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intermediate commit

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Value: [1e-10 to 1e-5]
This commit is contained in:
Quinn Dougherty 2022-05-23 12:07:03 -04:00
parent bafcb4f7b8
commit 3a56d6fca4
6 changed files with 73 additions and 66 deletions
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26
packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Continuous.res
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@ -271,19 +271,19 @@ module T = Dist({
let variance = (t: t): float =>
XYShape.Analysis.getVarianceDangerously(t, mean, Analysis.getMeanOfSquares)
let klDivergence = (prediction: t, answer: t) => {
let newShape = XYShape.PointwiseCombination.combineAlongSupportOfSecondArgument(
PointSetDist_Scoring.KLDivergence.integrand,
prediction.xyShape,
answer.xyShape,
)
newShape->E.R2.fmap(x => x->make->integralEndY)
}
let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
let priorPdf = prior->E.O2.fmap((shape, x) => XYShape.XtoY.linear(x, shape.xyShape))
let predictionPdf = x => XYShape.XtoY.linear(x, prediction.xyShape)
PointSetDist_Scoring.LogScoreWithPointResolution.score(~priorPdf, ~predictionPdf, ~answer)
}
// let klDivergence = (prediction: t, answer: t) => {
// let newShape = XYShape.PointwiseCombination.combineAlongSupportOfSecondArgument(
// PointSetDist_Scoring.KLDivergence.integrand,
// prediction.xyShape,
// answer.xyShape,
// )
// newShape->E.R2.fmap(x => x->make->integralEndY)
// }
// let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
// let priorPdf = prior->E.O2.fmap((shape, x) => XYShape.XtoY.linear(x, shape.xyShape))
// let predictionPdf = x => XYShape.XtoY.linear(x, prediction.xyShape)
// PointSetDist_Scoring.LogScoreWithPointResolution.score(~priorPdf, ~predictionPdf, ~answer)
// }
})
let isNormalized = (t: t): bool => {

20
packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Discrete.res
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@ -222,14 +222,14 @@ module T = Dist({
XYShape.Analysis.getVarianceDangerously(t, mean, getMeanOfSquares)
}
let klDivergence = (prediction: t, answer: t) => {
combinePointwise(
~fn=PointSetDist_Scoring.KLDivergence.integrand,
prediction,
answer,
)->E.R2.fmap(integralEndY)
}
let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
Error(Operation.NotYetImplemented)
}
// let klDivergence = (prediction: t, answer: t) => {
// combinePointwise(
// ~fn=PointSetDist_Scoring.KLDivergence.integrand,
// prediction,
// answer,
// )->E.R2.fmap(integralEndY)
// }
// let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
// Error(Operation.NotYetImplemented)
// }
})

16
packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Distributions.res
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@ -33,12 +33,12 @@ module type dist = {
let mean: t => float
let variance: t => float
let klDivergence: (t, t) => result<float, Operation.Error.t>
let logScoreWithPointResolution: (
~prediction: t,
~answer: float,
~prior: option<t>,
) => result<float, Operation.Error.t>
// let klDivergence: (t, t) => result<float, Operation.Error.t>
// let logScoreWithPointResolution: (
// ~prediction: t,
// ~answer: float,
// ~prior: option<t>,
// ) => result<float, Operation.Error.t>
}
module Dist = (T: dist) => {
@ -61,8 +61,8 @@ module Dist = (T: dist) => {
let mean = T.mean
let variance = T.variance
let integralEndY = T.integralEndY
let klDivergence = T.klDivergence
let logScoreWithPointResolution = T.logScoreWithPointResolution
// let klDivergence = T.klDivergence
// let logScoreWithPointResolution = T.logScoreWithPointResolution
let updateIntegralCache = T.updateIntegralCache

16
packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Mixed.res
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@ -301,14 +301,14 @@ module T = Dist({
}
}
let klDivergence = (prediction: t, answer: t) => {
let klDiscretePart = Discrete.T.klDivergence(prediction.discrete, answer.discrete)
let klContinuousPart = Continuous.T.klDivergence(prediction.continuous, answer.continuous)
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 klDivergence = (prediction: t, answer: t) => {
// let klDiscretePart = Discrete.T.klDivergence(prediction.discrete, answer.discrete)
// let klContinuousPart = Continuous.T.klDivergence(prediction.continuous, answer.continuous)
// 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 => {

