Gave SampleSetDist a private type
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@ -91,8 +91,9 @@ describe("toPointSet", () => {
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})
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test("on sample set distribution with under 4 points", () => {
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let sampleSet = SampleSetDist.make([0.0, 1.0, 2.0, 3.0]) -> E.R.toExn;
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let result =
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run(FromDist(ToDist(ToPointSet), SampleSet([0.0, 1.0, 2.0, 3.0])))->outputMap(
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run(FromDist(ToDist(ToPointSet), SampleSet(sampleSet)))->outputMap(
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FromDist(ToFloat(#Mean)),
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)
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expect(result)->toEqual(GenDistError(Other("Converting sampleSet to pointSet failed")))
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@ -4,12 +4,12 @@ open TestHelpers
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describe("Continuous and discrete splits", () => {
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makeTest(
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"splits (1)",
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SampleSet.Internals.T.splitContinuousAndDiscrete([1.432, 1.33455, 2.0]),
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SampleSetDist_ToPointSet.Internals.T.splitContinuousAndDiscrete([1.432, 1.33455, 2.0]),
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([1.432, 1.33455, 2.0], E.FloatFloatMap.empty()),
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)
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makeTest(
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"splits (2)",
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SampleSet.Internals.T.splitContinuousAndDiscrete([
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SampleSetDist_ToPointSet.Internals.T.splitContinuousAndDiscrete([
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1.432,
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1.33455,
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2.0,
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@ -26,13 +26,13 @@ describe("Continuous and discrete splits", () => {
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E.A.concatMany([sorted, sorted, sorted, sorted]) |> Belt.SortArray.stableSortBy(_, compare)
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}
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let (_, discrete1) = SampleSet.Internals.T.splitContinuousAndDiscrete(
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let (_, discrete1) = SampleSetDist_ToPointSet.Internals.T.splitContinuousAndDiscrete(
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makeDuplicatedArray(10),
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)
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let toArr1 = discrete1 |> E.FloatFloatMap.toArray
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makeTest("splitMedium at count=10", toArr1 |> Belt.Array.length, 10)
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let (_c, discrete2) = SampleSet.Internals.T.splitContinuousAndDiscrete(
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let (_c, discrete2) = SampleSetDist_ToPointSet.Internals.T.splitContinuousAndDiscrete(
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makeDuplicatedArray(500),
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)
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let toArr2 = discrete2 |> E.FloatFloatMap.toArray
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@ -128,7 +128,7 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
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->E.R2.fmap(r => Dist(r))
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->OutputLocal.fromResult
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| ToDist(ToSampleSet(n)) =>
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dist->GenericDist.sampleN(n)->E.R2.fmap(r => Dist(SampleSet(r)))->OutputLocal.fromResult
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dist->GenericDist.toSampleSetDist(n)->E.R2.fmap(r => Dist(SampleSet(r)))->OutputLocal.fromResult
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| ToDist(ToPointSet) =>
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dist
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->GenericDist.toPointSet(~xyPointLength, ~sampleCount, ())
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@ -2,16 +2,18 @@
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type t = GenericDist_Types.genericDist
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type error = GenericDist_Types.error
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type toPointSetFn = t => result<PointSetTypes.pointSetDist, error>
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type toSampleSetFn = t => result<array<float>, error>
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type toSampleSetFn = t => result<SampleSetDist.t, error>
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type scaleMultiplyFn = (t, float) => result<t, error>
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type pointwiseAddFn = (t, t) => result<t, error>
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let mapStringErrors = n => n->E.R2.errMap(r => Error(GenericDist_Types.Other(r)))
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let sampleN = (t: t, n) =>
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switch t {
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| PointSet(r) => Ok(PointSetDist.sampleNRendered(n, r))
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| Symbolic(r) => Ok(SymbolicDist.T.sampleN(n, r))
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| SampleSet(r) => Ok(SampleSet.sampleN(r, n))
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| SampleSet(r) => Ok(SampleSetDist.sampleN(r, n))
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}
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let toSampleSetDist = (t: t, n) => sampleN(t, n)->E.R.bind(SampleSetDist.make)->mapStringErrors
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let mapStringErrors = n => n->E.R2.errMap(r => Error(GenericDist_Types.Other(r)))
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let fromFloat = (f: float): t => Symbolic(SymbolicDist.Float.make(f))
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@ -63,7 +65,7 @@ let toPointSet = (
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| PointSet(pointSet) => Ok(pointSet)
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| Symbolic(r) => Ok(SymbolicDist.T.toPointSetDist(~xSelection, xyPointLength, r))
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| SampleSet(r) => {
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let response = SampleSet.toPointSetDist(
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let response = SampleSetDist.toPointSetDist(
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~samples=r,
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~samplingInputs={
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sampleCount: sampleCount,
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@ -167,8 +169,9 @@ module AlgebraicCombination = {
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t2: t,
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) => {
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let arithmeticOperation = Operation.Algebraic.toFn(arithmeticOperation)
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E.R.merge(toSampleSet(t1), toSampleSet(t2))->E.R2.fmap(((a, b)) => {
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Belt.Array.zip(a, b)->E.A2.fmap(((a, b)) => arithmeticOperation(a, b))
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E.R.merge(toSampleSet(t1), toSampleSet(t2))->E.R.bind(((a, b)) => {
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SampleSetDist.runMonteCarlo(arithmeticOperation, a, b)
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->mapStringErrors
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})
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}
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@ -200,13 +203,15 @@ module AlgebraicCombination = {
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| Some(Error(e)) => Error(Other(e))
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| None =>
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switch chooseConvolutionOrMonteCarlo(t1, t2) {
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| #CalculateWithMonteCarlo =>
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runMonteCarlo(
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toSampleSetFn,
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arithmeticOperation,
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t1,
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t2,
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)->E.R2.fmap(r => GenericDist_Types.SampleSet(r))
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| #CalculateWithMonteCarlo => {
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let sampleSetDist: result<SampleSetDist.t, error> = runMonteCarlo(
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toSampleSetFn,
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arithmeticOperation,
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t1,
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t2,
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)
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sampleSetDist->E.R2.fmap(r => GenericDist_Types.SampleSet(r))
