fix: SampleSet.fromDist works for discrete and mixed
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@ -31,9 +31,9 @@ let isSymbolic = (t: t) =>
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let sampleN = (t: t, n) =>
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switch t {
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| PointSet(r) => PointSetDist.sampleNRendered(n, r)
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| Symbolic(r) => SymbolicDist.T.sampleN(n, r)
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| PointSet(r) => PointSetDist.T.sampleN(r,n)
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| SampleSet(r) => SampleSetDist.sampleN(r, n)
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| Symbolic(r) => SymbolicDist.T.sampleN(n, r)
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}
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let sample = (t: t) => sampleN(t, 1)->E.A.first |> E.O.toExn("Should not have happened")
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@ -270,6 +270,25 @@ module T = Dist({
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}
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let variance = (t: t): float =>
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XYShape.Analysis.getVarianceDangerously(t, mean, Analysis.getMeanOfSquares)
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let doN = (n, fn) => {
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let items = Belt.Array.make(n, 0.0)
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for x in 0 to n - 1 {
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let _ = Belt.Array.set(items, x, fn())
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}
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items
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}
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let sample = (t: t): float => {
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let randomItem = Random.float(1.0)
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t |> integralYtoX(randomItem)
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}
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let sampleN = (dist, n) => {
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let integralCache = integral(dist)
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let distWithUpdatedIntegralCache = updateIntegralCache(Some(integralCache), dist)
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doN(n, () => sample(distWithUpdatedIntegralCache))
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}
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})
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let isNormalized = (t: t): bool => {
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@ -223,9 +223,9 @@ module T = Dist({
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let getMeanOfSquares = t => t |> shapeMap(XYShape.T.square) |> mean
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XYShape.Analysis.getVarianceDangerously(t, mean, getMeanOfSquares)
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}
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})
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let sampleN = (t: t, n): array<float> => {
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let normalized = t->T.normalize->getShape
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let normalized = t->normalize->getShape
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Stdlib.Random.sample(normalized.xs, {probs: normalized.ys, size: n})
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}
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})
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@ -33,6 +33,7 @@ module type dist = {
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let mean: t => float
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let variance: t => float
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let sampleN: (t, int) => array<float>
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}
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module Dist = (T: dist) => {
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@ -64,6 +65,8 @@ module Dist = (T: dist) => {
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let yToX = T.integralYtoX
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let sum = T.integralEndY
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}
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let sampleN = T.sampleN
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}
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module Common = {
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@ -270,38 +270,47 @@ module T = Dist({
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})
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}
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let mean = ({discrete, continuous}: t): float => {
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let discreteIntegralSum =({discrete}: t): float => Discrete.T.Integral.sum(discrete)
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let continuousIntegralSum =({continuous}: t): float => Continuous.T.Integral.sum(continuous)
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let integralSum =(t:t): float => discreteIntegralSum(t) +. continuousIntegralSum(t)
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let mean = ({discrete, continuous} as t: t): float => {
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let discreteMean = Discrete.T.mean(discrete)
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let continuousMean = Continuous.T.mean(continuous)
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// the combined mean is the weighted sum of the two:
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let discreteIntegralSum = Discrete.T.Integral.sum(discrete)
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let continuousIntegralSum = Continuous.T.Integral.sum(continuous)
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let totalIntegralSum = discreteIntegralSum +. continuousIntegralSum
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(discreteMean *. discreteIntegralSum +. continuousMean *. continuousIntegralSum) /.
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totalIntegralSum
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(discreteMean *. discreteIntegralSum(t) +. continuousMean *. continuousIntegralSum(t)) /.
