Formatter

packages/squiggle-lang/__tests__/SquiggleLibrary/SquiggleLibrary_FunctionRegistryLibrary_test.res

@@ -63,9 +63,15 @@ describe("FunctionRegistry Library", () => {
     testEvalToBe("SampleSet.fromList([3,5,2,3,5,2,3,5,2,3,3,5])", "Ok(Sample Set Distribution)")
     testEvalToBe("SampleSet.fromList([3,5,2,3,5,2,3,5,2,3,3,5])", "Ok(Sample Set Distribution)")
     testEvalToBe("SampleSet.fromFn({|| sample(normal(5,2))})", "Ok(Sample Set Distribution)")
-    testEvalToBe("SampleSet.min(SampleSet.fromDist(normal(50,2)), 2)", "Ok(Sample Set Distribution)")
+    testEvalToBe(
+      "SampleSet.min(SampleSet.fromDist(normal(50,2)), 2)",
+      "Ok(Sample Set Distribution)",
+    )
     testEvalToBe("mean(SampleSet.min(SampleSet.fromDist(normal(50,2)), 2))", "Ok(2)")
-    testEvalToBe("SampleSet.max(SampleSet.fromDist(normal(50,2)), 10)", "Ok(Sample Set Distribution)")
+    testEvalToBe(
+      "SampleSet.max(SampleSet.fromDist(normal(50,2)), 10)",
+      "Ok(Sample Set Distribution)",
+    )
     testEvalToBe(
       "addOne(t)=t+1; SampleSet.toList(SampleSet.map(SampleSet.fromList([1,2,3,4,5,6]), addOne))",
       "Ok([2,3,4,5,6,7])",

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

@@ -31,9 +31,9 @@ let isSymbolic = (t: t) =>
 
 let sampleN = (t: t, n) =>
   switch t {
-  | PointSet(r) => PointSetDist.sampleNRendered(n, r)
-  | Symbolic(r) => SymbolicDist.T.sampleN(n, r)
+  | PointSet(r) => PointSetDist.T.sampleN(r, n)
   | SampleSet(r) => SampleSetDist.sampleN(r, n)
+  | Symbolic(r) => SymbolicDist.T.sampleN(n, r)
   }
 
 let sample = (t: t) => sampleN(t, 1)->E.A.first |> E.O.toExn("Should not have happened")

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

@@ -270,6 +270,25 @@ module T = Dist({
   }
   let variance = (t: t): float =>
     XYShape.Analysis.getVarianceDangerously(t, mean, Analysis.getMeanOfSquares)
+
+  let doN = (n, fn) => {
+    let items = Belt.Array.make(n, 0.0)
+    for x in 0 to n - 1 {
+      let _ = Belt.Array.set(items, x, fn())
+    }
+    items
+  }
+
+  let sample = (t: t): float => {
+    let randomItem = Random.float(1.0)
+    t |> integralYtoX(randomItem)
+  }
+
+  let sampleN = (dist, n) => {
+    let integralCache = integral(dist)
+    let distWithUpdatedIntegralCache = updateIntegralCache(Some(integralCache), dist)
+    doN(n, () => sample(distWithUpdatedIntegralCache))
+  }
 })
 
 let isNormalized = (t: t): bool => {

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

@@ -223,9 +223,9 @@ module T = Dist({
     let getMeanOfSquares = t => t |> shapeMap(XYShape.T.square) |> mean
     XYShape.Analysis.getVarianceDangerously(t, mean, getMeanOfSquares)
   }
-})
 
   let sampleN = (t: t, n): array<float> => {
-  let normalized = t->T.normalize->getShape
+    let normalized = t->normalize->getShape
     Stdlib.Random.sample(normalized.xs, {probs: normalized.ys, size: n})
   }
+})

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

@@ -33,6 +33,7 @@ module type dist = {
 
   let mean: t => float
   let variance: t => float
+  let sampleN: (t, int) => array<float>
 }
 
 module Dist = (T: dist) => {
@@ -64,6 +65,8 @@ module Dist = (T: dist) => {
     let yToX = T.integralYtoX
     let sum = T.integralEndY
   }
+
+  let sampleN = T.sampleN
 }
 
 module Common = {

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

@@ -270,38 +270,49 @@ module T = Dist({
     })
   }
 
-  let mean = ({discrete, continuous}: t): float => {
+  let discreteIntegralSum = ({discrete}: t): float => Discrete.T.Integral.sum(discrete)
+  let continuousIntegralSum = ({continuous}: t): float => Continuous.T.Integral.sum(continuous)
+  let integralSum = (t: t): float => discreteIntegralSum(t) +. continuousIntegralSum(t)
+
+  let mean = ({discrete, continuous} as t: t): float => {
     let discreteMean = Discrete.T.mean(discrete)
     let continuousMean = Continuous.T.mean(continuous)
 
