Simple minX and maxX for distributions
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@ -16,15 +16,13 @@ let mixedDist =
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)
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|> GenericDistribution.renderIfNeeded(~sampleCount=1000);
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// "mm(floor(uniform(30,35)), normal(50,20), [.25,.5])",
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let timeDist =
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GenericDistribution.make(
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~generationSource=
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GuesstimatorString(
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"mm(floor(uniform(40, 50)), normal(50,10), [.5,.5])",
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),
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~generationSource=GuesstimatorString("floor(normal(30,2))"),
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~probabilityType=Pdf,
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~domain=Complete,
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~unit=TimeDistribution({zero: MomentRe.momentNow(), unit: `years}),
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~unit=TimeDistribution({zero: MomentRe.momentNow(), unit: `days}),
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(),
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)
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|> GenericDistribution.renderIfNeeded(~sampleCount=1000);
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@ -63,38 +63,55 @@ let continuousComponent = (p: DistributionTypes.pointsType) =>
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| Continuous(c) => Some(c)
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};
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module Cont = {
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[@react.component]
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let make = (~continuous, ~onHover, ~timeScale) => {
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let chart =
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React.useMemo1(
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() =>
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<CdfChart__Plain
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primaryDistribution=continuous
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color={`hex("333")}
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onHover
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timeScale
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/>,
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[|continuous|],
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);
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chart;
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let discreteScaleFactor = (p: DistributionTypes.pointsType) =>
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switch (p) {
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| Mixed(mixedShape) => Some(mixedShape.discreteProbabilityMassFraction)
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| Discrete(_) => None
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| Continuous(_) => None
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};
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};
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module Shapee = {
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[@react.component]
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let make = (~shape: DistributionTypes.pointsType, ~timeScale, ~onHover) => {
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let continuous = continuousComponent(shape);
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let discrete = discreteComponent(shape);
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let discreteScaleFactor = shape |> discreteScaleFactor;
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let continuous =
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continuousComponent(shape)
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|> E.O.bind(
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_,
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Shape.Continuous.scalePdf(
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~scaleTo=
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discreteScaleFactor
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|> E.O.fmap(r => 1. -. r)
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|> E.O.default(1.0),
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),
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);
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let discrete =
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discreteComponent(shape)
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|> E.O.fmap(
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Shape.Discrete.scaleYToTotal(
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discreteScaleFactor |> E.O.default(1.0),
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),
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);
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let minX = {
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Shape.Any.minX(shape);
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};
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let maxX = {
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Shape.Any.maxX(shape);
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};
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<div>
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{continuous
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|> E.O.React.fmapOrNull(continuous =>
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<Cont continuous onHover timeScale />
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)}
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{discrete
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|> E.O.React.fmapOrNull(r =>
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r |> Shape.Discrete.scaleYToTotal(0.3) |> Shape.Discrete.render
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<CdfChart__Plain
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primaryDistribution=continuous
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minX
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maxX
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?discrete
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color={`hex("333")}
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onHover
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timeScale
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/>
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)}
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{discrete |> E.O.React.fmapOrNull(Shape.Discrete.render)}
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</div>;
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};
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};
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@ -105,12 +122,19 @@ module GenericDist = {
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let (x, setX) = React.useState(() => 0.);
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let timeScale =
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genericDistribution.unit |> DistributionTypes.DistributionUnit.toJson;
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<div>
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{genericDistribution
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let chart =
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React.useMemo1(
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() => {
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genericDistribution
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|> DistributionTypes.shape
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|> E.O.React.fmapOrNull(shape => {
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<Shapee shape timeScale onHover={r => setX(_ => r)} />
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})}
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})
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},
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[|genericDistribution|],
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);
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<div>
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chart
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<table className="table-auto">
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<thead>
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<tr>
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@ -20,10 +20,26 @@ let renderIfNeeded =
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switch (t.generationSource) {
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| GuesstimatorString(s) =>
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let shape = Guesstimator.stringToMixedShape(~string=s, ~sampleCount, ());
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shape
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|> E.O.fmap((shape: DistributionTypes.mixedShape) =>
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let newShape =
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switch (shape) {
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| Some({
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continuous: {xs: [||], ys: [||]},
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discrete: {xs: [||], ys: [||]},
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}) =>
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None
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| Some({continuous, discrete: {xs: [||], ys: [||]}}) =>
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Some(Continuous(continuous))
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| Some({continuous: {xs: [||], ys: [||]}, discrete}) =>
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Some(Discrete(discrete))
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| Some(shape) => Some(Mixed(shape))
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| _ => None
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};
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Js.log(newShape);
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newShape
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|> E.O.fmap((shape: DistributionTypes.pointsType) =>
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make(
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~generationSource=Shape(Mixed(shape)),
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~generationSource=Shape(shape),
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~probabilityType=Cdf,
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~domain=t.domain,
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~unit=t.unit,
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@ -52,7 +68,7 @@ let normalizePdf = (t: DistributionTypes.pointsType) => {
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switch (t) {
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| Mixed({continuous, discrete, discreteProbabilityMassFraction}) =>
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continuous
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|> Shape.Continuous.normalizePdf
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|> Shape.Continuous.scalePdf(~scaleTo=1.0)
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|> E.O.fmap(r =>
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Mixed({
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continuous: r,
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@ -63,7 +79,7 @@ let normalizePdf = (t: DistributionTypes.pointsType) => {
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| Discrete(d) => Some(Discrete(d |> Shape.Discrete.scaleYToTotal(1.0)))
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| Continuous(continuousShape) =>
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continuousShape
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|> Shape.Continuous.normalizePdf
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|> Shape.Continuous.scalePdf(~scaleTo=1.0)
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|> E.O.fmap(r => Continuous(r))
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};
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};
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@ -17,6 +17,10 @@ module XYShape = {
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{"xs": t.xs, "ys": t.ys};
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};
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let minX = (t: t) => t.xs |> E.A.get(_, 0);
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// TODO: Check if this actually gets the last element, I'm not sure it does.