35
packages/squiggle-lang/src/rescript/Distributions/PointSetDist/PointSetDist.res
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@ -196,24 +196,27 @@ module T = Dist({
| Continuous(m) => Continuous.T.variance(m)
}
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)
| (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 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)
// | (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 logScore = (args: PointSetDist_Scoring.scoreArgs): result<float, Operation.Error.t> =>
PointSetDist_Scoring.logScore(args)
let pdf = (f: float, t: t) => {
let mixedPoint: PointSetTypes.mixedPoint = T.xToY(f, t)
mixedPoint.continuous +. mixedPoint.discrete

26
packages/squiggle-lang/src/rescript/Distributions/PointSetDist/PointSetDist_Scoring.res
View File

@ -1,4 +1,4 @@
type t = PointSetDist.pointSetDist
type t = PointSetTypes.pointSetDist
type continuousShape = PointSetTypes.continuousShape
type discreteShape = PointSetTypes.discreteShape
type mixedShape = PointSetTypes.mixedShape
@ -30,16 +30,16 @@ module WithDistAnswer = {
minusScaledLogOfQuot(~esti=estimateElement, ~answ=answerElement)
}
let sum = (~estimate: t, ~answer: t, ~integrateFn) =>
PointSetDist.combinePointwise(integrand, estimate, answer)->E.R2.fmap(integrateFn)
let sum = (~estimate: t, ~answer: t, ~combineFn, ~integrateFn) =>
combineFn(integrand, estimate, answer)->E.R2.fmap(integrateFn)
let sumWithPrior = (~estimate: t, ~answer: t, ~prior: t, ~integrateFn): result<
let sumWithPrior = (~estimate: t, ~answer: t, ~prior: t, ~combineFn, ~integrateFn): result<
float,
Operation.Error.t,
> => {
let kl1 = sum(~estimate, ~answer, ~integrateFn)
let kl2 = sum(~estimate=prior, ~answer, ~integrateFn)
E.R.merge(kl1, kl2)->E.R2.fmap(((k1', k2')) => kl1' -. kl2')
let kl1 = sum(~estimate, ~answer, ~combineFn, ~integrateFn)
let kl2 = sum(~estimate=prior, ~answer, ~combineFn, ~integrateFn)
E.R.merge(kl1, kl2)->E.R2.fmap(((kl1', kl2')) => kl1' -. kl2')
}
}
@ -69,11 +69,15 @@ module WithScalarAnswer = {
minusScaledLogOfQuot(~esti=numerator, ~answ=priorDensityOfAnswer)
}
}
let score = (~estimate: t, ~answer: t): result<float, Operation.Error.t> => {
let estimatePdf = x => XYShape.XtoY.linear(x, estimate.xyShape)
let score = (~estimate: t, ~answer: t, ~mapper): result<float, Operation.Error.t> => {
let pdf = (shape, ~x) => XYShape.XtoY.linear(x, shape.xyShape)
let estimatePdf = mapper((x => pdf(~x), x => pdf(~x), x => pdf(~x)))
score'(~estimatePdf, ~answer)
}
let scoreWithPrior = (~estimate: t, ~answer: t, ~prior: t): result<float, Operation.Error.t> => {
let scoreWithPrior = (~estimate: t, ~answer: t, ~prior: t, ~mapper): result<
float,
Operation.Error.t,
> => {
let estimatePdf = x => XYShape.XtoY.linear(x, estimate.xyShape)
let priorPdf = x => XYShape.XtoY.linear(x, prior.xyShape)
scoreWithPrior'(~estimatePdf, ~answer, ~priorPdf)
@ -100,7 +104,7 @@ module TwoScalars = {
}
}
let logScore = (args: scoreArgs, ~integrateFn): result<float, Operation.Error.t> =>
let logScore = (args: scoreArgs, ~combineFn, ~integrateFn): result<float, Operation.Error.t> =>
switch args {
| DistEstimateDistAnswer({estimate, answer, prior: None}) =>
WithDistAnswer.sum(~estimate, ~answer, ~integrateFn)