Working on code reorganization, doesn't compile yet

__tests__/Distributions__Test.re

@@ -386,10 +386,9 @@ describe("Shape", () => {
     let numSamples = 10000;
     open Distributions.Shape;
     let normal: SymbolicDist.dist = `Normal({mean, stdev});
-    let normalShape = SymbolicDist.GenericSimple.toShape(normal, numSamples);
+    let normalShape = TreeNode.toShape(numSamples, normal);
     let lognormal = SymbolicDist.Lognormal.fromMeanAndStdev(mean, stdev);
-    let lognormalShape =
+    let lognormalShape = TreeNode.toShape(numSamples, lognormal);
-      SymbolicDist.GenericSimple.toShape(lognormal, numSamples);
 
     makeTestCloseEquality(
       "Mean of a normal",

src/components/DistBuilder2.re

@@ -44,14 +44,14 @@ module DemoDist = {
             Distributions.DistPlus.make(
               ~shape=
                 Continuous(
-                  Distributions.Continuous.make(`Linear, {xs, ys}),
+                  Distributions.Continuous.make(`Linear, {xs, ys}, None),
                 ),
               ~domain=Complete,
               ~unit=UnspecifiedDistribution,
               ~guesstimatorString=None,
               (),
             )
-            |> Distributions.DistPlus.T.scaleToIntegralSum(~intendedSum=1.0);
+            |> Distributions.DistPlus.T.normalize;
           <DistPlusPlot distPlus />;
         };
     <Antd.Card title={"Distribution" |> R.ste}>

src/components/DistBuilder3.re

@@ -37,7 +37,7 @@ module DemoDist = {
     let parsed1 = MathJsParser.fromString(guesstimatorString);
     let shape =
       switch (parsed1) {
-      | Ok(r) => Some(SymbolicDist.toShape(10000, r))
+      | Ok(r) => Some(TreeNode.toShape(10000, r))
       | _ => None
       };
 

src/components/Drawer.re

@@ -177,6 +177,7 @@ module Convert = {
     let continuousShape: Types.continuousShape = {
       xyShape,
       interpolation: `Linear,
+      knownIntegralSum: None,
     };
     
     let integral = XYShape.Analysis.integrateContinuousShape(continuousShape);
@@ -188,6 +189,7 @@ module Convert = {
         ys,
       },
       interpolation: `Linear,
+      knownIntegralSum: Some(1.0),
     };
     continuousShape;
   };
@@ -387,7 +389,7 @@ module Draw = {
     let numSamples = 3000;
 
     let normal: SymbolicDist.dist = `Normal({mean, stdev});
-    let normalShape = SymbolicDist.GenericSimple.toShape(normal, numSamples);
+    let normalShape = TreeNode.toShape(numSamples, `DistData(`Symbolic(normal)));
     let xyShape: Types.xyShape =
       switch (normalShape) {
       | Mixed(_) => {xs: [||], ys: [||]}
@@ -667,9 +669,7 @@ module State = {
 
       /* create a cdf from a pdf */
       let _pdf =
-        Distributions.Continuous.T.scaleToIntegralSum(
+        Distributions.Continuous.T.normalize(
-          ~cache=None,
-          ~intendedSum=1.0,
           pdf,
         );
 

src/components/charts/DistPlusPlot.re

@@ -95,7 +95,7 @@ let table = (distPlus, x) => {
           </td>
           <td className="px-4 py-2 border ">
             {distPlus
-             |> Distributions.DistPlus.T.toScaledContinuous
+             |> Distributions.DistPlus.T.normalizedToContinuous
              |> E.O.fmap(
                   Distributions.Continuous.T.Integral.sum(~cache=None),
                 )
@@ -113,7 +113,7 @@ let table = (distPlus, x) => {
           </td>
           <td className="px-4 py-2 border ">
             {distPlus
-             |> Distributions.DistPlus.T.toScaledDiscrete
+             |> Distributions.DistPlus.T.normalizedToDiscrete
              |> E.O.fmap(Distributions.Discrete.T.Integral.sum(~cache=None))
              |> E.O.fmap(E.Float.with2DigitsPrecision)
              |> E.O.default("")
@@ -211,15 +211,13 @@ let percentiles = distPlus => {
   </div>;
 };
 
-let adjustBoth = discreteProbabilityMass => {
+let adjustBoth = discreteProbabilityMassFraction => {
-  let yMaxDiscreteDomainFactor = discreteProbabilityMass;
+  let yMaxDiscreteDomainFactor = discreteProbabilityMassFraction;
-  let yMaxContinuousDomainFactor = 1.0 -. discreteProbabilityMass;
+  let yMaxContinuousDomainFactor = 1.0 -. discreteProbabilityMassFraction;
-  let yMax =
+  let yMax = (yMaxDiscreteDomainFactor > 0.5 ? yMaxDiscreteDomainFactor : yMaxContinuousDomainFactor);
-    yMaxDiscreteDomainFactor > yMaxContinuousDomainFactor
-      ? yMaxDiscreteDomainFactor : yMaxContinuousDomainFactor;
   (
-    1.0 /. (yMaxDiscreteDomainFactor /. yMax),
+    yMax /. yMaxDiscreteDomainFactor,
-    1.0 /. (yMaxContinuousDomainFactor /. yMax),
+    yMax /. yMaxContinuousDomainFactor,
   );
 };
 
@@ -227,10 +225,10 @@ module DistPlusChart = {
   [@react.component]
   let make = (~distPlus: DistTypes.distPlus, ~config: chartConfig, ~onHover) => {
     open Distributions.DistPlus;
-    let discrete = distPlus |> T.toScaledDiscrete;
+    let discrete = distPlus |> T.normalizedToDiscrete |> E.O.fmap(Distributions.Discrete.getShape);
     let continuous =
       distPlus
-      |> T.toScaledContinuous
+      |> T.normalizedToContinuous
       |> E.O.fmap(Distributions.Continuous.getShape);
     let range = T.xTotalRange(distPlus);
 
@@ -254,10 +252,10 @@ module DistPlusChart = {
     };
 
     let timeScale = distPlus.unit |> DistTypes.DistributionUnit.toJson;
-    let toDiscreteProbabilityMass =
+    let discreteProbabilityMassFraction =
-      distPlus |> Distributions.DistPlus.T.toDiscreteProbabilityMass;
+      distPlus |> Distributions.DistPlus.T.toDiscreteProbabilityMassFraction;
     let (yMaxDiscreteDomainFactor, yMaxContinuousDomainFactor) =
-      adjustBoth(toDiscreteProbabilityMass);
+      adjustBoth(discreteProbabilityMassFraction);
     <DistributionPlot
       xScale={config.xLog ? "log" : "linear"}
       yScale={config.yLog ? "log" : "linear"}

src/distPlus/distribution/DistTypes.re

@@ -17,14 +17,18 @@ type xyShape = {
 type continuousShape = {
   xyShape,
   interpolation: [ | `Stepwise | `Linear],
+  knownIntegralSum: option(float),
 };
 
-type discreteShape = xyShape;
+type discreteShape = {
+  xyShape,
+  knownIntegralSum: option(float),
+};
 
 type mixedShape = {
   continuous: continuousShape,
   discrete: discreteShape,
-  discreteProbabilityMassFraction: float,
+//  discreteProbabilityMassFraction: float,
 };
 
 type shapeMonad('a, 'b, 'c) =

src/distPlus/distribution/MixedShapeBuilder.re

@@ -8,14 +8,15 @@ type assumptions = {
   discreteProbabilityMass: option(float),
 };
 
-let buildSimple = (~continuous: option(DistTypes.continuousShape), ~discrete): option(DistTypes.shape) => {
+let buildSimple = (~continuous: option(DistTypes.continuousShape), ~discrete: option(DistTypes.discreteShape)): option(DistTypes.shape) => {
-  let continuous = continuous |> E.O.default(Distributions.Continuous.make(`Linear, {xs: [||], ys: [||]}))
+  let continuous = continuous |> E.O.default(Distributions.Continuous.make(`Linear, {xs: [||], ys: [||]}, Some(0.0)));
+  let discrete = discrete |> E.O.default(Distributions.Discrete.make({xs: [||], ys: [||]}, Some(0.0)));
   let cLength =
     continuous
     |> Distributions.Continuous.getShape
     |> XYShape.T.xs
     |> E.A.length;
-  let dLength = discrete |> XYShape.T.xs |> E.A.length;
+  let dLength = discrete |> Distributions.Discrete.getShape |> XYShape.T.xs |> E.A.length;
   switch (cLength, dLength) {
   | (0 | 1, 0) => None
   | (0 | 1, _) => Some(Discrete(discrete))
@@ -23,18 +24,12 @@ let buildSimple = (~continuous: option(DistTypes.continuousShape), ~discrete): o
   | (_, _) =>
     let discreteProbabilityMassFraction =
       Distributions.Discrete.T.Integral.sum(~cache=None, discrete);
-    let discrete =
+    let discrete = Distributions.Discrete.T.normalize(discrete);
-      Distributions.Discrete.T.scaleToIntegralSum(~intendedSum=1.0, discrete);
+    let continuous = Distributions.Continuous.T.normalize(continuous);
-    let continuous =
-      Distributions.Continuous.T.scaleToIntegralSum(
-        ~intendedSum=1.0,
-        continuous,
-      );
     let mixedDist =
       Distributions.Mixed.make(
         ~continuous,
-        ~discrete,
+        ~discrete
-        ~discreteProbabilityMassFraction,
       );
     Some(Mixed(mixedDist));
   };
@@ -42,7 +37,7 @@ let buildSimple = (~continuous: option(DistTypes.continuousShape), ~discrete): o
 
