Working on code reorganization, doesn't compile yet
This commit is contained in:
Sebastian Kosch
parent
214f3b9e58
commit
bd528571af
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@ -386,10 +386,9 @@ describe("Shape", () => {
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let numSamples = 10000;
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open Distributions.Shape;
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let normal: SymbolicDist.dist = `Normal({mean, stdev});
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let normalShape = SymbolicDist.GenericSimple.toShape(normal, numSamples);
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let normalShape = TreeNode.toShape(numSamples, normal);
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let lognormal = SymbolicDist.Lognormal.fromMeanAndStdev(mean, stdev);
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let lognormalShape =
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SymbolicDist.GenericSimple.toShape(lognormal, numSamples);
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let lognormalShape = TreeNode.toShape(numSamples, lognormal);
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makeTestCloseEquality(
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"Mean of a normal",
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@ -44,14 +44,14 @@ module DemoDist = {
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Distributions.DistPlus.make(
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~shape=
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Continuous(
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Distributions.Continuous.make(`Linear, {xs, ys}),
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Distributions.Continuous.make(`Linear, {xs, ys}, None),
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),
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~domain=Complete,
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~unit=UnspecifiedDistribution,
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~guesstimatorString=None,
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(),
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)
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|> Distributions.DistPlus.T.scaleToIntegralSum(~intendedSum=1.0);
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|> Distributions.DistPlus.T.normalize;
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<DistPlusPlot distPlus />;
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};
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<Antd.Card title={"Distribution" |> R.ste}>
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@ -37,7 +37,7 @@ module DemoDist = {
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let parsed1 = MathJsParser.fromString(guesstimatorString);
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let shape =
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switch (parsed1) {
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| Ok(r) => Some(SymbolicDist.toShape(10000, r))
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| Ok(r) => Some(TreeNode.toShape(10000, r))
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| _ => None
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};
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@ -177,6 +177,7 @@ module Convert = {
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let continuousShape: Types.continuousShape = {
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xyShape,
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interpolation: `Linear,
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knownIntegralSum: None,
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};
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let integral = XYShape.Analysis.integrateContinuousShape(continuousShape);
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@ -188,6 +189,7 @@ module Convert = {
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ys,
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},
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interpolation: `Linear,
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knownIntegralSum: Some(1.0),
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};
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continuousShape;
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};
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@ -387,7 +389,7 @@ module Draw = {
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let numSamples = 3000;
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let normal: SymbolicDist.dist = `Normal({mean, stdev});
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let normalShape = SymbolicDist.GenericSimple.toShape(normal, numSamples);
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let normalShape = TreeNode.toShape(numSamples, `DistData(`Symbolic(normal)));
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let xyShape: Types.xyShape =
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switch (normalShape) {
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| Mixed(_) => {xs: [||], ys: [||]}
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@ -667,9 +669,7 @@ module State = {
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/* create a cdf from a pdf */
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let _pdf =
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Distributions.Continuous.T.scaleToIntegralSum(
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~cache=None,
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~intendedSum=1.0,
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Distributions.Continuous.T.normalize(
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pdf,
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);
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@ -95,7 +95,7 @@ let table = (distPlus, x) => {
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</td>
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<td className="px-4 py-2 border ">
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{distPlus
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|> Distributions.DistPlus.T.toScaledContinuous
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|> Distributions.DistPlus.T.normalizedToContinuous
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|> E.O.fmap(
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Distributions.Continuous.T.Integral.sum(~cache=None),
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)
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@ -113,7 +113,7 @@ let table = (distPlus, x) => {
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</td>
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<td className="px-4 py-2 border ">
