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Add lean source and binary files (part 126)
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import Mathlib
import OAI.Probability.Perceptron.Variational.TiltMeanCenteredAbsSq
import OAI.Probability.Perceptron.Variational.GaussianCoupling
import OAI.Probability.Perceptron.Variational.IndexedGaussianCovariance
namespace OAI
noncomputable section
namespace SphericalPerceptronFreeEnergy
open MeasureTheory ProbabilityTheory Filter Set
open scoped Topology NNReal ENNReal BigOperators BoundedContinuousFunction
lemma sourceEnrichedFeature_add_single (N : ℕ) (p : Fin N → ℕ) (u : Fin N → ℝ)
(j : Fin N) (a : ℝ) (x : NormalizedSpin N) (i : EnrichedIndex N N p) :
sourceEnrichedFeature N p (u+Pi.single j a) x i =
sourceEnrichedFeature N p u x i+
a*perturbationAmplitude N (fun _ => 1) j*sourceGaussianTestFeature N p j x i := by
classical
cases i with
| inl i => simp [sourceEnrichedFeature,sourceGaussianTestFeature,enrichedFeature]
| inr i =>
rcases i with ⟨l,r⟩
by_cases hl : l=j
· subst l
simp only [sourceEnrichedFeature,sourceGaussianTestFeature,enrichedFeature,perturbationAmplitude,
Pi.add_apply,Pi.single_eq_same,mul_one]
ring
· simp [sourceEnrichedFeature,sourceGaussianTestFeature,enrichedFeature,perturbationAmplitude,
Pi.single_apply,hl]
lemma sourceGaussianRow_add_single {N k : ℕ} (p d : Fin N → ℕ) (h : Fin (k+1) → ℝ)
(u : Fin N → ℝ) (j : Fin N) (a : ℝ) (i : ℕ) (x : NormalizedSpin N×IndexedLeaf k) :
sourceGaussianRow p d h (u+Pi.single j a) i x =
sourceGaussianRow p d h u i x+
a*perturbationAmplitude N (fun _ => 1) j*sourceGaussianTestRow p d h j i x := by
unfold sourceGaussianRow sourceGaussianTestRow indexedGaussianRow finiteGaussianRow
simp_rw [sourceEnrichedFeature_add_single]
rw [Finset.mul_sum,← Finset.sum_add_distrib]
apply Finset.sum_congr rfl
intro q _
split_ifs <;> ring
lemma enrichedIndexedHamiltonian_add_single (n M k : ℕ) (f : ℝ →ᵇ ℝ)
(a : Fin M → Fin (n+1) → ℝ) (p d : Fin (n+1) → ℕ)
(h : Fin (k+1) → ℝ) (u : Fin (n+1) → ℝ) (j : Fin (n+1)) (v : ℝ) (g : ℕ → ℝ)
(x : NormalizedSpin (n+1)×IndexedLeaf k) :
enrichedIndexedHamiltonian n M k f a p d h (u+Pi.single j v) g x =
enrichedIndexedHamiltonian n M k f a p d h u g x+
v*perturbationAmplitude (n+1) (fun _ => 1) j*
countableGaussianField (sourceGaussianTestRow p d h j)
(indexedGaussianRowLength (I := EnrichedIndex (n+1) (n+1) p) k) g x := by
unfold enrichedIndexedHamiltonian countableGaussianHamiltonian
have hr : sourceGaussianRow p d h (u+Pi.single j v) = fun i x =>
sourceGaussianRow p d h u i x+
(v*perturbationAmplitude (n+1) (fun _ => 1) j)*sourceGaussianTestRow p d h j i x := by
funext i x
exact sourceGaussianRow_add_single p d h u j v i x
rw [hr,countableGaussianField_add,countableGaussianField_smul]
ring
lemma coupling_log_contDiff {S : Type*} [MeasurableSpace S] (μ : Measure S)
[IsProbabilityMeasure μ] {H Y : S → ℝ} (hH : Measurable H) (hY : Measurable Y)
(he : ∀ a : ℝ, Integrable (fun x => Real.exp (H x+a*Y x)) μ) :
ContDiff ℝ 2 (fun b => Real.log (tiltPartition μ (fun x => H x+b*Y x) 1)) := by
