It compiles!

This commit is contained in:
Sebastian Kosch 2020-06-26 21:29:21 -07:00
parent bd528571af
commit dc1ec1bb86
9 changed files with 751 additions and 468 deletions

View File

@ -24,7 +24,7 @@ let makeTestCloseEquality = (~only=false, str, item1, item2, ~digits) =>
describe("Shape", () => {
describe("Continuous", () => {
open Distributions.Continuous;
let continuous = make(`Linear, shape);
let continuous = make(`Linear, shape, None);
makeTest("minX", T.minX(continuous), 1.0);
makeTest("maxX", T.maxX(continuous), 8.0);
makeTest(
@ -57,7 +57,7 @@ describe("Shape", () => {
);
});
describe("when Stepwise", () => {
let continuous = make(`Stepwise, shape);
let continuous = make(`Stepwise, shape, None);
makeTest(
"at 4.0",
T.xToY(4., continuous),
@ -89,7 +89,7 @@ describe("Shape", () => {
"toLinear",
{
let continuous =
make(`Stepwise, {xs: [|1., 4., 8.|], ys: [|0.1, 5., 1.0|]});
make(`Stepwise, {xs: [|1., 4., 8.|], ys: [|0.1, 5., 1.0|]}, None);
continuous |> toLinear |> E.O.fmap(getShape);
},
Some({
@ -100,7 +100,7 @@ describe("Shape", () => {
makeTest(
"toLinear",
{
let continuous = make(`Stepwise, {xs: [|0.0|], ys: [|0.3|]});
let continuous = make(`Stepwise, {xs: [|0.0|], ys: [|0.3|]}, None);
continuous |> toLinear |> E.O.fmap(getShape);
},
Some({xs: [|0.0|], ys: [|0.3|]}),
@ -123,7 +123,7 @@ describe("Shape", () => {
makeTest(
"integralEndY",
continuous
|> T.scaleToIntegralSum(~intendedSum=1.0)
|> T.normalize //scaleToIntegralSum(~intendedSum=1.0)
|> T.Integral.sum(~cache=None),
1.0,
);
@ -135,12 +135,12 @@ describe("Shape", () => {
xs: [|1., 4., 8.|],
ys: [|0.3, 0.5, 0.2|],
};
let discrete = shape;
let discrete = make(shape, None);
makeTest("minX", T.minX(discrete), 1.0);
makeTest("maxX", T.maxX(discrete), 8.0);
makeTest(
"mapY",
T.mapY(r => r *. 2.0, discrete) |> (r => r.ys),
T.mapY(r => r *. 2.0, discrete) |> (r => getShape(r).ys),
[|0.6, 1.0, 0.4|],
);
makeTest(
@ -160,19 +160,22 @@ describe("Shape", () => {
);
makeTest(
"scaleBy",
T.scaleBy(~scale=4.0, discrete),
{xs: [|1., 4., 8.|], ys: [|1.2, 2.0, 0.8|]},
scaleBy(~scale=4.0, discrete),
make({xs: [|1., 4., 8.|], ys: [|1.2, 2.0, 0.8|]}, None),
);
makeTest(
"scaleToIntegralSum",
T.scaleToIntegralSum(~intendedSum=4.0, discrete),
{xs: [|1., 4., 8.|], ys: [|1.2, 2.0, 0.8|]},
"normalize, then scale by 4.0",
discrete
|> T.normalize
|> scaleBy(~scale=4.0),
make({xs: [|1., 4., 8.|], ys: [|1.2, 2.0, 0.8|]}, None),
);
makeTest(
"scaleToIntegralSum: back and forth",
discrete
|> T.scaleToIntegralSum(~intendedSum=4.0)
|> T.scaleToIntegralSum(~intendedSum=1.0),
|> T.normalize
|> scaleBy(~scale=4.0)
|> T.normalize,
discrete,
);
makeTest(
@ -181,12 +184,13 @@ describe("Shape", () => {
Distributions.Continuous.make(
`Stepwise,
{xs: [|1., 4., 8.|], ys: [|0.3, 0.8, 1.0|]},
None
),
);
makeTest(
"integral with 1 element",
T.Integral.get(~cache=None, {xs: [|0.0|], ys: [|1.0|]}),
Distributions.Continuous.make(`Stepwise, {xs: [|0.0|], ys: [|1.0|]}),
T.Integral.get(~cache=None, Distributions.Discrete.make({xs: [|0.0|], ys: [|1.0|]}, None)),
Distributions.Continuous.make(`Stepwise, {xs: [|0.0|], ys: [|1.0|]}, None),
);
makeTest(
"integralXToY",
@ -205,27 +209,22 @@ describe("Shape", () => {
describe("Mixed", () => {
open Distributions.Mixed;
let discrete: DistTypes.xyShape = {
let discreteShape: DistTypes.xyShape = {
xs: [|1., 4., 8.|],
ys: [|0.3, 0.5, 0.2|],
};
let discrete = Distributions.Discrete.make(discreteShape, None);
let continuous =
Distributions.Continuous.make(
`Linear,
{xs: [|3., 7., 14.|], ys: [|0.058, 0.082, 0.124|]},
None
)
|> Distributions.Continuous.T.scaleToIntegralSum(~intendedSum=1.0);
let mixed =
MixedShapeBuilder.build(
|> Distributions.Continuous.T.normalize; //scaleToIntegralSum(~intendedSum=1.0);
let mixed = Distributions.Mixed.make(
~continuous,
~discrete,
~assumptions={
continuous: ADDS_TO_CORRECT_PROBABILITY,
discrete: ADDS_TO_CORRECT_PROBABILITY,
discreteProbabilityMass: Some(0.5),
},
)
|> E.O.toExn("");
);
makeTest("minX", T.minX(mixed), 1.0);
makeTest("maxX", T.maxX(mixed), 14.0);
makeTest(
@ -243,9 +242,9 @@ describe("Shape", () => {
0.24775224775224775,
|],
},
None
),
~discrete={xs: [|1., 4., 8.|], ys: [|0.6, 1.0, 0.4|]},
~discreteProbabilityMassFraction=0.5,
~discrete=Distributions.Discrete.make({xs: [|1., 4., 8.|], ys: [|0.6, 1.0, 0.4|]}, None)
),
);
makeTest(
@ -266,7 +265,7 @@ describe("Shape", () => {
makeTest("integralEndY", T.Integral.sum(~cache=None, mixed), 1.0);
makeTest(
"scaleBy",
T.scaleBy(~scale=2.0, mixed),
Distributions.Mixed.scaleBy(~scale=2.0, mixed),
Distributions.Mixed.make(
~continuous=
Distributions.Continuous.make(
@ -279,9 +278,9 @@ describe("Shape", () => {
0.24775224775224775,
|],
},
None
),
~discrete={xs: [|1., 4., 8.|], ys: [|0.6, 1.0, 0.4|]},
~discreteProbabilityMassFraction=0.5,
~discrete=Distributions.Discrete.make({xs: [|1., 4., 8.|], ys: [|0.6, 1.0, 0.4|]}, None),
),
);
makeTest(
@ -302,34 +301,31 @@ describe("Shape", () => {
0.6913122927072927,
1.0,
|],
},
},
None,
),
);
});
describe("Distplus", () => {
open Distributions.DistPlus;
let discrete: DistTypes.xyShape = {
let discreteShape: DistTypes.xyShape = {
xs: [|1., 4., 8.|],
ys: [|0.3, 0.5, 0.2|],
};
let discrete = Distributions.Discrete.make(discreteShape, None);
let continuous =
Distributions.Continuous.make(
`Linear,
{xs: [|3., 7., 14.|], ys: [|0.058, 0.082, 0.124|]},
None
)
|> Distributions.Continuous.T.scaleToIntegralSum(~intendedSum=1.0);
|> Distributions.Continuous.T.normalize; //scaleToIntegralSum(~intendedSum=1.0);
let mixed =
MixedShapeBuilder.build(
Distributions.Mixed.make(
~continuous,
~discrete,
~assumptions={
continuous: ADDS_TO_CORRECT_PROBABILITY,
discrete: ADDS_TO_CORRECT_PROBABILITY,
discreteProbabilityMass: Some(0.5),
},
)
|> E.O.toExn("");
);
let distPlus =
Distributions.DistPlus.make(
~shape=Mixed(mixed),
@ -374,6 +370,7 @@ describe("Shape", () => {
1.0,
|],
},
None,
),
),
);
@ -386,9 +383,9 @@ describe("Shape", () => {
let numSamples = 10000;
open Distributions.Shape;
let normal: SymbolicDist.dist = `Normal({mean, stdev});
let normalShape = TreeNode.toShape(numSamples, normal);
let normalShape = TreeNode.toShape(numSamples, `DistData(`Symbolic(normal)));
let lognormal = SymbolicDist.Lognormal.fromMeanAndStdev(mean, stdev);
let lognormalShape = TreeNode.toShape(numSamples, lognormal);
let lognormalShape = TreeNode.toShape(numSamples, `DistData(`Symbolic(lognormal)));
makeTestCloseEquality(
"Mean of a normal",