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{distPlus
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|> Distributions.DistPlus.T.toScaledDiscrete
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|> Distributions.DistPlus.T.normalizedToDiscrete
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|> E.O.fmap(Distributions.Discrete.T.Integral.sum(~cache=None))
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|> E.O.fmap(E.Float.with2DigitsPrecision)
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|> E.O.default("")
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@ -211,15 +211,13 @@ let percentiles = distPlus => {
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</div>;
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};
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let adjustBoth = discreteProbabilityMass => {
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let yMaxDiscreteDomainFactor = discreteProbabilityMass;
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let yMaxContinuousDomainFactor = 1.0 -. discreteProbabilityMass;
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let yMax =
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yMaxDiscreteDomainFactor > yMaxContinuousDomainFactor
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? yMaxDiscreteDomainFactor : yMaxContinuousDomainFactor;
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let adjustBoth = discreteProbabilityMassFraction => {
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let yMaxDiscreteDomainFactor = discreteProbabilityMassFraction;
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let yMaxContinuousDomainFactor = 1.0 -. discreteProbabilityMassFraction;
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let yMax = (yMaxDiscreteDomainFactor > 0.5 ? yMaxDiscreteDomainFactor : yMaxContinuousDomainFactor);
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(
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1.0 /. (yMaxDiscreteDomainFactor /. yMax),
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1.0 /. (yMaxContinuousDomainFactor /. yMax),
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yMax /. yMaxDiscreteDomainFactor,
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yMax /. yMaxContinuousDomainFactor,
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);
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};
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@ -227,10 +225,10 @@ module DistPlusChart = {
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[@react.component]
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let make = (~distPlus: DistTypes.distPlus, ~config: chartConfig, ~onHover) => {
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open Distributions.DistPlus;
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let discrete = distPlus |> T.toScaledDiscrete;
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let discrete = distPlus |> T.normalizedToDiscrete |> E.O.fmap(Distributions.Discrete.getShape);
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let continuous =
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distPlus
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|> T.toScaledContinuous
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|> T.normalizedToContinuous
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|> E.O.fmap(Distributions.Continuous.getShape);
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let range = T.xTotalRange(distPlus);
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@ -254,10 +252,10 @@ module DistPlusChart = {
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};
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let timeScale = distPlus.unit |> DistTypes.DistributionUnit.toJson;
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let toDiscreteProbabilityMass =
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distPlus |> Distributions.DistPlus.T.toDiscreteProbabilityMass;
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let discreteProbabilityMassFraction =
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distPlus |> Distributions.DistPlus.T.toDiscreteProbabilityMassFraction;
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let (yMaxDiscreteDomainFactor, yMaxContinuousDomainFactor) =
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adjustBoth(toDiscreteProbabilityMass);
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adjustBoth(discreteProbabilityMassFraction);
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<DistributionPlot
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xScale={config.xLog ? "log" : "linear"}
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yScale={config.yLog ? "log" : "linear"}
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@ -17,14 +17,18 @@ type xyShape = {
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type continuousShape = {
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xyShape,
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interpolation: [ | `Stepwise | `Linear],
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knownIntegralSum: option(float),
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};
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type discreteShape = xyShape;
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type discreteShape = {
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xyShape,
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knownIntegralSum: option(float),
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};
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type mixedShape = {
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continuous: continuousShape,
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discrete: discreteShape,
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discreteProbabilityMassFraction: float,
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// discreteProbabilityMassFraction: float,
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};
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type shapeMonad('a, 'b, 'c) =
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File diff suppressed because it is too large
Load Diff
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@ -8,14 +8,15 @@ type assumptions = {
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discreteProbabilityMass: option(float),
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};
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let buildSimple = (~continuous: option(DistTypes.continuousShape), ~discrete): option(DistTypes.shape) => {
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let continuous = continuous |> E.O.default(Distributions.Continuous.make(`Linear, {xs: [||], ys: [||]}))
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let buildSimple = (~continuous: option(DistTypes.continuousShape), ~discrete: option(DistTypes.discreteShape)): option(DistTypes.shape) => {