 
 // TODO: Delete, only being used in tests
-let build = (~continuous, ~discrete, ~assumptions) =>
+/*let build = (~continuous, ~discrete, ~assumptions) =>
   switch (assumptions) {
   | {
       continuous: ADDS_TO_CORRECT_PROBABILITY,
@@ -102,4 +97,4 @@ let build = (~continuous, ~discrete, ~assumptions) =>
       ),
     );
   | _ => None
-  };
+  };*/

src/distPlus/distribution/XYShape.re

@@ -17,6 +17,7 @@ module T = {
   type ts = array(xyShape);
   let xs = (t: t) => t.xs;
   let ys = (t: t) => t.ys;
+  let length = (t: t) => E.A.length(t.xs);
   let empty = {xs: [||], ys: [||]};
   let minX = (t: t) => t |> xs |> E.A.Sorted.min |> extImp;
   let maxX = (t: t) => t |> xs |> E.A.Sorted.max |> extImp;
@@ -154,7 +155,9 @@ module XsConversion = {
 
   let proportionByProbabilityMass =
       (newLength: int, integral: T.t, t: T.t): T.t => {
-    equallyDivideXByMass(newLength, integral) |> _replaceWithXs(_, t);
+    integral
+    |> equallyDivideXByMass(newLength) // creates a new set of xs at evenly spaced percentiles
+    |> _replaceWithXs(_, t); // linearly interpolates new ys for the new xs
   };
 };
 
@@ -164,6 +167,7 @@ module Zipped = {
   let compareXs = ((x1, _), (x2, _)) => x1 > x2 ? 1 : 0;
   let sortByY = (t: zipped) => t |> E.A.stableSortBy(_, compareYs);
   let sortByX = (t: zipped) => t |> E.A.stableSortBy(_, compareXs);
+  let filterByX = (testFn: (float => bool), t: zipped) => t |> E.A.filter(((x, _)) => testFn(x));
 };
 
 module Combine = {
@@ -253,8 +257,8 @@ module Range = {
         Belt.Array.set(
           cumulativeY,
           x + 1,
-          (xs[x + 1] -. xs[x])
+          (xs[x + 1] -. xs[x])  // dx
-          *. ((ys[x] +. ys[x + 1]) /. 2.)
+          *. ((ys[x] +. ys[x + 1]) /. 2.) // (1/2) * (avgY)
           +. cumulativeY[x],
         );
       ();

src/distPlus/renderers/RenderTypes.re

@@ -43,7 +43,7 @@ module ShapeRenderer = {
   module Symbolic = {
     type inputs = {length: int};
     type outputs = {
-      graph: SymbolicDist.distTree,
+      graph: TreeNode.treeNode,
       shape: DistTypes.shape,
     };
     let make = (graph, shape) => {graph, shape};

src/distPlus/renderers/ShapeRenderer.re

@@ -21,7 +21,7 @@ let runSymbolic = (guesstimatorString, length) => {
   |> E.R.fmap(g =>
        RenderTypes.ShapeRenderer.Symbolic.make(
          g,
-         SymbolicDist.toShape(length, g),
+         TreeNode.toShape(length, g),
        )
      );
 };

src/distPlus/renderers/samplesRenderer/Guesstimator.re

@@ -4,10 +4,10 @@ type discrete = {
   ys: array(float),
 };
 
-let jsToDistDiscrete = (d: discrete): DistTypes.discreteShape => {
+let jsToDistDiscrete = (d: discrete): DistTypes.discreteShape => {xyShape: {
   xs: xsGet(d),
   ys: ysGet(d),
-};
+}, knownIntegralSum: None};
 
 [@bs.module "./GuesstimatorLibrary.js"]
 external stringToSamples: (string, int) => array(float) = "stringToSamples";

src/distPlus/renderers/samplesRenderer/Samples.re

@@ -115,11 +115,12 @@ module T = {
     Array.fast_sort(compare, samples);
     let (continuousPart, discretePart) = E.A.Sorted.Floats.split(samples);
     let length = samples |> E.A.length |> float_of_int;
-    let discrete: DistTypes.xyShape =
+    let discrete: DistTypes.discreteShape =
       discretePart
       |> E.FloatFloatMap.fmap(r => r /. length)
       |> E.FloatFloatMap.toArray
-      |> XYShape.T.fromZippedArray;
+      |> XYShape.T.fromZippedArray
+      |> Distributions.Discrete.make(_, None);
 
     let pdf =
       continuousPart |> E.A.length > 5
@@ -149,14 +150,14 @@ module T = {
                ~outputXYPoints=samplingInputs.outputXYPoints,
                formatUnitWidth(usedUnitWidth),
              )
-          |> Distributions.Continuous.make(`Linear)
+          |> Distributions.Continuous.make(`Linear, _, None)
           |> (r => Some((r, foo)));
         }
         : None;
     let shape =
       MixedShapeBuilder.buildSimple(
         ~continuous=pdf |> E.O.fmap(fst),
-        ~discrete,
+        ~discrete=Some(discrete),
       );
     let samplesParse: RenderTypes.ShapeRenderer.Sampling.outputs = {
       continuousParseParams: pdf |> E.O.fmap(snd),

src/distPlus/symbolic/MathJsParser.re

@@ -88,69 +88,69 @@ module MathAdtToDistDst = {
         );
   };
 
-  let normal: array(arg) => result(SymbolicDist.distTree, string) =
+  let normal: array(arg) => result(TreeNode.treeNode, string) =
     fun
     | [|Value(mean), Value(stdev)|] =>
-      Ok(`Simple(`Normal({mean, stdev})))
+      Ok(`DistData(`Symbolic(`Normal({mean, stdev}))))
     | _ => Error("Wrong number of variables in normal distribution");
 
-  let lognormal: array(arg) => result(SymbolicDist.distTree, string) =
+  let lognormal: array(arg) => result(TreeNode.treeNode, string) =
     fun
-    | [|Value(mu), Value(sigma)|] => Ok(`Simple(`Lognormal({mu, sigma})))
+    | [|Value(mu), Value(sigma)|] => Ok(`DistData(`Symbolic(`Lognormal({mu, sigma}))))
     | [|Object(o)|] => {
         let g = Js.Dict.get(o);
         switch (g("mean"), g("stdev"), g("mu"), g("sigma")) {
         | (Some(Value(mean)), Some(Value(stdev)), _, _) =>
-          Ok(`Simple(SymbolicDist.Lognormal.fromMeanAndStdev(mean, stdev)))
+          Ok(`DistData(`Symbolic(SymbolicDist.Lognormal.fromMeanAndStdev(mean, stdev))))
         | (_, _, Some(Value(mu)), Some(Value(sigma))) =>
-          Ok(`Simple(`Lognormal({mu, sigma})))
+          Ok(`DistData(`Symbolic(`Lognormal({mu, sigma}))))
         | _ => Error("Lognormal distribution would need mean and stdev")
         };
       }
     | _ => Error("Wrong number of variables in lognormal distribution");
 
-  let to_: array(arg) => result(SymbolicDist.distTree, string) =
+  let to_: array(arg) => result(TreeNode.treeNode, string) =
     fun
     | [|Value(low), Value(high)|] when low <= 0.0 && low < high=> {
-        Ok(`Simple(SymbolicDist.Normal.from90PercentCI(low, high)));
+        Ok(`DistData(`Symbolic(SymbolicDist.Normal.from90PercentCI(low, high))));
       }
     | [|Value(low), Value(high)|] when low < high => {
-        Ok(`Simple(SymbolicDist.Lognormal.from90PercentCI(low, high)));
+        Ok(`DistData(`Symbolic(SymbolicDist.Lognormal.from90PercentCI(low, high))));
       }
     | [|Value(_), Value(_)|] =>
       Error("Low value must be less than high value.")
     | _ => Error("Wrong number of variables in lognormal distribution");
 
-  let uniform: array(arg) => result(SymbolicDist.distTree, string) =
+  let uniform: array(arg) => result(TreeNode.treeNode, string) =
     fun
-    | [|Value(low), Value(high)|] => Ok(`Simple(`Uniform({low, high})))
+    | [|Value(low), Value(high)|] => Ok(`DistData(`Symbolic(`Uniform({low, high}))))
     | _ => Error("Wrong number of variables in lognormal distribution");
 
-  let beta: array(arg) => result(SymbolicDist.distTree, string) =
+  let beta: array(arg) => result(TreeNode.treeNode, string) =
     fun
-    | [|Value(alpha), Value(beta)|] => Ok(`Simple(`Beta({alpha, beta})))
+    | [|Value(alpha), Value(beta)|] => Ok(`DistData(`Symbolic(`Beta({alpha, beta}))))
     | _ => Error("Wrong number of variables in lognormal distribution");
 
-  let exponential: array(arg) => result(SymbolicDist.distTree, string) =
+  let exponential: array(arg) => result(TreeNode.treeNode, string) =
     fun
-    | [|Value(rate)|] => Ok(`Simple(`Exponential({rate: rate})))
+    | [|Value(rate)|] => Ok(`DistData(`Symbolic(`Exponential({rate: rate}))))
     | _ => Error("Wrong number of variables in Exponential distribution");
 
-  let cauchy: array(arg) => result(SymbolicDist.distTree, string) =
+  let cauchy: array(arg) => result(TreeNode.treeNode, string) =
     fun
     | [|Value(local), Value(scale)|] =>
-      Ok(`Simple(`Cauchy({local, scale})))
+      Ok(`DistData(`Symbolic(`Cauchy({local, scale}))))
     | _ => Error("Wrong number of variables in cauchy distribution");
 
