54 lines
1.9 KiB
Plaintext
54 lines
1.9 KiB
Plaintext
open Jest
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open Expect
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open TestHelpers
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describe("kl divergence", () => {
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let klDivergence = DistributionOperation.Constructors.klDivergence(~env)
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exception KlFailed
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test("of two uniforms is equal to the analytic expression", () => {
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let lowAnswer = 2.3526e0
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let highAnswer = 8.5382e0
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let lowPrediction = 2.3526e0
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let highPrediction = 1.2345e1
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let answer =
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uniformMakeR(lowAnswer, highAnswer)->E.R2.errMap(s => DistributionTypes.ArgumentError(s))
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let prediction =
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uniformMakeR(lowPrediction, highPrediction)->E.R2.errMap(s => DistributionTypes.ArgumentError(
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s,
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))
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// integral along the support of the answer of answer.pdf(x) times log of prediction.pdf(x) divided by answer.pdf(x) dx
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let analyticalKl = Js.Math.log((highPrediction -. lowPrediction) /. (highAnswer -. lowAnswer))
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let kl = E.R.liftJoin2(klDivergence, prediction, answer)
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switch kl {
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| Ok(kl') => kl'->expect->toBeCloseTo(analyticalKl)
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| Error(err) => {
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Js.Console.log(DistributionTypes.Error.toString(err))
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raise(KlFailed)
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}
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}
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})
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test("of two normals is equal to the formula", () => {
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// This test case comes via Nuño https://github.com/quantified-uncertainty/squiggle/issues/433
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let mean1 = 4.0
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let mean2 = 1.0
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let stdev1 = 1.0
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let stdev2 = 1.0
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let prediction =
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normalMakeR(mean1, stdev1)->E.R2.errMap(s => DistributionTypes.ArgumentError(s))
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let answer = normalMakeR(mean2, stdev2)->E.R2.errMap(s => DistributionTypes.ArgumentError(s))
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let analyticalKl =
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Js.Math.log(stdev2 /. stdev1) +.
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stdev1 ** 2.0 /. 2.0 /. stdev2 ** 2.0 +.
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(mean1 -. mean2) ** 2.0 /. 2.0 /. stdev2 ** 2.0 -. 0.5
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let kl = E.R.liftJoin2(klDivergence, prediction, answer)
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switch kl {
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| Ok(kl') => kl'->expect->toBeCloseTo(analyticalKl)
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| Error(err) => {
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Js.Console.log(DistributionTypes.Error.toString(err))
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raise(KlFailed)
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}
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}
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})
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})
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