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let continuous = continuous |> E.O.default(Distributions.Continuous.make(`Linear, {xs: [||], ys: [||]}, Some(0.0)));
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let discrete = discrete |> E.O.default(Distributions.Discrete.make({xs: [||], ys: [||]}, Some(0.0)));
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let cLength =
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continuous
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|> Distributions.Continuous.getShape
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|> XYShape.T.xs
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|> E.A.length;
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let dLength = discrete |> XYShape.T.xs |> E.A.length;
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let dLength = discrete |> Distributions.Discrete.getShape |> XYShape.T.xs |> E.A.length;
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switch (cLength, dLength) {
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| (0 | 1, 0) => None
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| (0 | 1, _) => Some(Discrete(discrete))
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@ -23,18 +24,12 @@ let buildSimple = (~continuous: option(DistTypes.continuousShape), ~discrete): o
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| (_, _) =>
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let discreteProbabilityMassFraction =
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Distributions.Discrete.T.Integral.sum(~cache=None, discrete);
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let discrete =
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Distributions.Discrete.T.scaleToIntegralSum(~intendedSum=1.0, discrete);
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let continuous =
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Distributions.Continuous.T.scaleToIntegralSum(
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~intendedSum=1.0,
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continuous,
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);
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let discrete = Distributions.Discrete.T.normalize(discrete);
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let continuous = Distributions.Continuous.T.normalize(continuous);
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let mixedDist =
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Distributions.Mixed.make(
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~continuous,
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~discrete,
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~discreteProbabilityMassFraction,
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~discrete
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);
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Some(Mixed(mixedDist));
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};
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@ -42,7 +37,7 @@ let buildSimple = (~continuous: option(DistTypes.continuousShape), ~discrete): o
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// TODO: Delete, only being used in tests
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let build = (~continuous, ~discrete, ~assumptions) =>
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/*let build = (~continuous, ~discrete, ~assumptions) =>
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switch (assumptions) {
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| {
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continuous: ADDS_TO_CORRECT_PROBABILITY,
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@ -102,4 +97,4 @@ let build = (~continuous, ~discrete, ~assumptions) =>
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),
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);
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| _ => None
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};
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};*/
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@ -17,6 +17,7 @@ module T = {
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type ts = array(xyShape);
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let xs = (t: t) => t.xs;
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let ys = (t: t) => t.ys;
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let length = (t: t) => E.A.length(t.xs);
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let empty = {xs: [||], ys: [||]};
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let minX = (t: t) => t |> xs |> E.A.Sorted.min |> extImp;
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let maxX = (t: t) => t |> xs |> E.A.Sorted.max |> extImp;
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@ -154,7 +155,9 @@ module XsConversion = {
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let proportionByProbabilityMass =
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(newLength: int, integral: T.t, t: T.t): T.t => {
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equallyDivideXByMass(newLength, integral) |> _replaceWithXs(_, t);
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integral
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|> equallyDivideXByMass(newLength) // creates a new set of xs at evenly spaced percentiles
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|> _replaceWithXs(_, t); // linearly interpolates new ys for the new xs
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};
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};
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@ -164,6 +167,7 @@ module Zipped = {
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let compareXs = ((x1, _), (x2, _)) => x1 > x2 ? 1 : 0;
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let sortByY = (t: zipped) => t |> E.A.stableSortBy(_, compareYs);
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let sortByX = (t: zipped) => t |> E.A.stableSortBy(_, compareXs);
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let filterByX = (testFn: (float => bool), t: zipped) => t |> E.A.filter(((x, _)) => testFn(x));
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};
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module Combine = {
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@ -253,8 +257,8 @@ module Range = {
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Belt.Array.set(
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cumulativeY,
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x + 1,
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(xs[x + 1] -. xs[x])
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*. ((ys[x] +. ys[x + 1]) /. 2.)
