openAI-math-dataset / lean /OAI /Analysis /CircleDomains /Probability /ClosedSubsetHitFusion.lean
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Add lean source and binary files (part 12)
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import OAI.Analysis.CircleDomains.Probability.MarkedHitFusion
namespace OAI
/-! The fusion theorem applied to a closed dust subset. Its tests are ordinary
ambient measures supported on the dust, exactly as in the modulus extraction. -/
open Set Function MeasureTheory
open scoped Topology ENNReal
noncomputable section
namespace CircleDomainRigidity
variable {X Z : Type*} [MetricSpace X] [CompactSpace X] [SecondCountableTopology X]
[MeasurableSpace X] [BorelSpace X]
[MeasurableSpace C(PathTime, X)] [BorelSpace C(PathTime, X)]
[MetricSpace Z] [SecondCountableTopology Z] [CompleteSpace Z]
theorem exists_hit_norm_mixture_on_closed_subset
(B : Set X) (hB : IsClosed B)
(law : Z → ProbabilityMeasure C(PathTime, X)) (hlaw : Continuous law)
{C : ℝ} (hC : 0 ≤ C)
(htest : ∀ D : Set X, D.Countable → D ⊆ B → ∀ ν : Measure X,
IsFiniteMeasure ν → NullSingletonClass ν → ν Bᶜ = 0 →
∃ z, PassesHitTests C (law z) D ν)
{ε : ℝ} (hε : 0 < ε) :
∃ (P : ProbabilityMeasure (ℕ → ℕ)) (e : C(ℕ → ℕ, Z)),
let μ := probabilityMixture P (law ∘ e)
(measurable_of_continuous_probability (hlaw.comp e.continuous))
(∀ F : Finset B, ∑ x ∈ F, pathHitReal μ x.val ^ 2 ≤ (C + ε) ^ 2) ∧
{x : X | x ∈ B ∧ 0 < pathHitProbability μ x}.Countable := by
classical
let : CompactSpace B := isCompact_iff_compactSpace.mp hB.isCompact
let ι : C(B, X) := ⟨Subtype.val, continuous_subtype_val⟩
have htest' : ∀ D : Set B, D.Countable → ∀ ν : Measure B,
IsFiniteMeasure ν → NullSingletonClass ν →
∃ z, MarkedHit.PassesHitTests ι C (law z) D ν := by
intro D hD ν hν hnull
let : IsFiniteMeasure ν := hν
let : NullSingletonClass ν := hnull
let ρ : Measure X := ν.map (Subtype.val : B → X)
have hρ : IsFiniteMeasure ρ := inferInstance
have hρnull : NullSingletonClass ρ := by
constructor
intro x
rw [show ρ = ν.map (Subtype.val : B → X) from rfl,
Measure.map_apply continuous_subtype_val.measurable (measurableSet_singleton x)]
by_cases hx : x ∈ B
· have he : (Subtype.val : B → X) ⁻¹' {x} = {⟨x, hx⟩} := by
ext y
simp only [mem_preimage, mem_singleton_iff, Subtype.ext_iff]
rw [he]
exact measure_singleton _
· have he : (Subtype.val : B → X) ⁻¹' {x} = ∅ := by
apply eq_empty_iff_forall_notMem.mpr
intro y hy
exact hx ((mem_singleton_iff.mp hy) ▸ y.property)
rw [he, measure_empty]
have hρB : ρ Bᶜ = 0 := by
rw [show ρ = ν.map (Subtype.val : B → X) from rfl,
Measure.map_apply continuous_subtype_val.measurable hB.measurableSet.compl]
simp
obtain ⟨z, hz⟩ := htest (Subtype.val '' D) (hD.image _) (by
rintro x ⟨y, _, rfl⟩
exact y.property) ρ hρ hρnull hρB
refine ⟨z, ?_, ?_⟩
· intro F hF
have hb := hz.1 (F.image (Subtype.val : B → X)) (by
intro x hx
obtain ⟨y, hy, rfl⟩ := Finset.mem_image.mp hx
exact mem_image_of_mem _ (hF hy))
simpa only [Finset.sum_image (fun _ _ _ _ h => Subtype.val_injective h),
MarkedHit.pathHitReal, MarkedHit.pathHitProbability, pathHitReal, ι,
ContinuousMap.coe_mk] using hb
· change ∫⁻ x, pathHitProbability (law z) (x : X) ∂ν = 0
rw [← lintegral_map (measurable_pathHitProbability_point (law z))
continuous_subtype_val.measurable]
exact hz.2
obtain ⟨P, e, hb⟩ := MarkedHit.exists_hit_norm_mixture_of_countable_tests ι hlaw hC htest' hε
refine ⟨P, e, hb, ?_⟩
have hcount := hb.countable_positive_support
have heq : Subtype.val '' {x : B | 0 < MarkedHit.pathHitProbability ι
(probabilityMixture P (law ∘ e)
(measurable_of_continuous_probability (hlaw.comp e.continuous))) x} =
{x : X | x ∈ B ∧ 0 < pathHitProbability
(probabilityMixture P (law ∘ e)
(measurable_of_continuous_probability (hlaw.comp e.continuous))) x} := by
ext x
constructor
· rintro ⟨y, hy, rfl⟩
exact ⟨y.property, hy⟩
· rintro ⟨hx, hh⟩
exact ⟨⟨x, hx⟩, hh, rfl⟩
rw [← heq]
exact hcount.image _
end CircleDomainRigidity
end
end OAI