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Add lean source and binary files (part 84)
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import OAI.LinearAlgebra.MatrixMultiplication.FieldParameters.Compatibility
import OAI.LinearAlgebra.MatrixMultiplication.FieldParameters.Traversal
namespace OAI
/-! Fixed rational distributions and their entropy and capacity formulas. -/
namespace MatrixMultiplication.AllFieldParameters
open scoped BigOperators
theorem stageBLaw_nonnegative (t u : Shape) : 0 ≤ stageBLaw t u := by
by_cases hu : u ∈ below t
· exact (stageBLaw_positive t u hu).le
· simp [stageBLaw, splitLaw, hu]
theorem orderedPairs_mem_iff (t : Shape) (i j : Fin 6) :
(i,j) ∈ orderedPairs t ↔ statisticWeight i + statisticWeight j = shapeMax t := by
constructor
· exact orderedPairs_support t (i,j)
· intro h
apply List.mem_flatMap.mpr
refine ⟨i, by simp, ?_⟩
apply List.mem_filterMap.mpr
exact ⟨j, by simp, by simp [h]⟩
theorem zeroPairLaw_nonnegative (t : Shape) (i j : Fin 6) : 0 ≤ zeroPairLaw t i j := by
unfold zeroPairLaw
split_ifs
· exact div_nonneg (orderedPairWeight_positive t i j).le
(list_sum_map_nonnegative _ _ (fun ij _ => (orderedPairWeight_positive t ij.1 ij.2).le))
· exact le_rfl
theorem zeroPairLaw_full_normalized (t : Shape) (ht : t ∈ zeroSecond) :
∑ ij : PairSlot, zeroPairLaw t ij.1 ij.2 = 1 := by
calc
_ = ∑ ij ∈ (orderedPairs t).toFinset, zeroPairLaw t ij.1 ij.2 := by
symm
apply Finset.sum_subset (Finset.subset_univ _)
intro ij _ hij
have hs : statisticWeight ij.1 + statisticWeight ij.2 ≠ shapeMax t := by
intro h
exact hij (List.mem_toFinset.mpr ((orderedPairs_mem_iff t ij.1 ij.2).mpr h))
simp [zeroPairLaw, hs]
_ = ((orderedPairs t).map (fun ij => zeroPairLaw t ij.1 ij.2)).sum :=
List.sum_toFinset _ (ordered_pairs_nodup t ht)
_ = 1 := zeroPairLaw_normalized t ht
def positiveHalfLaw (t : Shape) (w : Fin 3) (ij : PairSlot) : ℚ :=
((below t).map fun u => stageBLaw t u * littleLaw t u w ij.1 *
littleLaw t (complement t u) w ij.2).sum
theorem positiveHalfLaw_nonnegative (t : Shape) (w : Fin 3) (ij : PairSlot) :
0 ≤ positiveHalfLaw t w ij := by
apply list_sum_map_nonnegative
intro u _
exact mul_nonneg (mul_nonneg (stageBLaw_nonnegative t u)
(littleLaw_nonnegative t u w ij.1))
(littleLaw_nonnegative t (complement t u) w ij.2)
theorem sum_pair_mixture {A : Type*} (xs : List A) (mass : A → ℚ)
(left right : A → Fin 6 → ℚ)
(hl : ∀ x ∈ xs, ∑ i, left x i = 1)
(hr : ∀ x ∈ xs, ∑ i, right x i = 1) :
(∑ ij : PairSlot, (xs.map fun x => mass x * left x ij.1 * right x ij.2).sum) =
(xs.map mass).sum := by
induction xs with
| nil => simp
| cons x xs ih =>
have hleft := hl x (by simp)
have hright := hr x (by simp)
have hhead : (∑ ij : PairSlot, mass x * left x ij.1 * right x ij.2) = mass x := by
simp [Fintype.sum_prod_type, ← Finset.mul_sum, hleft, hright]
have htail := ih (fun y hy => hl y (by simp [hy])) (fun y hy => hr y (by simp [hy]))
simpa only [List.map_cons, List.sum_cons, Finset.sum_add_distrib, hhead] using
congrArg (fun q : ℚ => mass x + q) htail
theorem positiveHalfLaw_normalized (t : Shape) (ht : t ∈ positiveSecond) (w : Fin 3) :
∑ ij : PairSlot, positiveHalfLaw t w ij = 1 := by
unfold positiveHalfLaw
rw [sum_pair_mixture _ _ _ _ (fun u _ => littleLaw_normalized t u w)
(fun u _ => littleLaw_normalized t (complement t u) w)]
exact stageBLaw_normalized t ht