View File

@ -17,7 +17,7 @@ module FormConfig = [%lenses
//
sampleCount: string,
outputXYPoints: string,
truncateTo: string,
downsampleTo: string,
kernelWidth: string,
}
];
@ -25,7 +25,7 @@ module FormConfig = [%lenses
type options = {
sampleCount: int,
outputXYPoints: int,
truncateTo: option(int),
downsampleTo: option(int),
kernelWidth: option(float),
};
@ -115,7 +115,7 @@ type inputs = {
samplingInputs: RenderTypes.ShapeRenderer.Sampling.inputs,
guesstimatorString: string,
length: int,
shouldTruncateSampledDistribution: int,
shouldDownsampleSampledDistribution: int,
};
module DemoDist = {
@ -141,8 +141,8 @@ module DemoDist = {
kernelWidth: options.kernelWidth,
},
~distPlusIngredients,
~shouldTruncate=options.truncateTo |> E.O.isSome,
~recommendedLength=options.truncateTo |> E.O.default(10000),
~shouldDownsample=options.downsampleTo |> E.O.isSome,
~recommendedLength=options.downsampleTo |> E.O.default(10000),
(),
);
let response = DistPlusRenderer.run(inputs);
@ -182,7 +182,7 @@ let make = () => {
unit: "days",
sampleCount: "30000",
outputXYPoints: "10000",
truncateTo: "1000",
downsampleTo: "1000",
kernelWidth: "5",
},
(),
@ -210,7 +210,7 @@ let make = () => {
let sampleCount = reform.state.values.sampleCount |> Js.Float.fromString;
let outputXYPoints =
reform.state.values.outputXYPoints |> Js.Float.fromString;
let truncateTo = reform.state.values.truncateTo |> Js.Float.fromString;
let downsampleTo = reform.state.values.downsampleTo |> Js.Float.fromString;
let kernelWidth = reform.state.values.kernelWidth |> Js.Float.fromString;
let domain =
@ -252,20 +252,20 @@ let make = () => {
};
let options =
switch (sampleCount, outputXYPoints, truncateTo) {
switch (sampleCount, outputXYPoints, downsampleTo) {
| (_, _, _)
when
!Js.Float.isNaN(sampleCount)
&& !Js.Float.isNaN(outputXYPoints)
&& !Js.Float.isNaN(truncateTo)
&& !Js.Float.isNaN(downsampleTo)
&& sampleCount > 10.
&& outputXYPoints > 10. =>
Some({
sampleCount: sampleCount |> int_of_float,
outputXYPoints: outputXYPoints |> int_of_float,
truncateTo:
int_of_float(truncateTo) > 0
? Some(int_of_float(truncateTo)) : None,
downsampleTo:
int_of_float(downsampleTo) > 0
? Some(int_of_float(downsampleTo)) : None,
kernelWidth: kernelWidth == 0.0 ? None : Some(kernelWidth),
})
| _ => None
@ -287,7 +287,7 @@ let make = () => {
reform.state.values.unit,
reform.state.values.sampleCount,
reform.state.values.outputXYPoints,
reform.state.values.truncateTo,
reform.state.values.downsampleTo,
reform.state.values.kernelWidth,
reloader |> string_of_int,
|],
@ -481,7 +481,7 @@ let make = () => {
/>
</Col>
<Col span=4>
<FieldFloat field=FormConfig.TruncateTo label="Truncate To" />
<FieldFloat field=FormConfig.DownsampleTo label="Downsample To" />
</Col>
<Col span=4>
<FieldFloat field=FormConfig.KernelWidth label="Kernel Width" />

View File

@ -43,7 +43,7 @@ module DemoDist = {
let str =
switch (parsed1) {
| Ok(r) => SymbolicDist.toString(r)
| Ok(r) => TreeNode.toString(r)
| Error(e) => e
};
@ -58,7 +58,7 @@ module DemoDist = {
~guesstimatorString=None,
(),
)
|> Distributions.DistPlus.T.scaleToIntegralSum(~intendedSum=1.0);
|> Distributions.DistPlus.T.normalize;
<DistPlusPlot distPlus />;
})
|> E.O.default(ReasonReact.null);

