Merged with develop
This commit is contained in:
Ozzie Gooen
commit
ab88b0d012
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|
@ -31,9 +31,9 @@
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"@testing-library/user-event": "^14.2.0",
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"@types/jest": "^27.5.0",
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"@types/lodash": "^4.14.182",
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"@types/node": "^17.0.32",
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"@types/node": "^17.0.33",
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"@types/react": "^18.0.9",
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"@types/react-dom": "^18.0.2",
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"@types/react-dom": "^18.0.4",
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"@types/styled-components": "^5.1.24",
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"@types/webpack": "^5.28.0",
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"cross-env": "^7.0.3",
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|
|
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|
|
@ -31,6 +31,9 @@ describe("eval on distribution functions", () => {
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testEval("mean(normal(5,2))", "Ok(5)")
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testEval("mean(lognormal(1,2))", "Ok(20.085536923187668)")
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testEval("mean(gamma(5,5))", "Ok(25)")
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testEval("mean(bernoulli(0.2))", "Ok(0.2)")
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testEval("mean(bernoulli(0.8))", "Ok(0.8)")
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testEval("mean(logistic(5,1))", "Ok(5)")
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})
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describe("toString", () => {
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testEval("toString(normal(5,2))", "Ok('Normal(5,2)')")
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|
|
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@ -34,27 +34,28 @@
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],
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"author": "Quantified Uncertainty Research Institute",
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"dependencies": {
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"rescript": "^9.1.4",
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"@stdlib/stats": "^0.0.13",
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"jstat": "^1.9.5",
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"mathjs": "^10.5.2",
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"pdfast": "^0.2.0",
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"mathjs": "^10.5.2"
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"rescript": "^9.1.4"
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},
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"devDependencies": {
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"bisect_ppx": "^2.7.1",
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"lodash": "^4.17.21",
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"rescript-fast-check": "^1.1.1",
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"@glennsl/rescript-jest": "^0.9.0",
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"@istanbuljs/nyc-config-typescript": "^1.0.2",
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"@types/jest": "^27.5.0",
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"babel-plugin-transform-es2015-modules-commonjs": "^6.26.2",
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"bisect_ppx": "^2.7.1",
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"chalk": "^5.0.1",
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"codecov": "^3.8.3",
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"fast-check": "^2.25.0",
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"gentype": "^4.3.0",
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"jest": "^27.5.1",
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"lodash": "^4.17.21",
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"moduleserve": "^0.9.1",
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"nyc": "^15.1.0",
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"reanalyze": "^2.19.0",
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"rescript-fast-check": "^1.1.1",
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"ts-jest": "^27.1.4",
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"ts-loader": "^9.3.0",
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"ts-node": "^10.7.0",
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|
|
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@ -146,7 +146,16 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
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}
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| ToDist(Normalize) => dist->GenericDist.normalize->Dist
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| ToScore(KLDivergence(t2)) =>
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GenericDist.klDivergence(dist, t2, ~toPointSetFn)
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GenericDist.Score.klDivergence(dist, t2, ~toPointSetFn)
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->E.R2.fmap(r => Float(r))
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->OutputLocal.fromResult
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| ToScore(LogScore(answer, prior)) =>
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GenericDist.Score.logScoreWithPointResolution(
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~prediction=dist,
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~answer,
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~prior,
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~toPointSetFn,
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)
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->E.R2.fmap(r => Float(r))
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->OutputLocal.fromResult
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| ToBool(IsNormalized) => dist->GenericDist.isNormalized->Bool
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@ -263,6 +272,12 @@ module Constructors = {
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let normalize = (~env, dist) => C.normalize(dist)->run(~env)->toDistR
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let isNormalized = (~env, dist) => C.isNormalized(dist)->run(~env)->toBoolR
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let klDivergence = (~env, dist1, dist2) => C.klDivergence(dist1, dist2)->run(~env)->toFloatR
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let logScoreWithPointResolution = (
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~env,
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~prediction: DistributionTypes.genericDist,
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~answer: float,
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~prior: option<DistributionTypes.genericDist>,
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) => C.logScoreWithPointResolution(~prediction, ~answer, ~prior)->run(~env)->toFloatR
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let toPointSet = (~env, dist) => C.toPointSet(dist)->run(~env)->toDistR
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let toSampleSet = (~env, dist, n) => C.toSampleSet(dist, n)->run(~env)->toDistR
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let fromSamples = (~env, xs) => C.fromSamples(xs)->run(~env)->toDistR
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|
|
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@ -63,6 +63,13 @@ module Constructors: {
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@genType
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let klDivergence: (~env: env, genericDist, genericDist) => result<float, error>
