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Project-declaredLean 4.33.0-rc1 · mathlib@169c26b52a38

Ae mem version Space of realizable

Cslib.MachineLearning.PACLearning.ae_mem_versionSpace_of_realizable

Plain-language statement

Under iid sampling from the realizable joint distribution induced by c ∈ C and a probability measure P on α, the target concept c lies in the version space almost surely.

Exact Lean statement

theorem ae_mem_versionSpace_of_realizable
    [MeasurableSpace α] [MeasurableSpace β]
    {C : ConceptClass α β} {c : α → β} (hc : c ∈ C) (hcm : Measurable c)
    (hG : MeasurableSet {p : α × β | p.2 = c p.1})
    (P : Measure α) [IsProbabilityMeasure P] (m : ℕ) :
    ∀ᵐ S : LabeledSample α β m
      ∂(Measure.pi (fun _ : Fin m => P.map (fun x => (x, c x)))),
      c ∈ VersionSpace C S

Formal artifact

Lean source

Canonical source
Full Lean sourceLean 4
theorem ae_mem_versionSpace_of_realizable    [MeasurableSpace α] [MeasurableSpace β]    {C : ConceptClass α β} {c : α  β} (hc : c  C) (hcm : Measurable c)    (hG : MeasurableSet {p : α × β | p.2 = c p.1})    (P : Measure α) [IsProbabilityMeasure P] (m : ) :    ᵐ S : LabeledSample α β m      ∂(Measure.pi (fun _ : Fin m => P.map (fun x => (x, c x)))),      c  VersionSpace C S := by  have hφ : Measurable (fun x : α => (x, c x)) := by fun_prop  haveI : IsProbabilityMeasure (P.map (fun x : α => (x, c x))) :=    Measure.isProbabilityMeasure_map hφ.aemeasurable  rw [ae_iff]  have hsub : {S : Fin m  α × β | ¬ c  VersionSpace C S}       (Set.univ.pi (fun _ : Fin m => {p : α × β | p.2 = c p.1}))ᶜ := by    intro S hS hcontra    exact hS hc, by simp_all  have hcompl : (Measure.pi (fun _ : Fin m => P.map (fun x : α => (x, c x))))      ((Set.univ.pi (fun _ : Fin m => {p : α × β | p.2 = c p.1}))ᶜ) = 0 := by    rw [prob_compl_eq_one_sub (MeasurableSet.univ_pi fun _ => hG),        pi_map_graph_eq_one hcm P hG, tsub_self]  exact measure_mono_null hsub hcompl
Project
Lean Computer Science Library
License
Apache-2.0
Commit
f36649cff2c9
Source
Cslib/MachineLearning/PACLearning/VersionSpace.lean:263-283

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