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}
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| #CalculateWithConvolution =>
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runConvolution(
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toPointSetFn,
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@ -1,11 +1,12 @@
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type t = GenericDist_Types.genericDist
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type error = GenericDist_Types.error
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type toPointSetFn = t => result<PointSetTypes.pointSetDist, error>
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type toSampleSetFn = t => result<array<float>, error>
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type toSampleSetFn = t => result<SampleSetDist.t, error>
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type scaleMultiplyFn = (t, float) => result<t, error>
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type pointwiseAddFn = (t, t) => result<t, error>
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let sampleN: (t, int) => result<array<float>, error>
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let toSampleSetDist: (t, int) => Belt.Result.t<QuriSquiggleLang.SampleSetDist.t, error>
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let fromFloat: float => t
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@ -1,6 +1,6 @@
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type genericDist =
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| PointSet(PointSetTypes.pointSetDist)
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| SampleSet(SampleSet.t)
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| SampleSet(SampleSetDist.t)
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| Symbolic(SymbolicDistTypes.symbolicDist)
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@genType
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@ -0,0 +1,48 @@
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module T: {
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type t
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let make: array<float> => result<t, string>
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let get: t => array<float>
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} = {
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type t = array<float>
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let make = (a: array<float>) =>
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if E.A.length(a) > 5 {
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Ok(a)
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} else {
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Error("too small")
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}
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let get = (a: t) => a
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}
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include T
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// TODO: Refactor to raise correct error when not enough samples
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let toPointSetDist = (~samples: t, ~samplingInputs: SamplingInputs.samplingInputs, ()) =>
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SampleSetDist_ToPointSet.toPointSetDist(~samples=get(samples), ~samplingInputs, ())
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//Randomly get one sample from the distribution
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let sample = (t: t): float => {
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let i = E.Int.random(~min=0, ~max=E.A.length(get(t)) - 1)
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E.A.unsafe_get(get(t), i)
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}
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/*
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If asked for a length of samples shorter or equal the length of the distribution,
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return this first n samples of this distribution.
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Else, return n random samples of the distribution.
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The former helps in cases where multiple distributions are correlated.
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However, if n > length(t), then there's no clear right answer, so we just randomly
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sample everything.
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*/
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let sampleN = (t: t, n) => {
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if n <= E.A.length(get(t)) {
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E.A.slice(get(t), ~offset=0, ~len=n)
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} else {
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Belt.Array.makeBy(n, _ => sample(t))
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}
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}
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let runMonteCarlo = (fn: (float, float) => float, t1: t, t2: t) => {
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let samples = Belt.Array.zip(get(t1), get(t2))->E.A2.fmap(((a, b)) => fn(a, b))
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make(samples)
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}
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@ -1,8 +1,3 @@
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@genType
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type t = array<float>
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// TODO: Refactor to raise correct error when not enough samples
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module Internals = {
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module Types = {
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type samplingStats = {
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@ -145,25 +140,3 @@ let toPointSetDist = (
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samplesParse
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}
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//Randomly get one sample from the distribution
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let sample = (t: t): float => {
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let i = E.Int.random(~min=0, ~max=E.A.length(t) - 1)
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E.A.unsafe_get(t, i)
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}
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/*
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If asked for a length of samples shorter or equal the length of the distribution,
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return this first n samples of this distribution.
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Else, return n random samples of the distribution.
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The former helps in cases where multiple distributions are correlated.
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However, if n > length(t), then there's no clear right answer, so we just randomly
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sample everything.
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*/
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let sampleN = (t: t, n) => {
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if n <= E.A.length(t) {
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E.A.slice(t, ~offset=0, ~len=n)
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} else {
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Belt.Array.makeBy(n, _ => sample(t))
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}
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}
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@ -218,15 +218,15 @@ module SamplingDistribution = {
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algebraicOp,
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a,
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b,
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)
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) |> E.O.toResult("Could not get samples")
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let sampleSetDist = samples -> E.R.bind(SampleSetDist.make)
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let pointSetDist =
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samples
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|> E.O.fmap(r =>
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SampleSet.toPointSetDist(~samplingInputs=evaluationParams.samplingInputs, ~samples=r, ())
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)
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|> E.O.bind(_, r => r.pointSetDist)
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|> E.O.toResult("No response")
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sampleSetDist
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-> E.R2.fmap(r =>
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SampleSetDist.toPointSetDist(~samplingInputs=evaluationParams.samplingInputs, ~samples=r, ()))
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-> E.R.bind(r => r.pointSetDist |> E.O.toResult("combineShapesUsingSampling Error"))
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pointSetDist |> E.R.fmap(r => #Normalize(#RenderedDist(r)))
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})
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}
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