let ρ := μ.withDensity (fun x => ENNReal.ofReal (Real.exp (H x)))
have hf : cgf Y ρ = (fun b => Real.log (tiltPartition μ (fun x => H x+b*Y x) 1)) := by
funext b
exact congrArg Real.log (coupling_mgf_identity μ hH b)
rw [← hf,contDiff_iff_contDiffAt]
intro a
apply AnalyticAt.contDiffAt
apply analyticAt_cgf
rw [coupling_expSet μ hH hY he,interior_univ]
trivial
lemma coupling_log_convex {S : Type*} [MeasurableSpace S] (μ : Measure S)
[IsProbabilityMeasure μ] {H Y : S → ℝ} (hH : Measurable H) (hY : Measurable Y)
(he : ∀ a : ℝ, Integrable (fun x => Real.exp (H x+a*Y x)) μ) :
ConvexOn ℝ univ (fun b => Real.log (tiltPartition μ (fun x => H x+b*Y x) 1)) := by
have hs := coupling_log_contDiff μ hH hY he
apply convexOn_of_deriv2_nonneg' convex_univ
(hs.differentiable (by norm_num)).differentiableOn
( (ContDiff.deriv' (n := 1) hs).differentiable (by norm_num)).differentiableOn
intro a _
rw [← iteratedDeriv_eq_iterate,coupling_log_second_derivative μ hH hY he]
exact tiltMean_nonneg μ (fun _ => sq_nonneg _) 1
lemma convex_derivative_local_bound {F D : ℝ → ℝ}
(hc : ConvexOn ℝ univ F) (hd : ∀ x, HasDerivAt F (D x) x) (u v : ℝ)
(hv : v ∈ Ioo (u-1) (u+1)) :
|D v| ≤ |F (u-2)|+|F (u-1)|+|F (u+1)|+|F (u+2)| := by
have hmono : Monotone D := by
intro x y hxy
simpa only [(hd x).deriv,(hd y).deriv] using
hc.monotoneOn_deriv (fun x _ => (hd x).differentiableAt) (mem_univ x) (mem_univ y) hxy
have hl := hc.slope_le_of_hasDerivAt (mem_univ (u-2)) (mem_univ (u-1))
(by linarith : u-2 < u-1) (hd (u-1))
have hr := hc.le_slope_of_hasDerivAt (mem_univ (u+1)) (mem_univ (u+2))
(by linarith : u+1 < u+2) (hd (u+1))
have hl' : F (u-1)-F (u-2) ≤ D v := by
have hh : (u-1)-(u-2) = (1:ℝ) := by ring
simp only [slope,hh,inv_one,one_smul] at hl
exact hl.trans (hmono hv.1.le)
have hr' : D v ≤ F (u+2)-F (u+1) := by
have hh : (u+2)-(u+1) = (1:ℝ) := by ring
simp only [slope,hh,inv_one,one_smul] at hr
exact (hmono hv.2.le).trans hr
apply abs_le.mpr
constructor
· linarith [neg_abs_le (F (u-1)),le_abs_self (F (u-2)),abs_nonneg (F (u+1)),abs_nonneg (F (u+2))]
· linarith [le_abs_self (F (u+2)),neg_abs_le (F (u+1)),abs_nonneg (F (u-1)),abs_nonneg (F (u-2))]
lemma annealed_convex_hasDerivAt {Ω : Type*} [MeasurableSpace Ω] (P : Measure Ω)
{F D : ℝ → Ω → ℝ} (hF : ∀ t, AEStronglyMeasurable (F t) P)
(hD : ∀ t, AEStronglyMeasurable (D t) P) (hi : ∀ t, Integrable (F t) P)
(hc : ∀ᵐ ω ∂P, ConvexOn ℝ univ (fun t => F t ω) ∧ ∀ t, HasDerivAt (fun t => F t ω) (D t ω) t)
(u : ℝ) :
Integrable (D u) P ∧ HasDerivAt (fun t => ∫ ω, F t ω ∂P) (∫ ω, D u ω ∂P) u := by
let B : Ω → ℝ := fun ω => |F (u-2) ω|+|F (u-1) ω|+|F (u+1) ω|+|F (u+2) ω|
apply hasDerivAt_integral_of_dominated_loc_of_deriv_le
(Ioo_mem_nhds (by linarith : u-1 < u) (by linarith : u < u+1))
(Eventually.of_forall hF) (hi u) (hD u)
· filter_upwards [hc] with ω hω t ht
rw [Real.norm_eq_abs]
exact convex_derivative_local_bound hω.1 hω.2 u t ht
· exact (((hi (u-2)).abs.add (hi (u-1)).abs).add (hi (u+1)).abs).add (hi (u+2)).abs
· filter_upwards [hc] with ω hω t _
exact hω.2 t
end SphericalPerceptronFreeEnergy
end
end OAI