It compiles!
This commit is contained in:
Sebastian Kosch
parent
bd528571af
commit
dc1ec1bb86
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@ -24,7 +24,7 @@ let makeTestCloseEquality = (~only=false, str, item1, item2, ~digits) =>
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describe("Shape", () => {
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describe("Continuous", () => {
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open Distributions.Continuous;
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let continuous = make(`Linear, shape);
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let continuous = make(`Linear, shape, None);
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makeTest("minX", T.minX(continuous), 1.0);
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makeTest("maxX", T.maxX(continuous), 8.0);
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makeTest(
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@ -57,7 +57,7 @@ describe("Shape", () => {
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);
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});
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describe("when Stepwise", () => {
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let continuous = make(`Stepwise, shape);
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let continuous = make(`Stepwise, shape, None);
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makeTest(
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"at 4.0",
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T.xToY(4., continuous),
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@ -89,7 +89,7 @@ describe("Shape", () => {
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"toLinear",
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{
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let continuous =
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make(`Stepwise, {xs: [|1., 4., 8.|], ys: [|0.1, 5., 1.0|]});
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make(`Stepwise, {xs: [|1., 4., 8.|], ys: [|0.1, 5., 1.0|]}, None);
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continuous |> toLinear |> E.O.fmap(getShape);
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},
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Some({
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@ -100,7 +100,7 @@ describe("Shape", () => {
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makeTest(
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"toLinear",
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{
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let continuous = make(`Stepwise, {xs: [|0.0|], ys: [|0.3|]});
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let continuous = make(`Stepwise, {xs: [|0.0|], ys: [|0.3|]}, None);
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continuous |> toLinear |> E.O.fmap(getShape);
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},
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Some({xs: [|0.0|], ys: [|0.3|]}),
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@ -123,7 +123,7 @@ describe("Shape", () => {
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makeTest(
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"integralEndY",
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continuous
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|> T.scaleToIntegralSum(~intendedSum=1.0)
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|> T.normalize //scaleToIntegralSum(~intendedSum=1.0)
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|> T.Integral.sum(~cache=None),
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1.0,
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);
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@ -135,12 +135,12 @@ describe("Shape", () => {
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xs: [|1., 4., 8.|],
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ys: [|0.3, 0.5, 0.2|],
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};
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let discrete = shape;
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let discrete = make(shape, None);
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makeTest("minX", T.minX(discrete), 1.0);
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makeTest("maxX", T.maxX(discrete), 8.0);
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makeTest(
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"mapY",
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T.mapY(r => r *. 2.0, discrete) |> (r => r.ys),
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T.mapY(r => r *. 2.0, discrete) |> (r => getShape(r).ys),
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[|0.6, 1.0, 0.4|],
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);
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makeTest(
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@ -160,19 +160,22 @@ describe("Shape", () => {
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);
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makeTest(
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"scaleBy",
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T.scaleBy(~scale=4.0, discrete),
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{xs: [|1., 4., 8.|], ys: [|1.2, 2.0, 0.8|]},
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scaleBy(~scale=4.0, discrete),
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make({xs: [|1., 4., 8.|], ys: [|1.2, 2.0, 0.8|]}, None),
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);
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makeTest(
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"scaleToIntegralSum",
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T.scaleToIntegralSum(~intendedSum=4.0, discrete),
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{xs: [|1., 4., 8.|], ys: [|1.2, 2.0, 0.8|]},
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"normalize, then scale by 4.0",
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discrete
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|> T.normalize
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|> scaleBy(~scale=4.0),
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make({xs: [|1., 4., 8.|], ys: [|1.2, 2.0, 0.8|]}, None),
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);
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makeTest(
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"scaleToIntegralSum: back and forth",
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discrete
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|> T.scaleToIntegralSum(~intendedSum=4.0)
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|> T.scaleToIntegralSum(~intendedSum=1.0),
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|> T.normalize
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|> scaleBy(~scale=4.0)
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|> T.normalize,
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discrete,
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);
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makeTest(
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@ -181,12 +184,13 @@ describe("Shape", () => {
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Distributions.Continuous.make(
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`Stepwise,
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{xs: [|1., 4., 8.|], ys: [|0.3, 0.8, 1.0|]},
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None
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),
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);
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makeTest(
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"integral with 1 element",
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T.Integral.get(~cache=None, {xs: [|0.0|], ys: [|1.0|]}),
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Distributions.Continuous.make(`Stepwise, {xs: [|0.0|], ys: [|1.0|]}),
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T.Integral.get(~cache=None, Distributions.Discrete.make({xs: [|0.0|], ys: [|1.0|]}, None)),
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Distributions.Continuous.make(`Stepwise, {xs: [|0.0|], ys: [|1.0|]}, None),
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);
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makeTest(
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"integralXToY",
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@ -205,27 +209,22 @@ describe("Shape", () => {
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describe("Mixed", () => {
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open Distributions.Mixed;
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let discrete: DistTypes.xyShape = {
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let discreteShape: DistTypes.xyShape = {
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xs: [|1., 4., 8.|],
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ys: [|0.3, 0.5, 0.2|],
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};
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let discrete = Distributions.Discrete.make(discreteShape, None);
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let continuous =
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Distributions.Continuous.make(
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`Linear,
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{xs: [|3., 7., 14.|], ys: [|0.058, 0.082, 0.124|]},
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None
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)
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|> Distributions.Continuous.T.scaleToIntegralSum(~intendedSum=1.0);
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let mixed =
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MixedShapeBuilder.build(
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|> Distributions.Continuous.T.normalize; //scaleToIntegralSum(~intendedSum=1.0);
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let mixed = Distributions.Mixed.make(
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~continuous,
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~discrete,
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~assumptions={
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continuous: ADDS_TO_CORRECT_PROBABILITY,
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discrete: ADDS_TO_CORRECT_PROBABILITY,
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discreteProbabilityMass: Some(0.5),
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},
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)
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|> E.O.toExn("");
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);
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makeTest("minX", T.minX(mixed), 1.0);
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makeTest("maxX", T.maxX(mixed), 14.0);
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makeTest(
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@ -243,9 +242,9 @@ describe("Shape", () => {
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0.24775224775224775,
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|],
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},
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None
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),
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~discrete={xs: [|1., 4., 8.|], ys: [|0.6, 1.0, 0.4|]},
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~discreteProbabilityMassFraction=0.5,
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~discrete=Distributions.Discrete.make({xs: [|1., 4., 8.|], ys: [|0.6, 1.0, 0.4|]}, None)
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),
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);
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makeTest(
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@ -266,7 +265,7 @@ describe("Shape", () => {
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makeTest("integralEndY", T.Integral.sum(~cache=None, mixed), 1.0);
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makeTest(
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"scaleBy",
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T.scaleBy(~scale=2.0, mixed),
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Distributions.Mixed.scaleBy(~scale=2.0, mixed),
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Distributions.Mixed.make(
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~continuous=
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Distributions.Continuous.make(
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@ -279,9 +278,9 @@ describe("Shape", () => {
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0.24775224775224775,
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|],
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},
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None
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),
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~discrete={xs: [|1., 4., 8.|], ys: [|0.6, 1.0, 0.4|]},
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~discreteProbabilityMassFraction=0.5,
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~discrete=Distributions.Discrete.make({xs: [|1., 4., 8.|], ys: [|0.6, 1.0, 0.4|]}, None),
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),
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);
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makeTest(
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@ -303,33 +302,30 @@ describe("Shape", () => {
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1.0,
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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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});
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describe("Distplus", () => {
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open Distributions.DistPlus;
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let discrete: DistTypes.xyShape = {
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let discreteShape: DistTypes.xyShape = {
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xs: [|1., 4., 8.|],
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ys: [|0.3, 0.5, 0.2|],
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};
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let discrete = Distributions.Discrete.make(discreteShape, None);
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let continuous =
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Distributions.Continuous.make(
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`Linear,
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{xs: [|3., 7., 14.|], ys: [|0.058, 0.082, 0.124|]},
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None
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)
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|> Distributions.Continuous.T.scaleToIntegralSum(~intendedSum=1.0);
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|> Distributions.Continuous.T.normalize; //scaleToIntegralSum(~intendedSum=1.0);
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let mixed =
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MixedShapeBuilder.build(
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Distributions.Mixed.make(
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~continuous,
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~discrete,