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let maxX = (t: t) => t.xs |> (r => E.A.get(r, E.A.length(r) - 1));
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let zip = t => Belt.Array.zip(t.xs, t.ys);
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let fmap = (t: t, y): t => {xs: t.xs, ys: t.ys |> E.A.fmap(y)};
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@ -101,6 +105,8 @@ module XYShape = {
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};
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module Continuous = {
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let minX = XYShape.minX;
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let maxX = XYShape.maxX;
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let fromArrays = XYShape.fromArrays;
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let toJs = XYShape.toJs;
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let toPdf = XYShape.Range.derivative;
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@ -114,11 +120,16 @@ module Continuous = {
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let normalizeCdf = (continuousShape: continuousShape) =>
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continuousShape |> XYShape.scaleCdfTo(~scaleTo=1.0);
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let normalizePdf = (continuousShape: continuousShape) =>
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continuousShape |> toCdf |> E.O.fmap(normalizeCdf) |> E.O.bind(_, toPdf);
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let scalePdf = (~scaleTo=1.0, continuousShape: continuousShape) =>
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continuousShape
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|> toCdf
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|> E.O.fmap(XYShape.scaleCdfTo(~scaleTo))
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|> E.O.bind(_, toPdf);
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};
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module Discrete = {
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let minX = XYShape.minX;
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let maxX = XYShape.maxX;
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type t = discreteShape;
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let fromArrays = XYShape.fromArrays;
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let toJs = XYShape.toJs;
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@ -147,6 +158,7 @@ module Discrete = {
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// TODO: This has a clear bug where it returns the Y value of the first item,
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// even if X is less than the X of the first item.
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// It has a second bug that it assumes things are triangular, instead of interpolating via steps.
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let findIntegralY = (f, t: t) => {
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t |> XYShape.accumulateYs |> CdfLibrary.Distribution.findY(f);
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};
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@ -159,6 +171,22 @@ module Discrete = {
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};
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};
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let min = (f1: option(float), f2: option(float)) =>
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switch (f1, f2) {
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| (Some(f1), Some(f2)) => Some(f1 < f2 ? f1 : f2)
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| (Some(f1), None) => Some(f1)
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| (None, Some(f2)) => Some(f2)
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| (None, None) => None
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};
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let max = (f1: option(float), f2: option(float)) =>
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switch (f1, f2) {
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| (Some(f1), Some(f2)) => Some(f1 > f2 ? f1 : f2)
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| (Some(f1), None) => Some(f1)
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| (None, Some(f2)) => Some(f2)
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| (None, None) => None
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};
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module Mixed = {
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let make = (~continuous, ~discrete, ~discreteProbabilityMassFraction) => {
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continuous,
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discreteProbabilityMassFraction,
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};
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let minX = (t: DistributionTypes.mixedShape) =>
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min(t.continuous |> Continuous.minX, t.discrete |> Discrete.minX);
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let maxX = (t: DistributionTypes.mixedShape) =>
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min(t.continuous |> Continuous.maxX, t.discrete |> Discrete.maxX);
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let mixedMultiply =
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(
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t: DistributionTypes.mixedShape,
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@ -197,10 +231,6 @@ module Mixed = {
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| _ => None
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};
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};
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//Do the math to add these distributions together
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// let integral =
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// (x: float, t: DistributionTypes.mixedShape): option(XYShape.t) => {
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// };
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};
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module Any = {
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@ -223,6 +253,20 @@ module Any = {
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Continuous.findIntegralY(x, continuousShape)
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};
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let minX = (t: t) =>
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switch (t) {
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| Mixed(m) => Mixed.minX(m)
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| Discrete(discreteShape) => Discrete.minX(discreteShape)
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| Continuous(continuousShape) => Continuous.minX(continuousShape)
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};
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let maxX = (t: t) =>
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switch (t) {
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| Mixed(m) => Mixed.maxX(m)
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| Discrete(discreteShape) => Discrete.maxX(discreteShape)
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| Continuous(continuousShape) => Continuous.maxX(continuousShape)
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};
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// TODO: This is wrong. The discrete component should be made continuous when integrating.
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let pdfToCdf = (t: t) =>
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switch (t) {
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