,xs)) then _ else _)
split
· exact trueComplement_small hl
· intro cs hc
change cs∈[baseMask (b,xs)] at hc
rcases List.mem_singleton.mp hc with rfl
exact hl
theorem extend_small {n : ℕ} {o : Data} (ho : Small n o) {hs ns : List Bool} (hh : hs.length≤n) (hn : ns.length≤n) :
Small n (extend (o,hs,ns)) := by
intro cs hc
change cs∈Code.append (o.2,[hs,ns]) at hc
rw [Code.append_eval] at hc
rcases List.mem_append.mp hc with hc | hc
· exact ho cs hc
· simp only [List.mem_cons,List.not_mem_nil,or_false] at hc
rcases hc with rfl | rfl <;> assumption
theorem combine_small {n : ℕ} {os : List Data} (h : ∀ o∈os, Small n o) : Small n (combine os) := by
intro cs hc
change cs∈Code.flatten (os.map Prod.snd) at hc
rw [Code.flatten_eval] at hc
obtain ⟨ss,hs,hcs⟩ := List.mem_flatten.mp hc
obtain ⟨o,ho,rfl⟩ := List.mem_map.mp hs
exact h o ho cs hcs
end RawOrientation
end MeanPayoff.TreeDevice.Certified
end
end OAI