Pulled out XYShape to be more separate

packages/squiggle-lang/src/js/index.ts

@@ -1,7 +1,7 @@
 import {runAll} from '../rescript/ProgramEvaluator.gen';
 import type { Inputs_SamplingInputs_t as SamplingInputs, exportEnv, exportType, exportDistribution} from '../rescript/ProgramEvaluator.gen';
 export type { SamplingInputs, exportEnv, exportDistribution }
-export type {t as DistPlus} from '../rescript/pointSetDist/DistPlus.gen';
+export type {t as DistPlus} from '../rescript/OldInterpreter/DistPlus.gen';
 
 export let defaultSamplingInputs : SamplingInputs = {
   sampleCount : 10000,

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

@@ -0,0 +1,92 @@
+type genericDist =
+  | PointSet(PointSetTypes.pointSetDist)
+  | SampleSet(array<float>)
+  | Symbolic(SymbolicDistTypes.symbolicDist)
+
+type error =
+  | NotYetImplemented
+  | Unreachable
+  | DistributionVerticalShiftIsInvalid
+  | Other(string)
+
+module Operation = {
+  type direction =
+    | Algebraic
+    | Pointwise
+
+  type arithmeticOperation = [
+    | #Add
+    | #Multiply
+    | #Subtract
+    | #Divide
+    | #Exponentiate
+    | #Logarithm
+  ]
+
+  let arithmeticToFn = (arithmetic: arithmeticOperation) =>
+    switch arithmetic {
+    | #Add => \"+."
+    | #Multiply => \"*."
+    | #Subtract => \"-."
+    | #Exponentiate => \"**"
+    | #Divide => \"/."
+    | #Logarithm => (a, b) => log(a) /. log(b)
+    }
+
+  type toFloat = [
+    | #Cdf(float)
+    | #Inv(float)
+    | #Pdf(float)
+    | #Mean
+    | #Sample
+  ]
+}
+
+module DistributionOperation = {
+  type toDist =
+    | Normalize
+    | ToPointSet
+    | ToSampleSet(int)
+    | Truncate(option<float>, option<float>)
+    | Inspect
+
+  type toFloatArray = Sample(int)
+
+  type fromDist =
+    | ToFloat(Operation.toFloat)
+    | ToDist(toDist)
+    | ToDistCombination(Operation.direction, Operation.arithmeticOperation, [#Dist(genericDist) | #Float(float)])
+    | ToString
+
+  type singleParamaterFunction =
+    | FromDist(fromDist)
+    | FromFloat(fromDist)
+
+  type genericFunctionCallInfo =
+    | FromDist(fromDist, genericDist)
+    | FromFloat(fromDist, float)
+    | Mixture(array<(genericDist, float)>)
+
+  let distCallToString = (distFunction: fromDist): string =>
+    switch distFunction {
+    | ToFloat(#Cdf(r)) => `cdf(${E.Float.toFixed(r)})`
+    | ToFloat(#Inv(r)) => `inv(${E.Float.toFixed(r)})`
+    | ToFloat(#Mean) => `mean`
+    | ToFloat(#Pdf(r)) => `pdf(${E.Float.toFixed(r)})`
+    | ToFloat(#Sample) => `sample`
+    | ToDist(Normalize) => `normalize`
+    | ToDist(ToPointSet) => `toPointSet`
+    | ToDist(ToSampleSet(r)) => `toSampleSet(${E.I.toString(r)})`
+    | ToDist(Truncate(_, _)) => `truncate`
+    | ToDist(Inspect) => `inspect`
+    | ToString => `toString`
+    | ToDistCombination(Algebraic, _, _) => `algebraic`
+    | ToDistCombination(Pointwise, _, _) => `pointwise`
+    }
+
+  let toString = (d: genericFunctionCallInfo): string =>
+    switch d {
+    | FromDist(f, _) | FromFloat(f, _) => distCallToString(f)
+    | Mixture(_) => `mixture`
+    }
+}

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

@@ -118,13 +118,14 @@ let combineShapesContinuousContinuous = (
   | #Logarithm => (m1, m2) => log(m1) /. log(m2)
   } // note: here, mInv2 = mean(1 / t2) ~= 1 / mean(t2)
 
-  // TODO: I don't know what the variances are for exponentatiation
+  // TODO: I don't know what the variances are for exponentatiation or logarithms
   // converts the variances and means of the two inputs into the variance of the output
   let combineVariancesFn = switch op {
   | #Add => (v1, v2, _, _) => v1 +. v2
   | #Subtract => (v1, v2, _, _) => v1 +. v2
   | #Multiply => (v1, v2, m1, m2) => v1 *. v2 +. v1 *. m2 ** 2. +. v2 *. m1 ** 2.
   | #Exponentiate => (v1, v2, m1, m2) => v1 *. v2 +. v1 *. m2 ** 2. +. v2 *. m1 ** 2.
+  | #Logarithm => (v1, v2, m1, m2) => v1 *. v2 +. v1 *. m2 ** 2. +. v2 *. m1 ** 2.
   | #Divide => (v1, vInv2, m1, mInv2) => v1 *. vInv2 +. v1 *. mInv2 ** 2. +. vInv2 *. m1 ** 2.
   }
 