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@genType
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let logScoreWithPointResolution: (
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~env: env,
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~prediction: genericDist,
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~answer: float,
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~prior: option<genericDist>,
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) => result<float, error>
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@genType
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let toPointSet: (~env: env, genericDist) => result<genericDist, error>
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@genType
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let toSampleSet: (~env: env, genericDist, int) => result<genericDist, error>
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|
|
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|
|
@ -92,7 +92,7 @@ module DistributionOperation = {
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| ToString
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| ToSparkline(int)
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type toScore = KLDivergence(genericDist)
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type toScore = KLDivergence(genericDist) | LogScore(float, option<genericDist>)
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type fromDist =
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| ToFloat(toFloat)
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|
|
@ -121,6 +121,7 @@ module DistributionOperation = {
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| ToFloat(#Sample) => `sample`
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| ToFloat(#IntegralSum) => `integralSum`
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| ToScore(KLDivergence(_)) => `klDivergence`
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| ToScore(LogScore(x, _)) => `logScore against ${E.Float.toFixed(x)}`
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| ToDist(Normalize) => `normalize`
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| ToDist(ToPointSet) => `toPointSet`
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| ToDist(ToSampleSet(r)) => `toSampleSet(${E.I.toString(r)})`
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|
|
@ -162,6 +163,10 @@ module Constructors = {
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let truncate = (dist, left, right): t => FromDist(ToDist(Truncate(left, right)), dist)
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let inspect = (dist): t => FromDist(ToDist(Inspect), dist)
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let klDivergence = (dist1, dist2): t => FromDist(ToScore(KLDivergence(dist2)), dist1)
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let logScoreWithPointResolution = (~prediction, ~answer, ~prior): t => FromDist(
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ToScore(LogScore(answer, prior)),
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prediction,
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)
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let scalePower = (dist, n): t => FromDist(ToDist(Scale(#Power, n)), dist)
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let scaleLogarithm = (dist, n): t => FromDist(ToDist(Scale(#Logarithm, n)), dist)
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let scaleLogarithmWithThreshold = (dist, n, eps): t => FromDist(
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|
|
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@ -59,11 +59,44 @@ let integralEndY = (t: t): float =>
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let isNormalized = (t: t): bool => Js.Math.abs_float(integralEndY(t) -. 1.0) < 1e-7
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let klDivergence = (t1, t2, ~toPointSetFn: toPointSetFn): result<float, error> => {
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let pointSets = E.R.merge(toPointSetFn(t1), toPointSetFn(t2))
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pointSets |> E.R2.bind(((a, b)) =>
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PointSetDist.T.klDivergence(a, b)->E.R2.errMap(x => DistributionTypes.OperationError(x))
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)
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module Score = {
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let klDivergence = (prediction, answer, ~toPointSetFn: toPointSetFn): result<float, error> => {
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let pointSets = E.R.merge(toPointSetFn(prediction), toPointSetFn(answer))
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pointSets |> E.R2.bind(((predi, ans)) =>
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PointSetDist.T.klDivergence(predi, ans)->E.R2.errMap(x => DistributionTypes.OperationError(x))
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)
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}
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let logScoreWithPointResolution = (
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~prediction: DistributionTypes.genericDist,
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~answer: float,
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~prior: option<DistributionTypes.genericDist>,
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~toPointSetFn: toPointSetFn,
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): result<float, error> => {
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switch prior {
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| Some(prior') =>
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E.R.merge(toPointSetFn(prior'), toPointSetFn(prediction))->E.R.bind(((
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prior'',
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prediction'',
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)) =>
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PointSetDist.T.logScoreWithPointResolution(
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~prediction=prediction'',
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~answer,
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~prior=prior''->Some,
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)->E.R2.errMap(x => DistributionTypes.OperationError(x))
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)
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| None =>
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prediction
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->toPointSetFn
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->E.R.bind(x =>
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PointSetDist.T.logScoreWithPointResolution(
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~prediction=x,
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~answer,
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~prior=None,
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)->E.R2.errMap(x => DistributionTypes.OperationError(x))
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)
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}
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}
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}
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let toFloatOperation = (
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|
|
|
|||
|
|
@ -23,7 +23,15 @@ let toFloatOperation: (
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~distToFloatOperation: DistributionTypes.DistributionOperation.toFloat,
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) => result<float, error>
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let klDivergence: (t, t, ~toPointSetFn: toPointSetFn) => result<float, error>
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module Score: {
