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 SFormal artifact
Lean source
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 := 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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