More substantial CR; more named args
Value: [1e-6 to 1e-2]
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1d2bb556de
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9e7319ed57
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@ -150,7 +150,12 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
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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(dist, answer, prior, ~toPointSetFn)
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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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@ -267,8 +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 = (~env, prediction, answer, prior) =>
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C.logScoreWithPointResolution(prediction, answer, prior)->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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@ -65,9 +65,9 @@ module Constructors: {
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@genType
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let logScoreWithPointResolution: (
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~env: env,
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genericDist,
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float,
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option<genericDist>,
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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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@ -162,9 +162,9 @@ 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 = (prior, prediction, answer): t => FromDist(
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ToScore(LogScore(prediction, answer)),
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prior,
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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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@ -68,18 +68,21 @@ module Score = {
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}
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let logScoreWithPointResolution = (
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prediction,
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answer,
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prior,
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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(((a, b)) =>
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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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b,
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answer,
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a->Some,
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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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@ -87,9 +90,9 @@ module Score = {
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->toPointSetFn
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->E.R.bind(x =>
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PointSetDist.T.logScoreWithPointResolution(
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x,
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answer,
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None,
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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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@ -26,9 +26,9 @@ let toFloatOperation: (
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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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t,
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float,
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option<t>,
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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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@ -279,7 +279,7 @@ module T = Dist({
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)
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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 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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@ -229,7 +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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let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
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Error(Operation.NotYetImplemented)
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}
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})
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@ -34,7 +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: (t, float, option<t>) => result<float, Operation.Error.t>
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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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) => result<float, Operation.Error.t>
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}
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module Dist = (T: dist) => {
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@ -306,7 +306,7 @@ module T = Dist({
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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))
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}
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let logScoreWithPointResolution = (prediction: t, answer: float, prior: option<t>) => {
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let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
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Error(Operation.NotYetImplemented)
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}
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})
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@ -203,11 +203,12 @@ module T = Dist({
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| (m1, m2) => Mixed.T.klDivergence(m1->toMixed, m2->toMixed)
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}
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let logScoreWithPointResolution = (prediction: t, answer: float, prior: option<t>) => {
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let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
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switch (prior, prediction) {
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| (Some(Continuous(t1)), Continuous(t2)) =>
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Continuous.T.logScoreWithPointResolution(t2, answer, t1->Some)
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| (None, Continuous(t2)) => Continuous.T.logScoreWithPointResolution(t2, answer, None)
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Continuous.T.logScoreWithPointResolution(~prediction=t2, ~answer, ~prior=t1->Some)
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| (None, Continuous(t2)) =>
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Continuous.T.logScoreWithPointResolution(~prediction=t2, ~answer, ~prior=None)
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| _ => Error(Operation.NotYetImplemented)
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}
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}
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@ -24,7 +24,7 @@ module LogScoreWithPointResolution = {
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): result<float, Operation.Error.t> => {
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let numerator = answer->predictionPdf
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if numerator < 0.0 {
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Operation.ComplexNumberError->Error
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Operation.PdfInvalidError->Error
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} else if numerator == 0.0 {
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infinity->Ok
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} else {
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@ -162,24 +162,6 @@ module Helpers = {
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}
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}
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}
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let constructNonNormalizedPointSet = (
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~supportOf: DistributionTypes.genericDist,
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fn: float => float,
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env: DistributionOperation.env,
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): DistributionTypes.genericDist => {
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let cdf = x => toFloatFn(#Cdf(x), supportOf, ~env)
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let leftEndpoint = cdf(MagicNumbers.Epsilon.ten)
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let rightEndpoint = cdf(1.0 -. MagicNumbers.Epsilon.ten)
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let xs = switch (leftEndpoint, rightEndpoint) {
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| (Some(Float(a)), Some(Float(b))) =>
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E.A.Floats.range(a, b, MagicNumbers.Environment.defaultXYPointLength)
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| _ => []
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}
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{xs: xs, ys: E.A.fmap(fn, xs)}
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->Continuous.make
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->PointSetTypes.Continuous
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->DistributionTypes.PointSet
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}
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let klDivergenceWithPrior = (
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prediction: DistributionTypes.genericDist,
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@ -620,6 +620,7 @@ module A = {
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| Some(o) => o
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| None => []
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}
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// REturns `None` there are no non-`None` elements
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let rec arrSomeToSomeArr = (optionals: array<option<'a>>): option<array<'a>> => {
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let optionals' = optionals->Belt.List.fromArray
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switch optionals' {
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@ -631,17 +632,7 @@ module A = {
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}
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}
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}
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let rec firstSome = (optionals: array<option<'a>>): option<'a> => {
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let optionals' = optionals->Belt.List.fromArray
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switch optionals' {
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| list{} => None
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| list{x, ...xs} =>
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switch x {
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| Some(_) => x
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| None => xs->Belt.List.toArray->firstSome
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}
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}
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}
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let firstSome = x => Belt.Array.getBy(x, O.isSome)
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}
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module R = {
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@ -55,7 +55,7 @@ type operationError =
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| ComplexNumberError
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| InfinityError
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| NegativeInfinityError
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| LogicallyInconsistentPathwayError
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| PdfInvalidError
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| NotYetImplemented // should be removed when `klDivergence` for mixed and discrete is implemented.
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@genType
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@ -69,7 +69,7 @@ module Error = {
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| ComplexNumberError => "Operation returned complex result"
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| InfinityError => "Operation returned positive infinity"
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| NegativeInfinityError => "Operation returned negative infinity"
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| LogicallyInconsistentPathwayError => "This pathway should have been logically unreachable"
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| PdfInvalidError => "This Pdf is invalid"
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| NotYetImplemented => "This pathway is not yet implemented"
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}
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}
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