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let klDivergence: (t, t, ~toPointSetFn: toPointSetFn) => result<float, error>
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let logScoreWithPointResolution: (
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~prediction: t,
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~answer: float,
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~prior: option<t>,
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~toPointSetFn: toPointSetFn,
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) => result<float, error>
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}
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@genType
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let toPointSet: (
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|
|
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|
|
@ -277,13 +277,12 @@ module T = Dist({
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prediction.xyShape,
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answer.xyShape,
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)
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let xyShapeToContinuous: XYShape.xyShape => t = xyShape => {
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xyShape: xyShape,
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interpolation: #Linear,
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integralSumCache: None,
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integralCache: None,
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}
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newShape->E.R2.fmap(x => x->xyShapeToContinuous->integralEndY)
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newShape->E.R2.fmap(x => x->make->integralEndY)
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}
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let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
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let priorPdf = prior->E.O2.fmap((shape, x) => XYShape.XtoY.linear(x, shape.xyShape))
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let predictionPdf = x => XYShape.XtoY.linear(x, prediction.xyShape)
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PointSetDist_Scoring.LogScoreWithPointResolution.score(~priorPdf, ~predictionPdf, ~answer)
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}
|
||||
})
|
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|
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|
|
|
|||
|
|
@ -229,4 +229,7 @@ module T = Dist({
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answer,
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)->E.R2.fmap(integralEndY)
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}
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let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
|
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Error(Operation.NotYetImplemented)
|
||||
}
|
||||
})
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|
|
|
|||
|
|
@ -34,6 +34,11 @@ module type dist = {
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let mean: t => float
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let variance: t => float
|
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let klDivergence: (t, t) => result<float, Operation.Error.t>
|
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let logScoreWithPointResolution: (
|
||||
~prediction: t,
|
||||
~answer: float,
|
||||
~prior: option<t>,
|
||||
) => result<float, Operation.Error.t>
|
||||
}
|
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|
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module Dist = (T: dist) => {
|
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|
|
@ -57,6 +62,7 @@ module Dist = (T: dist) => {
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let variance = T.variance
|
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let integralEndY = T.integralEndY
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let klDivergence = T.klDivergence
|
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let logScoreWithPointResolution = T.logScoreWithPointResolution
|
||||
|
||||
let updateIntegralCache = T.updateIntegralCache
|
||||
|
||||
|
|
|
|||
|
|
@ -306,6 +306,9 @@ module T = Dist({
|
|||
let klContinuousPart = Continuous.T.klDivergence(prediction.continuous, answer.continuous)
|
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E.R.merge(klDiscretePart, klContinuousPart)->E.R2.fmap(t => fst(t) +. snd(t))
|
||||
}
|
||||
let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
|
||||
Error(Operation.NotYetImplemented)
|
||||
}
|
||||
})
|
||||
|
||||
let combineAlgebraically = (op: Operation.convolutionOperation, t1: t, t2: t): t => {
|
||||
|
|
|
|||
|
|
@ -196,13 +196,22 @@ module T = Dist({
|
|||
| Continuous(m) => Continuous.T.variance(m)
|
||||
}
|
||||
|
||||
let klDivergence = (t1: t, t2: t) =>
|
||||
switch (t1, t2) {
|
||||
let klDivergence = (prediction: t, answer: t) =>
|
||||
switch (prediction, answer) {
|
||||
| (Continuous(t1), Continuous(t2)) => Continuous.T.klDivergence(t1, t2)
|
||||
| (Discrete(t1), Discrete(t2)) => Discrete.T.klDivergence(t1, t2)
|
||||
| (Mixed(t1), Mixed(t2)) => Mixed.T.klDivergence(t1, t2)
|
||||
| _ => Error(NotYetImplemented)
|
||||
| (m1, m2) => Mixed.T.klDivergence(m1->toMixed, m2->toMixed)
|
||||
}
|
||||
|
||||
let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
|
||||
switch (prior, prediction) {
|
||||
| (Some(Continuous(t1)), Continuous(t2)) =>
|
||||
Continuous.T.logScoreWithPointResolution(~prediction=t2, ~answer, ~prior=t1->Some)
|
||||
| (None, Continuous(t2)) =>
|
||||
Continuous.T.logScoreWithPointResolution(~prediction=t2, ~answer, ~prior=None)
|
||||
| _ => Error(Operation.NotYetImplemented)
|
||||
}
|
||||
}
|
||||
})
|
||||
|
||||
let pdf = (f: float, t: t) => {
|
||||
|
|
|
|||
|
|
@ -14,3 +14,33 @@ module KLDivergence = {
|
|||
quot < 0.0 ? Error(Operation.ComplexNumberError) : Ok(-.answerElement *. logFn(quot))
|
||||
}
|
||||
}
|
||||
|
||||
module LogScoreWithPointResolution = {
|
||||
let logFn = Js.Math.log
|
||||
let score = (
|
||||
~priorPdf: option<float => float>,
|
||||
~predictionPdf: float => float,
|
||||
~answer: float,
|
||||
): result<float, Operation.Error.t> => {
|
||||
let numerator = answer->predictionPdf
|
||||
if numerator < 0.0 {
|
||||
Operation.PdfInvalidError->Error
|
||||
} else if numerator == 0.0 {
|
||||
infinity->Ok
|
||||
} else {
|
||||
-.(
|
||||
switch priorPdf {
|
||||
| None => numerator->logFn
|
||||
| Some(f) => {
|
||||
let priorDensityOfAnswer = f(answer)
|
||||
if priorDensityOfAnswer == 0.0 {
|
||||
neg_infinity
|
||||
} else {
|
||||
(numerator /. priorDensityOfAnswer)->logFn
|
||||
}
|
||||
}
|
||||
}
|
||||
)->Ok
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -216,6 +216,50 @@ module Uniform = {
|
|||
}
|
||||
}
|
||||
|
||||
module Logistic = {
|
||||
type t = logistic
|
||||
let make = (location, scale) =>
|
||||
scale > 0.0
|
||||
? Ok(#Logistic({location: location, scale: scale}))
|
||||
: Error("Scale must be positive")
|
||||
|
||||
let pdf = (x, t: t) => Stdlib.Logistic.pdf(x, t.location, t.scale)
|
||||
let cdf = (x, t: t) => Stdlib.Logistic.cdf(x, t.location, t.scale)
|
||||
let inv = (p, t: t) => Stdlib.Logistic.quantile(p, t.location, t.scale)
|
||||
let sample = (t: t) => {
|
||||
let s = Uniform.sample({low: 0.0, high: 1.0})
|
||||
inv(s, t)
|
||||
}
|
||||
let mean = (t: t) => Ok(Stdlib.Logistic.mean(t.location, t.scale))
|
||||
let toString = ({location, scale}: t) => j`Logistic($location,$scale)`
|
||||
}
|
||||
|
||||
module Bernoulli = {
|
||||
type t = bernoulli
|
||||
let make = p =>
|
||||
p >= 0.0 && p <= 1.0
|
||||
? Ok(#Bernoulli({p: p}))
|
||||
: Error("Bernoulli parameter must be between 0 and 1")
|
||||
let pmf = (x, t: t) => Stdlib.Bernoulli.pmf(x, t.p)
|
||||
|
||||
//Bernoulli is a discrete distribution, so it doesn't really have a pdf().
|
||||
//We fake this for now with the pmf function, but this should be fixed at some point.
