logScore
now in interface.
Value: [1e-4 to 1e-1]
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parent
978e149913
commit
51310819a1
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@ -145,7 +145,11 @@ 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(prediction, answer)) =>
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GenericDist.Score.logScore(dist, prediction, answer, ~toPointSetFn)
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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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@ -262,6 +266,8 @@ 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 logScore = (~env, prior, prediction, answer) =>
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C.logScore(prior, prediction, answer)->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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@ -62,6 +62,8 @@ 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 logScore: (~env: env, genericDist, genericDist, float) => 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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@ -91,7 +91,7 @@ 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(genericDist, float)
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type fromDist =
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| ToFloat(toFloat)
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@ -120,6 +120,7 @@ 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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@ -161,6 +162,10 @@ 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 logScore = (prior, prediction, answer): t => FromDist(
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ToScore(LogScore(prediction, answer)),
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prior,
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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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@ -59,11 +59,20 @@ 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 = (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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}
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let logScore = (prior, prediction, answer, ~toPointSetFn: toPointSetFn): result<float, error> => {
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let pointSets = E.R.merge(toPointSetFn(prior), toPointSetFn(prediction))
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pointSets |> E.R2.bind(((a, b)) =>
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PointSetDist.T.logScore(a, b, answer)->E.R2.errMap(x => DistributionTypes.OperationError(x))
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)
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}
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}
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let toFloatOperation = (
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@ -23,7 +23,10 @@ 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 logScore: (t, t, float, ~toPointSetFn: toPointSetFn) => result<float, error>
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}
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@genType
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let toPointSet: (
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@ -287,10 +287,6 @@ 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 logScoreAgainstImproperPrior = (prediction: t, answer: float) => {
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let prior = make({xs: prediction.xyShape.xs, ys: E.A.fmap(_ => 1.0, prediction.xyShape.xs)})
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logScore(prior, prediction, answer)
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}
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})
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let isNormalized = (t: t): bool => {
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@ -232,7 +232,4 @@ module T = Dist({
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let logScore = (prior: t, prediction: t, answer: float) => {
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Error(Operation.NotYetImplemented)
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}
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let logScoreAgainstImproperPrior = (prediction: t, answer: float) => {
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Error(Operation.NotYetImplemented)
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}
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})
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@ -35,7 +35,6 @@ module type dist = {
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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 logScore: (t, t, float) => result<float, Operation.Error.t>
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let logScoreAgainstImproperPrior: (t, float) => result<float, Operation.Error.t>
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}
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module Dist = (T: dist) => {
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@ -60,7 +59,6 @@ module Dist = (T: dist) => {
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let integralEndY = T.integralEndY
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let klDivergence = T.klDivergence
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let logScore = T.logScore
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let logScoreAgainstImproperPrior = T.logScoreAgainstImproperPrior
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let updateIntegralCache = T.updateIntegralCache
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@ -309,9 +309,6 @@ module T = Dist({
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let logScore = (prior: t, prediction: t, answer: float) => {
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Error(Operation.NotYetImplemented)
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}
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let logScoreAgainstImproperPrior = (prediction: t, answer: float) => {
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Error(Operation.NotYetImplemented)
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}
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})
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let combineAlgebraically = (op: Operation.convolutionOperation, t1: t, t2: t): t => {
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@ -12,6 +12,7 @@ module Epsilon = {
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module Environment = {
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let defaultXYPointLength = 1000
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let defaultSampleCount = 10000
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let sparklineLength = 20
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}
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module OpCost = {
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@ -157,6 +157,20 @@ 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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): option<DistributionTypes.genericDist> => {
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switch supportOf {
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| PointSet(Continuous(dist)) =>
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{xs: dist.xyShape.xs, ys: E.A.fmap(fn, dist.xyShape.xs)}
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->Continuous.make
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->Continuous
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->PointSet
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->Some
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| _ => None
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}
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}
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}
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module SymbolicConstructors = {
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@ -219,7 +233,8 @@ let dispatchToGenericOutput = (call: ExpressionValue.functionCall, _environment)
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| ("mean", [EvDistribution(dist)]) => Helpers.toFloatFn(#Mean, dist)
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| ("integralSum", [EvDistribution(dist)]) => Helpers.toFloatFn(#IntegralSum, dist)
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| ("toString", [EvDistribution(dist)]) => Helpers.toStringFn(ToString, dist)
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| ("toSparkline", [EvDistribution(dist)]) => Helpers.toStringFn(ToSparkline(20), dist)
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| ("toSparkline", [EvDistribution(dist)]) =>
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Helpers.toStringFn(ToSparkline(MagicNumbers.Environment.sparklineLength), dist)
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| ("toSparkline", [EvDistribution(dist), EvNumber(n)]) =>
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Helpers.toStringFn(ToSparkline(Belt.Float.toInt(n)), dist)
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| ("exp", [EvDistribution(a)]) =>
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@ -233,6 +248,21 @@ let dispatchToGenericOutput = (call: ExpressionValue.functionCall, _environment)
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| ("normalize", [EvDistribution(dist)]) => Helpers.toDistFn(Normalize, dist)
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| ("klDivergence", [EvDistribution(a), EvDistribution(b)]) =>
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Some(runGenericOperation(FromDist(ToScore(KLDivergence(b)), a)))
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| ("logScore", [EvDistribution(prior), EvDistribution(prediction), EvNumber(answer)])
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| (
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"logScore",
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[EvDistribution(prior), EvDistribution(prediction), EvDistribution(Symbolic(#Float(answer)))],
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) =>
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Some(runGenericOperation(FromDist(ToScore(LogScore(prediction, answer)), prior)))
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| ("logScoreAgainstImproperPrior", [EvDistribution(prediction), EvNumber(answer)])
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| (
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"logScoreAgainstImproperPrior",
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[EvDistribution(prediction), EvDistribution(Symbolic(#Float(answer)))],
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) =>
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E.O.fmap(
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d => runGenericOperation(FromDist(ToScore(LogScore(prediction, answer)), d)),
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Helpers.constructNonNormalizedPointSet(~supportOf=prediction, _ => 1.0),
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)
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| ("isNormalized", [EvDistribution(dist)]) => Helpers.toBoolFn(IsNormalized, dist)
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| ("toPointSet", [EvDistribution(dist)]) => Helpers.toDistFn(ToPointSet, dist)
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| ("scaleLog", [EvDistribution(dist)]) =>
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