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(xs[x + 1] -. xs[x]) // dx
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*. ((ys[x] +. ys[x + 1]) /. 2.) // (1/2) * (avgY)
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+. cumulativeY[x],
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);
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();
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@ -43,7 +43,7 @@ module ShapeRenderer = {
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module Symbolic = {
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type inputs = {length: int};
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type outputs = {
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graph: SymbolicDist.distTree,
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graph: TreeNode.treeNode,
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shape: DistTypes.shape,
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};
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let make = (graph, shape) => {graph, shape};
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@ -21,7 +21,7 @@ let runSymbolic = (guesstimatorString, length) => {
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|> E.R.fmap(g =>
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RenderTypes.ShapeRenderer.Symbolic.make(
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g,
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SymbolicDist.toShape(length, g),
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TreeNode.toShape(length, g),
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)
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);
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};
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@ -4,10 +4,10 @@ type discrete = {
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ys: array(float),
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};
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let jsToDistDiscrete = (d: discrete): DistTypes.discreteShape => {
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let jsToDistDiscrete = (d: discrete): DistTypes.discreteShape => {xyShape: {
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xs: xsGet(d),
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ys: ysGet(d),
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};
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}, knownIntegralSum: None};
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[@bs.module "./GuesstimatorLibrary.js"]
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external stringToSamples: (string, int) => array(float) = "stringToSamples";
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@ -115,11 +115,12 @@ module T = {
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Array.fast_sort(compare, samples);
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let (continuousPart, discretePart) = E.A.Sorted.Floats.split(samples);
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let length = samples |> E.A.length |> float_of_int;
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let discrete: DistTypes.xyShape =
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let discrete: DistTypes.discreteShape =
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discretePart
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|> E.FloatFloatMap.fmap(r => r /. length)
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|> E.FloatFloatMap.toArray
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|> XYShape.T.fromZippedArray;
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|> XYShape.T.fromZippedArray
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|> Distributions.Discrete.make(_, None);
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let pdf =
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continuousPart |> E.A.length > 5
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@ -149,14 +150,14 @@ module T = {
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~outputXYPoints=samplingInputs.outputXYPoints,
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formatUnitWidth(usedUnitWidth),
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)
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|> Distributions.Continuous.make(`Linear)
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|> Distributions.Continuous.make(`Linear, _, None)
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|> (r => Some((r, foo)));
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}
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: None;
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let shape =
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MixedShapeBuilder.buildSimple(
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~continuous=pdf |> E.O.fmap(fst),
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~discrete,
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~discrete=Some(discrete),
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);
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let samplesParse: RenderTypes.ShapeRenderer.Sampling.outputs = {
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continuousParseParams: pdf |> E.O.fmap(snd),
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@ -88,69 +88,69 @@ module MathAdtToDistDst = {
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);
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};
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let normal: array(arg) => result(SymbolicDist.distTree, string) =
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let normal: array(arg) => result(TreeNode.treeNode, string) =
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fun
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| [|Value(mean), Value(stdev)|] =>
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Ok(`Simple(`Normal({mean, stdev})))
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Ok(`DistData(`Symbolic(`Normal({mean, stdev}))))
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| _ => Error("Wrong number of variables in normal distribution");
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let lognormal: array(arg) => result(SymbolicDist.distTree, string) =
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let lognormal: array(arg) => result(TreeNode.treeNode, string) =
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fun
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| [|Value(mu), Value(sigma)|] => Ok(`Simple(`Lognormal({mu, sigma})))
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| [|Value(mu), Value(sigma)|] => Ok(`DistData(`Symbolic(`Lognormal({mu, sigma}))))
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| [|Object(o)|] => {
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let g = Js.Dict.get(o);
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switch (g("mean"), g("stdev"), g("mu"), g("sigma")) {
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| (Some(Value(mean)), Some(Value(stdev)), _, _) =>
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Ok(`Simple(SymbolicDist.Lognormal.fromMeanAndStdev(mean, stdev)))
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Ok(`DistData(`Symbolic(SymbolicDist.Lognormal.fromMeanAndStdev(mean, stdev))))
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| (_, _, Some(Value(mu)), Some(Value(sigma))) =>
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Ok(`Simple(`Lognormal({mu, sigma})))
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Ok(`DistData(`Symbolic(`Lognormal({mu, sigma}))))
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| _ => Error("Lognormal distribution would need mean and stdev")
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};
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}
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| _ => Error("Wrong number of variables in lognormal distribution");
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let to_: array(arg) => result(SymbolicDist.distTree, string) =
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let to_: array(arg) => result(TreeNode.treeNode, string) =
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fun
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| [|Value(low), Value(high)|] when low <= 0.0 && low < high=> {
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Ok(`Simple(SymbolicDist.Normal.from90PercentCI(low, high)));
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Ok(`DistData(`Symbolic(SymbolicDist.Normal.from90PercentCI(low, high))));
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}
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| [|Value(low), Value(high)|] when low < high => {
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Ok(`Simple(SymbolicDist.Lognormal.from90PercentCI(low, high)));
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Ok(`DistData(`Symbolic(SymbolicDist.Lognormal.from90PercentCI(low, high))));
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}
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| [|Value(_), Value(_)|] =>
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Error("Low value must be less than high value.")