|
||||
let pdf = (x, t: t) => Stdlib.Bernoulli.pmf(x, t.p)
|
||||
let cdf = (x, t: t) => Stdlib.Bernoulli.cdf(x, t.p)
|
||||
let inv = (p, t: t) => Stdlib.Bernoulli.quantile(p, t.p)
|
||||
let mean = (t: t) => Ok(Stdlib.Bernoulli.mean(t.p))
|
||||
let min = (t: t) => t.p == 1.0 ? 1.0 : 0.0
|
||||
let max = (t: t) => t.p == 0.0 ? 0.0 : 1.0
|
||||
let sample = (t: t) => {
|
||||
let s = Uniform.sample({low: 0.0, high: 1.0})
|
||||
inv(s, t)
|
||||
}
|
||||
let toString = ({p}: t) => j`Bernoulli($p)`
|
||||
let toPointSetDist = ({p}: t): PointSetTypes.pointSetDist => Discrete(
|
||||
Discrete.make(~integralSumCache=Some(1.0), {xs: [0.0, 1.0], ys: [1.0 -. p, p]}),
|
||||
)
|
||||
}
|
||||
|
||||
module Gamma = {
|
||||
type t = gamma
|
||||
let make = (shape: float, scale: float) => {
|
||||
|
|
@ -252,6 +296,9 @@ module Float = {
|
|||
let mean = (t: t) => Ok(t)
|
||||
let sample = (t: t) => t
|
||||
let toString = (t: t) => j`Delta($t)`
|
||||
let toPointSetDist = (t: t): PointSetTypes.pointSetDist => Discrete(
|
||||
Discrete.make(~integralSumCache=Some(1.0), {xs: [t], ys: [1.0]}),
|
||||
)
|
||||
}
|
||||
|
||||
module From90thPercentile = {
|
||||
|
|
@ -275,9 +322,11 @@ module T = {
|
|||
| #Cauchy(n) => Cauchy.pdf(x, n)
|
||||
| #Gamma(n) => Gamma.pdf(x, n)
|
||||
| #Lognormal(n) => Lognormal.pdf(x, n)
|
||||
| #Logistic(n) => Logistic.pdf(x, n)
|
||||
| #Uniform(n) => Uniform.pdf(x, n)
|
||||
| #Beta(n) => Beta.pdf(x, n)
|
||||
| #Float(n) => Float.pdf(x, n)
|
||||
| #Bernoulli(n) => Bernoulli.pdf(x, n)
|
||||
}
|
||||
|
||||
let cdf = (x, dist) =>
|
||||
|
|
@ -287,10 +336,12 @@ module T = {
|
|||
| #Exponential(n) => Exponential.cdf(x, n)
|
||||
| #Cauchy(n) => Cauchy.cdf(x, n)
|
||||
| #Gamma(n) => Gamma.cdf(x, n)
|
||||
| #Logistic(n) => Logistic.cdf(x, n)
|
||||
| #Lognormal(n) => Lognormal.cdf(x, n)
|
||||
| #Uniform(n) => Uniform.cdf(x, n)
|
||||
| #Beta(n) => Beta.cdf(x, n)
|
||||
| #Float(n) => Float.cdf(x, n)
|
||||
| #Bernoulli(n) => Bernoulli.cdf(x, n)
|
||||
}
|
||||
|
||||
let inv = (x, dist) =>
|
||||
|
|
@ -300,10 +351,12 @@ module T = {
|
|||
| #Exponential(n) => Exponential.inv(x, n)
|
||||
| #Cauchy(n) => Cauchy.inv(x, n)
|
||||
| #Gamma(n) => Gamma.inv(x, n)
|
||||
| #Logistic(n) => Logistic.inv(x, n)
|
||||
| #Lognormal(n) => Lognormal.inv(x, n)
|
||||
| #Uniform(n) => Uniform.inv(x, n)
|
||||
| #Beta(n) => Beta.inv(x, n)
|
||||
| #Float(n) => Float.inv(x, n)
|
||||
| #Bernoulli(n) => Bernoulli.inv(x, n)
|
||||
}
|
||||
|
||||
let sample: symbolicDist => float = x =>
|
||||
|
|
@ -313,10 +366,12 @@ module T = {
|
|||
| #Exponential(n) => Exponential.sample(n)
|
||||
| #Cauchy(n) => Cauchy.sample(n)
|
||||
| #Gamma(n) => Gamma.sample(n)
|
||||
| #Logistic(n) => Logistic.sample(n)
|
||||
| #Lognormal(n) => Lognormal.sample(n)
|
||||
| #Uniform(n) => Uniform.sample(n)
|
||||
| #Beta(n) => Beta.sample(n)
|
||||
| #Float(n) => Float.sample(n)
|
||||
| #Bernoulli(n) => Bernoulli.sample(n)
|
||||
}
|
||||
|
||||
let doN = (n, fn) => {
|
||||
|
|
@ -336,10 +391,12 @@ module T = {
|
|||
| #Cauchy(n) => Cauchy.toString(n)
|
||||
| #Normal(n) => Normal.toString(n)
|
||||
| #Gamma(n) => Gamma.toString(n)
|
||||
| #Logistic(n) => Logistic.toString(n)
|
||||
| #Lognormal(n) => Lognormal.toString(n)
|
||||
| #Uniform(n) => Uniform.toString(n)
|
||||
| #Beta(n) => Beta.toString(n)
|
||||
| #Float(n) => Float.toString(n)
|
||||
| #Bernoulli(n) => Bernoulli.toString(n)
|
||||
}
|
||||
|
||||
let min: symbolicDist => float = x =>
|
||||
|
|
@ -349,8 +406,10 @@ module T = {
|
|||
| #Cauchy(n) => Cauchy.inv(minCdfValue, n)
|
||||
| #Normal(n) => Normal.inv(minCdfValue, n)
|
||||
| #Lognormal(n) => Lognormal.inv(minCdfValue, n)
|
||||
| #Logistic(n) => Logistic.inv(minCdfValue, n)
|
||||
| #Gamma(n) => Gamma.inv(minCdfValue, n)
|
||||
| #Uniform({low}) => low
|
||||
| #Bernoulli(n) => Bernoulli.min(n)
|
||||
| #Beta(n) => Beta.inv(minCdfValue, n)
|
||||
| #Float(n) => n
|
||||
}
|
||||
|
|