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~assumptions={
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continuous: ADDS_TO_CORRECT_PROBABILITY,
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discrete: ADDS_TO_CORRECT_PROBABILITY,
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discreteProbabilityMass: Some(0.5),
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},
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)
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|> E.O.toExn("");
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);
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let distPlus =
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Distributions.DistPlus.make(
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~shape=Mixed(mixed),
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@ -374,6 +370,7 @@ describe("Shape", () => {
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1.0,
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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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);
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@ -386,9 +383,9 @@ 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 = TreeNode.toShape(numSamples, normal);
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let normalShape = TreeNode.toShape(numSamples, `DistData(`Symbolic(normal)));
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let lognormal = SymbolicDist.Lognormal.fromMeanAndStdev(mean, stdev);
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let lognormalShape = TreeNode.toShape(numSamples, lognormal);
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let lognormalShape = TreeNode.toShape(numSamples, `DistData(`Symbolic(lognormal)));
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makeTestCloseEquality(
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"Mean of a normal",
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@ -17,7 +17,7 @@ module FormConfig = [%lenses
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//
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sampleCount: string,
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outputXYPoints: string,
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truncateTo: string,
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downsampleTo: string,
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kernelWidth: string,
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}
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];
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@ -25,7 +25,7 @@ module FormConfig = [%lenses
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type options = {
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sampleCount: int,
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outputXYPoints: int,
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truncateTo: option(int),
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downsampleTo: option(int),
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kernelWidth: option(float),
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};
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@ -115,7 +115,7 @@ type inputs = {
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samplingInputs: RenderTypes.ShapeRenderer.Sampling.inputs,
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guesstimatorString: string,
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length: int,
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shouldTruncateSampledDistribution: int,
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shouldDownsampleSampledDistribution: int,
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};
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module DemoDist = {
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@ -141,8 +141,8 @@ module DemoDist = {
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kernelWidth: options.kernelWidth,
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},
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~distPlusIngredients,
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~shouldTruncate=options.truncateTo |> E.O.isSome,
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~recommendedLength=options.truncateTo |> E.O.default(10000),
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~shouldDownsample=options.downsampleTo |> E.O.isSome,
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~recommendedLength=options.downsampleTo |> E.O.default(10000),
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(),
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);
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let response = DistPlusRenderer.run(inputs);
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@ -182,7 +182,7 @@ let make = () => {
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unit: "days",
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sampleCount: "30000",
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outputXYPoints: "10000",
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truncateTo: "1000",
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downsampleTo: "1000",
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kernelWidth: "5",
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},
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(),
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@ -210,7 +210,7 @@ let make = () => {
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let sampleCount = reform.state.values.sampleCount |> Js.Float.fromString;
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let outputXYPoints =
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reform.state.values.outputXYPoints |> Js.Float.fromString;
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let truncateTo = reform.state.values.truncateTo |> Js.Float.fromString;
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let downsampleTo = reform.state.values.downsampleTo |> Js.Float.fromString;
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let kernelWidth = reform.state.values.kernelWidth |> Js.Float.fromString;
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let domain =
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@ -252,20 +252,20 @@ let make = () => {
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};
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let options =
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switch (sampleCount, outputXYPoints, truncateTo) {
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switch (sampleCount, outputXYPoints, downsampleTo) {
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| (_, _, _)
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when
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!Js.Float.isNaN(sampleCount)
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&& !Js.Float.isNaN(outputXYPoints)
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&& !Js.Float.isNaN(truncateTo)
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&& !Js.Float.isNaN(downsampleTo)
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&& sampleCount > 10.
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&& outputXYPoints > 10. =>
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Some({
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sampleCount: sampleCount |> int_of_float,
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outputXYPoints: outputXYPoints |> int_of_float,
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truncateTo:
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int_of_float(truncateTo) > 0
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? Some(int_of_float(truncateTo)) : None,
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downsampleTo:
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int_of_float(downsampleTo) > 0
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? Some(int_of_float(downsampleTo)) : None,
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kernelWidth: kernelWidth == 0.0 ? None : Some(kernelWidth),
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})
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| _ => None
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@ -287,7 +287,7 @@ let make = () => {
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reform.state.values.unit,
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reform.state.values.sampleCount,
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reform.state.values.outputXYPoints,
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reform.state.values.truncateTo,
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reform.state.values.downsampleTo,
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reform.state.values.kernelWidth,
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reloader |> string_of_int,
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|],
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@ -481,7 +481,7 @@ let make = () => {
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/>
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</Col>
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<Col span=4>
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<FieldFloat field=FormConfig.TruncateTo label="Truncate To" />
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<FieldFloat field=FormConfig.DownsampleTo label="Downsample To" />
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</Col>
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<Col span=4>
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<FieldFloat field=FormConfig.KernelWidth label="Kernel Width" />
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|
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@ -43,7 +43,7 @@ module DemoDist = {
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let str =
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switch (parsed1) {
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| Ok(r) => SymbolicDist.toString(r)
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| Ok(r) => TreeNode.toString(r)
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| Error(e) => e
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};
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@ -58,7 +58,7 @@ module DemoDist = {
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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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|> E.O.default(ReasonReact.null);
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|
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@ -3,7 +3,8 @@ module type dist = {
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type integral;
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let minX: t => float;
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let maxX: t => float;
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let mapY: (~knownIntegralSumFn: float => option(float)=?, float => float, t) => t;
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let mapY:
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(~knownIntegralSumFn: float => option(float)=?, float => float, t) => t;
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let xToY: (float, t) => DistTypes.mixedPoint;
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let toShape: t => DistTypes.shape;
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let toContinuous: t => option(DistTypes.continuousShape);
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@ -13,6 +14,7 @@ module type dist = {
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let normalizedToDiscrete: t => option(DistTypes.discreteShape);
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let toDiscreteProbabilityMassFraction: t => float;
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let downsample: (~cache: option(integral)=?, int, t) => t;
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let truncate: (option(float), option(float), t) => t;
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let integral: (~cache: option(integral), t) => integral;
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let integralEndY: (~cache: option(integral), t) => float;
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@ -38,6 +40,7 @@ module Dist = (T: dist) => {
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let toContinuous = T.toContinuous;
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let toDiscrete = T.toDiscrete;
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let normalize = T.normalize;
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let truncate = T.truncate;
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let normalizedToContinuous = T.normalizedToContinuous;
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let normalizedToDiscrete = T.normalizedToDiscrete;
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let mean = T.mean;
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@ -52,7 +55,22 @@ module Dist = (T: dist) => {
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};
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};
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module Continuous {
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module Common = {
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let combineIntegralSums =
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(
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combineFn: (float, float) => option(float),
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t1KnownIntegralSum: option(float),
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t2KnownIntegralSum: option(float),
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) => {
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switch (t1KnownIntegralSum, t2KnownIntegralSum) {
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| (None, _)
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| (_, None) => None
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| (Some(s1), Some(s2)) => combineFn(s1, s2)
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};
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};
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};
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module Continuous = {
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type t = DistTypes.continuousShape;
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let getShape = (t: t) => t.xyShape;
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let interpolation = (t: t) => t.interpolation;
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@ -78,17 +96,21 @@ module Continuous {
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knownIntegralSum: Some(0.0),
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};
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let combine =
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(fn, t1: DistTypes.continuousShape, t2: DistTypes.continuousShape)
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(
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~knownIntegralSumsFn,
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fn,
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t1: DistTypes.continuousShape,
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t2: DistTypes.continuousShape,
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)
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: DistTypes.continuousShape => {
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// If we're adding the distributions, and we know the total of each, then we
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// can just sum them up. Otherwise, all bets are off.