View File

@ -3,7 +3,8 @@ module type dist = {
type integral;
let minX: t => float;
let maxX: t => float;
let mapY: (~knownIntegralSumFn: float => option(float)=?, float => float, t) => t;
let mapY:
(~knownIntegralSumFn: float => option(float)=?, float => float, t) => t;
let xToY: (float, t) => DistTypes.mixedPoint;
let toShape: t => DistTypes.shape;
let toContinuous: t => option(DistTypes.continuousShape);
@ -13,6 +14,7 @@ module type dist = {
let normalizedToDiscrete: t => option(DistTypes.discreteShape);
let toDiscreteProbabilityMassFraction: t => float;
let downsample: (~cache: option(integral)=?, int, t) => t;
let truncate: (option(float), option(float), t) => t;
let integral: (~cache: option(integral), t) => integral;
let integralEndY: (~cache: option(integral), t) => float;
@ -38,6 +40,7 @@ module Dist = (T: dist) => {
let toContinuous = T.toContinuous;
let toDiscrete = T.toDiscrete;
let normalize = T.normalize;
let truncate = T.truncate;
let normalizedToContinuous = T.normalizedToContinuous;
let normalizedToDiscrete = T.normalizedToDiscrete;
let mean = T.mean;
@ -52,7 +55,22 @@ module Dist = (T: dist) => {
};
};
module Continuous {
module Common = {
let combineIntegralSums =
(
combineFn: (float, float) => option(float),
t1KnownIntegralSum: option(float),
t2KnownIntegralSum: option(float),
) => {
switch (t1KnownIntegralSum, t2KnownIntegralSum) {
| (None, _)
| (_, None) => None
| (Some(s1), Some(s2)) => combineFn(s1, s2)
};
};
};
module Continuous = {
type t = DistTypes.continuousShape;
let getShape = (t: t) => t.xyShape;
let interpolation = (t: t) => t.interpolation;
@ -78,17 +96,21 @@ module Continuous {
knownIntegralSum: Some(0.0),
};
let combine =
(fn, t1: DistTypes.continuousShape, t2: DistTypes.continuousShape)
(
~knownIntegralSumsFn,
fn,
t1: DistTypes.continuousShape,
t2: DistTypes.continuousShape,
)
: DistTypes.continuousShape => {
// If we're adding the distributions, and we know the total of each, then we
// can just sum them up. Otherwise, all bets are off.
let combinedIntegralSum =
switch (fn, t1.knownIntegralSum, t2.knownIntegralSum) {
| (_, None, _)
| (_, _, None) => None
| ((+.), Some(s1), Some(s2)) => Some(s1 +. s2)
};
Common.combineIntegralSums(
knownIntegralSumsFn,
t1.knownIntegralSum,
t2.knownIntegralSum,
);
make(
`Linear,
@ -102,7 +124,6 @@ module Continuous {
combinedIntegralSum,
);
};
let reduce = (fn, items) => items |> E.A.fold_left(combine(fn), empty);
let toLinear = (t: t): option(t) => {
switch (t) {
@ -114,7 +135,19 @@ module Continuous {
};
};
let shapeFn = (fn, t: t) => t |> getShape |> fn;
let updateKnownIntegralSum = (knownIntegralSum, t: t): t => ({...t, knownIntegralSum});
let updateKnownIntegralSum = (knownIntegralSum, t: t): t => {
...t,
knownIntegralSum,
};
let reduce =
(
~knownIntegralSumsFn: (float, float) => option(float)=(_, _) => None,
fn,
continuousShapes,
) =>
continuousShapes
|> E.A.fold_left(combine(~knownIntegralSumsFn, fn), empty);
// Contracts every point in the continuous xyShape into a single dirac-Delta-like point,
// using the centerpoints between adjacent xs and the area under each trapezoid.
@ -128,11 +161,18 @@ module Continuous {
Belt.Array.set(
pointMassesY,
x,
(xs[x + 1] -. xs[x]) *. ((ys[x] +. ys[x + 1]) /. 2.)); // = dx * (1/2) * (avgY)
(xs[x + 1] -. xs[x]) *. ((ys[x] +. ys[x + 1]) /. 2.),
); // = dx * (1/2) * (avgY)
();
};
{xyShape: {xs: xs, ys: pointMassesY}, knownIntegralSum: t.knownIntegralSum};
{
xyShape: {
xs,
ys: pointMassesY,
},
knownIntegralSum: t.knownIntegralSum,
};
};
/* Performs a discrete convolution between two continuous distributions A and B.
@ -153,18 +193,25 @@ module Continuous {
let t1n = t1s |> XYShape.T.length;
let t2n = t2s |> XYShape.T.length;
let outXYShapes: array(array((float, float))) = Belt.Array.makeUninitializedUnsafe(t1n);
let outXYShapes: array(array((float, float))) =
Belt.Array.makeUninitializedUnsafe(t1n);
for (i in 0 to t1n - 1) {
// create a new distribution
let dxyShape: array((float, float)) = Belt.Array.makeUninitializedUnsafe(t2n);
let dxyShape: array((float, float)) =
Belt.Array.makeUninitializedUnsafe(t2n);
for (j in 0 to t2n - 1) {
let _ = Belt.Array.set(dxyShape, j, (fn(t1s.xs[i], t2s.xs[j]), t1s.ys[i] *. t2s.ys[j]));
let _ =
Belt.Array.set(
dxyShape,
j,
(fn(t1s.xs[i], t2s.xs[j]), t1s.ys[i] *. t2s.ys[j]),
);
();
}
};
let _ = Belt.Array.set(outXYShapes, i, dxyShape);
();
}
};
let combinedIntegralSum =
switch (t1.knownIntegralSum, t2.knownIntegralSum) {
@ -175,9 +222,9 @@ module Continuous {
outXYShapes
|> E.A.fmap(s => {
let xyShape = XYShape.T.fromZippedArray(s);
make(`Linear, xyShape, None);
})
let xyShape = XYShape.T.fromZippedArray(s);
make(`Linear, xyShape, None);
})
|> reduce((+.))
|> updateKnownIntegralSum(combinedIntegralSum);
};
@ -185,35 +232,22 @@ module Continuous {
let convolve = (fn, t1: t, t2: t) =>
convolveWithDiscrete(fn, t1, toDiscretePointMasses(t2));
let mapY = (~knownIntegralSumFn=(previousKnownIntegralSum => None), fn, t: t) => {
let mapY = (~knownIntegralSumFn=previousKnownIntegralSum => None, fn, t: t) => {
let u = E.O.bind(_, knownIntegralSumFn);
let yMapFn = shapeMap(XYShape.T.mapY(fn));
t |> yMapFn |> updateKnownIntegralSum(u(t.knownIntegralSum));
};
let scaleBy = (~scale=1.0, ~knownIntegralSum=None, t: t): t =>
t |> mapY((r: float) => r *. scale) |> updateKnownIntegralSum(knownIntegralSum);
let truncate = (leftCutoff: option(float), rightCutoff: option(float), t: t) => {
let truncatedZippedPairs =
t
|> getShape
|> XYShape.T.zip
|> XYShape.Zipped.filterByX(x => x >= E.O.default(neg_infinity, leftCutoff) || x <= E.O.default(infinity, rightCutoff));
let eps = (t |> getShape |> XYShape.T.xTotalRange) *. 0.0001;
let leftNewPoint = leftCutoff |> E.O.dimap(lc => [| (lc -. eps, 0.) |], _ => [||]);
let rightNewPoint = rightCutoff |> E.O.dimap(rc => [| (rc +. eps, 0.) |], _ => [||]);
let truncatedZippedPairsWithNewPoints =
E.A.concatMany([| leftNewPoint, truncatedZippedPairs, rightNewPoint |]);
let truncatedShape = XYShape.T.fromZippedArray(truncatedZippedPairsWithNewPoints);
make(`Linear, truncatedShape, None);
let scaleBy = (~scale=1.0, t: t): t => {
t
|> mapY((r: float) => r *. scale)
|> updateKnownIntegralSum(
E.O.bind(t.knownIntegralSum, v => Some(scale *. v)),
);
};
module T =
Dist({
type t = DistTypes.continuousShape;
@ -236,12 +270,31 @@ module Continuous {
|> DistTypes.MixedPoint.makeContinuous;
};
// let combineWithFn = (t1: t, t2: t, fn: (float, float) => float) => {
// switch(t1, t2){
// | ({interpolation: `Stepwise}, {interpolation: `Stepwise}) => 3.0
// | ({interpolation: `Linear}, {interpolation: `Linear}) => 3.0
// }
// };
let truncate =
(leftCutoff: option(float), rightCutoff: option(float), t: t) => {
let truncatedZippedPairs =
t
|> getShape
|> XYShape.T.zip
|> XYShape.Zipped.filterByX(x =>
x >= E.O.default(neg_infinity, leftCutoff)
|| x <= E.O.default(infinity, rightCutoff)
);
let eps = (t |> getShape |> XYShape.T.xTotalRange) *. 0.0001;
let leftNewPoint =
leftCutoff |> E.O.dimap(lc => [|(lc -. eps, 0.)|], _ => [||]);
let rightNewPoint =
rightCutoff |> E.O.dimap(rc => [|(rc +. eps, 0.)|], _ => [||]);
let truncatedZippedPairsWithNewPoints =
E.A.concatMany([|leftNewPoint, truncatedZippedPairs, rightNewPoint|]);
let truncatedShape =
XYShape.T.fromZippedArray(truncatedZippedPairsWithNewPoints);
make(`Linear, truncatedShape, None);
};
// TODO: This should work with stepwise plots.
let integral = (~cache, t) =>
@ -272,9 +325,9 @@ module Continuous {
let toDiscrete = _ => None;
let normalize = (t: t): t => {
let continuousIntegralSum = integralEndY(~cache=None, t);
scaleBy(~scale=(1. /. continuousIntegralSum), ~knownIntegralSum=Some(1.0), t);
t
|> scaleBy(~scale=1. /. integralEndY(~cache=None, t))
|> updateKnownIntegralSum(Some(1.0));
};
let normalizedToContinuous = t => Some(t); // TODO: this should be normalized
@ -316,40 +369,41 @@ module Discrete = {
let lastY = (t: t) => t |> getShape |> XYShape.T.lastY;
let combineIntegralSums = (combineFn: ((float, float) => option(float)), t1KnownIntegralSum: option(float), t2KnownIntegralSum: option(float)) => {
switch (t1KnownIntegralSum, t2KnownIntegralSum) {
| (None, _)
| (_, None) => None
| (Some(s1), Some(s2)) => combineFn(s1, s2)
};
};
let combine = (combineIntegralSumsFn, fn, t1: DistTypes.discreteShape, t2: DistTypes.discreteShape)
let combine =
(
~knownIntegralSumsFn,
fn,
t1: DistTypes.discreteShape,
t2: DistTypes.discreteShape,
)
: DistTypes.discreteShape => {
let combinedIntegralSum = combineIntegralSums(combineIntegralSumsFn, t1.knownIntegralSum, t2.knownIntegralSum);
let combinedIntegralSum =
Common.combineIntegralSums(
knownIntegralSumsFn,
t1.knownIntegralSum,
t2.knownIntegralSum,
);
make(
XYShape.Combine.combine(
~xsSelection=ALL_XS,
~xToYSelection=XYShape.XtoY.stepwiseIfAtX,
~fn, // stepwiseIfAtX returns option(float), so this fn needs to handle None, which is what the _default0 wrapper is for
~fn=((a, b) => fn(E.O.default(0.0, a), E.O.default(0.0, b))), // stepwiseIfAtX returns option(float), so this fn needs to handle None
t1.xyShape,
t2.xyShape,
),
combinedIntegralSum,
);
};
let _default0 = (fn, a, b) =>
fn(E.O.default(0.0, a), E.O.default(0.0, b));
let reduce = (fn, items) =>
items |> E.A.fold_left(combine((_, _) => None, _default0(fn)), empty);
// a special version of reduce that adds the results (which should be the most common case by far),
// and conveniently also adds the knownIntegralSums.