-  let triangular: array(arg) => result(SymbolicDist.distTree, string) =
+  let triangular: array(arg) => result(TreeNode.treeNode, string) =
     fun
     | [|Value(low), Value(medium), Value(high)|] =>
-      Ok(`Simple(`Triangular({low, medium, high})))
+      Ok(`DistData(`Symbolic(`Triangular({low, medium, high}))))
     | _ => Error("Wrong number of variables in triangle distribution");
 
   let multiModal =
       (
-        args: array(result(SymbolicDist.distTree, string)),
+        args: array(result(TreeNode.treeNode, string)),
         weights: option(array(float)),
       ) => {
     let weights = weights |> E.O.default([||]);
@@ -158,16 +158,8 @@ module MathAdtToDistDst = {
       args
       |> E.A.fmap(
            fun
-           | Ok(`Simple(d)) => Ok(`Simple(d))
+          | Ok(a) => a
-           | Ok(`Combination(t1, t2, op)) => Ok(`Combination(t1, t2, op))
-           | Ok(`PointwiseSum(t1, t2)) => Ok(`PointwiseSum(t1, t2))
-           | Ok(`PointwiseProduct(t1, t2)) => Ok(`PointwiseProduct(t1, t2))
-           | Ok(`Normalize(t)) => Ok(`Normalize(t))
-           | Ok(`LeftTruncate(t, x)) => Ok(`LeftTruncate(t, x))
-           | Ok(`RightTruncate(t, x)) => Ok(`RightTruncate(t, x))
-           | Ok(`Render(t)) => Ok(`Render(t))
           | Error(e) => Error(e)
-           | _ => Error("Unexpected dist")
           );
 
     let firstWithError = dists |> Belt.Array.getBy(_, Belt.Result.isError);
@@ -182,7 +174,7 @@ module MathAdtToDistDst = {
         |> E.A.fmapi((index, t) => {
             let w = weights |> E.A.get(_, index) |> E.O.default(1.0);
 
-            `VerticalScaling(t, `Simple(`Float(w)))
+            `Operation(`ScaleBy(`Multiply, t, `DistData(`Symbolic(`Float(w)))))
           });
 
         let pointwiseSum = components
@@ -196,7 +188,7 @@ module MathAdtToDistDst = {
     };
   };
 
-  let arrayParser = (args:array(arg)):result(SymbolicDist.distTree, string) => {
+  let arrayParser = (args:array(arg)):result(TreeNode.treeNode, string) => {
     let samples = args
     |> E.A.fmap(
           fun
@@ -207,18 +199,18 @@ module MathAdtToDistDst = {
     let outputs = Samples.T.fromSamples(samples);
     let pdf = outputs.shape |> E.O.bind(_,Distributions.Shape.T.toContinuous);
     let shape = pdf |> E.O.fmap(pdf => {
-      let _pdf = Distributions.Continuous.T.scaleToIntegralSum(~cache=None, ~intendedSum=1.0, pdf);
+      let _pdf = Distributions.Continuous.T.normalize(pdf);
       let cdf = Distributions.Continuous.T.integral(~cache=None, _pdf);
       SymbolicDist.ContinuousShape.make(_pdf, cdf)
     });
     switch(shape){
-      | Some(s) => Ok(`Simple(`ContinuousShape(s)))
+      | Some(s) => Ok(`DistData(`Symbolic(`ContinuousShape(s))))
       | None => Error("Rendering did not work")
     }
   }
 
 
-  let rec functionParser = (r): result(SymbolicDist.distTree, string) =>
+  let rec functionParser = (r): result(TreeNode.treeNode, string) =>
     r
     |> (
       fun
@@ -230,7 +222,7 @@ module MathAdtToDistDst = {
       | Fn({name: "exponential", args}) => exponential(args)
       | Fn({name: "cauchy", args}) => cauchy(args)
       | Fn({name: "triangular", args}) => triangular(args)
-      | Value(f) => Ok(`Simple(`Float(f)))
+      | Value(f) => Ok(`DistData(`Symbolic(`Float(f))))
       | Fn({name: "mm", args}) => {
           let weights =
             args
@@ -283,7 +275,7 @@ module MathAdtToDistDst = {
           args
           |> E.A.fmap(functionParser)
           |> (fun
-              | [|Ok(l), Ok(`Simple(`Float(0.0)))|] => Error("Division by zero")
+              | [|Ok(l), Ok(`DistData(`Symbolic(`Float(0.0))))|] => Error("Division by zero")
               | [|Ok(l), Ok(r)|] => Ok(`Combination(l, r, `DivideOperation))
               | _ => Error("Division needs two operands"))
       }
@@ -298,14 +290,14 @@ module MathAdtToDistDst = {
           args
           |> E.A.fmap(functionParser)
           |> (fun
-              | [|Ok(l), Ok(`Simple(`Float(r)))|] => Ok(`LeftTruncate(l, r))
+              | [|Ok(l), Ok(`DistData(`Symbolic(`Float(r))))|] => Ok(`LeftTruncate(l, r))
               | _ => Error("leftTruncate needs two arguments: the expression and the cutoff"))
       }
       | Fn({name: "rightTruncate", args}) => {
           args
           |> E.A.fmap(functionParser)
           |> (fun
-              | [|Ok(l), Ok(`Simple(`Float(r)))|] => Ok(`RightTruncate(l, r))
+              | [|Ok(l), Ok(`DistData(`Symbolic(`Float(r))))|] => Ok(`RightTruncate(l, r))
               | _ => Error("rightTruncate needs two arguments: the expression and the cutoff"))
       }
       | Fn({name}) => Error(name ++ ": function not supported")
@@ -314,18 +306,18 @@ module MathAdtToDistDst = {
         }
     );
 
-  let topLevel = (r): result(SymbolicDist.distTree, string) =>
+  let topLevel = (r): result(TreeNode.treeNode, string) =>
     r
     |> (
       fun
       | Fn(_) => functionParser(r)
-      | Value(r) => Ok(`Simple(`Float(r)))
+      | Value(r) => Ok(`DistData(`Symbolic(`Float(r))))
       | Array(r) => arrayParser(r)
       | Symbol(_) => Error("Symbol not valid as top level")
       | Object(_) => Error("Object not valid as top level")
     );
 
-  let run = (r): result(SymbolicDist.distTree, string) =>
+  let run = (r): result(TreeNode.treeNode, string) =>
     r |> MathAdtCleaner.run |> topLevel;
 };
 

src/distPlus/symbolic/SymbolicDist.re

@@ -36,7 +36,6 @@ type continuousShape = {
   cdf: DistTypes.continuousShape,
 };
 
-type contType = [ | `Continuous | `Discrete];
 
 type dist = [
   | `Normal(normal)
@@ -50,29 +49,6 @@ type dist = [
   | `Float(float) // Dirac delta at x. Practically useful only in the context of multimodals.
 ];
 
-type integral = float;
-type cutoffX = float;
-type operation = [
-    | `AddOperation
-    | `SubtractOperation
-    | `MultiplyOperation
-    | `DivideOperation
-    | `ExponentiateOperation
-];
-
-type distTree = [
-    | `Simple(dist)
-    | `Combination(distTree, distTree, operation)
-    | `PointwiseSum(distTree, distTree)
-    | `PointwiseProduct(distTree, distTree)
-    | `VerticalScaling(distTree, distTree)
-    | `Normalize(distTree)
-    | `LeftTruncate(distTree, cutoffX)
-    | `RightTruncate(distTree, cutoffX)
-    | `Render(distTree)
-]
-and weightedDists = array((distTree, float));
-
 module ContinuousShape = {
   type t = continuousShape;
   let make = (pdf, cdf): t => {pdf, cdf};
@@ -82,8 +58,9 @@ module ContinuousShape = {
     Distributions.Continuous.T.xToY(p, t.pdf).continuous;
   // TODO: Fix the sampling, to have it work correctly.
   let sample = (t: t) => 3.0;
+  // TODO: Fix the mean, to have it work correctly.
+  let mean = (t: t) => Ok(0.0);
   let toString = t => {j|CustomContinuousShape|j};
-  let contType: contType = `Continuous;
 };
 
 module Exponential = {
@@ -91,8 +68,8 @@ module Exponential = {
   let pdf = (x, t: t) => Jstat.exponential##pdf(x, t.rate);
   let inv = (p, t: t) => Jstat.exponential##inv(p, t.rate);
   let sample = (t: t) => Jstat.exponential##sample(t.rate);
+  let mean = (t: t) => Ok(Jstat.exponential##mean(t.rate));
   let toString = ({rate}: t) => {j|Exponential($rate)|j};
-  let contType: contType = `Continuous;
 };
 
 module Cauchy = {
@@ -100,8 +77,8 @@ module Cauchy = {
   let pdf = (x, t: t) => Jstat.cauchy##pdf(x, t.local, t.scale);
   let inv = (p, t: t) => Jstat.cauchy##inv(p, t.local, t.scale);
   let sample = (t: t) => Jstat.cauchy##sample(t.local, t.scale);
+  let mean = (t: t) => Error("Cauchy distributions have no mean value.")
   let toString = ({local, scale}: t) => {j|Cauchy($local, $scale)|j};
-  let contType: contType = `Continuous;
 };
 
 module Triangular = {
@@ -109,8 +86,8 @@ module Triangular = {
   let pdf = (x, t: t) => Jstat.triangular##pdf(x, t.low, t.high, t.medium);
   let inv = (p, t: t) => Jstat.triangular##inv(p, t.low, t.high, t.medium);
   let sample = (t: t) => Jstat.triangular##sample(t.low, t.high, t.medium);
+  let mean = (t: t) => Ok(Jstat.triangular##mean(t.low, t.high, t.medium));
   let toString = ({low, medium, high}: t) => {j|Triangular($low, $medium, $high)|j};
-  let contType: contType = `Continuous;
 };
 