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let combinedIntegralSum =
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switch (fn, t1.knownIntegralSum, t2.knownIntegralSum) {
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| (_, None, _)
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| (_, _, None) => None
|
||||
| ((+.), Some(s1), Some(s2)) => Some(s1 +. s2)
|
||||
};
|
||||
Common.combineIntegralSums(
|
||||
knownIntegralSumsFn,
|
||||
t1.knownIntegralSum,
|
||||
t2.knownIntegralSum,
|
||||
);
|
||||
|
||||
make(
|
||||
`Linear,
|
||||
|
|
@ -102,7 +124,6 @@ module Continuous {
|
|||
combinedIntegralSum,
|
||||
);
|
||||
};
|
||||
let reduce = (fn, items) => items |> E.A.fold_left(combine(fn), empty);
|
||||
|
||||
let toLinear = (t: t): option(t) => {
|
||||
switch (t) {
|
||||
|
|
@ -114,7 +135,19 @@ module Continuous {
|
|||
};
|
||||
};
|
||||
let shapeFn = (fn, t: t) => t |> getShape |> fn;
|
||||
let updateKnownIntegralSum = (knownIntegralSum, t: t): t => ({...t, knownIntegralSum});
|
||||
let updateKnownIntegralSum = (knownIntegralSum, t: t): t => {
|
||||
...t,
|
||||
knownIntegralSum,
|
||||
};
|
||||
|
||||
let reduce =
|
||||
(
|
||||
~knownIntegralSumsFn: (float, float) => option(float)=(_, _) => None,
|
||||
fn,
|
||||
continuousShapes,
|
||||
) =>
|
||||
continuousShapes
|
||||
|> E.A.fold_left(combine(~knownIntegralSumsFn, fn), empty);
|
||||
|
||||
// Contracts every point in the continuous xyShape into a single dirac-Delta-like point,
|
||||
// using the centerpoints between adjacent xs and the area under each trapezoid.
|
||||
|
|
@ -128,11 +161,18 @@ module Continuous {
|
|||
Belt.Array.set(
|
||||
pointMassesY,
|
||||
x,
|
||||
(xs[x + 1] -. xs[x]) *. ((ys[x] +. ys[x + 1]) /. 2.)); // = dx * (1/2) * (avgY)
|
||||
(xs[x + 1] -. xs[x]) *. ((ys[x] +. ys[x + 1]) /. 2.),
|
||||
); // = dx * (1/2) * (avgY)
|
||||
();
|
||||
};
|
||||
|
||||
{xyShape: {xs: xs, ys: pointMassesY}, knownIntegralSum: t.knownIntegralSum};
|
||||
{
|
||||
xyShape: {
|
||||
xs,
|
||||
ys: pointMassesY,
|
||||
},
|
||||
knownIntegralSum: t.knownIntegralSum,
|
||||
};
|
||||
};
|
||||
|
||||
/* Performs a discrete convolution between two continuous distributions A and B.
|
||||
|
|
@ -153,18 +193,25 @@ module Continuous {
|
|||
let t1n = t1s |> XYShape.T.length;
|
||||
let t2n = t2s |> XYShape.T.length;
|
||||
|
||||
let outXYShapes: array(array((float, float))) = Belt.Array.makeUninitializedUnsafe(t1n);
|
||||
let outXYShapes: array(array((float, float))) =
|
||||
Belt.Array.makeUninitializedUnsafe(t1n);
|
||||
|
||||
for (i in 0 to t1n - 1) {
|
||||
// create a new distribution
|
||||
let dxyShape: array((float, float)) = Belt.Array.makeUninitializedUnsafe(t2n);
|
||||
let dxyShape: array((float, float)) =
|
||||
Belt.Array.makeUninitializedUnsafe(t2n);
|
||||
for (j in 0 to t2n - 1) {
|
||||
let _ = Belt.Array.set(dxyShape, j, (fn(t1s.xs[i], t2s.xs[j]), t1s.ys[i] *. t2s.ys[j]));
|
||||
let _ =
|
||||
Belt.Array.set(
|
||||
dxyShape,
|
||||
j,
|
||||
(fn(t1s.xs[i], t2s.xs[j]), t1s.ys[i] *. t2s.ys[j]),
|
||||
);
|
||||
();
|
||||
}
|
||||
};
|
||||
let _ = Belt.Array.set(outXYShapes, i, dxyShape);
|
||||
();
|
||||
}
|
||||
};
|
||||
|
||||
let combinedIntegralSum =
|
||||
switch (t1.knownIntegralSum, t2.knownIntegralSum) {
|
||||
|
|
@ -185,35 +232,22 @@ module Continuous {
|
|||
let convolve = (fn, t1: t, t2: t) =>
|
||||
convolveWithDiscrete(fn, t1, toDiscretePointMasses(t2));
|
||||
|
||||
let mapY = (~knownIntegralSumFn=(previousKnownIntegralSum => None), fn, t: t) => {
|
||||
let mapY = (~knownIntegralSumFn=previousKnownIntegralSum => None, fn, t: t) => {
|
||||
let u = E.O.bind(_, knownIntegralSumFn);
|
||||
let yMapFn = shapeMap(XYShape.T.mapY(fn));
|
||||
|
||||
t |> yMapFn |> updateKnownIntegralSum(u(t.knownIntegralSum));
|
||||
};
|
||||
|
||||
let scaleBy = (~scale=1.0, ~knownIntegralSum=None, t: t): t =>
|
||||
t |> mapY((r: float) => r *. scale) |> updateKnownIntegralSum(knownIntegralSum);
|
||||
|
||||
let truncate = (leftCutoff: option(float), rightCutoff: option(float), t: t) => {
|
||||
let truncatedZippedPairs =
|
||||
let scaleBy = (~scale=1.0, t: t): t => {
|
||||
t
|
||||
|> getShape
|
||||
|> XYShape.T.zip
|
||||
|> XYShape.Zipped.filterByX(x => x >= E.O.default(neg_infinity, leftCutoff) || x <= E.O.default(infinity, rightCutoff));
|
||||
|
||||
let eps = (t |> getShape |> XYShape.T.xTotalRange) *. 0.0001;
|
||||
|
||||
let leftNewPoint = leftCutoff |> E.O.dimap(lc => [| (lc -. eps, 0.) |], _ => [||]);
|
||||
let rightNewPoint = rightCutoff |> E.O.dimap(rc => [| (rc +. eps, 0.) |], _ => [||]);
|
||||
|
||||
let truncatedZippedPairsWithNewPoints =
|
||||
E.A.concatMany([| leftNewPoint, truncatedZippedPairs, rightNewPoint |]);
|
||||
let truncatedShape = XYShape.T.fromZippedArray(truncatedZippedPairsWithNewPoints);
|
||||
|
||||
make(`Linear, truncatedShape, None);
|
||||
|> mapY((r: float) => r *. scale)
|
||||
|> updateKnownIntegralSum(
|
||||
E.O.bind(t.knownIntegralSum, v => Some(scale *. v)),
|
||||
);
|
||||
};
|
||||
|
||||
|
||||
module T =
|
||||
Dist({
|
||||
type t = DistTypes.continuousShape;
|
||||
|
|
@ -236,12 +270,31 @@ module Continuous {
|
|||
|> DistTypes.MixedPoint.makeContinuous;
|
||||
};
|
||||
|
||||
// let combineWithFn = (t1: t, t2: t, fn: (float, float) => float) => {
|
||||
// switch(t1, t2){
|
||||
// | ({interpolation: `Stepwise}, {interpolation: `Stepwise}) => 3.0
|
||||
// | ({interpolation: `Linear}, {interpolation: `Linear}) => 3.0
|
||||
// }
|
||||
// };
|
||||
let truncate =
|
||||
(leftCutoff: option(float), rightCutoff: option(float), t: t) => {
|
||||
let truncatedZippedPairs =
|
||||
t
|
||||
|> getShape
|
||||
|> XYShape.T.zip
|
||||
|> XYShape.Zipped.filterByX(x =>
|
||||
x >= E.O.default(neg_infinity, leftCutoff)
|
||||
|| x <= E.O.default(infinity, rightCutoff)
|
||||
);
|
||||
|
||||
let eps = (t |> getShape |> XYShape.T.xTotalRange) *. 0.0001;
|
||||
|
||||
let leftNewPoint =
|
||||
leftCutoff |> E.O.dimap(lc => [|(lc -. eps, 0.)|], _ => [||]);
|
||||
let rightNewPoint =
|
||||
rightCutoff |> E.O.dimap(rc => [|(rc +. eps, 0.)|], _ => [||]);
|
||||
|
||||
let truncatedZippedPairsWithNewPoints =
|
||||
E.A.concatMany([|leftNewPoint, truncatedZippedPairs, rightNewPoint|]);
|
||||
let truncatedShape =
|
||||
XYShape.T.fromZippedArray(truncatedZippedPairsWithNewPoints);
|
||||
|
||||
make(`Linear, truncatedShape, None);
|
||||
};
|
||||
|
||||
// TODO: This should work with stepwise plots.