@ -363,7 +422,9 @@ module T = {
|
|||
| #Normal(n) => Normal.inv(maxCdfValue, n)
|
||||
| #Gamma(n) => Gamma.inv(maxCdfValue, n)
|
||||
| #Lognormal(n) => Lognormal.inv(maxCdfValue, n)
|
||||
| #Logistic(n) => Logistic.inv(maxCdfValue, n)
|
||||
| #Beta(n) => Beta.inv(maxCdfValue, n)
|
||||
| #Bernoulli(n) => Bernoulli.max(n)
|
||||
| #Uniform({high}) => high
|
||||
| #Float(n) => n
|
||||
}
|
||||
|
|
@ -376,8 +437,10 @@ module T = {
|
|||
| #Normal(n) => Normal.mean(n)
|
||||
| #Lognormal(n) => Lognormal.mean(n)
|
||||
| #Beta(n) => Beta.mean(n)
|
||||
| #Logistic(n) => Logistic.mean(n)
|
||||
| #Uniform(n) => Uniform.mean(n)
|
||||
| #Gamma(n) => Gamma.mean(n)
|
||||
| #Bernoulli(n) => Bernoulli.mean(n)
|
||||
| #Float(n) => Float.mean(n)
|
||||
}
|
||||
|
||||
|
|
@ -453,7 +516,8 @@ module T = {
|
|||
d: symbolicDist,
|
||||
): PointSetTypes.pointSetDist =>
|
||||
switch d {
|
||||
| #Float(v) => Discrete(Discrete.make(~integralSumCache=Some(1.0), {xs: [v], ys: [1.0]}))
|
||||
| #Float(v) => Float.toPointSetDist(v)
|
||||
| #Bernoulli(v) => Bernoulli.toPointSetDist(v)
|
||||
| _ =>
|
||||
let xs = interpolateXs(~xSelection, d, sampleCount)
|
||||
let ys = xs |> E.A.fmap(x => pdf(x, d))
|
||||
|
|
|
|||
|
|
@ -36,6 +36,13 @@ type gamma = {
|
|||
scale: float,
|
||||
}
|
||||
|
||||
type logistic = {
|
||||
location: float,
|
||||
scale: float,
|
||||
}
|
||||
|
||||
type bernoulli = {p: float}
|
||||
|
||||
@genType
|
||||
type symbolicDist = [
|
||||
| #Normal(normal)
|
||||
|
|
@ -47,6 +54,8 @@ type symbolicDist = [
|
|||
| #Triangular(triangular)
|
||||
| #Gamma(gamma)
|
||||
| #Float(float)
|
||||
| #Bernoulli(bernoulli)
|
||||
| #Logistic(logistic)
|
||||
]
|
||||
|
||||
type analyticalSimplificationResult = [
|
||||
|
|
|
|||
|
|
@ -12,6 +12,7 @@ module Epsilon = {
|
|||
module Environment = {
|
||||
let defaultXYPointLength = 1000
|
||||
let defaultSampleCount = 10000
|
||||
let sparklineLength = 20
|
||||
}
|
||||
|
||||
module OpCost = {
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
module ExpressionValue = ReducerInterface_ExpressionValue
|
||||
type expressionValue = ReducerInterface_ExpressionValue.expressionValue
|
||||
type expressionValue = ExpressionValue.expressionValue
|
||||
|
||||
module Helpers = {
|
||||
let arithmeticMap = r =>
|
||||
|
|
@ -162,12 +162,27 @@ module Helpers = {
|
|||
}
|
||||
}
|
||||
}
|
||||
|
||||
let klDivergenceWithPrior = (
|
||||
prediction: DistributionTypes.genericDist,
|
||||
answer: DistributionTypes.genericDist,
|
||||
prior: DistributionTypes.genericDist,
|
||||
env: DistributionOperation.env,
|
||||
) => {
|
||||
let term1 = DistributionOperation.Constructors.klDivergence(~env, prediction, answer)
|
||||
let term2 = DistributionOperation.Constructors.klDivergence(~env, prior, answer)
|
||||
switch E.R.merge(term1, term2)->E.R2.fmap(((a, b)) => a -. b) {
|
||||
| Ok(x) => x->DistributionOperation.Float->Some
|
||||
| Error(_) => None
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
module SymbolicConstructors = {
|
||||
let oneFloat = name =>
|
||||
switch name {
|
||||
| "exponential" => Ok(SymbolicDist.Exponential.make)
|
||||
| "bernoulli" => Ok(SymbolicDist.Bernoulli.make)
|
||||
| _ => Error("Unreachable state")
|
||||
}
|
||||
|
||||
|
|
@ -177,6 +192,7 @@ module SymbolicConstructors = {
|
|||
| "uniform" => Ok(SymbolicDist.Uniform.make)
|
||||
| "beta" => Ok(SymbolicDist.Beta.make)
|
||||
| "lognormal" => Ok(SymbolicDist.Lognormal.make)
|
||||
| "logistic" => Ok(SymbolicDist.Logistic.make)
|
||||
| "cauchy" => Ok(SymbolicDist.Cauchy.make)
|
||||
| "gamma" => Ok(SymbolicDist.Gamma.make)
|
||||
| "to" => Ok(SymbolicDist.From90thPercentile.make)
|
||||
|
|
@ -204,14 +220,21 @@ let dispatchToGenericOutput = (
|
|||
): option<DistributionOperation.outputType> => {
|
||||
let (fnName, args) = call
|
||||
switch (fnName, args) {
|
||||
| ("exponential" as fnName, [EvNumber(f)]) =>
|
||||
| (("exponential" | "bernoulli") as fnName, [EvNumber(f)]) =>
|