 module Normal = {
@@ -124,8 +101,26 @@ module Normal = {
   };
   let inv = (p, t: t) => Jstat.normal##inv(p, t.mean, t.stdev);
   let sample = (t: t) => Jstat.normal##sample(t.mean, t.stdev);
+  let mean = (t: t) => Ok(Jstat.normal##mean(t.mean, t.stdev));
   let toString = ({mean, stdev}: t) => {j|Normal($mean,$stdev)|j};
-  let contType: contType = `Continuous;
+
+  let add = (n1: t, n2: t) => {
+    let mean = n1.mean +. n2.mean;
+    let stdev = sqrt(n1.stdev ** 2. +. n2.stdev ** 2.);
+    `Normal({mean, stdev});
+  };
+  let subtract = (n1: t, n2: t) => {
+    let mean = n1.mean -. n2.mean;
+    let stdev = sqrt(n1.stdev ** 2. +. n2.stdev ** 2.);
+    `Normal({mean, stdev});
+  };
+
+  // TODO: is this useful here at all? would need the integral as well ...
+  let pointwiseProduct = (n1: t, n2: t) => {
+    let mean = (n1.mean *. n2.stdev**2. +. n2.mean *. n1.stdev**2.) /. (n1.stdev**2. +. n2.stdev**2.);
+    let stdev = 1. /. ((1. /. n1.stdev**2.) +. (1. /. n2.stdev**2.));
+    `Normal({mean, stdev});
+  };
 };
 
 module Beta = {
@@ -133,17 +128,17 @@ module Beta = {
   let pdf = (x, t: t) => Jstat.beta##pdf(x, t.alpha, t.beta);
   let inv = (p, t: t) => Jstat.beta##inv(p, t.alpha, t.beta);
   let sample = (t: t) => Jstat.beta##sample(t.alpha, t.beta);
+  let mean = (t: t) => Ok(Jstat.beta##mean(t.alpha, t.beta));
   let toString = ({alpha, beta}: t) => {j|Beta($alpha,$beta)|j};
-  let contType: contType = `Continuous;
 };
 
 module Lognormal = {
   type t = lognormal;
   let pdf = (x, t: t) => Jstat.lognormal##pdf(x, t.mu, t.sigma);
   let inv = (p, t: t) => Jstat.lognormal##inv(p, t.mu, t.sigma);
+  let mean = (t: t) => Ok(Jstat.lognormal##mean(t.mu, t.sigma));
   let sample = (t: t) => Jstat.lognormal##sample(t.mu, t.sigma);
   let toString = ({mu, sigma}: t) => {j|Lognormal($mu,$sigma)|j};
-  let contType: contType = `Continuous;
   let from90PercentCI = (low, high) => {
     let logLow = Js.Math.log(low);
     let logHigh = Js.Math.log(high);
@@ -163,6 +158,17 @@ module Lognormal = {
       );
     `Lognormal({mu, sigma});
   };
+
+  let multiply = (l1, l2) => {
+    let mu = l1.mu +. l2.mu;
+    let sigma = l1.sigma +. l2.sigma;
+    `Lognormal({mu, sigma})
+  };
+  let divide = (l1, l2) => {
+    let mu = l1.mu -. l2.mu;
+    let sigma = l1.sigma +. l2.sigma;
+    `Lognormal({mu, sigma})
+  };
 };
 
 module Uniform = {
@@ -170,20 +176,20 @@ module Uniform = {
   let pdf = (x, t: t) => Jstat.uniform##pdf(x, t.low, t.high);
   let inv = (p, t: t) => Jstat.uniform##inv(p, t.low, t.high);
   let sample = (t: t) => Jstat.uniform##sample(t.low, t.high);
+  let mean = (t: t) => Ok(Jstat.uniform##mean(t.low, t.high));
   let toString = ({low, high}: t) => {j|Uniform($low,$high)|j};
-  let contType: contType = `Continuous;
 };
 
 module Float = {
   type t = float;
   let pdf = (x, t: t) => x == t ? 1.0 : 0.0;
   let inv = (p, t: t) => p < t ? 0.0 : 1.0;
+  let mean = (t: t) => Ok(t);
   let sample = (t: t) => t;
   let toString = Js.Float.toString;
-  let contType: contType = `Discrete;
 };
 
-module GenericSimple = {
+module GenericDistFunctions = {
   let minCdfValue = 0.0001;
   let maxCdfValue = 0.9999;
 
@@ -200,19 +206,6 @@ module GenericSimple = {
     | `ContinuousShape(n) => ContinuousShape.pdf(x, n)
     };
 
-  let contType = (dist: dist): contType =>
-    switch (dist) {
-    | `Normal(_) => Normal.contType
-    | `Triangular(_) => Triangular.contType
-    | `Exponential(_) => Exponential.contType
-    | `Cauchy(_) => Cauchy.contType
-    | `Lognormal(_) => Lognormal.contType
-    | `Uniform(_) => Uniform.contType
-    | `Beta(_) => Beta.contType
-    | `Float(_) => Float.contType
-    | `ContinuousShape(_) => ContinuousShape.contType
-    };
-
   let inv = (x, dist) =>
     switch (dist) {
     | `Normal(n) => Normal.inv(x, n)
@@ -274,15 +267,18 @@ module GenericSimple = {
     | `Uniform({high}) => high
     | `Float(n) => n;
 
-  /* This function returns a list of x's at which to evaluate the overall distribution (for rendering).
+  let mean: dist => result(float, string) =
-        This function is called separately for each individual distribution.
+    fun
+    | `Triangular(n) => Triangular.mean(n)
+    | `Exponential(n) => Exponential.mean(n)
+    | `Cauchy(n) => Cauchy.mean(n)
+    | `Normal(n) => Normal.mean(n)
+    | `Lognormal(n) => Lognormal.mean(n)
+    | `Beta(n) => Beta.mean(n)
+    | `ContinuousShape(n) => ContinuousShape.mean(n)
+    | `Uniform(n) => Uniform.mean(n)
+    | `Float(n) => Float.mean(n)
 
-        When called with xSelection=`Linear, this function will return (n) x's, evenly
-        distributed between the min and max of the distribution (whatever those are defined to be above).
-
-        When called with xSelection=`ByWeight, this function will distribute the x's such as to
-        match the cumulative shape of the distribution. This is slower but may give better results.
-     */
   let interpolateXs =
     (~xSelection: [ | `Linear | `ByWeight]=`Linear, dist: dist, n) => {
     switch (xSelection, dist) {
@@ -297,423 +293,5 @@ module GenericSimple = {
       ys |> E.A.fmap(y => inv(y, dist));
     };
   };
-
-  let toShape =
-      (~xSelection: [ | `Linear | `ByWeight]=`Linear, dist: dist, n)
-      : DistTypes.shape => {
-    switch (dist) {
-    | `ContinuousShape(n) => n.pdf |> Distributions.Continuous.T.toShape
-    | dist =>
-      let xs = interpolateXs(~xSelection, dist, n);
-      let ys = xs |> E.A.fmap(r => pdf(r, dist));
-      XYShape.T.fromArrays(xs, ys)
-      |> Distributions.Continuous.make(`Linear, _)
-      |> Distributions.Continuous.T.toShape;
-    };
-  };
 };
 