|
||||
let integral = (~cache, t) =>
|
||||
|
|
@ -272,9 +325,9 @@ module Continuous {
|
|||
let toDiscrete = _ => None;
|
||||
|
||||
let normalize = (t: t): t => {
|
||||
let continuousIntegralSum = integralEndY(~cache=None, t);
|
||||
|
||||
scaleBy(~scale=(1. /. continuousIntegralSum), ~knownIntegralSum=Some(1.0), t);
|
||||
t
|
||||
|> scaleBy(~scale=1. /. integralEndY(~cache=None, t))
|
||||
|> updateKnownIntegralSum(Some(1.0));
|
||||
};
|
||||
|
||||
let normalizedToContinuous = t => Some(t); // TODO: this should be normalized
|
||||
|
|
@ -316,40 +369,41 @@ module Discrete = {
|
|||
|
||||
let lastY = (t: t) => t |> getShape |> XYShape.T.lastY;
|
||||
|
||||
let combineIntegralSums = (combineFn: ((float, float) => option(float)), t1KnownIntegralSum: option(float), t2KnownIntegralSum: option(float)) => {
|
||||
switch (t1KnownIntegralSum, t2KnownIntegralSum) {
|
||||
| (None, _)
|
||||
| (_, None) => None
|
||||
| (Some(s1), Some(s2)) => combineFn(s1, s2)
|
||||
};
|
||||
};
|
||||
|
||||
let combine = (combineIntegralSumsFn, fn, t1: DistTypes.discreteShape, t2: DistTypes.discreteShape)
|
||||
let combine =
|
||||
(
|
||||
~knownIntegralSumsFn,
|
||||
fn,
|
||||
t1: DistTypes.discreteShape,
|
||||
t2: DistTypes.discreteShape,
|
||||
)
|
||||
: DistTypes.discreteShape => {
|
||||
|
||||
let combinedIntegralSum = combineIntegralSums(combineIntegralSumsFn, t1.knownIntegralSum, t2.knownIntegralSum);
|
||||
let combinedIntegralSum =
|
||||
Common.combineIntegralSums(
|
||||
knownIntegralSumsFn,
|
||||
t1.knownIntegralSum,
|
||||
t2.knownIntegralSum,
|
||||
);
|
||||
|
||||
make(
|
||||
XYShape.Combine.combine(
|
||||
~xsSelection=ALL_XS,
|
||||
~xToYSelection=XYShape.XtoY.stepwiseIfAtX,
|
||||
~fn, // stepwiseIfAtX returns option(float), so this fn needs to handle None, which is what the _default0 wrapper is for
|
||||
~fn=((a, b) => fn(E.O.default(0.0, a), E.O.default(0.0, b))), // stepwiseIfAtX returns option(float), so this fn needs to handle None
|
||||
t1.xyShape,
|
||||
t2.xyShape,
|
||||
),
|
||||
combinedIntegralSum,
|
||||
);
|
||||
};
|
||||
let _default0 = (fn, a, b) =>
|
||||
fn(E.O.default(0.0, a), E.O.default(0.0, b));
|
||||
let reduce = (fn, items) =>
|
||||
items |> E.A.fold_left(combine((_, _) => None, _default0(fn)), empty);
|
||||
// a special version of reduce that adds the results (which should be the most common case by far),
|
||||
// and conveniently also adds the knownIntegralSums.
|
||||
let reduceAdd = (fn, items) =>
|
||||
items |> E.A.fold_left(combine((s1, s2) => Some(s1 +. s2), _default0((+.))), empty);
|
||||
|
||||
let updateKnownIntegralSum = (knownIntegralSum, t: t): t => ({...t, knownIntegralSum});
|
||||
let reduce = (~knownIntegralSumsFn=(_, _) => None, fn, discreteShapes): DistTypes.discreteShape =>
|
||||
discreteShapes
|
||||
|> E.A.fold_left(combine(~knownIntegralSumsFn, fn), empty);
|
||||
|
||||
let updateKnownIntegralSum = (knownIntegralSum, t: t): t => {
|
||||
...t,
|
||||
knownIntegralSum,
|
||||
};
|
||||
|
||||
let convolve = (fn, t1: t, t2: t) => {
|
||||
let t1s = t1 |> getShape;
|
||||
|
|
@ -357,7 +411,12 @@ module Discrete = {
|
|||
let t1n = t1s |> XYShape.T.length;
|
||||
let t2n = t2s |> XYShape.T.length;
|
||||
|
||||
let combinedIntegralSum = combineIntegralSums((s1, s2) => Some(s1 *. s2), t1.knownIntegralSum, t2.knownIntegralSum);
|
||||
let combinedIntegralSum =
|
||||
Common.combineIntegralSums(
|
||||
(s1, s2) => Some(s1 *. s2),
|
||||
t1.knownIntegralSum,
|
||||
t2.knownIntegralSum,
|
||||
);
|
||||
|
||||
let xToYMap = E.FloatFloatMap.empty();
|
||||
|
||||
|
|
@ -368,8 +427,8 @@ module Discrete = {
|
|||
let my = t1s.ys[i] *. t2s.ys[j];
|
||||
let _ = Belt.MutableMap.set(xToYMap, x, cv +. my);
|
||||
();
|
||||
}
|
||||
}
|
||||
};
|
||||
};
|
||||
|
||||
let rxys = xToYMap |> E.FloatFloatMap.toArray |> XYShape.Zipped.sortByX;
|
||||
|
||||
|
|
@ -378,25 +437,19 @@ module Discrete = {
|
|||
make(convolvedShape, combinedIntegralSum);
|
||||
};
|
||||
|
||||
let mapY = (~knownIntegralSumFn=(previousKnownIntegralSum => None), fn, t: t) => {
|
||||
let mapY = (~knownIntegralSumFn=previousKnownIntegralSum => None, fn, t: t) => {
|
||||
let u = E.O.bind(_, knownIntegralSumFn);
|
||||
let yMapFn = shapeMap(XYShape.T.mapY(fn));
|
||||
|
||||
t |> yMapFn |> updateKnownIntegralSum(u(t.knownIntegralSum));
|
||||
};
|
||||
|
||||
let scaleBy = (~scale=1.0, ~knownIntegralSum=None, t: t): t =>
|
||||
t |> mapY((r: float) => r *. scale) |> updateKnownIntegralSum(knownIntegralSum);
|
||||
|
||||
let truncate = (leftCutoff: option(float), rightCutoff: option(float), t: t) => {
|
||||
let truncatedShape =
|
||||
let scaleBy = (~scale=1.0, t: t): t => {
|
||||
t
|
||||
|> getShape
|
||||
|> XYShape.T.zip
|
||||
|> XYShape.Zipped.filterByX(x => x >= E.O.default(neg_infinity, leftCutoff) || x <= E.O.default(infinity, rightCutoff))
|
||||
|> XYShape.T.fromZippedArray;
|
||||
|
||||
make(truncatedShape, None);
|
||||
|> mapY((r: float) => r *. scale)
|
||||
|> updateKnownIntegralSum(
|
||||
E.O.bind(t.knownIntegralSum, v => Some(scale *. v)),
|
||||
);
|
||||
};
|
||||
|
||||
module T =
|
||||
|
|
@ -414,7 +467,8 @@ module Discrete = {
|
|||
)
|
||||
};
|
||||
let integralEndY = (~cache, t: t) =>
|
||||
t.knownIntegralSum |> E.O.default(t |> integral(~cache) |> Continuous.lastY);
|
||||
t.knownIntegralSum
|
||||
|> E.O.default(t |> integral(~cache) |> Continuous.lastY);
|
||||
let minX = shapeFn(XYShape.T.minX);
|
||||
let maxX = shapeFn(XYShape.T.maxX);
|
||||
let toDiscreteProbabilityMassFraction = _ => 1.0;
|
||||
|
|
@ -424,9 +478,9 @@ module Discrete = {
|
|||
let toDiscrete = t => Some(t);
|
||||
|
||||
let normalize = (t: t): t => {
|
||||
let discreteIntegralSum = integralEndY(~cache=None, t);
|
||||
|
||||
scaleBy(~scale=(1. /. discreteIntegralSum), ~knownIntegralSum=Some(1.0), t);
|
||||
t
|
||||
|> scaleBy(~scale=1. /. integralEndY(~cache=None, t))
|
||||
|> updateKnownIntegralSum(Some(1.0));
|
||||
};
|
||||
|
||||
let normalizedToContinuous = _ => None;
|
||||
|
|
@ -448,6 +502,21 @@ module Discrete = {
|
|||
make(clippedShape, None); // if someone needs the sum, they'll have to recompute it
|
||||
};
|
||||
|
||||
let truncate =
|
||||
(leftCutoff: option(float), rightCutoff: option(float), t: t): t => {
|
||||
let truncatedShape =
|
||||
t
|
||||
|> getShape
|
||||
|> XYShape.T.zip
|
||||
|> XYShape.Zipped.filterByX(x =>
|
||||
x >= E.O.default(neg_infinity, leftCutoff)
|
||||
|| x <= E.O.default(infinity, rightCutoff)
|
||||
)
|
||||
|> XYShape.T.fromZippedArray;
|
||||
|
||||
make(truncatedShape, None);
|
||||
};
|
||||
|
||||
let xToY = (f, t) =>
|
||||
t
|
||||
|> getShape
|
||||
|
|
@ -477,53 +546,43 @@ module Discrete = {
|
|||
XYShape.Analysis.getVarianceDangerously(t, mean, getMeanOfSquares);
|
||||
};
|
||||
});
|
||||
|
||||
};
|
||||
|
||||
// TODO: I think this shouldn't assume continuous/discrete are normalized to 1.0, and thus should not need the discreteProbabilityMassFraction being separate.