||||
SymbolicConstructors.oneFloat(fnName)
|
||||
->E.R.bind(r => r(f))
|
||||
->SymbolicConstructors.symbolicResultToOutput
|
||||
| ("delta", [EvNumber(f)]) =>
|
||||
SymbolicDist.Float.makeSafe(f)->SymbolicConstructors.symbolicResultToOutput
|
||||
| (
|
||||
("normal" | "uniform" | "beta" | "lognormal" | "cauchy" | "gamma" | "to") as fnName,
|
||||
("normal"
|
||||
| "uniform"
|
||||
| "beta"
|
||||
| "lognormal"
|
||||
| "cauchy"
|
||||
| "gamma"
|
||||
| "to"
|
||||
| "logistic") as fnName,
|
||||
[EvNumber(f1), EvNumber(f2)],
|
||||
) =>
|
||||
SymbolicConstructors.twoFloat(fnName)
|
||||
|
|
@ -227,7 +250,8 @@ let dispatchToGenericOutput = (
|
|||
| ("mean", [EvDistribution(dist)]) => Helpers.toFloatFn(#Mean, dist, ~env)
|
||||
| ("integralSum", [EvDistribution(dist)]) => Helpers.toFloatFn(#IntegralSum, dist, ~env)
|
||||
| ("toString", [EvDistribution(dist)]) => Helpers.toStringFn(ToString, dist, ~env)
|
||||
| ("toSparkline", [EvDistribution(dist)]) => Helpers.toStringFn(ToSparkline(20), dist, ~env)
|
||||
| ("toSparkline", [EvDistribution(dist)]) =>
|
||||
Helpers.toStringFn(ToSparkline(MagicNumbers.Environment.sparklineLength), dist, ~env)
|
||||
| ("toSparkline", [EvDistribution(dist), EvNumber(n)]) =>
|
||||
Helpers.toStringFn(ToSparkline(Belt.Float.toInt(n)), dist, ~env)
|
||||
| ("exp", [EvDistribution(a)]) =>
|
||||
|
|
@ -240,8 +264,28 @@ let dispatchToGenericOutput = (
|
|||
~env,
|
||||
)->Some
|
||||
| ("normalize", [EvDistribution(dist)]) => Helpers.toDistFn(Normalize, dist, ~env)
|
||||
| ("klDivergence", [EvDistribution(a), EvDistribution(b)]) =>
|
||||
Some(DistributionOperation.run(FromDist(ToScore(KLDivergence(b)), a), ~env))
|
||||
| ("klDivergence", [EvDistribution(prediction), EvDistribution(answer)]) =>
|
||||
Some(DistributionOperation.run(FromDist(ToScore(KLDivergence(answer)), prediction), ~env))
|
||||
| ("klDivergence", [EvDistribution(prediction), EvDistribution(answer), EvDistribution(prior)]) =>
|
||||
Helpers.klDivergenceWithPrior(prediction, answer, prior, env)
|
||||
| (
|
||||
"logScoreWithPointAnswer",
|
||||
[EvDistribution(prediction), EvNumber(answer), EvDistribution(prior)],
|
||||
)
|
||||
| (
|
||||
"logScoreWithPointAnswer",
|
||||
[EvDistribution(prediction), EvDistribution(Symbolic(#Float(answer))), EvDistribution(prior)],
|
||||
) =>
|
||||
DistributionOperation.run(
|
||||
FromDist(ToScore(LogScore(answer, prior->Some)), prediction),
|
||||
~env,
|
||||
)->Some
|
||||
| ("logScoreWithPointAnswer", [EvDistribution(prediction), EvNumber(answer)])
|
||||
| (
|
||||
"logScoreWithPointAnswer",
|
||||
[EvDistribution(prediction), EvDistribution(Symbolic(#Float(answer)))],
|
||||
) =>
|
||||
DistributionOperation.run(FromDist(ToScore(LogScore(answer, None)), prediction), ~env)->Some
|
||||
| ("isNormalized", [EvDistribution(dist)]) => Helpers.toBoolFn(IsNormalized, dist, ~env)
|
||||
| ("toPointSet", [EvDistribution(dist)]) => Helpers.toDistFn(ToPointSet, dist, ~env)
|
||||
| ("scaleLog", [EvDistribution(dist)]) =>
|
||||
|
|
|
|||
|
|
@ -623,6 +623,19 @@ module A = {
|
|||
| Some(o) => o
|
||||
| None => []
|
||||
}
|
||||
// REturns `None` there are no non-`None` elements
|
||||
let rec arrSomeToSomeArr = (optionals: array<option<'a>>): option<array<'a>> => {
|
||||
let optionals' = optionals->Belt.List.fromArray
|
||||
switch optionals' {
|
||||
| list{} => []->Some
|
||||
| list{x, ...xs} =>
|
||||
switch x {
|
||||
| Some(_) => xs->Belt.List.toArray->arrSomeToSomeArr
|
||||
| None => None
|
||||
}
|
||||
}
|
||||
}
|
||||
let firstSome = x => Belt.Array.getBy(x, O.isSome)
|
||||
}
|
||||
|
||||
module R = {
|
||||
|
|
|
|||
|
|
@ -55,8 +55,8 @@ type operationError =
|
|||
| ComplexNumberError
|
||||
| InfinityError
|
||||
| NegativeInfinityError
|
||||
| LogicallyInconsistentPathwayError
|
||||
| SampleMapNeedsNtoNFunction
|
||||
| PdfInvalidError
|
||||
| NotYetImplemented // should be removed when `klDivergence` for mixed and discrete is implemented.
