Initialized logScore
and logScoreAgainstImproperPrior
Value: [1e-5 to 6e-3]
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parent
937458cd05
commit
978e149913
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@ -277,13 +277,19 @@ 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 logScore = (prior: t, prediction: t, answer: float) => {
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let newShape = XYShape.PointwiseCombination.combineAlongSupportOfSecondArgument(
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PointSetDist_Scoring.LogScore.integrand(~answer),
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prior.xyShape,
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prediction.xyShape,
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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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@ -229,4 +229,10 @@ 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 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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@ -34,6 +34,8 @@ 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 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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@ -57,6 +59,8 @@ 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 logScore = T.logScore
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let logScoreAgainstImproperPrior = T.logScoreAgainstImproperPrior
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let updateIntegralCache = T.updateIntegralCache
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@ -306,6 +306,12 @@ 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 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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@ -196,13 +196,22 @@ module T = Dist({
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| Continuous(m) => Continuous.T.variance(m)
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}
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let klDivergence = (t1: t, t2: t) =>
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switch (t1, t2) {
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let klDivergence = (prediction: t, answer: t) =>
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switch (prediction, answer) {
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| (Continuous(t1), Continuous(t2)) => Continuous.T.klDivergence(t1, t2)
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| (Discrete(t1), Discrete(t2)) => Discrete.T.klDivergence(t1, t2)
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| (Mixed(t1), Mixed(t2)) => Mixed.T.klDivergence(t1, t2)
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| _ => Error(NotYetImplemented)
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| (m1, m2) => Mixed.T.klDivergence(m1->toMixed, m2->toMixed)
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}
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let logScore = (prior: t, prediction: t, answer: float) => {
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switch (prior, prediction) {
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| (Continuous(t1), Continuous(t2)) => Continuous.T.logScore(t1, t2, answer)
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| _ => Error(Operation.NotYetImplemented)
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}
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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 pdf = (f: float, t: t) => {
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@ -14,3 +14,20 @@ module KLDivergence = {
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quot < 0.0 ? Error(Operation.ComplexNumberError) : Ok(-.answerElement *. logFn(quot))
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}
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}
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/*
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*/
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module LogScore = {
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let logFn = Js.Math.log
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let integrand = (priorElement: float, predictionElement: float, ~answer: float) => {
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if answer == 0.0 {
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Ok(0.0)
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} else if predictionElement == 0.0 {
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Ok(infinity)
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} else {
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let quot = predictionElement /. priorElement
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quot < 0.0 ? Error(Operation.ComplexNumberError) : Ok(-.answer *. logFn(quot /. answer))
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}
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}
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}
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