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integralSum(t)
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}
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let variance = ({discrete, continuous} as t: t): float => {
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// the combined mean is the weighted sum of the two:
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let discreteIntegralSum = Discrete.T.Integral.sum(discrete)
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let continuousIntegralSum = Continuous.T.Integral.sum(continuous)
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let totalIntegralSum = discreteIntegralSum +. continuousIntegralSum
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let _discreteIntegralSum = discreteIntegralSum(t)
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let _integralSum = integralSum(t)
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let getMeanOfSquares = ({discrete, continuous}: t) => {
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let discreteMean = discrete |> Discrete.shapeMap(XYShape.T.square) |> Discrete.T.mean
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let continuousMean = continuous |> Continuous.Analysis.getMeanOfSquares
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(discreteMean *. discreteIntegralSum +. continuousMean *. continuousIntegralSum) /.
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totalIntegralSum
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let continuousMean = continuous -> Continuous.Analysis.getMeanOfSquares
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(discreteMean *. discreteIntegralSum(t) +. continuousMean *. continuousIntegralSum(t)) /.
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integralSum(t)
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}
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switch discreteIntegralSum /. totalIntegralSum {
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switch _discreteIntegralSum /. _integralSum {
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| 1.0 => Discrete.T.variance(discrete)
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| 0.0 => Continuous.T.variance(continuous)
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| _ => XYShape.Analysis.getVarianceDangerously(t, mean, getMeanOfSquares)
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}
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}
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let sampleN = (t: t, n:int): array<float> => {
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let discreteIntegralSum = discreteIntegralSum(t);
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let integralSum = integralSum(t);
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let discreteSampleLength:int = (Js.Int.toFloat(n) *. discreteIntegralSum /. integralSum) -> E.Float.toInt
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let continuousSampleLength = n - discreteSampleLength;
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let continuousSamples = t.continuous ->Continuous.T.normalize-> Continuous.T.sampleN( continuousSampleLength)
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let discreteSamples = t.discrete ->Discrete.T.normalize->Discrete.T.sampleN(discreteSampleLength)
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Js.log3("Samples", continuousSamples, discreteSamples);
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E.A.concat(discreteSamples, continuousSamples) -> E.A.shuffle
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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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@ -198,6 +198,13 @@ module T = Dist({
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| Discrete(m) => Discrete.T.variance(m)
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| Continuous(m) => Continuous.T.variance(m)
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}
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let sampleN = (t: t, int): array<float> =>
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switch t {
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| Mixed(m) => Mixed.T.sampleN(m,int)
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| Discrete(m) => Discrete.T.sampleN(m,int)
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| Continuous(m) => Continuous.T.sampleN(m,int)
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}
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})
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let logScore = (args: PointSetDist_Scoring.scoreArgs): result<float, Operation.Error.t> =>
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@ -235,12 +242,6 @@ let isFloat = (t: t) =>
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| _ => false
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}
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let sampleNRendered = (n, dist) => {
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let integralCache = T.Integral.get(dist)
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let distWithUpdatedIntegralCache = T.updateIntegralCache(Some(integralCache), dist)
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doN(n, () => sample(distWithUpdatedIntegralCache))
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}
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let operate = (distToFloatOp: Operation.distToFloatOperation, s): float =>
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switch distToFloatOp {
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| #Pdf(f) => pdf(f, s)
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@ -139,7 +139,7 @@ let mixture = (values: array<(t, float)>, intendedLength: int) => {
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->Belt.Array.mapWithIndex((i, (_, weight)) => (E.I.toFloat(i), weight /. totalWeight))
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->XYShape.T.fromZippedArray
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->Discrete.make
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->Discrete.sampleN(intendedLength)
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->Discrete.T.sampleN(intendedLength)
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let dists = values->E.A2.fmap(E.Tuple2.first)->E.A2.fmap(T.get)
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let samples =
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discreteSamples
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@ -559,6 +559,7 @@ module A = {
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let isEmpty = r => length(r) < 1
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let stableSortBy = Belt.SortArray.stableSortBy
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let toNoneIfEmpty = r => isEmpty(r) ? None : Some(r)
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let shuffle = Belt.Array.shuffle
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let toRanges = (a: array<'a>) =>
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switch a |> Belt.Array.length {
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| 0
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