|
||||
module Mixed = {
|
||||
type t = DistTypes.mixedShape;
|
||||
let make = (~continuous, ~discrete): t => {
|
||||
continuous,
|
||||
discrete,
|
||||
};
|
||||
let make = (~continuous, ~discrete): t => {continuous, discrete};
|
||||
|
||||
let totalLength = (t: t): int => {
|
||||
let continuousLength = t.continuous |> Continuous.getShape |> XYShape.T.length;
|
||||
let continuousLength =
|
||||
t.continuous |> Continuous.getShape |> XYShape.T.length;
|
||||
let discreteLength = t.discrete |> Discrete.getShape |> XYShape.T.length;
|
||||
|
||||
continuousLength + discreteLength;
|
||||
};
|
||||
|
||||
// TODO: Put into scaling module
|
||||
//let normalizeMixedPoint = (t, f) => f *. discreteProbabilityMassFraction;*/
|
||||
let scaleBy = (~scale=1.0, {discrete, continuous}: t): t => {
|
||||
let scaledDiscrete = Discrete.scaleBy(~scale, discrete);
|
||||
let scaledContinuous = Continuous.scaleBy(~scale, continuous);
|
||||
make(~discrete=scaledDiscrete, ~continuous=scaledContinuous);
|
||||
};
|
||||
|
||||
//TODO: Warning: This currently computes the integral, which is expensive.
|
||||
/*let scaleContinuousFn =
|
||||
({discreteProbabilityMassFraction}: DistTypes.mixedShape, f) =>
|
||||
f *. (1.0 -. discreteProbabilityMassFraction); */
|
||||
let toContinuous = ({continuous}: t) => Some(continuous);
|
||||
let toDiscrete = ({discrete}: t) => Some(discrete);
|
||||
|
||||
//TODO: Warning: This currently computes the integral, which is expensive.
|
||||
let combine = (~knownIntegralSumsFn, fn, t1: t, t2: t) => {
|
||||
let reducedDiscrete =
|
||||
[|t1, t2|]
|
||||
|> E.A.fmap(toDiscrete)
|
||||
|> E.A.O.concatSomes
|
||||
|> Discrete.reduce(~knownIntegralSumsFn, fn);
|
||||
|
||||
// Normalizes to 1.0.
|
||||
/*let scaleContinuous = ({discreteProbabilityMassFraction}: t, continuous) =>
|
||||
// get only the continuous, and scale it to the respective
|
||||
continuous
|
||||
|> Continuous.T.scaleToIntegralSum(
|
||||
~intendedSum=1.0 -. discreteProbabilityMassFraction,
|
||||
);
|
||||
let reducedContinuous =
|
||||
[|t1, t2|]
|
||||
|> E.A.fmap(toContinuous)
|
||||
|> E.A.O.concatSomes
|
||||
|> Continuous.reduce(~knownIntegralSumsFn, fn);
|
||||
|
||||
let scaleDiscrete = ({discreteProbabilityMassFraction}: t, disrete) =>
|
||||
disrete
|
||||
|> Discrete.T.scaleToIntegralSum(
|
||||
~intendedSum=discreteProbabilityMassFraction,
|
||||
);*/
|
||||
|
||||
let truncate = (leftCutoff: option(float), rightCutoff: option(float), {discrete, continuous}: t) => {
|
||||
let truncatedDiscrete = Discrete.truncate(leftCutoff, rightCutoff, discrete);
|
||||
let truncatedContinuous = Continuous.truncate(leftCutoff, rightCutoff, continuous);
|
||||
|
||||
make(~discrete=truncatedDiscrete, ~continuous=truncatedContinuous);
|
||||
make(~discrete=reducedDiscrete, ~continuous=reducedContinuous);
|
||||
};
|
||||
|
||||
module T =
|
||||
|
|
@ -536,19 +595,40 @@ module Mixed = {
|
|||
let maxX = ({continuous, discrete}: t) =>
|
||||
max(Continuous.T.maxX(continuous), Discrete.T.maxX(discrete));
|
||||
let toShape = (t: t): DistTypes.shape => Mixed(t);
|
||||
let toContinuous = ({continuous}: t) => Some(continuous);
|
||||
let toDiscrete = ({discrete}: t) => Some(discrete);
|
||||
|
||||
let toContinuous = toContinuous;
|
||||
let toDiscrete = toDiscrete;
|
||||
|
||||
let truncate =
|
||||
(
|
||||
leftCutoff: option(float),
|
||||
rightCutoff: option(float),
|
||||
{discrete, continuous}: t,
|
||||
) => {
|
||||
let truncatedContinuous = Continuous.T.truncate(leftCutoff, rightCutoff, continuous);
|
||||
let truncatedDiscrete = Discrete.T.truncate(leftCutoff, rightCutoff, discrete);
|
||||
|
||||
make(~discrete=truncatedDiscrete, ~continuous=truncatedContinuous);
|
||||
};
|
||||
|
||||
let normalize = (t: t): t => {
|
||||
let continuousIntegralSum = Continuous.T.Integral.sum(~cache=None, t.continuous);
|
||||
let discreteIntegralSum = Discrete.T.Integral.sum(~cache=None, t.discrete);
|
||||
let continuousIntegralSum =
|
||||
Continuous.T.Integral.sum(~cache=None, t.continuous);
|
||||
let discreteIntegralSum =
|
||||
Discrete.T.Integral.sum(~cache=None, t.discrete);
|
||||
let totalIntegralSum = continuousIntegralSum +. discreteIntegralSum;
|
||||
|
||||
let newContinuousSum = continuousIntegralSum /. totalIntegralSum;
|
||||
let newDiscreteSum = discreteIntegralSum /. totalIntegralSum;
|
||||
|
||||
let normalizedContinuous = Continuous.scaleBy(~scale=(1. /. newContinuousSum), ~knownIntegralSum=Some(newContinuousSum), t.continuous);
|
||||
let normalizedDiscrete = Discrete.scaleBy(~scale=(1. /. newDiscreteSum), ~knownIntegralSum=Some(newDiscreteSum), t.discrete);
|
||||
let normalizedContinuous =
|
||||
t.continuous
|
||||
|> Continuous.scaleBy(~scale=1. /. newContinuousSum)
|
||||
|> Continuous.updateKnownIntegralSum(Some(newContinuousSum));
|
||||
let normalizedDiscrete =
|
||||
t.discrete
|
||||
|> Discrete.scaleBy(~scale=1. /. newDiscreteSum)
|
||||
|> Discrete.updateKnownIntegralSum(Some(newDiscreteSum));
|
||||
|
||||
make(~continuous=normalizedContinuous, ~discrete=normalizedDiscrete);
|
||||
};
|
||||
|
|
@ -563,8 +643,10 @@ module Mixed = {
|
|||
};
|
||||
|
||||
let toDiscreteProbabilityMassFraction = ({discrete, continuous}: t) => {
|
||||
let discreteIntegralSum = Discrete.T.Integral.sum(~cache=None, discrete);
|
||||
let continuousIntegralSum = Continuous.T.Integral.sum(~cache=None, continuous);
|
||||
let discreteIntegralSum =
|
||||
Discrete.T.Integral.sum(~cache=None, discrete);
|
||||
let continuousIntegralSum =
|
||||
Continuous.T.Integral.sum(~cache=None, continuous);
|
||||
let totalIntegralSum = discreteIntegralSum +. continuousIntegralSum;
|
||||
|
||||
discreteIntegralSum /. totalIntegralSum;
|
||||
|
|
@ -575,20 +657,25 @@ module Mixed = {
|
|||
// The easiest way to do this is to simply go by the previous probability masses.