let reduceAdd = (fn, items) =>
items |> E.A.fold_left(combine((s1, s2) => Some(s1 +. s2), _default0((+.))), empty);
let updateKnownIntegralSum = (knownIntegralSum, t: t): t => ({...t, knownIntegralSum});
let reduce = (~knownIntegralSumsFn=(_, _) => None, fn, discreteShapes): DistTypes.discreteShape =>
discreteShapes
|> E.A.fold_left(combine(~knownIntegralSumsFn, fn), empty);
let updateKnownIntegralSum = (knownIntegralSum, t: t): t => {
...t,
knownIntegralSum,
};
let convolve = (fn, t1: t, t2: t) => {
let t1s = t1 |> getShape;
@ -357,7 +411,12 @@ module Discrete = {
let t1n = t1s |> XYShape.T.length;
let t2n = t2s |> XYShape.T.length;
let combinedIntegralSum = combineIntegralSums((s1, s2) => Some(s1 *. s2), t1.knownIntegralSum, t2.knownIntegralSum);
let combinedIntegralSum =
Common.combineIntegralSums(
(s1, s2) => Some(s1 *. s2),
t1.knownIntegralSum,
t2.knownIntegralSum,
);
let xToYMap = E.FloatFloatMap.empty();
@ -368,8 +427,8 @@ module Discrete = {
let my = t1s.ys[i] *. t2s.ys[j];
let _ = Belt.MutableMap.set(xToYMap, x, cv +. my);
();
}
}
};
};
let rxys = xToYMap |> E.FloatFloatMap.toArray |> XYShape.Zipped.sortByX;
@ -378,25 +437,19 @@ module Discrete = {
make(convolvedShape, combinedIntegralSum);
};
let mapY = (~knownIntegralSumFn=(previousKnownIntegralSum => None), fn, t: t) => {
let mapY = (~knownIntegralSumFn=previousKnownIntegralSum => None, fn, t: t) => {
let u = E.O.bind(_, knownIntegralSumFn);
let yMapFn = shapeMap(XYShape.T.mapY(fn));
t |> yMapFn |> updateKnownIntegralSum(u(t.knownIntegralSum));
};
let scaleBy = (~scale=1.0, ~knownIntegralSum=None, t: t): t =>
t |> mapY((r: float) => r *. scale) |> updateKnownIntegralSum(knownIntegralSum);
let truncate = (leftCutoff: option(float), rightCutoff: option(float), t: t) => {
let truncatedShape =
t
|> getShape
|> XYShape.T.zip
|> XYShape.Zipped.filterByX(x => x >= E.O.default(neg_infinity, leftCutoff) || x <= E.O.default(infinity, rightCutoff))
|> XYShape.T.fromZippedArray;
make(truncatedShape, None);
let scaleBy = (~scale=1.0, t: t): t => {
t
|> mapY((r: float) => r *. scale)
|> updateKnownIntegralSum(
E.O.bind(t.knownIntegralSum, v => Some(scale *. v)),
);
};
module T =
@ -414,7 +467,8 @@ module Discrete = {
)
};
let integralEndY = (~cache, t: t) =>
t.knownIntegralSum |> E.O.default(t |> integral(~cache) |> Continuous.lastY);
t.knownIntegralSum
|> E.O.default(t |> integral(~cache) |> Continuous.lastY);
let minX = shapeFn(XYShape.T.minX);
let maxX = shapeFn(XYShape.T.maxX);
let toDiscreteProbabilityMassFraction = _ => 1.0;
@ -424,9 +478,9 @@ module Discrete = {
let toDiscrete = t => Some(t);
let normalize = (t: t): t => {
let discreteIntegralSum = integralEndY(~cache=None, t);
scaleBy(~scale=(1. /. discreteIntegralSum), ~knownIntegralSum=Some(1.0), t);
t
|> scaleBy(~scale=1. /. integralEndY(~cache=None, t))
|> updateKnownIntegralSum(Some(1.0));
};
let normalizedToContinuous = _ => None;
@ -448,6 +502,21 @@ module Discrete = {
make(clippedShape, None); // if someone needs the sum, they'll have to recompute it
};
let truncate =
(leftCutoff: option(float), rightCutoff: option(float), t: t): t => {
let truncatedShape =
t
|> getShape
|> XYShape.T.zip
|> XYShape.Zipped.filterByX(x =>
x >= E.O.default(neg_infinity, leftCutoff)
|| x <= E.O.default(infinity, rightCutoff)
)
|> XYShape.T.fromZippedArray;
make(truncatedShape, None);
};
let xToY = (f, t) =>
t
|> getShape
@ -477,53 +546,43 @@ module Discrete = {
XYShape.Analysis.getVarianceDangerously(t, mean, getMeanOfSquares);
};
});
};
// TODO: I think this shouldn't assume continuous/discrete are normalized to 1.0, and thus should not need the discreteProbabilityMassFraction being separate.
module Mixed = {
type t = DistTypes.mixedShape;
let make = (~continuous, ~discrete): t => {
continuous,
discrete,
};
let make = (~continuous, ~discrete): t => {continuous, discrete};
let totalLength = (t: t): int => {
let continuousLength = t.continuous |> Continuous.getShape |> XYShape.T.length;
let continuousLength =
t.continuous |> Continuous.getShape |> XYShape.T.length;
let discreteLength = t.discrete |> Discrete.getShape |> XYShape.T.length;
continuousLength + discreteLength;
};
// TODO: Put into scaling module
//let normalizeMixedPoint = (t, f) => f *. discreteProbabilityMassFraction;*/
let scaleBy = (~scale=1.0, {discrete, continuous}: t): t => {
let scaledDiscrete = Discrete.scaleBy(~scale, discrete);
let scaledContinuous = Continuous.scaleBy(~scale, continuous);
make(~discrete=scaledDiscrete, ~continuous=scaledContinuous);
};
//TODO: Warning: This currently computes the integral, which is expensive.
/*let scaleContinuousFn =
({discreteProbabilityMassFraction}: DistTypes.mixedShape, f) =>
f *. (1.0 -. discreteProbabilityMassFraction); */
let toContinuous = ({continuous}: t) => Some(continuous);
let toDiscrete = ({discrete}: t) => Some(discrete);
//TODO: Warning: This currently computes the integral, which is expensive.
let combine = (~knownIntegralSumsFn, fn, t1: t, t2: t) => {
let reducedDiscrete =
[|t1, t2|]
|> E.A.fmap(toDiscrete)
|> E.A.O.concatSomes
|> Discrete.reduce(~knownIntegralSumsFn, fn);
// Normalizes to 1.0.
/*let scaleContinuous = ({discreteProbabilityMassFraction}: t, continuous) =>
// get only the continuous, and scale it to the respective
continuous
|> Continuous.T.scaleToIntegralSum(
~intendedSum=1.0 -. discreteProbabilityMassFraction,
);
let reducedContinuous =
[|t1, t2|]
|> E.A.fmap(toContinuous)
|> E.A.O.concatSomes
|> Continuous.reduce(~knownIntegralSumsFn, fn);
let scaleDiscrete = ({discreteProbabilityMassFraction}: t, disrete) =>
disrete
|> Discrete.T.scaleToIntegralSum(
~intendedSum=discreteProbabilityMassFraction,
);*/
let truncate = (leftCutoff: option(float), rightCutoff: option(float), {discrete, continuous}: t) => {
let truncatedDiscrete = Discrete.truncate(leftCutoff, rightCutoff, discrete);
let truncatedContinuous = Continuous.truncate(leftCutoff, rightCutoff, continuous);
make(~discrete=truncatedDiscrete, ~continuous=truncatedContinuous);
make(~discrete=reducedDiscrete, ~continuous=reducedContinuous);
};
module T =
@ -536,19 +595,40 @@ module Mixed = {
let maxX = ({continuous, discrete}: t) =>
max(Continuous.T.maxX(continuous), Discrete.T.maxX(discrete));
let toShape = (t: t): DistTypes.shape => Mixed(t);
let toContinuous = ({continuous}: t) => Some(continuous);
let toDiscrete = ({discrete}: t) => Some(discrete);
let toContinuous = toContinuous;
let toDiscrete = toDiscrete;
let truncate =
(
leftCutoff: option(float),
rightCutoff: option(float),
{discrete, continuous}: t,
) => {
let truncatedContinuous = Continuous.T.truncate(leftCutoff, rightCutoff, continuous);
let truncatedDiscrete = Discrete.T.truncate(leftCutoff, rightCutoff, discrete);
make(~discrete=truncatedDiscrete, ~continuous=truncatedContinuous);
};
let normalize = (t: t): t => {
let continuousIntegralSum = Continuous.T.Integral.sum(~cache=None, t.continuous);
let discreteIntegralSum = Discrete.T.Integral.sum(~cache=None, t.discrete);
let continuousIntegralSum =
Continuous.T.Integral.sum(~cache=None, t.continuous);
let discreteIntegralSum =
Discrete.T.Integral.sum(~cache=None, t.discrete);
let totalIntegralSum = continuousIntegralSum +. discreteIntegralSum;
let newContinuousSum = continuousIntegralSum /. totalIntegralSum;
let newDiscreteSum = discreteIntegralSum /. totalIntegralSum;
let normalizedContinuous = Continuous.scaleBy(~scale=(1. /. newContinuousSum), ~knownIntegralSum=Some(newContinuousSum), t.continuous);
let normalizedDiscrete = Discrete.scaleBy(~scale=(1. /. newDiscreteSum), ~knownIntegralSum=Some(newDiscreteSum), t.discrete);
let normalizedContinuous =
t.continuous
|> Continuous.scaleBy(~scale=1. /. newContinuousSum)
|> Continuous.updateKnownIntegralSum(Some(newContinuousSum));
let normalizedDiscrete =
t.discrete
|> Discrete.scaleBy(~scale=1. /. newDiscreteSum)
|> Discrete.updateKnownIntegralSum(Some(newDiscreteSum));
make(~continuous=normalizedContinuous, ~discrete=normalizedDiscrete);
};
@ -563,8 +643,10 @@ module Mixed = {
};
let toDiscreteProbabilityMassFraction = ({discrete, continuous}: t) => {
let discreteIntegralSum = Discrete.T.Integral.sum(~cache=None, discrete);
let continuousIntegralSum = Continuous.T.Integral.sum(~cache=None, continuous);
let discreteIntegralSum =
Discrete.T.Integral.sum(~cache=None, discrete);
let continuousIntegralSum =
Continuous.T.Integral.sum(~cache=None, continuous);
let totalIntegralSum = discreteIntegralSum +. continuousIntegralSum;
discreteIntegralSum /. totalIntegralSum;
@ -575,20 +657,25 @@ module Mixed = {
// The easiest way to do this is to simply go by the previous probability masses.
// The cache really isn't helpful here, because we would need two separate caches
let discreteIntegralSum = Discrete.T.Integral.sum(~cache=None, discrete);
let continuousIntegralSum = Continuous.T.Integral.sum(~cache=None, continuous);
let discreteIntegralSum =
Discrete.T.Integral.sum(~cache=None, discrete);
let continuousIntegralSum =
Continuous.T.Integral.sum(~cache=None, continuous);
let totalIntegralSum = discreteIntegralSum +. continuousIntegralSum;
let downsampledDiscrete =
Discrete.T.downsample(
int_of_float(float_of_int(count) *. (discreteIntegralSum /. totalIntegralSum)),
int_of_float(
float_of_int(count) *. (discreteIntegralSum /. totalIntegralSum),
),
discrete,
);
let downsampledContinuous =
Continuous.T.downsample(
int_of_float(
