Added sampleN from Stdlib to allow for correct sampling of discrete distributions

packages/squiggle-lang/__tests__/Stdlib_test.res

@@ -0,0 +1,16 @@
+open Jest
+open Expect
+
+let makeTest = (~only=false, str, item1, item2) =>
+  only
+    ? Only.test(str, () => expect(item1)->toEqual(item2))
+    : test(str, () => expect(item1)->toEqual(item2))
+
+describe("Stdlib", () => {
+  makeTest("min", Stdlib.Random.sample([1.0, 2.0], {probs: [0.5, 0.5], size: 10}) |> E.A.length, 10)
+  makeTest(
+    "min",
+    Stdlib.Random.sample([1.0, 2.0], {probs: [0.5, 0.5], size: 10}) |> E.A.uniq |> E.A.Floats.sort,
+    [1.0, 2.0],
+  )
+})

packages/squiggle-lang/package.json

@@ -18,6 +18,7 @@
     "benchmark": "ts-node benchmark/conversion_tests.ts",
     "test": "jest",
     "test:ts": "jest __tests__/TS/",
+    "test:stdlib": "jest __tests__/Stdlib_test.bs.js",
     "test:rescript": "jest --modulePathIgnorePatterns=__tests__/TS/*",
     "test:watch": "jest --watchAll",
     "test:fnRegistry": "jest __tests__/SquiggleLibrary/SquiggleLibrary_FunctionRegistryLibrary_test.bs.js",

packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Discrete.res

@@ -224,3 +224,8 @@ module T = Dist({
     XYShape.Analysis.getVarianceDangerously(t, mean, getMeanOfSquares)
   }
 })
+
+let sampleN = (t: t, n): array<float> => {
+  let normalized = t |> T.normalize |> getShape
+  Stdlib.Random.sample(normalized.xs, {probs: normalized.ys, size: n})
+}

packages/squiggle-lang/src/rescript/Distributions/PointSetDist/PointSetDist.res

@@ -257,3 +257,7 @@ let toSparkline = (t: t, bucketCount): result<string, PointSetTypes.sparklineErr
   ->E.O2.fmap(Continuous.downsampleEquallyOverX(bucketCount))
   ->E.O2.toResult(PointSetTypes.CannotSparklineDiscrete)
   ->E.R2.fmap(r => Continuous.getShape(r).ys->Sparklines.create())
+
+let makeDiscrete = (d):t => Discrete(d)
+let makeContinuous = (d):t => Continuous(d)
+let makeMixed = (d):t => Mixed(d)

packages/squiggle-lang/src/rescript/Distributions/SampleSetDist/SampleSetDist.res

@@ -134,12 +134,19 @@ let percentile = (t, f) => T.get(t)->E.A.Floats.percentile(f)
 
 let mixture = (values: array<(t, float)>, intendedLength: int) => {
   let totalWeight = values->E.A2.fmap(E.Tuple2.second)->E.A.Floats.sum
+  let discreteSamples =
     values
-  ->E.A2.fmap(((dist, weight)) => {
-    let adjustedWeight = weight /. totalWeight
-    let samplesToGet = adjustedWeight *. E.I.toFloat(intendedLength) |> E.Float.toInt
-    sampleN(dist, samplesToGet)
+    ->Belt.Array.mapWithIndex((i, (_, weight)) => (E.I.toFloat(i), weight /. totalWeight))
+    ->XYShape.T.fromZippedArray
+    ->Discrete.make
+    ->Discrete.sampleN(intendedLength)
+  let dists = values->E.A2.fmap(E.Tuple2.first)->E.A2.fmap(T.get)
+  let samples =
+    discreteSamples
+    ->Belt.Array.mapWithIndex((index, distIndexToChoose) => {
+      let chosenDist = E.A.get(dists, E.Float.toInt(distIndexToChoose))
+      chosenDist |> E.O2.bind(E.A.get(_, index))
     })
-  ->E.A.concatMany
-  ->T.make
+    ->E.A.O.openIfAllSome
+  (samples |> E.O.toExn("Mixture unreachable error"))->T.make
 }

packages/squiggle-lang/src/rescript/Utility/Stdlib.res

@@ -38,3 +38,11 @@ module Logistic = {
   @module external variance: (float, float) => float = "@stdlib/stats/base/dists/logistic/variance"
   let variance = variance
 }
+
+module Random = {
+  type sampleArgs = {
+    probs: array<float>,
+    size: int,
+  }
+  @module external sample: (array<float>, sampleArgs) => array<float> = "@stdlib/random/sample"
+}