openAI-math-dataset / lean /OAI /Computability /VertexCover /Repetition /SelectedCommonMarginal.lean
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Add lean source and binary files (part 64)
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import Mathlib
import OAI.Computability.VertexCover.Repetition.SelectedJointGame
import OAI.Computability.VertexCover.Repetition.QuestionMarginal
import OAI.Computability.VertexCover.Repetition.DistributionMaps
import OAI.Computability.VertexCover.Repetition.SelectedCommon
namespace OAI
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namespace UniqueGames.Foundations.Repetition
open scoped BigOperators
open Games Information
noncomputable section
theorem pushforward_snd_weight_eq_secondMarginal {A B : Type*}
[Fintype A] [Fintype B] (μ : FiniteDistribution (A × B)) :
(μ.pushforward Prod.snd).weight = secondMarginal μ.weight := by
classical
funext b
simp [FiniteDistribution.pushforward, secondMarginal, Fintype.sum_prod_type]
variable {I T X Y : Type*} [Fintype I] [DecidableEq I] [Fintype T]
[Fintype X] [Fintype Y] [DecidableEq X] [DecidableEq Y]
def revealDataSplitEquiv (j : I) :
(T × (I → X ⊕ Y)) ≃ ((T × ({i : I // i ≠ j} → X ⊕ Y)) × (X ⊕ Y)) where
toFun z := ((z.1, fun i => z.2 i.1), z.2 j)
invFun z := (z.1.1, mergeAt j z.2 z.1.2)
left_inv z := by
apply Prod.ext
· rfl
· funext i
by_cases h : i = j <;> simp [mergeAt, h]
right_inv z := by
apply Prod.ext
· apply Prod.ext
· rfl
· funext i
exact mergeAt_other j z.2 z.1.2 i
· exact mergeAt_self j z.2 z.1.2
theorem partialRevealMarginal_secondMarginal
(μ : FiniteDistribution (X × Y)) (j : I)
(likelihood : T → (I → X × Y) → ℝ) :
secondMarginal (partialRevealMarginal μ j likelihood) =
secondMarginal (fullRevealMarginal μ j likelihood) := by
classical
funext q
calc
_ = ∑ z : (T × ({i : I // i ≠ j} → X ⊕ Y)) × (X ⊕ Y),
maskedJoint (partialRevealMarginal μ j likelihood) (z,q) := by
simp [secondMarginal, Fintype.sum_prod_type, maskedJoint]
_ = _ := by
apply Fintype.sum_equiv (revealDataSplitEquiv (T := T) j).symm
intro z
exact (fullRevealMarginal_merge μ j likelihood z.1.1 z.1.2 z.2 q).symm
variable {Q₁ Q₂ A₁ A₂ : Type*}
[Fintype Q₁] [Fintype Q₂] [Fintype A₁] [Fintype A₂]
[DecidableEq Q₁] [DecidableEq Q₂] {n : Nat}
def selectedObservationDataEquiv (selected : Finset (Fin n)) :
(SelectedInput Q₁ Q₂ selected × SelectedLabels (A₁ := A₁) (A₂ := A₂) selected) ≃
(((selected → Q₁ × Q₂) × SelectedLabels (A₁ := A₁) (A₂ := A₂) selected) ×
({i : Fin n // i ∉ selected} → Q₁ ⊕ Q₂)) where
toFun z := ((z.1.1,z.2),z.1.2)
invFun z := ((z.1.1,z.2),z.1.2)
left_inv _ := rfl
right_inv _ := rfl
theorem selectedFullReveal_secondMarginal (G : Game Q₁ Q₂ A₁ A₂)
(strategy : Strategy (Fin n → Q₁) (Fin n → Q₂) (Fin n → A₁) (Fin n → A₂))
(selected : Finset (Fin n)) (j : {i : Fin n // i ∉ selected}) :
secondMarginal (fullRevealMarginal G.questions j
(selectedOutsideLikelihood G strategy selected)) =
secondMarginal (selectedRawMarginal G strategy selected j) := by
classical
funext q
apply Fintype.sum_equiv
(selectedObservationDataEquiv (Q₁ := Q₁) (Q₂ := Q₂)
(A₁ := A₁) (A₂ := A₂) selected).symm
intro z
exact (selectedRawMarginal_fullReveal G strategy selected j (z,q)).symm
theorem selectedRawMarginal_secondMarginal (G : Game Q₁ Q₂ A₁ A₂)
(strategy : Strategy (Fin n → Q₁) (Fin n → Q₂) (Fin n → A₁) (Fin n → A₂))
(selected : Finset (Fin n)) (positive : 0 < G.selectedSuccess strategy selected)
(j : {i : Fin n // i ∉ selected}) :
secondMarginal (selectedRawMarginal G strategy selected j) =
(selectedQuestionMarginal G strategy selected positive j.1).weight := by
classical
funext q
calc
_ = (selectedJointLaw G strategy selected positive).probability
(fun z => decide (z.2 j = q)) := by
simp only [secondMarginal, selectedRawMarginal, FiniteDistribution.probability,
selectedJointLaw, toGameLaw, Fintype.sum_prod_type, decide_eq_true_eq]
_ = ((G.repetition n).questions.condition (G.selectedWins strategy selected) positive).probability
(fun questions => decide ((questions.1 j.1,questions.2 j.1) = q)) := by
have h := selectedJointLaw_question_probability G strategy selected positive
(fun questions => decide ((questions.1 j.1,questions.2 j.1) = q))
simpa [selectedQuestionTuple, mergeCoordinates, j.property] using h
_ = _ := by
rw [FiniteDistribution.weight_eq_probability_singleton, selectedQuestionMarginal,
FiniteDistribution.probability_pushforward]
theorem selectedCommonLaw_secondMarginal (G : Game Q₁ Q₂ A₁ A₂)
(strategy : Strategy (Fin n → Q₁) (Fin n → Q₂) (Fin n → A₁) (Fin n → A₂))
(selected : Finset (Fin n)) (positive : 0 < G.selectedSuccess strategy selected)
(j : {i : Fin n // i ∉ selected}) :
secondMarginal (selectedCommonLaw G strategy selected positive j).weight =
(selectedQuestionMarginal G strategy selected positive j.1).weight := by
change secondMarginal (partialRevealMarginal G.questions j
(selectedOutsideLikelihood G strategy selected)) = _
rw [partialRevealMarginal_secondMarginal, selectedFullReveal_secondMarginal]
exact selectedRawMarginal_secondMarginal G strategy selected positive j
theorem selectedCommonLaw_questions_pushforward (G : Game Q₁ Q₂ A₁ A₂)
(strategy : Strategy (Fin n → Q₁) (Fin n → Q₂) (Fin n → A₁) (Fin n → A₂))
(selected : Finset (Fin n)) (positive : 0 < G.selectedSuccess strategy selected)
(j : {i : Fin n // i ∉ selected}) :
(selectedCommonLaw G strategy selected positive j).pushforward Prod.snd =
selectedQuestionMarginal G strategy selected positive j.1 := by
classical
apply FiniteDistribution.eq_of_weight_eq
intro q
exact (congrFun (pushforward_snd_weight_eq_secondMarginal
(selectedCommonLaw G strategy selected positive j)) q).trans
(congrFun (selectedCommonLaw_secondMarginal G strategy selected positive j) q)
end
end UniqueGames.Foundations.Repetition
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end OAI