float_of_int(count) *. (continuousIntegralSum /. totalIntegralSum),
float_of_int(count)
*. (continuousIntegralSum /. totalIntegralSum),
),
continuous,
);
@ -596,23 +683,20 @@ module Mixed = {
{discrete: downsampledDiscrete, continuous: downsampledContinuous};
};
let normalizedToContinuous = (t: t) =>
Some(normalize(t).continuous);
let normalizedToContinuous = (t: t) => Some(normalize(t).continuous);
let normalizedToDiscrete = ({discrete} as t: t) =>
Some(normalize(t).discrete);
let normalizedToDiscrete = ({discrete} as t: t) =>
Some(normalize(t).discrete);
let integral =
(
~cache,
{continuous, discrete}: t,
) => {
let integral = (~cache, {continuous, discrete}: t) => {
switch (cache) {
| Some(cache) => cache
| None => {
| None =>
// note: if the underlying shapes aren't normalized, then these integrals won't be either!
let continuousIntegral = Continuous.T.Integral.get(~cache=None, continuous);
let discreteIntegral = Discrete.T.Integral.get(~cache=None, discrete);
let continuousIntegral =
Continuous.T.Integral.get(~cache=None, continuous);
let discreteIntegral =
Discrete.T.Integral.get(~cache=None, discrete);
Continuous.make(
`Linear,
@ -623,7 +707,6 @@ module Mixed = {
),
None,
);
}
};
};
@ -648,14 +731,26 @@ module Mixed = {
// This pipes all ys (continuous and discrete) through fn.
// If mapY is a linear operation, we might be able to update the knownIntegralSums as well;
// if not, they'll be set to None.
let mapY = (~knownIntegralSumFn=(previousIntegralSum => None), fn, {discrete, continuous}: t): t => {
let mapY =
(
~knownIntegralSumFn=previousIntegralSum => None,
fn,
{discrete, continuous}: t,
)
: t => {
let u = E.O.bind(_, knownIntegralSumFn);
let yMappedDiscrete =
discrete |> Discrete.T.mapY(fn) |> Discrete.updateKnownIntegralSum(u(discrete.knownIntegralSum));
discrete
|> Discrete.T.mapY(fn)
|> Discrete.updateKnownIntegralSum(u(discrete.knownIntegralSum));
let yMappedContinuous =
continuous |> Continuous.T.mapY(fn) |> Continuous.updateKnownIntegralSum(u(continuous.knownIntegralSum));
continuous
|> Continuous.T.mapY(fn)
|> Continuous.updateKnownIntegralSum(
u(continuous.knownIntegralSum),
);
{
discrete: yMappedDiscrete,
@ -668,34 +763,55 @@ module Mixed = {
let continuousMean = Continuous.T.mean(continuous);
// the combined mean is the weighted sum of the two:
let discreteIntegralSum = Discrete.T.Integral.sum(~cache=None, discrete);
let continuousIntegralSum = Continuous.T.Integral.sum(~cache=None, continuous);
let discreteIntegralSum =
Discrete.T.Integral.sum(~cache=None, discrete);
let continuousIntegralSum =
Continuous.T.Integral.sum(~cache=None, continuous);
let totalIntegralSum = discreteIntegralSum +. continuousIntegralSum;
(discreteMean *. discreteIntegralSum +. continuousMean *. continuousIntegralSum) /. totalIntegralSum;
(
discreteMean
*. discreteIntegralSum
+. continuousMean
*. continuousIntegralSum
)
/. totalIntegralSum;
};
let variance = ({discrete, continuous} as t: t): float => {
// the combined mean is the weighted sum of the two:
let discreteIntegralSum = Discrete.T.Integral.sum(~cache=None, discrete);
let continuousIntegralSum = Continuous.T.Integral.sum(~cache=None, continuous);
let discreteIntegralSum =
Discrete.T.Integral.sum(~cache=None, discrete);
let continuousIntegralSum =
Continuous.T.Integral.sum(~cache=None, continuous);
let totalIntegralSum = discreteIntegralSum +. continuousIntegralSum;
let getMeanOfSquares = ({discrete, continuous} as t: t) => {
let discreteMean = discrete |> Discrete.shapeMap(XYShape.Analysis.squareXYShape) |> Discrete.T.mean;
let continuousMean = continuous |> XYShape.Analysis.getMeanOfSquaresContinuousShape;
(discreteMean *. discreteIntegralSum +. continuousMean *. continuousIntegralSum) /. totalIntegralSum
let discreteMean =
discrete
|> Discrete.shapeMap(XYShape.Analysis.squareXYShape)
|> Discrete.T.mean;
let continuousMean =
continuous |> XYShape.Analysis.getMeanOfSquaresContinuousShape;
(
discreteMean
*. discreteIntegralSum
+. continuousMean
*. continuousIntegralSum
)
/. totalIntegralSum;
};
switch (discreteIntegralSum /. totalIntegralSum) {
| 1.0 => Discrete.T.variance(discrete)
| 0.0 => Continuous.T.variance(continuous)
| _ => XYShape.Analysis.getVarianceDangerously(t, mean, getMeanOfSquares)
| _ =>
XYShape.Analysis.getVarianceDangerously(t, mean, getMeanOfSquares)
};
};
});
let convolve = (fn: ((float, float) => float), t1: t, t2: t): t => {
let convolve = (fn: (float, float) => float, t1: t, t2: t): t => {
// Discrete convolution can cause a huge increase in the number of samples,
// so we'll first downsample.
@ -713,16 +829,21 @@ module Mixed = {
// continuous (*) continuous => continuous, but also
// discrete (*) continuous => continuous (and vice versa). We have to take care of all combos and then combine them:
let ccConvResult = Continuous.convolve(fn, t1d.continuous, t2d.continuous);
let dcConvResult = Continuous.convolveWithDiscrete(fn, t2d.continuous, t1d.discrete);
let cdConvResult = Continuous.convolveWithDiscrete(fn, t1d.continuous, t2d.discrete);
let continuousConvResult = Continuous.reduce((+.), [|ccConvResult, dcConvResult, cdConvResult|]);
let ccConvResult =
Continuous.convolve(fn, t1d.continuous, t2d.continuous);
let dcConvResult =
Continuous.convolveWithDiscrete(fn, t2d.continuous, t1d.discrete);
let cdConvResult =
Continuous.convolveWithDiscrete(fn, t1d.continuous, t2d.discrete);
let continuousConvResult =
Continuous.reduce((+.), [|ccConvResult, dcConvResult, cdConvResult|]);
// ... finally, discrete (*) discrete => discrete, obviously:
let discreteConvResult = Discrete.convolve(fn, t1d.discrete, t2d.discrete);
let discreteConvResult =
Discrete.convolve(fn, t1d.discrete, t2d.discrete);
{discrete: discreteConvResult, continuous: continuousConvResult};
}
};
};
module Shape = {
@ -741,43 +862,31 @@ module Shape = {
| Continuous(m) => Continuous(fn3(m))
};
let toMixed = mapToAll((
m => m,
d => Mixed.make(~discrete=d, ~continuous=Continuous.empty),
c => Mixed.make(~discrete=Discrete.empty, ~continuous=c),
));
let toMixed =
mapToAll((
m => m,
d => Mixed.make(~discrete=d, ~continuous=Continuous.empty),
c => Mixed.make(~discrete=Discrete.empty, ~continuous=c),
));
let convolve = (fn, t1: t, t2: t): t => {
Mixed(Mixed.convolve(fn, toMixed(t1), toMixed(t2)));
};
let downsample = (~cache=None, i, t) =>
fmap((
Mixed.T.downsample(i),
Discrete.T.downsample(i),
Continuous.T.downsample(i),
), t);
let normalize =
fmap((
Mixed.T.normalize,
Discrete.T.normalize,
Continuous.T.normalize,
));
let truncate (leftCutoff, rightCutoff, t): t =
fmap((
Mixed.truncate(leftCutoff, rightCutoff),
Discrete.truncate(leftCutoff, rightCutoff),
Continuous.truncate(leftCutoff, rightCutoff),
), t);
let combine = (~knownIntegralSumsFn=(_, _) => None, fn, t1: t, t2: t) =>
switch ((t1, t2)) {
| (Continuous(m1), Continuous(m2)) => DistTypes.Continuous(Continuous.combine(~knownIntegralSumsFn, fn, m1, m2))
| (Discrete(m1), Discrete(m2)) => DistTypes.Discrete(Discrete.combine(~knownIntegralSumsFn, fn, m1, m2))
| (m1, m2) => {
DistTypes.Mixed(Mixed.combine(~knownIntegralSumsFn, fn, toMixed(m1), toMixed(m2)))
}
};
module T =
Dist({
type t = DistTypes.shape;
type integral = DistTypes.continuousShape;
let xToY = (f: float) =>
mapToAll((
Mixed.T.xToY(f),
@ -789,9 +898,31 @@ module Shape = {
let toContinuous = t => None;
let toDiscrete = t => None;
let downsample = (~cache=None, i, t) => t;
let toDiscreteProbabilityMassFraction = t => 0.0;
let normalize = t => t;
let downsample = (~cache=None, i, t) =>
fmap(
(
Mixed.T.downsample(i),
Discrete.T.downsample(i),
Continuous.T.downsample(i),
),
t,
);
let truncate = (leftCutoff, rightCutoff, t): t =>
fmap(
(
Mixed.T.truncate(leftCutoff, rightCutoff),
Discrete.T.truncate(leftCutoff, rightCutoff),
Continuous.T.truncate(leftCutoff, rightCutoff),
),
t,
);
let toDiscreteProbabilityMassFraction = t => 0.0;
let normalize =
fmap((Mixed.T.normalize, Discrete.T.normalize, Continuous.T.normalize));
let toContinuous =
mapToAll((
Mixed.T.toContinuous,
@ -853,7 +984,7 @@ module Shape = {
));
};
let maxX = mapToAll((Mixed.T.maxX, Discrete.T.maxX, Continuous.T.maxX));
let mapY = (~knownIntegralSumFn=(previousIntegralSum => None), fn) =>
let mapY = (~knownIntegralSumFn=previousIntegralSum => None, fn) =>
fmap((
Mixed.T.mapY(~knownIntegralSumFn, fn),
Discrete.T.mapY(~knownIntegralSumFn, fn),
@ -935,14 +1066,18 @@ module DistPlus = {
let toDiscrete = shapeFn(Shape.T.toDiscrete);
let normalize = (t: t): t => {
let normalizedShape =
t |> toShape |> Shape.T.normalize;
t |> updateShape(normalizedShape);
let normalizedShape = t |> toShape |> Shape.T.normalize;
t |> updateShape(normalizedShape);
// TODO: also adjust for domainIncludedProbabilityMass here.
};
let truncate = (leftCutoff, rightCutoff, t: t): t => {
let truncatedShape = t |> toShape |> Shape.T.truncate(leftCutoff, rightCutoff);
t |> updateShape(truncatedShape);
};
// TODO: replace this with
let normalizedToContinuous = (t: t) => {
t
@ -980,7 +1115,13 @@ module DistPlus = {
let downsample = (~cache=None, i, t): t =>
updateShape(t |> toShape |> Shape.T.downsample(i), t);
// todo: adjust for limit, maybe?
let mapY = (~knownIntegralSumFn=(previousIntegralSum => None), fn, {shape, _} as t: t): t =>
let mapY =
(
~knownIntegralSumFn=previousIntegralSum => None,
fn,
{shape, _} as t: t,
)
: t =>
Shape.T.mapY(~knownIntegralSumFn, fn, shape) |> updateShape(_, t);
let integralEndY = (~cache as _, t: t) =>