theorem stageB_complement_permutation : ∀ t ∈ positiveSecond,
((below t).map (complement t)).Perm (below t) := by decide +kernel
theorem stageB_complement_involution (t : Shape) (ht : t ∈ positiveSecond)
(u : Shape) (hu : u ∈ below t) : complement t (complement t u) = u := by
funext w
exact Nat.sub_sub_self ((stageB_support_complement t ht u hu).2 w)
theorem positiveHalfLaw_swap (t : Shape) (ht : t ∈ positiveSecond)
(w : Fin 3) (i j : Fin 6) : positiveHalfLaw t w (i,j) = positiveHalfLaw t w (j,i) := by
let f := fun u => stageBLaw t u * littleLaw t u w i * littleLaw t (complement t u) w j
let g := fun u => stageBLaw t u * littleLaw t u w j * littleLaw t (complement t u) w i
have hp := (stageB_complement_permutation t ht).map f
have hm : ((below t).map (complement t)).map f = (below t).map g := by
rw [List.map_map]
apply List.map_congr_left
intro u hu
dsimp only [Function.comp_def, f, g]
rw [stageBLaw_complement t u ht hu, stageB_complement_involution t ht u hu]
ring
change ((below t).map f).sum = ((below t).map g).sum
rw [← hp.sum_eq, hm]
def halfMaxAxis (t : Shape) : Fin 3 :=
(([0,1,2] : List (Fin 3)).find? (fun w => t w == shapeMax t)).getD 0
def halfLaw (t : Shape) (w : Fin 3) (ij : PairSlot) : ℚ :=
if positive t then positiveHalfLaw t w ij
else if t w = 0 then if ij = (0,0) then 1 else 0
else if w = halfMaxAxis t then zeroPairLaw t ij.1 ij.2
else zeroPairLaw t (kappa ij.1) (kappa ij.2)
theorem halfLaw_positive_parent (t : Shape) (ht : t ∈ positiveSecond)
(w : Fin 3) (ij : PairSlot) : halfLaw t w ij = positiveHalfLaw t w ij := by
have hp : positive t = true := (List.mem_filter.mp ht).2
simp [halfLaw, hp]
theorem halfLaw_swap (t : Shape) (ht : t ∈ shapes 8) (w : Fin 3) (i j : Fin 6) :
halfLaw t w (i,j) = halfLaw t w (j,i) := by
by_cases hp : positive t = true
· simp only [halfLaw, hp, ite_true]
exact positiveHalfLaw_swap t (List.mem_filter.mpr ⟨ht, hp⟩) w i j
· unfold halfLaw
simp only [hp, Bool.false_eq_true, ite_false]
by_cases hw : t w = 0
· simp [hw, Prod.mk.injEq, and_comm]
· simp only [hw, ite_false]
by_cases hm : w = halfMaxAxis t
· simpa only [hm, ite_true] using zeroPairLaw_swap t i j
· simpa only [hm, ite_false] using zeroPairLaw_swap t (kappa i) (kappa j)
theorem halfLaw_nonnegative (t : Shape) (w : Fin 3) (ij : PairSlot) :
0 ≤ halfLaw t w ij := by
unfold halfLaw
split_ifs
· exact positiveHalfLaw_nonnegative t w ij
· norm_num
· exact le_rfl
· exact zeroPairLaw_nonnegative t ij.1 ij.2
· exact zeroPairLaw_nonnegative t (kappa ij.1) (kappa ij.2)
def kappaEquiv : Fin 6 ≃ Fin 6 where
toFun := kappa
invFun := kappa
left_inv := kappa_involution
right_inv := kappa_involution
theorem sum_pair_kappa (f : PairSlot → ℚ) :
(∑ ij : PairSlot, f (kappa ij.1, kappa ij.2)) = ∑ ij, f ij :=
(Equiv.prodCongr kappaEquiv kappaEquiv).sum_comp f
theorem halfLaw_normalized (t : Shape) (ht : t ∈ shapes 8) (w : Fin 3) :
∑ ij : PairSlot, halfLaw t w ij = 1 := by
by_cases hp : positive t = true
· simp only [halfLaw, hp, ite_true]
exact positiveHalfLaw_normalized t (List.mem_filter.mpr ⟨ht, hp⟩) w
· have hz : t ∈ zeroSecond := by simp [zeroSecond, ht, hp]
simp only [halfLaw, hp, Bool.false_eq_true, ite_false]
by_cases hw : t w = 0
· simp [hw]
· simp only [hw, ite_false]
by_cases hm : w = halfMaxAxis t
· simpa only [hm, ite_true] using zeroPairLaw_full_normalized t hz
