Download lean/OAI/Computability/DepthThree/EntropyThreshold.lean from roseking/openAI-math-dataset: direct link, hf CLI and curl.
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2.15 kB
| import Mathlib.Analysis.SpecialFunctions.Log.NegMulLog | |
| import Mathlib.Analysis.SpecificLimits.Basic | |
| namespace OAI | |
| open Filter | |
| open scoped Topology | |
| namespace DepthThreeLowerBound | |
| noncomputable def binaryEntropy (x : ℝ) : ℝ := | |
| -(x * Real.log x + (1 - x) * Real.log (1 - x)) / Real.log 2 | |
| @[simp] theorem binaryEntropy_zero : binaryEntropy 0 = 0 := by | |
| simp [binaryEntropy] | |
| @[simp] theorem binaryEntropy_one : binaryEntropy 1 = 0 := by | |
| simp [binaryEntropy] | |
| theorem binaryEntropy_mul_log_two (x : ℝ) : | |
| binaryEntropy x * Real.log 2 = | |
| -(x * Real.log x + (1 - x) * Real.log (1 - x)) := by | |
| exact div_mul_cancel₀ _ (ne_of_gt (Real.log_pos one_lt_two)) | |
| theorem binaryEntropy_nonneg {x : ℝ} (hx₀ : 0 ≤ x) (hx₁ : x ≤ 1) : | |
| 0 ≤ binaryEntropy x := by | |
| apply div_nonneg | |
| · exact neg_nonneg.mpr (add_nonpos | |
| (Real.mul_log_nonpos hx₀ hx₁) | |
| (Real.mul_log_nonpos (sub_nonneg.mpr hx₁) (sub_le_self _ hx₀))) | |
| · exact (Real.log_pos one_lt_two).le | |
| theorem continuous_binaryEntropy : Continuous binaryEntropy := by | |
| unfold binaryEntropy | |
| exact ((Real.continuous_mul_log.add | |
| (Real.continuous_mul_log.comp (continuous_const.sub continuous_id))).neg).div_const _ | |
| theorem tendsto_binaryEntropy_reciprocal : | |
| Tendsto (fun r : ℕ => binaryEntropy (1 / (r : ℝ))) atTop (𝓝 0) := by | |
| simpa only [binaryEntropy_zero, Function.comp_def] using | |
| (continuous_binaryEntropy.tendsto 0).comp tendsto_one_div_atTop_nhds_zero_nat | |
| theorem exists_integer_entropy_threshold (F : ℕ) {ε : ℝ} (hε : 0 < ε) : | |
| ∃ r : ℕ, 2 ≤ r ∧ (F : ℝ) * binaryEntropy (1 / (r : ℝ)) ≤ ε := by | |
| have hlim : Tendsto (fun r : ℕ => (F : ℝ) * binaryEntropy (1 / (r : ℝ))) | |
| atTop (𝓝 0) := by | |
| simpa only [mul_zero] using | |
| (tendsto_const_nhds.mul tendsto_binaryEntropy_reciprocal : | |
| Tendsto (fun r : ℕ => (F : ℝ) * binaryEntropy (1 / (r : ℝ))) | |
| atTop (𝓝 ((F : ℝ) * 0))) | |
| obtain ⟨N, hN⟩ := eventually_atTop.1 (hlim.eventually (eventually_lt_nhds hε)) | |
| exact ⟨max N 2, le_max_right _ _, (hN _ (le_max_left _ _)).le⟩ | |
| end DepthThreeLowerBound | |
| end OAI | |