View File

@ -1,13 +1,13 @@
let truncateIfShould =
let downsampleIfShould =
(
{recommendedLength, shouldTruncate}: RenderTypes.DistPlusRenderer.inputs,
{recommendedLength, shouldDownsample}: RenderTypes.DistPlusRenderer.inputs,
outputs: RenderTypes.ShapeRenderer.Combined.outputs,
dist,
) => {
let willTruncate =
shouldTruncate
let willDownsample =
shouldDownsample
&& RenderTypes.ShapeRenderer.Combined.methodUsed(outputs) == `Sampling;
willTruncate ? dist |> Distributions.DistPlus.T.truncate(recommendedLength) : dist;
willDownsample ? dist |> Distributions.DistPlus.T.downsample(recommendedLength) : dist;
};
let run =
@ -21,7 +21,7 @@ let run =
~guesstimatorString=Some(inputs.distPlusIngredients.guesstimatorString),
(),
)
|> Distributions.DistPlus.T.scaleToIntegralSum(~intendedSum=1.0);
|> Distributions.DistPlus.T.normalize;
let outputs =
ShapeRenderer.run({
samplingInputs: inputs.samplingInputs,
@ -32,6 +32,6 @@ let run =
});
let shape = outputs |> RenderTypes.ShapeRenderer.Combined.getShape;
let dist =
shape |> E.O.fmap(toDist) |> E.O.fmap(truncateIfShould(inputs, outputs));
shape |> E.O.fmap(toDist) |> E.O.fmap(downsampleIfShould(inputs, outputs));
RenderTypes.DistPlusRenderer.Outputs.make(outputs, dist);
};

View File

@ -75,7 +75,7 @@ module ShapeRenderer = {
module DistPlusRenderer = {
let defaultRecommendedLength = 10000;
let defaultShouldTruncate = true;
let defaultShouldDownsample = true;
type ingredients = {
guesstimatorString: string,
domain: DistTypes.domain,
@ -85,7 +85,7 @@ module DistPlusRenderer = {
distPlusIngredients: ingredients,
samplingInputs: ShapeRenderer.Sampling.inputs,
recommendedLength: int,
shouldTruncate: bool,
shouldDownsample: bool,
};
module Ingredients = {
let make =
@ -105,7 +105,7 @@ module DistPlusRenderer = {
(
~samplingInputs=ShapeRenderer.Sampling.Inputs.empty,
~recommendedLength=defaultRecommendedLength,
~shouldTruncate=defaultShouldTruncate,
~shouldDownsample=defaultShouldDownsample,
~distPlusIngredients,
(),
)
@ -113,7 +113,7 @@ module DistPlusRenderer = {
distPlusIngredients,
samplingInputs,
recommendedLength,
shouldTruncate,
shouldDownsample,
};
type outputs = {
shapeRenderOutputs: ShapeRenderer.Combined.outputs,