|
||||
|
||||
@genType
|
||||
|
|
@ -70,8 +70,8 @@ module Error = {
|
|||
| ComplexNumberError => "Operation returned complex result"
|
||||
| InfinityError => "Operation returned positive infinity"
|
||||
| NegativeInfinityError => "Operation returned negative infinity"
|
||||
| LogicallyInconsistentPathwayError => "This pathway should have been logically unreachable"
|
||||
| SampleMapNeedsNtoNFunction => "SampleMap needs a function that converts a number to a number"
|
||||
| PdfInvalidError => "This Pdf is invalid"
|
||||
| NotYetImplemented => "This pathway is not yet implemented"
|
||||
}
|
||||
}
|
||||
|
|
|
|||
40
packages/squiggle-lang/src/rescript/Utility/Stdlib.res
Normal file
40
packages/squiggle-lang/src/rescript/Utility/Stdlib.res
Normal file
|
|
@ -0,0 +1,40 @@
|
|||
module Bernoulli = {
|
||||
@module external cdf: (float, float) => float = "@stdlib/stats/base/dists/bernoulli/cdf"
|
||||
let cdf = cdf
|
||||
|
||||
@module external pmf: (float, float) => float = "@stdlib/stats/base/dists/bernoulli/pmf"
|
||||
let pmf = pmf
|
||||
|
||||
@module external quantile: (float, float) => float = "@stdlib/stats/base/dists/bernoulli/quantile"
|
||||
let quantile = quantile
|
||||
|
||||
@module external mean: float => float = "@stdlib/stats/base/dists/bernoulli/mean"
|
||||
let mean = mean
|
||||
|
||||
@module external stdev: float => float = "@stdlib/stats/base/dists/bernoulli/stdev"
|
||||
let stdev = stdev
|
||||
|
||||
@module external variance: float => float = "@stdlib/stats/base/dists/bernoulli/variance"
|
||||
let variance = variance
|
||||
}
|
||||
|
||||
module Logistic = {
|
||||
@module external cdf: (float, float, float) => float = "@stdlib/stats/base/dists/logistic/cdf"
|
||||
let cdf = cdf
|
||||
|
||||
@module external pdf: (float, float, float) => float = "@stdlib/stats/base/dists/logistic/pdf"
|
||||
let pdf = pdf
|
||||
|
||||
@module
|
||||
external quantile: (float, float, float) => float = "@stdlib/stats/base/dists/logistic/quantile"
|
||||
let quantile = quantile
|
||||
|
||||
@module external mean: (float, float) => float = "@stdlib/stats/base/dists/logistic/mean"
|
||||
let mean = mean
|
||||
|
||||
@module external stdev: (float, float) => float = "@stdlib/stats/base/dists/logistic/stdev"
|
||||
let stdev = stdev
|
||||
|
||||
@module external variance: (float, float) => float = "@stdlib/stats/base/dists/logistic/variance"
|
||||
let variance = variance
|
||||
}
|
||||
|
|
@ -16,7 +16,7 @@
|
|||
"@docusaurus/preset-classic": "2.0.0-beta.20",
|
||||
"@quri/squiggle-components": "^0.2.20",
|
||||
"clsx": "^1.1.1",
|
||||
"prism-react-renderer": "^1.2.1",
|
||||
"prism-react-renderer": "^1.3.3",
|
||||
"react": "^18.1.0",
|
||||
"react-dom": "^18.1.0",
|
||||
"remark-math": "^3",
|
||||
|
|
|
|||
355
yarn.lock
355
yarn.lock
|
|
@ -2502,6 +2502,335 @@
|
|||
eval "^0.1.8"
|
||||
webpack-sources "^1.4.3"
|
||||
|
||||
"@stdlib/array@^0.0.x":
|
||||
version "0.0.12"
|
||||
resolved "https://registry.yarnpkg.com/@stdlib/array/-/array-0.0.12.tgz#12f40ab95bb36d424cdad991f29fc3cb491ee29e"
|
||||
integrity sha512-nDksiuvRC1dSTHrf5yOGQmlRwAzSKV8MdFQwFSvLbZGGhi5Y4hExqea5HloLgNVouVs8lnAFi2oubSM4Mc7YAg==
|
||||
dependencies:
|
||||
"@stdlib/assert" "^0.0.x"
|
||||
"@stdlib/blas" "^0.0.x"
|
||||
"@stdlib/complex" "^0.0.x"
|
||||
"@stdlib/constants" "^0.0.x"
|
||||
"@stdlib/math" "^0.0.x"
|
||||
"@stdlib/symbol" "^0.0.x"
|
||||
"@stdlib/types" "^0.0.x"
|
||||
"@stdlib/utils" "^0.0.x"
|
||||
|
||||
"@stdlib/assert@^0.0.x":
|
||||
version "0.0.12"
|
||||
resolved "https://registry.yarnpkg.com/@stdlib/assert/-/assert-0.0.12.tgz#1648c9016e5041291f55a6464abcc4069c5103ce"
|
||||
integrity sha512-38FxFf+ZoQZbdc+m09UsWtaCmzd/2e7im0JOaaFYE7icmRfm+4KiE9BRvBT4tIn7ioLB2f9PsBicKjIsf+tY1w==
|
||||
dependencies:
|
||||
"@stdlib/array" "^0.0.x"
|
||||
"@stdlib/cli" "^0.0.x"
|
||||
"@stdlib/complex" "^0.0.x"
|
||||
"@stdlib/constants" "^0.0.x"
|
||||
"@stdlib/fs" "^0.0.x"
|
||||
"@stdlib/math" "^0.0.x"
|
||||
"@stdlib/ndarray" "^0.0.x"
