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Factored continuous part of normal and uniform kldivergence into it's

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own function

Value: [1e-4 to 1e-3]
This commit is contained in:
Quinn Dougherty 2022-05-11 14:14:10 -04:00
parent c13f49a7bc
commit 95fe117ef0
1 changed files with 10 additions and 12 deletions
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22
packages/squiggle-lang/__tests__/Distributions/KlDivergence_test.res
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@ -3,6 +3,12 @@ open Expect
open TestHelpers open TestHelpers
open GenericDist_Fixtures open GenericDist_Fixtures
let klNormalUniform = (mean, stdev, low, high): float =>
-.Js.Math.log((high -. low) /. Js.Math.sqrt(2.0 *. MagicNumbers.Math.pi *. stdev ** 2.0)) +.
1.0 /.
stdev ** 2.0 *.
(mean ** 2.0 -. (high +. low) *. mean +. (low ** 2.0 +. high *. low +. high ** 2.0) /. 3.0)
describe("klDivergence: continuous -> continuous -> float", () => { describe("klDivergence: continuous -> continuous -> float", () => {
let klDivergence = DistributionOperation.Constructors.klDivergence(~env) let klDivergence = DistributionOperation.Constructors.klDivergence(~env)
@ -63,11 +69,7 @@ describe("klDivergence: continuous -> continuous -> float", () => {
let prediction = normalDist10 let prediction = normalDist10
let answer = uniformDist let answer = uniformDist
let kl = klDivergence(prediction, answer) let kl = klDivergence(prediction, answer)
let analyticalKl = let analyticalKl = klNormalUniform(10.0, 2.0, 9.0, 10.0)
-.Js.Math.log((10.0 -. 9.0) /. Js.Math.sqrt(2.0 *. MagicNumbers.Math.pi *. 2.0 ** 2.0)) +.
1.0 /.
2.0 ** 2.0 *.
(10.0 ** 2.0 -. (10.0 +. 9.0) *. 10.0 +. (9.0 ** 2.0 +. 10.0 *. 9.0 +. 10.0 ** 2.0) /. 3.0)
switch kl { switch kl {
| Ok(kl') => kl'->expect->toBeSoCloseTo(analyticalKl, ~digits=3) | Ok(kl') => kl'->expect->toBeSoCloseTo(analyticalKl, ~digits=3)
| Error(err) => { | Error(err) => {
@ -122,17 +124,13 @@ describe("klDivergence: mixed -> mixed -> float", () => {
| (Dist(a''), Dist(b'')) => (a'', b'') | (Dist(a''), Dist(b'')) => (a'', b'')
| _ => raise(MixtureFailed) | _ => raise(MixtureFailed)
} }
test("finite klDivergence returns is correct", () => { test("finite klDivergence return is correct", () => {
let prediction = b let prediction = b
let answer = a let answer = a
let kl = klDivergence(prediction, answer) let kl = klDivergence(prediction, answer)
// high = 10; low = 9; mean = 10; stdev = 2 // high = 10; low = 9; mean = 10; stdev = 2
let analyticalKlContinuousPart = let analyticalKlContinuousPart = klNormalUniform(10.0, 2.0, 9.0, 10.0)
Js.Math.log(Js.Math.sqrt(2.0 *. MagicNumbers.Math.pi *. 2.0 ** 2.0) /. (10.0 -. 9.0)) +. let analyticalKlDiscretePart = 2.0 /. 3.0 *. Js.Math.log(2.0 /. 3.0)
1.0 /.
2.0 ** 2.0 *.
(10.0 ** 2.0 -. (10.0 +. 9.0) *. 10.0 +. (9.0 ** 2.0 +. 10.0 *. 9.0 +. 10.0 ** 2.0) /. 3.0)
let analyticalKlDiscretePart = -2.0 /. 3.0 *. Js.Math.log(3.0 /. 2.0)
switch kl { switch kl {
| Ok(kl') => | Ok(kl') =>
kl'->expect->toBeSoCloseTo(analyticalKlContinuousPart +. analyticalKlDiscretePart, ~digits=0) kl'->expect->toBeSoCloseTo(analyticalKlContinuousPart +. analyticalKlDiscretePart, ~digits=0)