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

@@ -1,6 +1,47 @@
 open Distributions
 
 type t = PointSetTypes.continuousShape
+
+module Analysis = {
+  let integrate = (
+    ~indefiniteIntegralStepwise=(p, h1) => h1 *. p,
+    ~indefiniteIntegralLinear=(p, a, b) => a *. p +. b *. p ** 2.0 /. 2.0,
+    t: t,
+  ): float => {
+    let xs = t.xyShape.xs
+    let ys = t.xyShape.ys
+
+    E.A.reducei(xs, 0.0, (acc, _x, i) => {
+      let areaUnderIntegral = // TODO Take this switch statement out of the loop body
+      switch (t.interpolation, i) {
+      | (_, 0) => 0.0
+      | (#Stepwise, _) =>
+        indefiniteIntegralStepwise(xs[i], ys[i - 1]) -.
+        indefiniteIntegralStepwise(xs[i - 1], ys[i - 1])
+      | (#Linear, _) =>
+        let x1 = xs[i - 1]
+        let x2 = xs[i]
+        if x1 == x2 {
+          0.0
+        } else {
+          let h1 = ys[i - 1]
+          let h2 = ys[i]
+          let b = (h1 -. h2) /. (x1 -. x2)
+          let a = h1 -. b *. x1
+          indefiniteIntegralLinear(x2, a, b) -. indefiniteIntegralLinear(x1, a, b)
+        }
+      }
+      acc +. areaUnderIntegral
+    })
+  }
+
+  let getMeanOfSquares = (t: t) => {
+    let indefiniteIntegralLinear = (p, a, b) => a *. p ** 3.0 /. 3.0 +. b *. p ** 4.0 /. 4.0
+    let indefiniteIntegralStepwise = (p, h1) => h1 *. p ** 3.0 /. 3.0
+    integrate(~indefiniteIntegralStepwise, ~indefiniteIntegralLinear, t)
+  }
+}
+
 let getShape = (t: t) => t.xyShape
 let interpolation = (t: t) => t.interpolation
 let make = (~interpolation=#Linear, ~integralSumCache=None, ~integralCache=None, xyShape): t => {
@@ -194,7 +235,7 @@ module T = Dist({
     let indefiniteIntegralStepwise = (p, h1) => h1 *. p ** 2.0 /. 2.0
     let indefiniteIntegralLinear = (p, a, b) => a *. p ** 2.0 /. 2.0 +. b *. p ** 3.0 /. 3.0
 
-    XYShape.Analysis.integrateContinuousShape(
+    Analysis.integrate(
       ~indefiniteIntegralStepwise,
       ~indefiniteIntegralLinear,
       t,
@@ -204,7 +245,7 @@ module T = Dist({
     XYShape.Analysis.getVarianceDangerously(
       t,
       mean,
-      XYShape.Analysis.getMeanOfSquaresContinuousShape,
+      Analysis.getMeanOfSquares,
     )
 })
 

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

@@ -209,8 +209,9 @@ module T = Dist({
     let s = getShape(t)
     E.A.reducei(s.xs, 0.0, (acc, x, i) => acc +. x *. s.ys[i])
   }
+
   let variance = (t: t): float => {
-    let getMeanOfSquares = t => t |> shapeMap(XYShape.Analysis.squareXYShape) |> mean
+    let getMeanOfSquares = t => t |> shapeMap(XYShape.T.square) |> mean
     XYShape.Analysis.getVarianceDangerously(t, mean, getMeanOfSquares)
   }
 })

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

@@ -213,8 +213,8 @@ module T = Dist({
 
     let getMeanOfSquares = ({discrete, continuous}: t) => {
       let discreteMean =
-        discrete |> Discrete.shapeMap(XYShape.Analysis.squareXYShape) |> Discrete.T.mean
-      let continuousMean = continuous |> XYShape.Analysis.getMeanOfSquaresContinuousShape
+        discrete |> Discrete.shapeMap(XYShape.T.square) |> Discrete.T.mean
+      let continuousMean = continuous |> Continuous.Analysis.getMeanOfSquares
       (discreteMean *. discreteIntegralSum +. continuousMean *. continuousIntegralSum) /.
         totalIntegralSum
     }

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

@@ -14,21 +14,10 @@ type distributionType = [
   | #CDF
 ]
 