|
||||
|
||||
// The cache really isn't helpful here, because we would need two separate caches
|
||||
let discreteIntegralSum = Discrete.T.Integral.sum(~cache=None, discrete);
|
||||
let continuousIntegralSum = Continuous.T.Integral.sum(~cache=None, continuous);
|
||||
let discreteIntegralSum =
|
||||
Discrete.T.Integral.sum(~cache=None, discrete);
|
||||
let continuousIntegralSum =
|
||||
Continuous.T.Integral.sum(~cache=None, continuous);
|
||||
let totalIntegralSum = discreteIntegralSum +. continuousIntegralSum;
|
||||
|
||||
let downsampledDiscrete =
|
||||
Discrete.T.downsample(
|
||||
int_of_float(float_of_int(count) *. (discreteIntegralSum /. totalIntegralSum)),
|
||||
int_of_float(
|
||||
float_of_int(count) *. (discreteIntegralSum /. totalIntegralSum),
|
||||
),
|
||||
discrete,
|
||||
);
|
||||
|
||||
let downsampledContinuous =
|
||||
Continuous.T.downsample(
|
||||
int_of_float(
|
||||
float_of_int(count) *. (continuousIntegralSum /. totalIntegralSum),
|
||||
float_of_int(count)
|
||||
*. (continuousIntegralSum /. totalIntegralSum),
|
||||
),
|
||||
continuous,
|
||||
);
|
||||
|
|
@ -596,23 +683,20 @@ module Mixed = {
|
|||
{discrete: downsampledDiscrete, continuous: downsampledContinuous};
|
||||
};
|
||||
|
||||
let normalizedToContinuous = (t: t) =>
|
||||
Some(normalize(t).continuous);
|
||||
let normalizedToContinuous = (t: t) => Some(normalize(t).continuous);
|
||||
|
||||
let normalizedToDiscrete = ({discrete} as t: t) =>
|
||||
Some(normalize(t).discrete);
|
||||
|
||||
let integral =
|
||||
(
|
||||
~cache,
|
||||
{continuous, discrete}: t,
|
||||
) => {
|
||||
let integral = (~cache, {continuous, discrete}: t) => {
|
||||
switch (cache) {
|
||||
| Some(cache) => cache
|
||||
| None => {
|
||||
| None =>
|
||||
// note: if the underlying shapes aren't normalized, then these integrals won't be either!
|
||||
let continuousIntegral = Continuous.T.Integral.get(~cache=None, continuous);
|
||||
let discreteIntegral = Discrete.T.Integral.get(~cache=None, discrete);
|
||||
let continuousIntegral =
|
||||
Continuous.T.Integral.get(~cache=None, continuous);
|
||||
let discreteIntegral =
|
||||
Discrete.T.Integral.get(~cache=None, discrete);
|
||||
|
||||
Continuous.make(
|
||||
`Linear,
|
||||
|
|
@ -623,7 +707,6 @@ module Mixed = {
|
|||
),
|
||||
None,
|
||||
);
|
||||
}
|
||||
};
|
||||
};
|
||||
|
||||
|
|
@ -648,14 +731,26 @@ module Mixed = {
|
|||
// This pipes all ys (continuous and discrete) through fn.
|
||||
// If mapY is a linear operation, we might be able to update the knownIntegralSums as well;
|
||||
// if not, they'll be set to None.
|
||||
let mapY = (~knownIntegralSumFn=(previousIntegralSum => None), fn, {discrete, continuous}: t): t => {
|
||||
let mapY =
|
||||
(
|
||||
~knownIntegralSumFn=previousIntegralSum => None,
|
||||
fn,
|
||||
{discrete, continuous}: t,
|
||||
)
|
||||
: t => {
|
||||
let u = E.O.bind(_, knownIntegralSumFn);
|
||||
|
||||
let yMappedDiscrete =
|
||||
discrete |> Discrete.T.mapY(fn) |> Discrete.updateKnownIntegralSum(u(discrete.knownIntegralSum));
|
||||
discrete
|
||||
|> Discrete.T.mapY(fn)
|
||||
|> Discrete.updateKnownIntegralSum(u(discrete.knownIntegralSum));
|
||||
|
||||
let yMappedContinuous =
|
||||
continuous |> Continuous.T.mapY(fn) |> Continuous.updateKnownIntegralSum(u(continuous.knownIntegralSum));
|
||||
continuous
|
||||
|> Continuous.T.mapY(fn)
|
||||
|> Continuous.updateKnownIntegralSum(
|
||||
u(continuous.knownIntegralSum),
|
||||
);
|
||||
|
||||
{
|
||||
discrete: yMappedDiscrete,
|
||||
|
|
@ -668,34 +763,55 @@ module Mixed = {
|
|||
let continuousMean = Continuous.T.mean(continuous);
|
||||
|
||||
// the combined mean is the weighted sum of the two:
|
||||
let discreteIntegralSum = Discrete.T.Integral.sum(~cache=None, discrete);
|
||||
let continuousIntegralSum = Continuous.T.Integral.sum(~cache=None, continuous);
|
||||
let discreteIntegralSum =
|
||||
Discrete.T.Integral.sum(~cache=None, discrete);
|
||||
let continuousIntegralSum =
|
||||
Continuous.T.Integral.sum(~cache=None, continuous);
|
||||
let totalIntegralSum = discreteIntegralSum +. continuousIntegralSum;
|
||||
|
||||
(discreteMean *. discreteIntegralSum +. continuousMean *. continuousIntegralSum) /. totalIntegralSum;
|
||||
(
|
||||
discreteMean
|
||||
*. discreteIntegralSum
|
||||
+. continuousMean
|
||||
*. continuousIntegralSum
|
||||
)
|
||||
/. totalIntegralSum;
|
||||
};
|
||||
|
||||
let variance = ({discrete, continuous} as t: t): float => {
|
||||
// the combined mean is the weighted sum of the two:
|
||||
let discreteIntegralSum = Discrete.T.Integral.sum(~cache=None, discrete);
|
||||
let continuousIntegralSum = Continuous.T.Integral.sum(~cache=None, continuous);
|
||||
let discreteIntegralSum =
|
||||
Discrete.T.Integral.sum(~cache=None, discrete);
|
||||
let continuousIntegralSum =
|
||||
Continuous.T.Integral.sum(~cache=None, continuous);
|
||||
let totalIntegralSum = discreteIntegralSum +. continuousIntegralSum;
|
||||
|
||||
let getMeanOfSquares = ({discrete, continuous} as t: t) => {
|
||||
let discreteMean = discrete |> Discrete.shapeMap(XYShape.Analysis.squareXYShape) |> Discrete.T.mean;
|
||||
let continuousMean = continuous |> XYShape.Analysis.getMeanOfSquaresContinuousShape;
|
||||
(discreteMean *. discreteIntegralSum +. continuousMean *. continuousIntegralSum) /. totalIntegralSum
|
||||
let discreteMean =
|
||||
discrete
|
||||
|> Discrete.shapeMap(XYShape.Analysis.squareXYShape)
|
||||
|> Discrete.T.mean;
|
||||
let continuousMean =
|
||||
continuous |> XYShape.Analysis.getMeanOfSquaresContinuousShape;
|
||||
(
|
||||
discreteMean
|
||||
*. discreteIntegralSum
|
||||
+. continuousMean
|
||||
*. continuousIntegralSum
|
||||
)
|
||||
/. totalIntegralSum;
|
||||
};
|
||||
|
||||
switch (discreteIntegralSum /. totalIntegralSum) {
|
||||
| 1.0 => Discrete.T.variance(discrete)
|
||||
| 0.0 => Continuous.T.variance(continuous)
|
||||
| _ => XYShape.Analysis.getVarianceDangerously(t, mean, getMeanOfSquares)
|
||||
| _ =>
|
||||
XYShape.Analysis.getVarianceDangerously(t, mean, getMeanOfSquares)
|
||||
};
|
||||
};
|
||||
});
|
||||
|
||||
let convolve = (fn: ((float, float) => float), t1: t, t2: t): t => {
|
||||
let convolve = (fn: (float, float) => float, t1: t, t2: t): t => {
|
||||
// Discrete convolution can cause a huge increase in the number of samples,
|
||||
// so we'll first downsample.