View File

@ -154,16 +154,17 @@ module MathAdtToDistDst = {
weights: option(array(float)),
) => {
let weights = weights |> E.O.default([||]);
let dists =
/*let dists: =
args
|> E.A.fmap(
fun
| Ok(a) => a
| Error(e) => Error(e)
);
);*/
let firstWithError = dists |> Belt.Array.getBy(_, Belt.Result.isError);
let withoutErrors = dists |> E.A.fmap(E.R.toOption) |> E.A.O.concatSomes;
let firstWithError = args |> Belt.Array.getBy(_, Belt.Result.isError);
let withoutErrors = args |> E.A.fmap(E.R.toOption) |> E.A.O.concatSomes;
switch (firstWithError) {
| Some(Error(e)) => Error(e)
@ -174,16 +175,16 @@ module MathAdtToDistDst = {
|> E.A.fmapi((index, t) => {
let w = weights |> E.A.get(_, index) |> E.O.default(1.0);
`Operation(`ScaleBy(`Multiply, t, `DistData(`Symbolic(`Float(w)))))
`Operation(`ScaleOperation(`Multiply, t, `DistData(`Symbolic(`Float(w)))))
});
let pointwiseSum = components
|> Js.Array.sliceFrom(1)
|> E.A.fold_left((acc, x) => {
`PointwiseSum(acc, x)
`Operation(`PointwiseOperation(`Add, acc, x))
}, E.A.unsafe_get(components, 0))
Ok(`Normalize(pointwiseSum))
Ok(`Operation(`Normalize(pointwiseSum)))
}
};
};
@ -254,21 +255,21 @@ module MathAdtToDistDst = {
args
|> E.A.fmap(functionParser)
|> (fun
| [|Ok(l), Ok(r)|] => Ok(`Combination(l, r, `AddOperation))
| [|Ok(l), Ok(r)|] => Ok(`Operation(`StandardOperation(`Add, l, r)))
| _ => Error("Addition needs two operands"))
}
| Fn({name: "subtract", args}) => {
args
|> E.A.fmap(functionParser)
|> (fun
| [|Ok(l), Ok(r)|] => Ok(`Combination(l, r, `SubtractOperation))
| [|Ok(l), Ok(r)|] => Ok(`Operation(`StandardOperation(`Subtract, l, r)))
| _ => Error("Subtraction needs two operands"))
}
| Fn({name: "multiply", args}) => {
args
|> E.A.fmap(functionParser)
|> (fun
| [|Ok(l), Ok(r)|] => Ok(`Combination(l, r, `MultiplyOperation))
| [|Ok(l), Ok(r)|] => Ok(`Operation(`StandardOperation(`Multiply, l, r)))
| _ => Error("Multiplication needs two operands"))
}
| Fn({name: "divide", args}) => {
@ -276,28 +277,37 @@ module MathAdtToDistDst = {
|> E.A.fmap(functionParser)
|> (fun
| [|Ok(l), Ok(`DistData(`Symbolic(`Float(0.0))))|] => Error("Division by zero")
| [|Ok(l), Ok(r)|] => Ok(`Combination(l, r, `DivideOperation))
| [|Ok(l), Ok(r)|] => Ok(`Operation(`StandardOperation(`Divide, l, r)))
| _ => Error("Division needs two operands"))
}
| Fn({name: "pow", args}) => {
args
|> E.A.fmap(functionParser)
|> (fun
| [|Ok(l), Ok(r)|] => Ok(`Combination(l, r, `ExponentiateOperation))
| [|Ok(l), Ok(r)|] => Ok(`Operation(`StandardOperation(`Exponentiate, l, r)))
| _ => Error("Division needs two operands")
| _ => Error("Exponentiations needs two operands"))
}
| Fn({name: "leftTruncate", args}) => {
args
|> E.A.fmap(functionParser)
|> (fun
| [|Ok(l), Ok(`DistData(`Symbolic(`Float(r))))|] => Ok(`LeftTruncate(l, r))
| [|Ok(d), Ok(`DistData(`Symbolic(`Float(lc))))|] => Ok(`Operation(`Truncate(Some(lc), None, d)))
| _ => Error("leftTruncate needs two arguments: the expression and the cutoff"))
}
| Fn({name: "rightTruncate", args}) => {
args
|> E.A.fmap(functionParser)
|> (fun
| [|Ok(l), Ok(`DistData(`Symbolic(`Float(r))))|] => Ok(`RightTruncate(l, r))
| [|Ok(d), Ok(`DistData(`Symbolic(`Float(rc))))|] => Ok(`Operation(`Truncate(None, Some(rc), d)))
| _ => Error("rightTruncate needs two arguments: the expression and the cutoff"))
}
| Fn({name: "truncate", args}) => {
args
|> E.A.fmap(functionParser)
|> (fun
| [|Ok(d), Ok(`DistData(`Symbolic(`Float(lc)))), Ok(`DistData(`Symbolic(`Float(rc))))|] => Ok(`Operation(`Truncate(Some(lc), Some(rc), d)))
// TODO: allow on-the-fly evaluations of FloatFromDists to be used as cutoff arguments here.
| _ => Error("rightTruncate needs two arguments: the expression and the cutoff"))
}
| Fn({name}) => Error(name ++ ": function not supported")