-module DistTree = {
-  type nodeResult = [
-    | `Simple(dist)
-    // RenderedShape: continuous xyShape, discrete xyShape, total value.
-    | `RenderedShape(DistTypes.continuousShape, DistTypes.discreteShape, integral)
-  ];
-
-  let evaluateDistribution = (d: dist): nodeResult => {
-    `Simple(d)
-  };
-
-  // This is a performance bottleneck!
-  // Using raw JS here so we can use native for loops and access array elements
-  // directly, without option checks.
-  let jsContinuousCombinationConvolve: (array(float), array(float), array(float), array(float), float => float => float) => array(array((float, float))) = [%bs.raw
-  {|
-  function (s1xs, s1ys, s2xs, s2ys, func) {
-    // For continuous-continuous convolution, use linear interpolation.
-    // Let's assume we got downsampled distributions
-
-    const outXYShapes = new Array(s1xs.length);
-    for (let i = 0; i < s1xs.length; i++) {
-      // create a new distribution
-      const dxyShape = new Array(s2xs.length);
-      for (let j = 0; j < s2xs.length; j++) {
-        dxyShape[j] = [func(s1xs[i], s2xs[j]), (s1ys[i] * s2ys[j])];
-      }
-      outXYShapes[i] = dxyShape;
-    }
-
-    return outXYShapes;
-  }
-  |}];
-
-  let jsDiscreteCombinationConvolve: (array(float), array(float), array(float), array(float), float => float => float) => (array(float), array(float)) = [%bs.raw
-  {|
-  function (s1xs, s1ys, s2xs, s2ys, func) {
-    const r = new Map();
-
-    for (let i = 0; i < s1xs.length; i++) {
-      for (let j = 0; j < s2xs.length; j++) {
-
-        const x = func(s1xs[i], s2xs[j]);
-        const cv = r.get(x) | 0;
-
-        r.set(x, cv + s1ys[i] * s2ys[j]);   // add up the ys, if same x
-      }
-    }
-
-    const rxys = [...r.entries()];
-    rxys.sort(([x1, y1], [x2, y2]) => x1 - x2);
-
-    const rxs = new Array(rxys.length);
-    const rys = new Array(rxys.length);
-
-    for (let i = 0; i < rxys.length; i++) {
-      rxs[i] = rxys[i][0];
-      rys[i] = rxys[i][1];
-    }
-
-    return [rxs, rys];
-  }
-  |}];
-
-  let funcFromOp = (op: operation) => {
-    switch (op) {
-    | `AddOperation => (+.)
-    | `SubtractOperation => (-.)
-    | `MultiplyOperation => (*.)
-    | `DivideOperation => (/.)
-    | `ExponentiateOperation => (**)
-    }
-  }
-
-  let renderDistributionToXYShape = (d: dist, n: int): (DistTypes.continuousShape, DistTypes.discreteShape) => {
-    // render the distribution into an XY shape
-    switch (d) {
-    | `Float(v) => (Distributions.Continuous.empty, {xs: [|v|], ys: [|1.0|]})
-    | _ => {
-        let xs = GenericSimple.interpolateXs(~xSelection=`ByWeight, d, n);
-        let ys = xs |> E.A.fmap(x => GenericSimple.pdf(x, d));
-        (Distributions.Continuous.make(`Linear, {xs: xs, ys: ys}), XYShape.T.empty)
-      }
-    }
-  };
-
-  let combinationDistributionOfXYShapes = (sc1: DistTypes.continuousShape, // continuous shape
-                                        sd1: DistTypes.discreteShape, // discrete shape
-                                        sc2: DistTypes.continuousShape,
-                                        sd2: DistTypes.discreteShape, func): (DistTypes.continuousShape, DistTypes.discreteShape) => {
-
-    // First, deal with the discrete-discrete convolution:
-    let (ddxs, ddys) = jsDiscreteCombinationConvolve(sd1.xs, sd1.ys, sd2.xs, sd2.ys, func);
-    let ddxy: DistTypes.discreteShape = {xs: ddxs, ys: ddys};
-
-    // Then, do the other three:
-    let downsample = (sc: DistTypes.continuousShape) => {
-      let scLength = E.A.length(sc.xyShape.xs);
-      let scSqLength = sqrt(float_of_int(scLength));
-      scSqLength > 10. ? Distributions.Continuous.T.truncate(int_of_float(scSqLength), sc) : sc;
-    };
-
-    let combinePointConvolutionResults = ca => ca |> E.A.fmap(s => {
-        // s is an array of (x, y) objects
-        let (xs, ys) = Belt.Array.unzip(s);
-        Distributions.Continuous.make(`Linear, {xs, ys});
-      })
-    |> Distributions.Continuous.reduce((+.));
-
-    let sc1d = downsample(sc1);
-    let sc2d = downsample(sc2);
-
-    let ccxy = jsContinuousCombinationConvolve(sc1d.xyShape.xs, sc1d.xyShape.ys, sc2d.xyShape.xs, sc2d.xyShape.ys, func) |> combinePointConvolutionResults;
-    let dcxy = jsContinuousCombinationConvolve(sc1d.xyShape.xs, sc1d.xyShape.ys, sc2d.xyShape.xs, sc2d.xyShape.ys, func) |> combinePointConvolutionResults;
-    let cdxy = jsContinuousCombinationConvolve(sc1d.xyShape.xs, sc1d.xyShape.ys, sc2d.xyShape.xs, sc2d.xyShape.ys, func) |> combinePointConvolutionResults;
-    let continuousShapeSum = Distributions.Continuous.reduce((+.), [|ccxy, dcxy, cdxy|]);
-
-    (continuousShapeSum, ddxy)
-  };
-
-  let evaluateCombinationDistribution = (et1: nodeResult, et2: nodeResult, op: operation, n: int) => {
-    /* return either a Distribution or a RenderedShape. Must integrate to 1. */
-
-    let func = funcFromOp(op);
-    switch ((et1, et2, op)) {
-    /* Known cases: replace symbolic with symbolic distribution */
-    | (`Simple(`Float(v1)), `Simple(`Float(v2)), _) => {
-        `Simple(`Float(func(v1, v2)))
-      }
-
-    | (`Simple(`Normal(n2)), `Simple(`Float(v1)), `AddOperation)
-    | (`Simple(`Float(v1)), `Simple(`Normal(n2)), `AddOperation) => {
-        let n: normal = {mean: v1 +. n2.mean, stdev: n2.stdev};
-        `Simple(`Normal(n))
-      }
-
-    | (`Simple(`Normal(n1)), `Simple(`Normal(n2)), `AddOperation) => {
-        let n: normal = {mean: n1.mean +. n2.mean, stdev: sqrt(n1.stdev ** 2. +. n2.stdev ** 2.)};
-        `Simple(`Normal(n));
-      }
-
-    | (`Simple(`Normal(n1)), `Simple(`Normal(n2)), `SubtractOperation) => {
-        let n: normal = {mean: n1.mean -. n2.mean, stdev: sqrt(n1.stdev ** 2. +. n2.stdev ** 2.)};
-        `Simple(`Normal(n));
-      }
-
-    | (`Simple(`Lognormal(l1)), `Simple(`Lognormal(l2)), `MultiplyOperation) => {
-        let l: lognormal = {mu: l1.mu +. l2.mu, sigma: l1.sigma +. l2.sigma};
-        `Simple(`Lognormal(l));
-      }
-
-    | (`Simple(`Lognormal(l1)), `Simple(`Lognormal(l2)), `DivideOperation) => {
-        let l: lognormal = {mu: l1.mu -. l2.mu, sigma: l1.sigma +. l2.sigma};
-        `Simple(`Lognormal(l));
-      }
-
-
-    /* General cases: convolve the XYShapes */
-    | (`Simple(d1), `Simple(d2), _) => {
-        let (sc1, sd1) = renderDistributionToXYShape(d1, n);
-        let (sc2, sd2) = renderDistributionToXYShape(d2, n);
-        let (sc, sd) = combinationDistributionOfXYShapes(sc1, sd1, sc2, sd2, func);
-        `RenderedShape(sc, sd, 1.0)
-    }
-    | (`Simple(d1), `RenderedShape(sc2, sd2, i2), _)
-    | (`RenderedShape(sc2, sd2, i2), `Simple(d1), _) => {
-        let (sc1, sd1) = renderDistributionToXYShape(d1, n);
-        let (sc, sd) = combinationDistributionOfXYShapes(sc1, sd1, sc2, sd2, func);
-        `RenderedShape(sc, sd, i2)
-    }
-    | (`RenderedShape(sc1, sd1, i1), `RenderedShape(sc2, sd2, i2), _) => {
-        // sum of two multimodals that have a continuous and discrete each.
-        let (sc, sd) = combinationDistributionOfXYShapes(sc1, sd1, sc2, sd2, func);
-        `RenderedShape(sc, sd, i1);
-      }
-    }
-  };
-
-  let evaluatePointwiseSum = (et1: nodeResult, et2: nodeResult, n: int) => {
-    switch ((et1, et2)) {
-    /* Known cases: */
-    | (`Simple(`Float(v1)), `Simple(`Float(v2))) => {
-        v1 == v2
-        ? `RenderedShape(Distributions.Continuous.empty, Distributions.Discrete.make({xs: [|v1|], ys: [|2.|]}), 2.)
-        : `RenderedShape(Distributions.Continuous.empty, Distributions.Discrete.empty, 0.) // TODO: add warning: shouldn't pointwise add scalars.
-    }
-    | (`Simple(`Float(v1)), `Simple(d2))
-    | (`Simple(d2), `Simple(`Float(v1))) => {
-        let sd1: DistTypes.xyShape = {xs: [|v1|], ys: [|1.|]};
-        let (sc2, sd2) = renderDistributionToXYShape(d2, n);
-        `RenderedShape(sc2, Distributions.Discrete.reduce((+.), [|sd1, sd2|]), 2.)
-    }
-    | (`Simple(d1), `Simple(d2)) => {
-        let (sc1, sd1) = renderDistributionToXYShape(d1, n);
-        let (sc2, sd2) = renderDistributionToXYShape(d2, n);
-        `RenderedShape(Distributions.Continuous.reduce((+.), [|sc1, sc2|]), Distributions.Discrete.reduce((+.), [|sd1, sd2|]), 2.)
-    }
-    | (`Simple(d1), `RenderedShape(sc2, sd2, i2))
-    | (`RenderedShape(sc2, sd2, i2), `Simple(d1)) => {
-        let (sc1, sd1) = renderDistributionToXYShape(d1, n);