|
||||
"@stdlib/number" "^0.0.x"
|
||||
"@stdlib/os" "^0.0.x"
|
||||
"@stdlib/process" "^0.0.x"
|
||||
"@stdlib/regexp" "^0.0.x"
|
||||
"@stdlib/streams" "^0.0.x"
|
||||
"@stdlib/string" "^0.0.x"
|
||||
"@stdlib/symbol" "^0.0.x"
|
||||
"@stdlib/types" "^0.0.x"
|
||||
"@stdlib/utils" "^0.0.x"
|
||||
|
||||
"@stdlib/bigint@^0.0.x":
|
||||
version "0.0.11"
|
||||
resolved "https://registry.yarnpkg.com/@stdlib/bigint/-/bigint-0.0.11.tgz#c416a1d727001c55f4897e6424124199d638f2fd"
|
||||
integrity sha512-uz0aYDLABAYyqxaCSHYbUt0yPkXYUCR7TrVvHN+UUD3i8FZ02ZKcLO+faKisDyxKEoSFTNtn3Ro8Ir5ebOlVXQ==
|
||||
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|
||||
"@types/node@*", "@types/node@^17.0.33", "@types/node@^17.0.5":
|
||||
version "17.0.33"
|
||||
resolved "https://registry.yarnpkg.com/@types/node/-/node-17.0.33.tgz#3c1879b276dc63e73030bb91165e62a4509cd506"
|
||||
integrity sha512-miWq2m2FiQZmaHfdZNcbpp9PuXg34W5JZ5CrJ/BaS70VuhoJENBEQybeiYSaPBRNq6KQGnjfEnc/F3PN++D+XQ==
|
||||
|
||||
"@types/node@^14.0.10":
|
||||
version "14.18.16"
|
||||
|
|
@ -4108,10 +4437,10 @@
|
|||
resolved "https://registry.yarnpkg.com/@types/range-parser/-/range-parser-1.2.4.tgz#cd667bcfdd025213aafb7ca5915a932590acdcdc"
|
||||
integrity sha512-EEhsLsD6UsDM1yFhAvy0Cjr6VwmpMWqFBCb9w07wVugF7w9nfajxLuVmngTIpgS6svCnm6Vaw+MZhoDCKnOfsw==
|
||||
|
||||
"@types/react-dom@^18.0.0", "@types/react-dom@^18.0.2":
|
||||
version "18.0.3"
|
||||
resolved "https://registry.yarnpkg.com/@types/react-dom/-/react-dom-18.0.3.tgz#a022ea08c75a476fe5e96b675c3e673363853831"
|
||||
integrity sha512-1RRW9kst+67gveJRYPxGmVy8eVJ05O43hg77G2j5m76/RFJtMbcfAs2viQ2UNsvvDg8F7OfQZx8qQcl6ymygaQ==
|
||||
"@types/react-dom@^18.0.0", "@types/react-dom@^18.0.4":
|
||||
version "18.0.4"
|
||||
resolved "https://registry.yarnpkg.com/@types/react-dom/-/react-dom-18.0.4.tgz#dcbcadb277bcf6c411ceff70069424c57797d375"
|
||||
integrity sha512-FgTtbqPOCI3dzZPZoC2T/sx3L34qxy99ITWn4eoSA95qPyXDMH0ALoAqUp49ITniiJFsXUVBtalh/KffMpg21Q==
|
||||
dependencies:
|
||||
"@types/react" "*"
|
||||
|
||||
|
|
@ -13678,10 +14007,10 @@ pretty-time@^1.1.0:
|
|||
resolved "https://registry.yarnpkg.com/pretty-time/-/pretty-time-1.1.0.tgz#ffb7429afabb8535c346a34e41873adf3d74dd0e"
|
||||
integrity sha512-28iF6xPQrP8Oa6uxE6a1biz+lWeTOAPKggvjB8HAs6nVMKZwf5bG++632Dx614hIWgUPkgivRfG+a8uAXGTIbA==
|
||||
|
||||
prism-react-renderer@^1.2.1, prism-react-renderer@^1.3.1:
|
||||
version "1.3.1"
|
||||
resolved "https://registry.yarnpkg.com/prism-react-renderer/-/prism-react-renderer-1.3.1.tgz#88fc9d0df6bed06ca2b9097421349f8c2f24e30d"
|
||||
integrity sha512-xUeDMEz074d0zc5y6rxiMp/dlC7C+5IDDlaEUlcBOFE2wddz7hz5PNupb087mPwTt7T9BrFmewObfCBuf/LKwQ==
|
||||
prism-react-renderer@^1.3.1, prism-react-renderer@^1.3.3:
|
||||
version "1.3.3"
|
||||
resolved "https://registry.yarnpkg.com/prism-react-renderer/-/prism-react-renderer-1.3.3.tgz#9b5a4211a6756eee3c96fee9a05733abc0b0805c"
|
||||
integrity sha512-Viur/7tBTCH2HmYzwCHmt2rEFn+rdIWNIINXyg0StiISbDiIhHKhrFuEK8eMkKgvsIYSjgGqy/hNyucHp6FpoQ==
|
||||
|
||||
prismjs@^1.21.0, prismjs@^1.28.0:
|
||||
version "1.28.0"
|
||||
|
|
@ -14375,7 +14704,7 @@ read-pkg@^5.2.0:
|
|||
parse-json "^5.0.0"
|
||||
type-fest "^0.6.0"
|
||||
|
||||
"readable-stream@1 || 2", readable-stream@^2.0.0, readable-stream@^2.0.1, readable-stream@^2.0.2, readable-stream@^2.1.5, readable-stream@^2.2.2, readable-stream@^2.3.3, readable-stream@^2.3.6, readable-stream@~2.3.6:
|
||||
"readable-stream@1 || 2", readable-stream@^2.0.0, readable-stream@^2.0.1, readable-stream@^2.0.2, readable-stream@^2.1.4, readable-stream@^2.1.5, readable-stream@^2.2.2, readable-stream@^2.3.3, readable-stream@^2.3.6, readable-stream@~2.3.6:
|
||||
version "2.3.7"
|
||||
resolved "https://registry.yarnpkg.com/readable-stream/-/readable-stream-2.3.7.tgz#1eca1cf711aef814c04f62252a36a62f6cb23b57"
|
||||
integrity sha512-Ebho8K4jIbHAxnuxi7o42OrZgF/ZTNcsZj6nRKyUmkhLFq8CHItp/fy6hQZuZmP/n3yZ9VBUbp4zz/mX8hmYPw==
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user