-    // the combined mean is the weighted sum of the two:
-    let discreteIntegralSum = Discrete.T.Integral.sum(discrete)
-    let continuousIntegralSum = Continuous.T.Integral.sum(continuous)
-    let totalIntegralSum = discreteIntegralSum +. continuousIntegralSum
-
-    (discreteMean *. discreteIntegralSum +. continuousMean *. continuousIntegralSum) /.
-      totalIntegralSum
+    (discreteMean *. discreteIntegralSum(t) +. continuousMean *. continuousIntegralSum(t)) /.
+      integralSum(t)
   }
 
   let variance = ({discrete, continuous} as t: t): float => {
     // the combined mean is the weighted sum of the two:
-    let discreteIntegralSum = Discrete.T.Integral.sum(discrete)
-    let continuousIntegralSum = Continuous.T.Integral.sum(continuous)
-    let totalIntegralSum = discreteIntegralSum +. continuousIntegralSum
 
+    let _discreteIntegralSum = discreteIntegralSum(t)
+    let _integralSum = integralSum(t)
     let getMeanOfSquares = ({discrete, continuous}: t) => {
       let discreteMean = discrete |> Discrete.shapeMap(XYShape.T.square) |> Discrete.T.mean
-      let continuousMean = continuous |> Continuous.Analysis.getMeanOfSquares
-      (discreteMean *. discreteIntegralSum +. continuousMean *. continuousIntegralSum) /.
-        totalIntegralSum
+      let continuousMean = continuous->Continuous.Analysis.getMeanOfSquares
+      (discreteMean *. discreteIntegralSum(t) +. continuousMean *. continuousIntegralSum(t)) /.
+        integralSum(t)
     }
 
-    switch discreteIntegralSum /. totalIntegralSum {
+    switch _discreteIntegralSum /. _integralSum {
     | 1.0 => Discrete.T.variance(discrete)
     | 0.0 => Continuous.T.variance(continuous)
     | _ => XYShape.Analysis.getVarianceDangerously(t, mean, getMeanOfSquares)
     }
   }
+
+  let sampleN = (t: t, n: int): array<float> => {
+    let discreteIntegralSum = discreteIntegralSum(t)
+    let integralSum = integralSum(t)
+    let discreteSampleLength: int =
+      (Js.Int.toFloat(n) *. discreteIntegralSum /. integralSum)->E.Float.toInt
+    let continuousSampleLength = n - discreteSampleLength
+    let continuousSamples =
+      t.continuous->Continuous.T.normalize->Continuous.T.sampleN(continuousSampleLength)
+    let discreteSamples = t.discrete->Discrete.T.normalize->Discrete.T.sampleN(discreteSampleLength)
+    Js.log3("Samples", continuousSamples, discreteSamples)
+    E.A.concat(discreteSamples, continuousSamples)->E.A.shuffle
+  }
 })
 
 let combineAlgebraically = (op: Operation.convolutionOperation, t1: t, t2: t): t => {

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

@@ -198,6 +198,13 @@ module T = Dist({
     | Discrete(m) => Discrete.T.variance(m)
     | Continuous(m) => Continuous.T.variance(m)
     }
+
+  let sampleN = (t: t, int): array<float> =>
+    switch t {
+    | Mixed(m) => Mixed.T.sampleN(m, int)
+    | Discrete(m) => Discrete.T.sampleN(m, int)
+    | Continuous(m) => Continuous.T.sampleN(m, int)
+    }
 })
 
 let logScore = (args: PointSetDist_Scoring.scoreArgs): result<float, Operation.Error.t> =>
@@ -235,12 +242,6 @@ let isFloat = (t: t) =>
   | _ => false
   }
 
-let sampleNRendered = (n, dist) => {
-  let integralCache = T.Integral.get(dist)
-  let distWithUpdatedIntegralCache = T.updateIntegralCache(Some(integralCache), dist)
-  doN(n, () => sample(distWithUpdatedIntegralCache))
-}
-
 let operate = (distToFloatOp: Operation.distToFloatOperation, s): float =>
   switch distToFloatOp {
   | #Pdf(f) => pdf(f, s)

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

@@ -139,7 +139,7 @@ let mixture = (values: array<(t, float)>, intendedLength: int) => {
     ->Belt.Array.mapWithIndex((i, (_, weight)) => (E.I.toFloat(i), weight /. totalWeight))
     ->XYShape.T.fromZippedArray
     ->Discrete.make
-    ->Discrete.sampleN(intendedLength)
+    ->Discrete.T.sampleN(intendedLength)
   let dists = values->E.A2.fmap(E.Tuple2.first)->E.A2.fmap(T.get)
   let samples =
     discreteSamples

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

@@ -559,6 +559,7 @@ module A = {
   let isEmpty = r => length(r) < 1
   let stableSortBy = Belt.SortArray.stableSortBy
   let toNoneIfEmpty = r => isEmpty(r) ? None : Some(r)
+  let shuffle = Belt.Array.shuffle
   let toRanges = (a: array<'a>) =>
     switch a |> Belt.Array.length {
     | 0