-        `RenderedShape(Distributions.Continuous.reduce((+.), [|sc1, sc2|]), Distributions.Discrete.reduce((+.), [|sd1, sd2|]), 1. +. i2)
-    }
-    | (`RenderedShape(sc1, sd1, i1), `RenderedShape(sc2, sd2, i2)) => {
-        `RenderedShape(Distributions.Continuous.reduce((+.), [|sc1, sc2|]), Distributions.Discrete.reduce((+.), [|sd1, sd2|]), i1 +. i2)
-    }
-    }
-  };
-
-  let evaluatePointwiseProduct = (et1: nodeResult, et2: nodeResult, n: int) => {
-    switch ((et1, et2)) {
-    /* Known cases: */
-    | (`Simple(`Float(v1)), `Simple(`Float(v2))) => {
-        v1 == v2
-        ? `RenderedShape(Distributions.Continuous.empty, Distributions.Discrete.make({xs: [|v1|], ys: [|1.|]}), 1.)
-        : `RenderedShape(Distributions.Continuous.empty, Distributions.Discrete.empty, 0.) // TODO: add warning: shouldn't pointwise multiply scalars.
-    }
-    | (`Simple(`Float(v1)), `Simple(d2)) => {
-        // evaluate d2 at v1
-        let y = GenericSimple.pdf(v1, d2);
-        `RenderedShape(Distributions.Continuous.empty, Distributions.Discrete.make({xs: [|v1|], ys: [|y|]}), y)
-    }
-    | (`Simple(d1), `Simple(`Float(v2))) => {
-        // evaluate d1 at v2
-        let y = GenericSimple.pdf(v2, d1);
-        `RenderedShape(Distributions.Continuous.empty, Distributions.Discrete.make({xs: [|v2|], ys: [|y|]}), y)
-    }
-    | (`Simple(`Normal(n1)), `Simple(`Normal(n2))) => {
-        let mean = (n1.mean *. n2.stdev**2. +. n2.mean *. n1.stdev**2.) /. (n1.stdev**2. +. n2.stdev**2.);
-        let stdev = 1. /. ((1. /. n1.stdev**2.) +. (1. /. n2.stdev**2.));
-        let integral = 0; // TODO
-        `RenderedShape(Distributions.Continuous.empty, Distributions.Discrete.empty, 0.)
-    }
-    /* General cases */
-    | (`Simple(d1), `Simple(d2)) => {
-        // NOT IMPLEMENTED YET
-        // TODO: evaluate integral properly
-        let (sc1, sd1) = renderDistributionToXYShape(d1, n);
-        let (sc2, sd2) = renderDistributionToXYShape(d2, n);
-        `RenderedShape(Distributions.Continuous.empty, Distributions.Discrete.empty, 0.)
-    }
-    | (`Simple(d1), `RenderedShape(sc2, sd2, i2)) => {
-        // NOT IMPLEMENTED YET
-        // TODO: evaluate integral properly
-        let (sc1, sd1) = renderDistributionToXYShape(d1, n);
-        `RenderedShape(Distributions.Continuous.empty, Distributions.Discrete.empty, 0.)
-    }
-    | (`RenderedShape(sc1, sd1, i1), `Simple(d1)) => {
-        // NOT IMPLEMENTED YET
-        // TODO: evaluate integral properly
-        let (sc2, sd2) = renderDistributionToXYShape(d1, n);
-        `RenderedShape(Distributions.Continuous.empty, Distributions.Discrete.empty, 0.)
-    }
-    | (`RenderedShape(sc1, sd1, i1), `RenderedShape(sc2, sd2, i2)) => {
-        `RenderedShape(Distributions.Continuous.empty, Distributions.Discrete.empty, 0.)
-    }
-    }
-  };
-
-
-  let evaluateNormalize = (et: nodeResult, n: int) => {
-    // just divide everything by the integral.
-    switch (et) {
-    | `RenderedShape(sc, sd, 0.) => {
-        `RenderedShape(Distributions.Continuous.empty, Distributions.Discrete.empty, 0.)
-      }
-    | `RenderedShape(sc, sd, i) => {
-        // loop through all ys and divide them by i
-        let normalize = (s: DistTypes.xyShape): DistTypes.xyShape => {xs: s.xs, ys: s.ys |> E.A.fmap(y => y /. i)};
-
-        let scn = sc |> Distributions.Continuous.shapeMap(normalize);
-        let sdn = sd |> normalize;
-
-        `RenderedShape(scn, sdn, 1.)
-    }
-    | `Simple(d) => `Simple(d) // any kind of atomic dist should already be normalized -- TODO: THIS IS ACTUALLY FALSE! E.g. pointwise product of normal * normal
-    }
-  };
-
-  let evaluateTruncate = (et: nodeResult, xc: cutoffX, compareFunc: (float, float) => bool, n: int) => {
-    let cut = (s: DistTypes.xyShape): DistTypes.xyShape => {
-      let (xs, ys) = s.ys
-        |> Belt.Array.zip(s.xs)
-        |> E.A.filter(((x, y)) => compareFunc(x, xc))
-        |> Belt.Array.unzip
-
-      let cutShape: DistTypes.xyShape = {xs, ys};
-      cutShape;
-    };
-
-    switch (et) {
-      | `Simple(d) => {
-          let (sc, sd) = renderDistributionToXYShape(d, n);
-
-          let scc = sc |> Distributions.Continuous.shapeMap(cut);
-          let sdc = sd |> cut;
-
-          let newIntegral = 1.; // TODO
-
-          `RenderedShape(scc, sdc, newIntegral);
-      }
-      | `RenderedShape(sc, sd, i) => {
-          let scc = sc |> Distributions.Continuous.shapeMap(cut);
-          let sdc = sd |> cut;
-
-          let newIntegral = 1.; // TODO
-
-          `RenderedShape(scc, sdc, newIntegral);
-      }
-    }
-  };
-
-  let evaluateVerticalScaling = (et1: nodeResult, et2: nodeResult, n: int) => {
-    let scale = (i: float, s: DistTypes.xyShape): DistTypes.xyShape => {xs: s.xs, ys: s.ys |> E.A.fmap(y => y *. i)};
-
-    switch ((et1, et2)) {
-      | (`Simple(`Float(v)), `Simple(d))
-      | (`Simple(d), `Simple(`Float(v))) => {
-          let (sc, sd) = renderDistributionToXYShape(d, n);
-
-          let scc = sc |> Distributions.Continuous.shapeMap(scale(v));
-          let sdc = sd |> scale(v);
-
-          let newIntegral = v; // TODO
-
-          `RenderedShape(scc, sdc, newIntegral);
-      }
-      | (`Simple(`Float(v)), `RenderedShape(sc, sd, i))
-      | (`RenderedShape(sc, sd, i), `Simple(`Float(v))) => {
-          let scc = sc |> Distributions.Continuous.shapeMap(scale(v));
-          let sdc = sd |> scale(v);
-
-          let newIntegral = v; // TODO
-
-          `RenderedShape(scc, sdc, newIntegral);
-        }
-      | _ => `RenderedShape(Distributions.Continuous.empty, Distributions.Discrete.empty, 0.) // TODO: give warning
-    }
-  }
-
-  let renderNode = (et: nodeResult, n: int) => {
-    switch (et) {
-    | `Simple(d) => {
-          let (sc, sd) = renderDistributionToXYShape(d, n);
-          `RenderedShape(sc, sd, 1.0);
-      }
-    | s => s
-    }
-  }
-
-  let rec evaluateNode = (treeNode: distTree, n: int): nodeResult => {
-    // returns either a new symbolic distribution
-    switch (treeNode) {
-    | `Simple(d) => evaluateDistribution(d)
-    | `Combination(t1, t2, op) => evaluateCombinationDistribution(evaluateNode(t1, n), evaluateNode(t2, n), op, n)
-    | `PointwiseSum(t1, t2) => evaluatePointwiseSum(evaluateNode(t1, n), evaluateNode(t2, n), n)
-    | `PointwiseProduct(t1, t2) => evaluatePointwiseProduct(evaluateNode(t1, n), evaluateNode(t2, n), n)
-    | `VerticalScaling(t1, t2) => evaluateVerticalScaling(evaluateNode(t1, n), evaluateNode(t2, n), n)
-    | `Normalize(t) => evaluateNormalize(evaluateNode(t, n), n)
-    | `LeftTruncate(t, x) => evaluateTruncate(evaluateNode(t, n), x, (>=), n)
-    | `RightTruncate(t, x) => evaluateTruncate(evaluateNode(t, n), x, (<=), n)
-    | `Render(t) => renderNode(evaluateNode(t, n), n)
-    }
-  };
-
-  let toShape = (treeNode: distTree, n: int) => {
-    let treeShape = evaluateNode(`Render(`Normalize(treeNode)), n);
-
-    switch (treeShape) {
-    | `Simple(_) => E.O.toExn("No shape found!", None)
-    | `RenderedShape(sc, sd, _) => {
-      let shape = MixedShapeBuilder.buildSimple(~continuous=Some(sc), ~discrete=sd);
-
-      shape |> E.O.toExt("");
-    }
-    }
-  };
-
-  let rec toString = (treeNode: distTree): string => {
-    let stringFromOp = op => switch (op) {
-      | `AddOperation => " + "
-      | `SubtractOperation => " - "
-      | `MultiplyOperation => " * "
-      | `DivideOperation => " / "
-      | `ExponentiateOperation => "^"
-    };
-
-    switch (treeNode) {
-    | `Simple(d) => GenericSimple.toString(d)
-    | `Combination(t1, t2, op) => toString(t1) ++ stringFromOp(op) ++ toString(t2)
-    | `PointwiseSum(t1, t2) => toString(t1) ++ " .+ " ++ toString(t2)
-    | `PointwiseProduct(t1, t2) => toString(t1) ++ " .* " ++ toString(t2)
-    | `VerticalScaling(t1, t2) => toString(t1) ++ " @ " ++ toString(t2)
-    | `Normalize(t) => "normalize(" ++ toString(t) ++ ")"
-    | `LeftTruncate(t, x) => "leftTruncate(" ++ toString(t) ++ ", " ++ string_of_float(x) ++ ")"
-    | `RightTruncate(t, x) => "rightTruncate(" ++ toString(t) ++ ", " ++ string_of_float(x) ++ ")"
-    | `Render(t) => toString(t)
-    }
-  };
-};
-
-let toString = (treeNode: distTree) => DistTree.toString(treeNode)
-
-let toShape = (sampleCount: int, treeNode: distTree) =>
-  DistTree.toShape(treeNode, sampleCount) //~xSelection=`ByWeight,