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| _ => Error("Wrong number of variables in lognormal distribution");
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let uniform: array(arg) => result(SymbolicDist.distTree, string) =
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let uniform: array(arg) => result(TreeNode.treeNode, string) =
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fun
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| [|Value(low), Value(high)|] => Ok(`Simple(`Uniform({low, high})))
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| [|Value(low), Value(high)|] => Ok(`DistData(`Symbolic(`Uniform({low, high}))))
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| _ => Error("Wrong number of variables in lognormal distribution");
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let beta: array(arg) => result(SymbolicDist.distTree, string) =
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let beta: array(arg) => result(TreeNode.treeNode, string) =
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fun
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| [|Value(alpha), Value(beta)|] => Ok(`Simple(`Beta({alpha, beta})))
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| [|Value(alpha), Value(beta)|] => Ok(`DistData(`Symbolic(`Beta({alpha, beta}))))
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| _ => Error("Wrong number of variables in lognormal distribution");
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let exponential: array(arg) => result(SymbolicDist.distTree, string) =
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let exponential: array(arg) => result(TreeNode.treeNode, string) =
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fun
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| [|Value(rate)|] => Ok(`Simple(`Exponential({rate: rate})))
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| [|Value(rate)|] => Ok(`DistData(`Symbolic(`Exponential({rate: rate}))))
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| _ => Error("Wrong number of variables in Exponential distribution");
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let cauchy: array(arg) => result(SymbolicDist.distTree, string) =
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let cauchy: array(arg) => result(TreeNode.treeNode, string) =
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fun
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| [|Value(local), Value(scale)|] =>
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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([||]);
|
||||
|
|
@ -158,17 +158,9 @@ module MathAdtToDistDst = {
|
|||
args
|
||||
|> E.A.fmap(
|
||||
fun
|
||||
| Ok(`Simple(d)) => Ok(`Simple(d))
|
||||
| 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")
|
||||
);
|
||||
| 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;
|
||||
|
|
@ -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;
|
||||
};
|
||||
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
@ -274,22 +267,25 @@ 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).
|
||||
This function is called separately for each individual distribution.
|
||||
let mean: dist => result(float, string) =
|
||||
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) => {
|
||||
(~xSelection: [ | `Linear | `ByWeight]=`Linear, dist: dist, n) => {
|
||||
switch (xSelection, dist) {
|
||||
| (`Linear, _) => E.A.Floats.range(min(dist), max(dist), n)
|
||||
| (`ByWeight, `Uniform(n)) =>
|
||||
// In `ByWeight mode, uniform distributions get special treatment because we need two x's
|
||||
// on either side for proper rendering (just left and right of the discontinuities).
|
||||
// In `ByWeight mode, uniform distributions get special treatment because we need two x's
|
||||
// on either side for proper rendering (just left and right of the discontinuities).
|
||||
let dx = 0.00001 *. (n.high -. n.low);
|
||||
[|n.low -. dx, n.low +. dx, n.high -. dx, n.high +. dx|];
|
||||
| (`ByWeight, _) =>
|
||||
|
|
@ -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,
|
||||
|
|
|
|||
414
src/distPlus/symbolic/TreeNode.re
Normal file
414
src/distPlus/symbolic/TreeNode.re
Normal file
|
|
@ -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)
|
||||
}
|
||||
};
|
||||
|
|
@ -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 = {
|
||||
.
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user