View File

@ -1,69 +1,60 @@
/* 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 = [
type 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 = [
];
type standardOperation = [
| `Add
| `Multiply
| `Subtract
| `Divide
| `Exponentiate
] and pointwiseOperation = [
| `Add
| `Multiply
] and scaleOperation = [
| `Multiply
| `Log
];
type pointwiseOperation = [ | `Add | `Multiply];
type scaleOperation = [ | `Multiply | `Exponentiate | `Log];
type distToFloatOperation = [ | `Pdf(float) | `Inv(float) | `Mean | `Sample];
/* TreeNodes are either Data (i.e. symbolic or rendered distributions) or Operations. */
type treeNode = [
| `DistData(distData) // a leaf node that describes a distribution
| `Operation(operation) // an operation on two child nodes
]
and scaleBy = treeNode and leftCutoff = option(float) and rightCutoff = option(float)
and distToFloatOperation = [
| `Pdf(float)
| `Cdf(float)
| `Inv(float)
| `Sample
and operation = [
| // binary operations
`StandardOperation(
standardOperation,
treeNode,
treeNode,
)
// unary operations
| `PointwiseOperation(pointwiseOperation, treeNode, treeNode) // always evaluates to `DistData(`RenderedShape(...))
| `ScaleOperation(scaleOperation, treeNode, treeNode) // always evaluates to `DistData(`RenderedShape(...))
| `Render(treeNode) // always evaluates to `DistData(`RenderedShape(...))
| `Truncate // always evaluates to `DistData(`RenderedShape(...))
(
option(float),
option(float),
treeNode,
) // leftCutoff and rightCutoff
| `Normalize // always evaluates to `DistData(`RenderedShape(...))
// leftCutoff and rightCutoff
(
treeNode,
)
| `FloatFromDist // always evaluates to `DistData(`RenderedShape(...))
// leftCutoff and rightCutoff
(
distToFloatOperation,
treeNode,
)
];
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. */
@ -154,207 +145,328 @@ module TreeNode = {
};
};
let evaluateNumerically = (standardOp, renderParams, t1, t2) => {
let evaluateNumerically = (standardOp, operationToDistData, t1, t2) => {
let func = funcFromOp(standardOp);
// TODO: downsample the two shapes
let renderedShape1 = t1 |> renderToShape(renderParams);
let renderedShape2 = t2 |> renderToShape(renderParams);
// force rendering into shapes
let renderedShape1 = operationToDistData(`Render(t1));
let renderedShape2 = operationToDistData(`Render(t2));
// 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))))
switch (renderedShape1, renderedShape2) {
| (
Ok(`DistData(`RenderedShape(s1))),
Ok(`DistData(`RenderedShape(s2))),
) =>
Ok(
`DistData(
`RenderedShape(Distributions.Shape.convolve(func, s1, s2)),
),
)
| (Error(e1), _) => Error(e1)
| (_, Error(e2)) => Error(e2)
| _ => Error("Could not render shapes.")
};
};
let evaluateToDistData =
(standardOp: standardOperation, renderParams, t1: t, t2: t): result(treeNode, string) =>
(standardOp: standardOperation, operationToDistData, 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),
| `Operation(_) =>
// if not, run the convolution
evaluateNumerically(standardOp, operationToDistData, 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) ));
| `Multiply => ( *. )
| `Exponentiate => ( ** )
| `Log => ((a, b) => log(a) /. log(b));
let knownIntegralSumFnFromOp =
fun
| `Multiply => (a, b) => Some(a *. b)
| `Multiply => ((a, b) => Some(a *. b))
| `Exponentiate => ((_, _) => None)
| `Log => ((_, _) => None);
let evaluateToDistData = (scaleOp, renderParams, t, scaleBy) => {
let evaluateToDistData = (scaleOp, operationToDistData, t, scaleBy) => {
// scaleBy has to be a single float, otherwise we'll return an error.
let fn = fnFromOp(scaleOp);
let knownIntegralSumFn = knownIntegralSumFnFromOp(scaleOp);
let renderedShape = t |> renderToShape(renderParams);
let scaleByMeanValue = mean(renderParams, scaleBy);
switch ((renderedShape, scaleByMeanValue)) {
let renderedShape = operationToDistData(`Render(t));
switch (renderedShape, scaleBy) {
| (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))))
}
| (
Ok(`DistData(`RenderedShape(rs))),
`DistData(`Symbolic(`Float(sm))),
) =>
Ok(
`DistData(
`RenderedShape(
Distributions.Shape.T.mapY(
~knownIntegralSumFn=knownIntegralSumFn(sm),
fn(sm),
rs,
),
),
),
)
| (_, _) => Error("Can only scale by float values.")
};
};
};
module PointwiseOperation = {
let funcFromOp: (pointwiseOperation => ((float, float) => float)) =
fun
| `Add => (+.)
| `Multiply => ( *. );
let pointwiseAdd = (operationToDistData, t1, t2) => {
let renderedShape1 = operationToDistData(`Render(t1));
let renderedShape2 = operationToDistData(`Render(t2));
let evaluateToDistData = (pointwiseOp, renderParams, t1, t2) => {
let func = funcFromOp(pointwiseOp);
let renderedShape1 = t1 |> renderToShape(renderParams);
let renderedShape2 = t2 |> renderToShape(renderParams);
switch ((renderedShape1, renderedShape2)) {
| (Error(e1), _) => Error(e1)
| (_, Error(e2)) => Error(e2)
| (Ok(`DistData(`RenderedShape(rs1))), Ok(`DistData(`RenderedShape(rs2)))) => Ok(`DistData(`RenderedShape(Distributions.Shape.combine(~knownIntegralSumsFn=(a, b) => Some(a +. b), (+.), rs1, rs2))))
| _ => Error("Could not perform pointwise addition.")
};
};
// TODO: figure out integral, diff between pointwiseAdd and pointwiseProduct and other stuff
// Distributions.Shape.reduce(func, renderedShape1, renderedShape2);
let pointwiseMultiply = (operationToDistData, t1, t2) => {
// TODO: construct a function that we can easily sample from, to construct
// a RenderedShape. Use the xMin and xMax of the rendered shapes to tell the sampling function where to look.
Error("Pointwise multiplication not yet supported.");
};
Error("Pointwise operations currently not supported.")
let evaluateToDistData = (pointwiseOp, operationToDistData, t1, t2) => {
switch (pointwiseOp) {
| `Add => pointwiseAdd(operationToDistData, t1, t2)
| `Multiply => pointwiseMultiply(operationToDistData, t1, t2)
}
};
};
module Truncate = {
module Simplify = {
let tryTruncatingNothing: simplifier = fun
| `Operation(`Truncate(None, None, `DistData(d))) => Ok(`DistData(d))
| t => Ok(t);
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 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));
let originalTreeNode =
`Operation(`Truncate((leftCutoff, rightCutoff, t)));
originalTreeNode
|> tryTruncatingNothing
|> E.R.bind(_, tryTruncatingUniform);
originalTreeNode
|> tryTruncatingNothing
|> E.R.bind(_, tryTruncatingUniform);
};
};
let evaluateNumerically = (leftCutoff, rightCutoff, renderParams, t) => {
let evaluateNumerically =
(leftCutoff, rightCutoff, operationToDistData, 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);
let renderedShape = operationToDistData(`Render(t));
E.R.bind(renderedShape, rs => {
let truncatedShape = rs |> Distributions.Shape.truncate(leftCutoff, rightCutoff);
switch (renderedShape) {
| Ok(`DistData(`RenderedShape(rs))) =>
let truncatedShape =
rs |> Distributions.Shape.T.truncate(leftCutoff, rightCutoff);
Ok(`DistData(`RenderedShape(rs)));
});
| Error(e1) => Error(e1)
| _ => Error("Could not truncate distribution.")
};
};
let evaluateToDistData = (leftCutoff: option(float), rightCutoff: option(float), renderParams, t: treeNode): result(treeNode, string) => {
let evaluateToDistData =
(
leftCutoff: option(float),
rightCutoff: option(float),
operationToDistData,
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),
| `Operation(_) =>
evaluateNumerically(
leftCutoff,
rightCutoff,
operationToDistData,
t,
),
); // if not, run the convolution
};
};
};
module Normalize = {
let rec evaluateToDistData = (renderParams, t: treeNode): result(treeNode, string) => {
let rec evaluateToDistData =
(operationToDistData, t: treeNode): result(treeNode, string) => {
switch (t) {
| `DistData(`Symbolic(_)) => Ok(t)
| `DistData(`RenderedShape(s)) => {
let normalized = Distributions.Shape.normalize(s);
| `DistData(`RenderedShape(s)) =>
let normalized = Distributions.Shape.T.normalize(s);
Ok(`DistData(`RenderedShape(normalized)));
}
| `Operation(op) => E.R.bind(renderParams.operationToDistData(renderParams.sampleCount, op), evaluateToDistData(renderParams))
}
}
| `Operation(op) =>
E.R.bind(
operationToDistData(op),
evaluateToDistData(operationToDistData),
)
};
};
};
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 value =
switch (distToFloatOp) {
| `Pdf(f) => Ok(SymbolicDist.GenericDistFunctions.pdf(f, s))
| `Inv(f) => Ok(SymbolicDist.GenericDistFunctions.inv(f, s))
| `Sample => Ok(SymbolicDist.GenericDistFunctions.sample(s))
| `Mean => SymbolicDist.GenericDistFunctions.mean(s)
};
E.R.bind(value, v => Ok(`DistData(`Symbolic(`Float(v)))));
};
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 evaluateFromRenderedShape =
(distToFloatOp: distToFloatOperation, rs: DistTypes.shape)
: result(treeNode, string) => {
Ok(`DistData(`Symbolic(`Float(Distributions.Shape.T.mean(rs)))));
};
let rec evaluateToDistData = (distToFloatOp: distToFloatOperation, renderParams, t: treeNode): result(treeNode, string) => {
let rec evaluateToDistData =
(
distToFloatOp: distToFloatOperation,
operationToDistData,
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))
}
}
| `DistData(`RenderedShape(rs)) =>
evaluateFromRenderedShape(distToFloatOp, rs)
| `Operation(op) =>
E.R.bind(
operationToDistData(op),
evaluateToDistData(distToFloatOp, operationToDistData),
)
};
};
};
module Render = {
let evaluateToRenderedShape = (renderParams, t: treeNode): result(t, string) => {
E.R.bind(renderToShape(renderParams, t), rs => Ok(`DistData(`RenderedShape(rs))));
}
let rec evaluateToRenderedShape =
(operationToDistData: operation => result(t, string), sampleCount: int, t: treeNode)
: result(t, string) => {
switch (t) {
| `DistData(`RenderedShape(s)) => Ok(`DistData(`RenderedShape(s))) // already a rendered shape, we're done here
| `DistData(`Symbolic(d)) =>
switch (d) {
| `Float(v) =>
Ok(
`DistData(
`RenderedShape(
Discrete(
Distributions.Discrete.make(
{xs: [|v|], ys: [|1.0|]},
Some(1.0),
),
),
),
),
)
| _ =>
let xs =
SymbolicDist.GenericDistFunctions.interpolateXs(
~xSelection=`ByWeight,
d,
sampleCount,
);
let ys =
xs |> E.A.fmap(x => SymbolicDist.GenericDistFunctions.pdf(x, d));
Ok(
`DistData(
`RenderedShape(
Continuous(
Distributions.Continuous.make(
`Linear,
{xs, ys},
Some(1.0),
),
),
),
),
);
}
| `Operation(op) =>
E.R.bind(
operationToDistData(op),
evaluateToRenderedShape(operationToDistData, sampleCount),
)
};
};
};
let rec operationToDistData =
(sampleCount: int, op: operation): result(t, string) => {
(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
standardOp,
operationToDistData(sampleCount),
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,
operationToDistData(sampleCount),
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)
ScaleOperation.evaluateToDistData(
scaleOp,
operationToDistData(sampleCount),
t,
scaleBy,
)
| `Truncate(leftCutoff, rightCutoff, t) =>
Truncate.evaluateToDistData(
leftCutoff,
rightCutoff,
operationToDistData(sampleCount),
t,
)
| `FloatFromDist(distToFloatOp, t) =>
FloatFromDist.evaluateToDistData(distToFloatOp, operationToDistData(sampleCount), t)
| `Normalize(t) => Normalize.evaluateToDistData(operationToDistData(sampleCount), t)
| `Render(t) =>
Render.evaluateToRenderedShape(operationToDistData(sampleCount), sampleCount, t)
};
};
@ -372,7 +484,8 @@ module TreeNode = {
};
let rec toString = (t: t): string => {
let stringFromStandardOperation = fun
let stringFromStandardOperation =
fun
| `Add => " + "
| `Subtract => " - "
| `Multiply => " * "
@ -384,31 +497,53 @@ module TreeNode = {
| `Add => " .+ "
| `Multiply => " .* ";
let stringFromFloatFromDistOperation =
fun
| `Pdf(f) => "pdf(x=$f, "
| `Inv(f) => "inv(c=$f, "
| `Sample => "sample("
| `Mean => "mean(";
switch (t) {
| `DistData(`Symbolic(d)) => SymbolicDist.GenericDistFunctions.toString(d)
| `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(`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(`FloatFromDist(floatFromDistOp, t)) => stringFromFloatFromDistOperation(floatFromDistOp) ++ toString(t) ++ ")"
| `Operation(`Truncate(lc, rc, t)) =>
"truncate("
++ toString(t)
++ ", "
++ E.O.dimap(Js.Float.toString, () => "-inf", lc)
++ ", "
++ E.O.dimap(Js.Float.toString, () => "inf", rc)
++ ")"
| `Operation(`Render(t)) => toString(t)
}
};
};
};
let toShape = (sampleCount: int, treeNode: treeNode) => {
let renderResult = TreeNode.toDistData(`Operation(`Render(treeNode)), sampleCount);
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(`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)
}
| Error(message) => E.O.toExn("No shape found, error: " ++ message, None)
};
};
let toString = (treeNode: treeNode) =>
TreeNode.toString(treeNode);

View File

@ -22,7 +22,7 @@ let propValue = (t: Prop.Value.t) => {
RenderTypes.DistPlusRenderer.make(
~distPlusIngredients=r,
~recommendedLength=10000,
~shouldTruncate=true,
~shouldDownsample=true,
(),
)
|> DistPlusRenderer.run