src/distPlus/symbolic/TreeNode.re

@@ -0,0 +1,414 @@
+/* This module represents a tree node. */
+
+/* TreeNodes are either Data (i.e. symbolic or rendered distributions) or Operations. */
+type treeNode = [
+    | `DistData(distData)
+    | `Operation(operation)
+] and distData = [
+  | `Symbolic(SymbolicDist.dist)
+  | `RenderedShape(DistTypes.shape)
+] and operation = [
+  // binary operations
+  | `StandardOperation(standardOperation, treeNode, treeNode)
+  | `PointwiseOperation(pointwiseOperation, treeNode, treeNode)
+  | `ScaleOperation(scaleOperation, treeNode, scaleBy)
+  // unary operations
+  | `Render(treeNode) // always evaluates to `DistData(`RenderedShape(...))
+  | `Truncate(leftCutoff, rightCutoff, treeNode)
+  | `Normalize(treeNode)
+  // direct evaluations of dists (e.g. cdf, sample)
+  | `FloatFromDist(distToFloatOperation, treeNode)
+] and standardOperation = [
+  | `Add
+  | `Multiply
+  | `Subtract
+  | `Divide
+  | `Exponentiate
+] and pointwiseOperation = [
+  | `Add
+  | `Multiply
+] and scaleOperation = [
+  | `Multiply
+  | `Log
+]
+and scaleBy = treeNode and leftCutoff = option(float) and rightCutoff = option(float)
+and distToFloatOperation = [
+  | `Pdf(float)
+  | `Cdf(float)
+  | `Inv(float)
+  | `Sample
+];
+
+module TreeNode = {
+  type t = treeNode;
+  type simplifier = treeNode => result(treeNode, string);
+
+  type renderParams = {
+    operationToDistData: (int, operation) => result(t, string),
+    sampleCount: int,
+  }
+
+  let rec renderToShape = (renderParams, t: t): result(DistTypes.shape, string) => {
+    switch (t) {
+    | `DistData(`RenderedShape(s)) => Ok(s) // already a rendered shape, we're done here
+    | `DistData(`Symbolic(d)) =>
+      switch (d) {
+      | `Float(v) =>
+        Ok(Discrete(Distributions.Discrete.make({xs: [|v|], ys: [|1.0|]}, Some(1.0))));
+      | _ =>
+        let xs = SymbolicDist.GenericDistFunctions.interpolateXs(~xSelection=`ByWeight, d, renderParams.sampleCount);
+        let ys = xs |> E.A.fmap(x => SymbolicDist.GenericDistFunctions.pdf(x, d));
+        Ok(Continuous(Distributions.Continuous.make(`Linear, {xs, ys}, Some(1.0))));
+      }
+    | `Operation(op) => E.R.bind(renderParams.operationToDistData(renderParams.sampleCount, op), renderToShape(renderParams))
+    };
+  };
+
+  /* The following modules encapsulate everything we can do with
+   * different kinds of operations. */
+
+  /* Given two random variables A and B, this returns the distribution
+     of a new variable that is the result of the operation on A and B.
+     For instance, normal(0, 1) + normal(1, 1) -> normal(1, 2).
+     In general, this is implemented via convolution. */
+  module StandardOperation = {
+    let funcFromOp: (standardOperation, float, float) => float =
+      fun
+      | `Add => (+.)
+      | `Subtract => (-.)
+      | `Multiply => ( *. )
+      | `Divide => (/.)
+      | `Exponentiate => ( ** );
+
+    module Simplify = {
+      let tryCombiningFloats: simplifier =
+        fun
+        | `Operation(
+            `StandardOperation(
+              `Divide,
+              `DistData(`Symbolic(`Float(v1))),
+              `DistData(`Symbolic(`Float(0.))),
+            ),
+          ) =>
+          Error("Cannot divide $v1 by zero.")
+        | `Operation(
+            `StandardOperation(
+              standardOp,
+              `DistData(`Symbolic(`Float(v1))),
+              `DistData(`Symbolic(`Float(v2))),
+            ),
+          ) => {
+            let func = funcFromOp(standardOp);
+            Ok(`DistData(`Symbolic(`Float(func(v1, v2)))));
+          }
+        | t => Ok(t);
+
+      let tryCombiningNormals: simplifier =
+        fun
+        | `Operation(
+            `StandardOperation(
+              `Add,
+              `DistData(`Symbolic(`Normal(n1))),
+              `DistData(`Symbolic(`Normal(n2))),
+            ),
+          ) =>
+          Ok(`DistData(`Symbolic(SymbolicDist.Normal.add(n1, n2))))
+        | `Operation(
+            `StandardOperation(
+              `Subtract,
+              `DistData(`Symbolic(`Normal(n1))),
+              `DistData(`Symbolic(`Normal(n2))),
+            ),
+          ) =>
+          Ok(`DistData(`Symbolic(SymbolicDist.Normal.subtract(n1, n2))))
+        | t => Ok(t);
+
+      let tryCombiningLognormals: simplifier =
+        fun
+        | `Operation(
+            `StandardOperation(
+              `Multiply,
+              `DistData(`Symbolic(`Lognormal(l1))),
+              `DistData(`Symbolic(`Lognormal(l2))),
+            ),
+          ) =>
+          Ok(`DistData(`Symbolic(SymbolicDist.Lognormal.multiply(l1, l2))))
+        | `Operation(
+            `StandardOperation(
+              `Divide,
+              `DistData(`Symbolic(`Lognormal(l1))),
+              `DistData(`Symbolic(`Lognormal(l2))),
+            ),
+          ) =>
+          Ok(`DistData(`Symbolic(SymbolicDist.Lognormal.divide(l1, l2))))
+        | t => Ok(t);
+
+      let attempt = (standardOp, t1: t, t2: t): result(treeNode, string) => {
+        let originalTreeNode =
+          `Operation(`StandardOperation((standardOp, t1, t2)));
+
+        originalTreeNode
+        |> tryCombiningFloats
+        |> E.R.bind(_, tryCombiningNormals)
+        |> E.R.bind(_, tryCombiningLognormals);
+      };
+    };
+
+    let evaluateNumerically = (standardOp, renderParams, t1, t2) => {
+      let func = funcFromOp(standardOp);
+
+      // TODO: downsample the two shapes
+      let renderedShape1 = t1 |> renderToShape(renderParams);
+      let renderedShape2 = t2 |> renderToShape(renderParams);
+
+      // This will most likely require a mixed
+
+      switch ((renderedShape1, renderedShape2)) {
+        | (Error(e1), _) => Error(e1)
+        | (_, Error(e2)) => Error(e2)
+        | (Ok(s1), Ok(s2)) => Ok(`DistData(`RenderedShape(Distributions.Shape.convolve(func, s1, s2))))
+      };
+    };
+
+    let evaluateToDistData =
+        (standardOp: standardOperation, renderParams, t1: t, t2: t): result(treeNode, string) =>
+      standardOp
+      |> Simplify.attempt(_, t1, t2)
+      |> E.R.bind(
+           _,
+           fun
+           | `DistData(d) => Ok(`DistData(d)) // the analytical simplifaction worked, nice!
+           | `Operation(_) => // if not, run the convolution
+             evaluateNumerically(standardOp, renderParams, t1, t2),
+         );
+  };
+
+  module ScaleOperation = {
+    let rec mean = (renderParams, t: t): result(float, string) => {
+      switch (t) {
+      | `DistData(`RenderedShape(s)) => Ok(Distributions.Shape.T.mean(s))
+      | `DistData(`Symbolic(s)) => SymbolicDist.GenericDistFunctions.mean(s)
+          // evaluating the operation returns result(treeNode(distData)). We then want to make sure
+      | `Operation(op) => E.R.bind(renderParams.operationToDistData(renderParams.sampleCount, op), mean(renderParams))
+      }
+    };
+
+    let fnFromOp =
+      fun
+      | `Multiply => (*.)
+      | `Log => ((a, b) => ( log(a) /. log(b) ));
+
+    let knownIntegralSumFnFromOp =
+      fun
+      | `Multiply => (a, b) => Some(a *. b)
+      | `Log => ((_, _) => None);
+
+    let evaluateToDistData = (scaleOp, renderParams, t, scaleBy) => {
+      let fn = fnFromOp(scaleOp);
+      let knownIntegralSumFn = knownIntegralSumFnFromOp(scaleOp);
+      let renderedShape = t |> renderToShape(renderParams);
+      let scaleByMeanValue = mean(renderParams, scaleBy);
+
+      switch ((renderedShape, scaleByMeanValue)) {
+      | (Error(e1), _) => Error(e1)
+      | (_, Error(e2)) => Error(e2)
+      | (Ok(rs), Ok(sm)) =>
+            Ok(`DistData(`RenderedShape(Distributions.Shape.T.mapY(~knownIntegralSumFn=knownIntegralSumFn(sm), fn(sm), rs))))
+      }
+    };
+  };
+
+  module PointwiseOperation = {
+    let funcFromOp: (pointwiseOperation => ((float, float) => float)) =
+      fun
+      | `Add => (+.)
+      | `Multiply => ( *. );
+
+    let evaluateToDistData = (pointwiseOp, renderParams, t1, t2) => {
+      let func = funcFromOp(pointwiseOp);
+      let renderedShape1 = t1 |> renderToShape(renderParams);
+      let renderedShape2 = t2 |> renderToShape(renderParams);
+
+      // TODO: figure out integral, diff between pointwiseAdd and pointwiseProduct and other stuff
+      // Distributions.Shape.reduce(func, renderedShape1, renderedShape2);
+
+      Error("Pointwise operations currently not supported.")
+    };
+  };
+
+  module Truncate = {
+    module Simplify = {
+      let tryTruncatingNothing: simplifier = fun
+      | `Operation(`Truncate(None, None, `DistData(d))) => Ok(`DistData(d))
+      | t => Ok(t);
+
+      let tryTruncatingUniform: simplifier = fun
+      | `Operation(`Truncate(lc, rc, `DistData(`Symbolic(`Uniform(u))))) => {
+          // just create a new Uniform distribution
+          let newLow = max(E.O.default(neg_infinity, lc), u.low);
+          let newHigh = min(E.O.default(infinity, rc), u.high);
+          Ok(`DistData(`Symbolic(`Uniform({low: newLow, high: newHigh}))));
+        }
+      | t => Ok(t);
+
+      let attempt = (leftCutoff, rightCutoff, t): result(treeNode, string) => {
+          let originalTreeNode = `Operation(`Truncate(leftCutoff, rightCutoff, t));
+
+          originalTreeNode
+          |> tryTruncatingNothing
+          |> E.R.bind(_, tryTruncatingUniform);
+      };
+    };
+
+    let evaluateNumerically = (leftCutoff, rightCutoff, renderParams, t) => {
+      // TODO: use named args in renderToShape; if we're lucky we can at least get the tail
+      // of a distribution we otherwise wouldn't get at all
+      let renderedShape = t |> renderToShape(renderParams);
+
+      E.R.bind(renderedShape, rs => {
+        let truncatedShape = rs |> Distributions.Shape.truncate(leftCutoff, rightCutoff);
+        Ok(`DistData(`RenderedShape(rs)));
+      });
+    };
+
+    let evaluateToDistData = (leftCutoff: option(float), rightCutoff: option(float), renderParams, t: treeNode): result(treeNode, string) => {
+      t
+      |> Simplify.attempt(leftCutoff, rightCutoff)
+      |> E.R.bind(
+           _,
+           fun
+           | `DistData(d) => Ok(`DistData(d)) // the analytical simplifaction worked, nice!
+           | `Operation(_) => evaluateNumerically(leftCutoff, rightCutoff, renderParams, t),
+         ); // if not, run the convolution
+      };
+  };
+
+  module Normalize = {
+    let rec evaluateToDistData = (renderParams, t: treeNode): result(treeNode, string) => {
+      switch (t) {
+      | `DistData(`Symbolic(_)) => Ok(t)
+      | `DistData(`RenderedShape(s)) => {
+          let normalized = Distributions.Shape.normalize(s);
+        Ok(`DistData(`RenderedShape(normalized)));
+      }
+      | `Operation(op) => E.R.bind(renderParams.operationToDistData(renderParams.sampleCount, op), evaluateToDistData(renderParams))
+      }
+    }
+  };
+
+  module FloatFromDist = {
+    let evaluateFromSymbolic = (distToFloatOp: distToFloatOperation, s) => {
+      let value = switch (distToFloatOp) {
+        | `Pdf(f) => SymbolicDist.GenericDistFunctions.pdf(f, s)
+        | `Cdf(f) => 0.0
+        | `Inv(f) => SymbolicDist.GenericDistFunctions.inv(f, s)
+        | `Sample => SymbolicDist.GenericDistFunctions.sample(s)
+      }
+      Ok(`DistData(`Symbolic(`Float(value))));
+    };
+    let evaluateFromRenderedShape = (distToFloatOp: distToFloatOperation, rs: DistTypes.shape): result(treeNode, string) => {
+      // evaluate the pdf, cdf, get sample, etc. from the renderedShape rs
+      // Should be a float like Ok(`DistData(`Symbolic(Float(0.0))));
+      Error("Float from dist is not yet implemented.");
+    };
+    let rec evaluateToDistData = (distToFloatOp: distToFloatOperation, renderParams, t: treeNode): result(treeNode, string) => {
+      switch (t) {
+      | `DistData(`Symbolic(s)) => evaluateFromSymbolic(distToFloatOp, s) // we want to evaluate the distToFloatOp on the symbolic dist
+      | `DistData(`RenderedShape(rs)) => evaluateFromRenderedShape(distToFloatOp, rs)
+      | `Operation(op) => E.R.bind(renderParams.operationToDistData(renderParams.sampleCount, op), evaluateToDistData(distToFloatOp, renderParams))
+      }
+    }
+  };
+
+  module Render = {
+    let evaluateToRenderedShape = (renderParams, t: treeNode): result(t, string) => {
+      E.R.bind(renderToShape(renderParams, t), rs => Ok(`DistData(`RenderedShape(rs))));
+    }
+  };
+
+  let rec operationToDistData =
+      (sampleCount: int, op: operation): result(t, string) => {
+
+    // the functions that convert the Operation nodes to DistData nodes need to
+    // have a way to call this function on their children, if their children are themselves Operation nodes.
+
+    let renderParams: renderParams = {
+      operationToDistData: operationToDistData,
+      sampleCount: sampleCount,
+    };
+
+    switch (op) {
+    | `StandardOperation(standardOp, t1, t2) =>
+      StandardOperation.evaluateToDistData(
+        standardOp, renderParams, t1, t2 // we want to give it the option to render or simply leave it as is
+      )
+    | `PointwiseOperation(pointwiseOp, t1, t2) =>
+      PointwiseOperation.evaluateToDistData(
+        pointwiseOp,
+        renderParams,
+        t1,
+        t2,
+      )
+    | `ScaleOperation(scaleOp, t, scaleBy) =>
+      ScaleOperation.evaluateToDistData(scaleOp, renderParams, t, scaleBy)
+    | `Truncate(leftCutoff, rightCutoff, t) => Truncate.evaluateToDistData(leftCutoff, rightCutoff, renderParams, t)
+    | `FloatFromDist(distToFloatOp, t) => FloatFromDist.evaluateToDistData(distToFloatOp, renderParams, t)
+    | `Normalize(t) => Normalize.evaluateToDistData(renderParams, t)
+    | `Render(t) => Render.evaluateToRenderedShape(renderParams, t)
+    };
+  };
+
+  /* This function recursively goes through the nodes of the parse tree,
+     replacing each Operation node and its subtree with a Data node.
+     Whenever possible, the replacement produces a new Symbolic Data node,
+     but most often it will produce a RenderedShape.
+     This function is used mainly to turn a parse tree into a single RenderedShape
+     that can then be displayed to the user. */
+  let rec toDistData = (treeNode: t, sampleCount: int): result(t, string) => {
+    switch (treeNode) {
+    | `DistData(d) => Ok(`DistData(d))
+    | `Operation(op) => operationToDistData(sampleCount, op)
+    };
+  };
+
+  let rec toString = (t: t): string => {
+    let stringFromStandardOperation = fun
+      | `Add => " + "
+      | `Subtract => " - "
+      | `Multiply => " * "
+      | `Divide => " / "
+      | `Exponentiate => "^";
+
+    let stringFromPointwiseOperation =
+      fun
+      | `Add => " .+ "
+      | `Multiply => " .* ";
+
+    switch (t) {
+    | `DistData(`Symbolic(d)) => SymbolicDist.GenericDistFunctions.toString(d)
+    | `DistData(`RenderedShape(s)) => "[shape]"
+    | `Operation(`StandardOperation(op, t1, t2)) => toString(t1) ++ stringFromStandardOperation(op) ++ toString(t2)
+    | `Operation(`PointwiseOperation(op, t1, t2)) => toString(t1) ++ stringFromPointwiseOperation(op) ++ toString(t2)
+    | `Operation(`ScaleOperation(_scaleOp, t, scaleBy)) => toString(t) ++ " @ " ++ toString(scaleBy)
+    | `Operation(`Normalize(t)) => "normalize(" ++ toString(t) ++ ")"
+    | `Operation(`Truncate(lc, rc, t)) => "truncate(" ++ toString(t) ++ ", " ++ E.O.dimap(string_of_float, () => "-inf", lc) ++ ", " ++ E.O.dimap(string_of_float, () => "inf", rc) ++ ")"
+    | `Operation(`Render(t)) => toString(t)
+    }
+  };
+};
+
+let toShape = (sampleCount: int, treeNode: treeNode) => {
+  let renderResult = TreeNode.toDistData(`Operation(`Render(treeNode)), sampleCount);
+
+
+  switch (renderResult) {
+  | Ok(`DistData(`RenderedShape(rs))) => {
+      let continuous = Distributions.Shape.T.toContinuous(rs);
+      let discrete = Distributions.Shape.T.toDiscrete(rs);
+      let shape = MixedShapeBuilder.buildSimple(~continuous, ~discrete);
+      shape |> E.O.toExt("");
+    }
+  | Ok(_) => E.O.toExn("Rendering failed.", None)
+  | Error(message) => E.O.toExn("No shape found!", None)
+  }
+};