|
||||
|
||||
|
|
@ -713,16 +829,21 @@ module Mixed = {
|
|||
|
||||
// continuous (*) continuous => continuous, but also
|
||||
// discrete (*) continuous => continuous (and vice versa). We have to take care of all combos and then combine them:
|
||||
let ccConvResult = Continuous.convolve(fn, t1d.continuous, t2d.continuous);
|
||||
let dcConvResult = Continuous.convolveWithDiscrete(fn, t2d.continuous, t1d.discrete);
|
||||
let cdConvResult = Continuous.convolveWithDiscrete(fn, t1d.continuous, t2d.discrete);
|
||||
let continuousConvResult = Continuous.reduce((+.), [|ccConvResult, dcConvResult, cdConvResult|]);
|
||||
let ccConvResult =
|
||||
Continuous.convolve(fn, t1d.continuous, t2d.continuous);
|
||||
let dcConvResult =
|
||||
Continuous.convolveWithDiscrete(fn, t2d.continuous, t1d.discrete);
|
||||
let cdConvResult =
|
||||
Continuous.convolveWithDiscrete(fn, t1d.continuous, t2d.discrete);
|
||||
let continuousConvResult =
|
||||
Continuous.reduce((+.), [|ccConvResult, dcConvResult, cdConvResult|]);
|
||||
|
||||
// ... finally, discrete (*) discrete => discrete, obviously:
|
||||
let discreteConvResult = Discrete.convolve(fn, t1d.discrete, t2d.discrete);
|
||||
let discreteConvResult =
|
||||
Discrete.convolve(fn, t1d.discrete, t2d.discrete);
|
||||
|
||||
{discrete: discreteConvResult, continuous: continuousConvResult};
|
||||
}
|
||||
};
|
||||
};
|
||||
|
||||
module Shape = {
|
||||
|
|
@ -741,7 +862,8 @@ module Shape = {
|
|||
| Continuous(m) => Continuous(fn3(m))
|
||||
};
|
||||
|
||||
let toMixed = mapToAll((
|
||||
let toMixed =
|
||||
mapToAll((
|
||||
m => m,
|
||||
d => Mixed.make(~discrete=d, ~continuous=Continuous.empty),
|
||||
c => Mixed.make(~discrete=Discrete.empty, ~continuous=c),
|
||||
|
|
@ -751,33 +873,20 @@ module Shape = {
|
|||
Mixed(Mixed.convolve(fn, toMixed(t1), toMixed(t2)));
|
||||
};
|
||||
|
||||
let downsample = (~cache=None, i, t) =>
|
||||
fmap((
|
||||
Mixed.T.downsample(i),
|
||||
Discrete.T.downsample(i),
|
||||
Continuous.T.downsample(i),
|
||||
), t);
|
||||
|
||||
let normalize =
|
||||
fmap((
|
||||
Mixed.T.normalize,
|
||||
Discrete.T.normalize,
|
||||
Continuous.T.normalize,
|
||||
));
|
||||
|
||||
let truncate (leftCutoff, rightCutoff, t): t =
|
||||
fmap((
|
||||
Mixed.truncate(leftCutoff, rightCutoff),
|
||||
Discrete.truncate(leftCutoff, rightCutoff),
|
||||
Continuous.truncate(leftCutoff, rightCutoff),
|
||||
), t);
|
||||
let combine = (~knownIntegralSumsFn=(_, _) => None, fn, t1: t, t2: t) =>
|
||||
switch ((t1, t2)) {
|
||||
| (Continuous(m1), Continuous(m2)) => DistTypes.Continuous(Continuous.combine(~knownIntegralSumsFn, fn, m1, m2))
|
||||
| (Discrete(m1), Discrete(m2)) => DistTypes.Discrete(Discrete.combine(~knownIntegralSumsFn, fn, m1, m2))
|
||||
| (m1, m2) => {
|
||||
DistTypes.Mixed(Mixed.combine(~knownIntegralSumsFn, fn, toMixed(m1), toMixed(m2)))
|
||||
}
|
||||
};
|
||||
|
||||
module T =
|
||||
Dist({
|
||||
type t = DistTypes.shape;
|
||||
type integral = DistTypes.continuousShape;
|
||||
|
||||
|
||||
let xToY = (f: float) =>
|
||||
mapToAll((
|
||||
Mixed.T.xToY(f),
|
||||
|
|
@ -789,9 +898,31 @@ module Shape = {
|
|||
|
||||
let toContinuous = t => None;
|
||||
let toDiscrete = t => None;
|
||||
let downsample = (~cache=None, i, t) => t;
|
||||
|
||||
|
||||
let downsample = (~cache=None, i, t) =>
|
||||
fmap(
|
||||
(
|
||||
Mixed.T.downsample(i),
|
||||
Discrete.T.downsample(i),
|
||||
Continuous.T.downsample(i),
|
||||
),
|
||||
t,
|
||||
);
|
||||
|
||||
let truncate = (leftCutoff, rightCutoff, t): t =>
|
||||
fmap(
|
||||
(
|
||||
Mixed.T.truncate(leftCutoff, rightCutoff),
|
||||
Discrete.T.truncate(leftCutoff, rightCutoff),
|
||||
Continuous.T.truncate(leftCutoff, rightCutoff),
|
||||
),
|
||||
t,
|
||||
);
|
||||
|
||||
let toDiscreteProbabilityMassFraction = t => 0.0;
|
||||
let normalize = t => t;
|
||||
let normalize =
|
||||
fmap((Mixed.T.normalize, Discrete.T.normalize, Continuous.T.normalize));
|
||||
let toContinuous =
|
||||
mapToAll((
|
||||
Mixed.T.toContinuous,
|
||||
|
|
@ -853,7 +984,7 @@ module Shape = {
|
|||
));
|
||||
};
|
||||
let maxX = mapToAll((Mixed.T.maxX, Discrete.T.maxX, Continuous.T.maxX));
|
||||
let mapY = (~knownIntegralSumFn=(previousIntegralSum => None), fn) =>
|
||||
let mapY = (~knownIntegralSumFn=previousIntegralSum => None, fn) =>
|
||||
fmap((
|
||||
Mixed.T.mapY(~knownIntegralSumFn, fn),
|
||||
Discrete.T.mapY(~knownIntegralSumFn, fn),
|
||||
|
|
@ -935,14 +1066,18 @@ module DistPlus = {
|
|||
let toDiscrete = shapeFn(Shape.T.toDiscrete);
|
||||
|
||||
let normalize = (t: t): t => {
|
||||
let normalizedShape =
|
||||
t |> toShape |> Shape.T.normalize;
|
||||
let normalizedShape = t |> toShape |> Shape.T.normalize;
|
||||
|
||||
t |> updateShape(normalizedShape);
|
||||
|
||||
// TODO: also adjust for domainIncludedProbabilityMass here.
|
||||
};
|
||||
|
||||
let truncate = (leftCutoff, rightCutoff, t: t): t => {
|
||||
let truncatedShape = t |> toShape |> Shape.T.truncate(leftCutoff, rightCutoff);
|
||||
|
||||
t |> updateShape(truncatedShape);
|
||||
};
|
||||
|
||||
// TODO: replace this with
|
||||
let normalizedToContinuous = (t: t) => {
|
||||
t
|
||||
|
|
@ -980,7 +1115,13 @@ module DistPlus = {
|
|||
let downsample = (~cache=None, i, t): t =>
|
||||
updateShape(t |> toShape |> Shape.T.downsample(i), t);
|
||||
// todo: adjust for limit, maybe?