-type xyShape = {
-  xs: array<float>,
-  ys: array<float>,
-}
-
-type interpolationStrategy = [
-  | #Stepwise
-  | #Linear
-]
-type extrapolationStrategy = [
-  | #UseZero
-  | #UseOutermostPoints
-]
-
-type interpolator = (xyShape, int, float) => float
+type xyShape = XYShape.xyShape;
+type interpolationStrategy = XYShape.interpolationStrategy;
+type extrapolationStrategy = XYShape.extrapolationStrategy;
+type interpolator = XYShape.extrapolationStrategy;
 
 type rec continuousShape = {
   xyShape: xyShape,

packages/squiggle-lang/src/rescript/utility/XYShape.res

@@ -1,4 +1,18 @@
-open PointSetTypes
+type xyShape = {
+  xs: array<float>,
+  ys: array<float>,
+}
+
+type interpolationStrategy = [
+  | #Stepwise
+  | #Linear
+]
+type extrapolationStrategy = [
+  | #UseZero
+  | #UseOutermostPoints
+]
+
+type interpolator = (xyShape, int, float) => float
 
 let interpolate = (xMin: float, xMax: float, yMin: float, yMax: float, xIntended: float): float => {
   let minProportion = (xMax -. xIntended) /. (xMax -. xMin)
@@ -25,6 +39,7 @@ module T = {
   let xTotalRange = (t: t) => maxX(t) -. minX(t)
   let mapX = (fn, t: t): t => {xs: E.A.fmap(fn, t.xs), ys: t.ys}
   let mapY = (fn, t: t): t => {xs: t.xs, ys: E.A.fmap(fn, t.ys)}
+  let square = mapX(x => x ** 2.0)
   let zip = ({xs, ys}: t) => Belt.Array.zip(xs, ys)
   let fromArray = ((xs, ys)): t => {xs: xs, ys: ys}
   let fromArrays = (xs, ys): t => {xs: xs, ys: ys}
@@ -126,8 +141,8 @@ module XtoY = {
   /* Returns a between-points-interpolating function that can be used with PointwiseCombination.combine.
    Interpolation can either be stepwise (using the value on the left) or linear. Extrapolation can be `UseZero or `UseOutermostPoints. */
   let continuousInterpolator = (
-    interpolation: PointSetTypes.interpolationStrategy,
-    extrapolation: PointSetTypes.extrapolationStrategy,
+    interpolation: interpolationStrategy,
+    extrapolation: extrapolationStrategy,
   ): interpolator =>
     switch (interpolation, extrapolation) {
     | (#Linear, #UseZero) =>
@@ -392,49 +407,9 @@ let logScorePoint = (sampleCount, t1, t2) =>
   |> E.O.fmap(Pairs.y)
 
 module Analysis = {
-  let integrateContinuousShape = (
-    ~indefiniteIntegralStepwise=(p, h1) => h1 *. p,
-    ~indefiniteIntegralLinear=(p, a, b) => a *. p +. b *. p ** 2.0 /. 2.0,
-    t: PointSetTypes.continuousShape,
-  ): float => {
-    let xs = t.xyShape.xs
-    let ys = t.xyShape.ys
-
-    E.A.reducei(xs, 0.0, (acc, _x, i) => {
-      let areaUnderIntegral = // TODO Take this switch statement out of the loop body
-      switch (t.interpolation, i) {
-      | (_, 0) => 0.0
-      | (#Stepwise, _) =>
-        indefiniteIntegralStepwise(xs[i], ys[i - 1]) -.
-        indefiniteIntegralStepwise(xs[i - 1], ys[i - 1])
-      | (#Linear, _) =>
-        let x1 = xs[i - 1]
-        let x2 = xs[i]
-        if x1 == x2 {
-          0.0
-        } else {
-          let h1 = ys[i - 1]
-          let h2 = ys[i]
-          let b = (h1 -. h2) /. (x1 -. x2)
-          let a = h1 -. b *. x1
-          indefiniteIntegralLinear(x2, a, b) -. indefiniteIntegralLinear(x1, a, b)
-        }
-      }
-      acc +. areaUnderIntegral
-    })
-  }
-
-  let getMeanOfSquaresContinuousShape = (t: PointSetTypes.continuousShape) => {
-    let indefiniteIntegralLinear = (p, a, b) => a *. p ** 3.0 /. 3.0 +. b *. p ** 4.0 /. 4.0
-    let indefiniteIntegralStepwise = (p, h1) => h1 *. p ** 3.0 /. 3.0
-    integrateContinuousShape(~indefiniteIntegralStepwise, ~indefiniteIntegralLinear, t)
-  }
-
   let getVarianceDangerously = (t: 't, mean: 't => float, getMeanOfSquares: 't => float): float => {
     let meanSquared = mean(t) ** 2.0
     let meanOfSquares = getMeanOfSquares(t)
     meanOfSquares -. meanSquared
   }
-
-  let squareXYShape = T.mapX(x => x ** 2.0)
 }