· simp only [hm, ite_false]
rw [sum_pair_kappa (fun ij => zeroPairLaw t ij.1 ij.2)]
exact zeroPairLaw_full_normalized t hz
theorem positiveHalfLaw_outside_support (t : Shape) (ht : t ∈ positiveSecond)
(w : Fin 3) (ij : PairSlot)
(h : statisticWeight ij.1 + statisticWeight ij.2 ≠ t w) :
positiveHalfLaw t w ij = 0 := by
unfold positiveHalfLaw
have he : (below t).map (fun u => stageBLaw t u * littleLaw t u w ij.1 *
littleLaw t (complement t u) w ij.2) = (below t).map (fun _ => (0 : ℚ)) := by
apply List.map_congr_left
intro u hu
obtain ⟨hc, hb⟩ := stageB_support_complement t ht u hu
by_cases hi : statisticWeight ij.1 = u w
· have hj : statisticWeight ij.2 ≠ complement t u w := by
intro hj
apply h
rw [hi, hj]
change u w + (t w - u w) = t w
have hw := hb w
omega
rw [littleLaw_outside_support t (complement t u) w
(child_statistic_weight_bounded t ht (complement t u) hc w) ij.2 hj, mul_zero]
· rw [littleLaw_outside_support t u w
(child_statistic_weight_bounded t ht u hu w) ij.1 hi, mul_zero, zero_mul]
rw [he]
simp
theorem halfMaxAxis_spec : ∀ t ∈ shapes 8, t (halfMaxAxis t) = shapeMax t := by
decide +kernel
theorem zero_other_axis_spec : ∀ t ∈ zeroSecond, ∀ w : Fin 3,
t w ≠ 0 → w ≠ halfMaxAxis t → t w + shapeMax t = 8 := by decide +kernel
theorem statisticWeight_le_four (i : Fin 6) : statisticWeight i ≤ 4 := by
revert i
decide +kernel
theorem halfLaw_outside_support (t : Shape) (ht : t ∈ shapes 8)
(w : Fin 3) (ij : PairSlot)
(h : statisticWeight ij.1 + statisticWeight ij.2 ≠ t w) :
halfLaw t w ij = 0 := by
by_cases hp : positive t = true
· simp only [halfLaw, hp, ite_true]
exact positiveHalfLaw_outside_support t (List.mem_filter.mpr ⟨ht, hp⟩) w ij h
· have hz : t ∈ zeroSecond := by simp [zeroSecond, ht, hp]
simp only [halfLaw, hp, Bool.false_eq_true, ite_false]
by_cases hw : t w = 0
· have hij : ij ≠ (0,0) := by
intro hij
apply h
rw [hij, hw]
rfl
rw [ite_eq_left hw, ite_eq_right hij]
· simp only [hw, ite_false]
by_cases hm : w = halfMaxAxis t
· have hs : statisticWeight ij.1 + statisticWeight ij.2 ≠ shapeMax t := by
simpa only [hm, halfMaxAxis_spec t ht] using h
simp [hm, zeroPairLaw, hs]
· have hs : statisticWeight (kappa ij.1) + statisticWeight (kappa ij.2) ≠
shapeMax t := by
have htotal := zero_other_axis_spec t hz w hw hm
have hi := statisticWeight_le_four ij.1
have hj := statisticWeight_le_four ij.2
simp only [statisticWeight_kappa]
omega
simp [hm, zeroPairLaw, hs]
theorem zero_nonzero_axes_distinguished : ∀ t ∈ zeroSecond, ∀ w v : Fin 3,
t w ≠ 0 → t v ≠ 0 → w ≠ v →
(w = halfMaxAxis t ↔ v ≠ halfMaxAxis t) := by decide +kernel
theorem halfLaw_zero_complement (t : Shape) (ht : t ∈ zeroSecond)
(w v : Fin 3) (hw : t w ≠ 0) (hv : t v ≠ 0) (hwv : w ≠ v) (ij : PairSlot) :
halfLaw t w (kappa ij.1, kappa ij.2) = halfLaw t v ij := by
have hp : positive t ≠ true := by
have h := (List.mem_filter.mp ht).2
simpa using h
have hm := zero_nonzero_axes_distinguished t ht w v hw hv hwv
by_cases ha : w = halfMaxAxis t
· have hb : v ≠ halfMaxAxis t := hm.mp ha
have hmax : t (halfMaxAxis t) ≠ 0 := by simpa only [ha] using hw
simp [halfLaw, hp, hv, ha, hb, hmax]
· have hb : v = halfMaxAxis t := by
by_contra hne
exact ha (hm.mpr hne)
have hmax : t (halfMaxAxis t) ≠ 0 := by simpa only [hb] using hv
simp [halfLaw, hp, hw, ha, hb, hmax, kappa_involution]
end MatrixMultiplication.AllFieldParameters
end OAI