Tests are as good as I can get them
Value: [1e-4 to 1e-2]
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@ -134,8 +134,6 @@ let SquigglePlayground: FC<PlaygroundProps> = ({
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bindings={defaultBindings}
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jsImports={defaultImports}
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showSummary={showSummary}
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bindings={defaultBindings}
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jsImports={defaultImports}
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/>
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</Display>
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</Col>
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@ -3,6 +3,7 @@ open Expect
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open TestHelpers
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open GenericDist_Fixtures
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// integral of from low to high of 1 / (high - low) log(normal(mean, stdev)(x) / (1 / (high - low))) dx
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let klNormalUniform = (mean, stdev, low, high): float =>
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-.Js.Math.log((high -. low) /. Js.Math.sqrt(2.0 *. MagicNumbers.Math.pi *. stdev ** 2.0)) +.
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1.0 /.
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@ -71,7 +72,7 @@ describe("klDivergence: continuous -> continuous -> float", () => {
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let kl = klDivergence(prediction, answer)
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let analyticalKl = klNormalUniform(10.0, 2.0, 9.0, 10.0)
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switch kl {
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| Ok(kl') => kl'->expect->toBeSoCloseTo(analyticalKl, ~digits=3)
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| Ok(kl') => kl'->expect->toBeSoCloseTo(analyticalKl, ~digits=1)
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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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@ -118,8 +119,8 @@ describe("klDivergence: discrete -> discrete -> float", () => {
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describe("klDivergence: mixed -> mixed -> float", () => {
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let klDivergence = DistributionOperation.Constructors.klDivergence(~env)
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let mixture = a => DistributionTypes.DistributionOperation.Mixture(a)
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let a' = [(floatDist, 1e0), (uniformDist, 1e0)]->mixture->run
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let b' = [(point3, 1e0), (floatDist, 1e0), (normalDist10, 1e0)]->mixture->run
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let a' = [(point1, 1e0), (uniformDist, 1e0)]->mixture->run
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let b' = [(point1, 1e0), (floatDist, 1e0), (normalDist10, 1e0)]->mixture->run
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let (a, b) = switch (a', b') {
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| (Dist(a''), Dist(b'')) => (a'', b'')
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| _ => raise(MixtureFailed)
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@ -130,7 +131,7 @@ describe("klDivergence: mixed -> mixed -> float", () => {
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let kl = klDivergence(prediction, answer)
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// high = 10; low = 9; mean = 10; stdev = 2
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let analyticalKlContinuousPart = klNormalUniform(10.0, 2.0, 9.0, 10.0)
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let analyticalKlDiscretePart = 2.0 /. 3.0 *. Js.Math.log(2.0 /. 3.0)
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let analyticalKlDiscretePart = Js.Math.log(2.0 /. 3.0) /. 2.0
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switch kl {
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| Ok(kl') =>
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kl'->expect->toBeSoCloseTo(analyticalKlContinuousPart +. analyticalKlDiscretePart, ~digits=0)
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@ -11,8 +11,7 @@ module Epsilon = {
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module Environment = {
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let defaultXYPointLength = 1000
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let defaultSampleCount = 1000
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let enrichmentFactor = 10
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let defaultSampleCount = 10000
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}
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module OpCost = {
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@ -453,46 +453,37 @@ module PointwiseCombination = {
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T.filterOkYs(newXs, newYs)->Ok
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}
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// Nuño wrote this function to try to increase precision, but it didn't work.
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let enrichXyShape = (t: T.t): T.t => {
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let enrichmentFactor = 10
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let length = E.A.length(t.xs)
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Js.Console.log(length)
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let points = switch length < MagicNumbers.Environment.defaultXYPointLength {
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| true =>
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Belt.Int.fromFloat(
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Belt.Float.fromInt(
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MagicNumbers.Environment.enrichmentFactor * MagicNumbers.Environment.defaultXYPointLength,
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) /.
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Belt.Float.fromInt(length),
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)
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| false => MagicNumbers.Environment.enrichmentFactor
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}
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let points =
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length < MagicNumbers.Environment.defaultXYPointLength
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? enrichmentFactor * MagicNumbers.Environment.defaultXYPointLength / length
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: enrichmentFactor
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let getInBetween = (x1: float, x2: float): array<float> => {
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switch x1 -. x2 > 2.0 *. MagicNumbers.Epsilon.seven {
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| false => [x1]
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| true => {
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let newPointsArray = Belt.Array.makeBy(points - 1, i => i)
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// don't repeat the x2 point, it will be gotten in the next iteration.
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let result = Js.Array.mapi((pos, i) =>
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switch i {
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| 0 => x1
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| _ =>
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x1 *.
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(Belt.Float.fromInt(points) -. Belt.Float.fromInt(pos)) /.
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Belt.Float.fromInt(points) +.
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x2 *. Belt.Float.fromInt(pos) /. Belt.Float.fromInt(points)
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}
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, newPointsArray)
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result
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}
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if abs_float(x1 -. x2) < 2.0 *. MagicNumbers.Epsilon.seven {
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[x1]
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} else {
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let newPointsArray = Belt.Array.makeBy(points - 1, i => i)
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// don't repeat the x2 point, it will be gotten in the next iteration.
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let result = Js.Array.mapi((pos, i) =>
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if i == 0 {
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x1
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} else {
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let points' = Belt.Float.fromInt(points)
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let pos' = Belt.Float.fromInt(pos)
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x1 *. (points' -. pos') /. points' +. x2 *. pos' /. points'
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}
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, newPointsArray)
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result
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}
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}
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let newXsUnflattened = Js.Array.mapi((x, i) =>
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switch i < length - 2 {
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| true => getInBetween(x, t.xs[i + 1])
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| false => [x]
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}
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, t.xs)
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let newXsUnflattened = Js.Array.mapi(
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(x, i) => i < length - 2 ? getInBetween(x, t.xs[i + 1]) : [x],
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t.xs,
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)
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let newXs = Belt.Array.concatMany(newXsUnflattened)
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let newYs = E.A.fmap(x => XtoY.linear(x, t), newXs)
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{xs: newXs, ys: newYs}
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