src/distPlus/utility/Jstat.re

@@ -5,6 +5,7 @@ type normal = {
   [@bs.meth] "cdf": (float, float, float) => float,
   [@bs.meth] "inv": (float, float, float) => float,
   [@bs.meth] "sample": (float, float) => float,
+  [@bs.meth] "mean": (float, float) => float,
 };
 type lognormal = {
   .
@@ -12,6 +13,7 @@ type lognormal = {
   [@bs.meth] "cdf": (float, float, float) => float,
   [@bs.meth] "inv": (float, float, float) => float,
   [@bs.meth] "sample": (float, float) => float,
+  [@bs.meth] "mean": (float, float) => float,
 };
 type uniform = {
   .
@@ -19,6 +21,7 @@ type uniform = {
   [@bs.meth] "cdf": (float, float, float) => float,
   [@bs.meth] "inv": (float, float, float) => float,
   [@bs.meth] "sample": (float, float) => float,
+  [@bs.meth] "mean": (float, float) => float,
 };
 type beta = {
   .
@@ -26,6 +29,7 @@ type beta = {
   [@bs.meth] "cdf": (float, float, float) => float,
   [@bs.meth] "inv": (float, float, float) => float,
   [@bs.meth] "sample": (float, float) => float,
+  [@bs.meth] "mean": (float, float) => float,
 };
 type exponential = {
   .
@@ -33,6 +37,7 @@ type exponential = {
   [@bs.meth] "cdf": (float, float) => float,
   [@bs.meth] "inv": (float, float) => float,
   [@bs.meth] "sample": float => float,
+  [@bs.meth] "mean": float => float,
 };
 type cauchy = {
   .
@@ -47,6 +52,7 @@ type triangular = {
   [@bs.meth] "cdf": (float, float, float, float) => float,
   [@bs.meth] "inv": (float, float, float, float) => float,
   [@bs.meth] "sample": (float, float, float) => float,
+  [@bs.meth] "mean": (float, float, float) => float,
 };
 
 // Pareto doesn't have sample for some reason
@@ -61,6 +67,7 @@ type poisson = {
   [@bs.meth] "pdf": (float, float) => float,
   [@bs.meth] "cdf": (float, float) => float,
   [@bs.meth] "sample": float => float,
+  [@bs.meth] "mean": float => float,
 };
 type weibull = {
   .
@@ -68,6 +75,7 @@ type weibull = {
   [@bs.meth] "cdf": (float, float, float) => float,
   [@bs.meth] "inv": (float, float, float) => float,
   [@bs.meth] "sample": (float, float) => float,
+  [@bs.meth] "mean": (float, float) => float,
 };
 type binomial = {
   .