|
||||
let mapY = (~knownIntegralSumFn=(previousIntegralSum => None), fn, {shape, _} as t: t): t =>
|
||||
let mapY =
|
||||
(
|
||||
~knownIntegralSumFn=previousIntegralSum => None,
|
||||
fn,
|
||||
{shape, _} as t: t,
|
||||
)
|
||||
: t =>
|
||||
Shape.T.mapY(~knownIntegralSumFn, fn, shape) |> updateShape(_, t);
|
||||
|
||||
let integralEndY = (~cache as _, t: t) =>
|
||||
|
|
|
|||
|
|
@ -1,13 +1,13 @@
|
|||
let truncateIfShould =
|
||||
let downsampleIfShould =
|
||||
(
|
||||
{recommendedLength, shouldTruncate}: RenderTypes.DistPlusRenderer.inputs,
|
||||
{recommendedLength, shouldDownsample}: RenderTypes.DistPlusRenderer.inputs,
|
||||
outputs: RenderTypes.ShapeRenderer.Combined.outputs,
|
||||
dist,
|
||||
) => {
|
||||
let willTruncate =
|
||||
shouldTruncate
|
||||
let willDownsample =
|
||||
shouldDownsample
|
||||
&& RenderTypes.ShapeRenderer.Combined.methodUsed(outputs) == `Sampling;
|
||||
willTruncate ? dist |> Distributions.DistPlus.T.truncate(recommendedLength) : dist;
|
||||
willDownsample ? dist |> Distributions.DistPlus.T.downsample(recommendedLength) : dist;
|
||||
};
|
||||
|
||||
let run =
|
||||
|
|
@ -21,7 +21,7 @@ let run =
|
|||
~guesstimatorString=Some(inputs.distPlusIngredients.guesstimatorString),
|
||||
(),
|
||||
)
|
||||
|> Distributions.DistPlus.T.scaleToIntegralSum(~intendedSum=1.0);
|
||||
|> Distributions.DistPlus.T.normalize;
|
||||
let outputs =
|
||||
ShapeRenderer.run({
|
||||
samplingInputs: inputs.samplingInputs,
|
||||
|
|
@ -32,6 +32,6 @@ let run =
|
|||
});
|
||||
let shape = outputs |> RenderTypes.ShapeRenderer.Combined.getShape;
|
||||
let dist =
|
||||
shape |> E.O.fmap(toDist) |> E.O.fmap(truncateIfShould(inputs, outputs));
|
||||
shape |> E.O.fmap(toDist) |> E.O.fmap(downsampleIfShould(inputs, outputs));
|
||||
RenderTypes.DistPlusRenderer.Outputs.make(outputs, dist);
|
||||
};
|
||||
|
|
@ -75,7 +75,7 @@ module ShapeRenderer = {
|
|||
|
||||
module DistPlusRenderer = {
|
||||
let defaultRecommendedLength = 10000;
|
||||
let defaultShouldTruncate = true;
|
||||
let defaultShouldDownsample = true;
|
||||
type ingredients = {
|
||||
guesstimatorString: string,
|
||||
domain: DistTypes.domain,
|
||||
|
|
@ -85,7 +85,7 @@ module DistPlusRenderer = {
|
|||
distPlusIngredients: ingredients,
|
||||
samplingInputs: ShapeRenderer.Sampling.inputs,
|
||||
recommendedLength: int,
|
||||
shouldTruncate: bool,
|
||||
shouldDownsample: bool,
|
||||
};
|
||||
module Ingredients = {
|
||||
let make =
|
||||
|
|
@ -105,7 +105,7 @@ module DistPlusRenderer = {
|
|||
(
|
||||
~samplingInputs=ShapeRenderer.Sampling.Inputs.empty,
|
||||
~recommendedLength=defaultRecommendedLength,
|
||||
~shouldTruncate=defaultShouldTruncate,
|
||||
~shouldDownsample=defaultShouldDownsample,
|
||||
~distPlusIngredients,
|
||||
(),
|
||||
)
|
||||
|
|
@ -113,7 +113,7 @@ module DistPlusRenderer = {
|
|||
distPlusIngredients,
|
||||
samplingInputs,
|
||||
recommendedLength,
|
||||
shouldTruncate,
|
||||
shouldDownsample,
|
||||
};
|
||||
type outputs = {
|
||||
shapeRenderOutputs: ShapeRenderer.Combined.outputs,
|
||||
|
|
|
|||
|
|
@ -154,16 +154,17 @@ module MathAdtToDistDst = {
|
|||
weights: option(array(float)),
|
||||
) => {
|
||||
let weights = weights |> E.O.default([||]);
|
||||
let dists =
|
||||
|
||||
/*let dists: =
|
||||
args
|
||||
|> E.A.fmap(
|
||||
fun
|
||||
| Ok(a) => a
|
||||
| Error(e) => Error(e)
|
||||
);
|
||||
);*/
|
||||
|
||||
let firstWithError = dists |> Belt.Array.getBy(_, Belt.Result.isError);
|
||||
let withoutErrors = dists |> E.A.fmap(E.R.toOption) |> E.A.O.concatSomes;
|
||||
let firstWithError = args |> Belt.Array.getBy(_, Belt.Result.isError);
|
||||
let withoutErrors = args |> E.A.fmap(E.R.toOption) |> E.A.O.concatSomes;
|
||||
|
||||
switch (firstWithError) {
|
||||
| Some(Error(e)) => Error(e)
|
||||
|
|
@ -174,16 +175,16 @@ module MathAdtToDistDst = {
|
|||
|> E.A.fmapi((index, t) => {
|
||||
let w = weights |> E.A.get(_, index) |> E.O.default(1.0);
|
||||
|
||||
`Operation(`ScaleBy(`Multiply, t, `DistData(`Symbolic(`Float(w)))))
|
||||
`Operation(`ScaleOperation(`Multiply, t, `DistData(`Symbolic(`Float(w)))))
|
||||
});
|
||||
|
||||
let pointwiseSum = components
|
||||
|> Js.Array.sliceFrom(1)
|
||||
|> E.A.fold_left((acc, x) => {
|
||||
`PointwiseSum(acc, x)
|
||||
`Operation(`PointwiseOperation(`Add, acc, x))
|
||||
}, E.A.unsafe_get(components, 0))
|
||||
|
||||
Ok(`Normalize(pointwiseSum))
|
||||
Ok(`Operation(`Normalize(pointwiseSum)))
|
||||
}
|
||||
};
|
||||
};
|
||||
|
|
@ -254,21 +255,21 @@ module MathAdtToDistDst = {
|
|||
args
|
||||
|> E.A.fmap(functionParser)
|
||||
|> (fun
|
||||
| [|Ok(l), Ok(r)|] => Ok(`Combination(l, r, `AddOperation))
|
||||
| [|Ok(l), Ok(r)|] => Ok(`Operation(`StandardOperation(`Add, l, r)))
|
||||
| _ => Error("Addition needs two operands"))
|
||||
}
|
||||
| Fn({name: "subtract", args}) => {
|
||||
args
|
||||
|> E.A.fmap(functionParser)
|
||||
|> (fun
|
||||
| [|Ok(l), Ok(r)|] => Ok(`Combination(l, r, `SubtractOperation))
|
||||
| [|Ok(l), Ok(r)|] => Ok(`Operation(`StandardOperation(`Subtract, l, r)))
|
||||
| _ => Error("Subtraction needs two operands"))
|
||||
}
|
||||
| Fn({name: "multiply", args}) => {
|
||||
args
|
||||
|> E.A.fmap(functionParser)
|
||||
|> (fun
|
||||
| [|Ok(l), Ok(r)|] => Ok(`Combination(l, r, `MultiplyOperation))
|
||||
| [|Ok(l), Ok(r)|] => Ok(`Operation(`StandardOperation(`Multiply, l, r)))
|
||||
| _ => Error("Multiplication needs two operands"))
|
||||
}
|
||||
| Fn({name: "divide", args}) => {
|
||||
|
|
@ -276,28 +277,37 @@ module MathAdtToDistDst = {
|
|||
|> E.A.fmap(functionParser)
|
||||
|> (fun
|
||||
| [|Ok(l), Ok(`DistData(`Symbolic(`Float(0.0))))|] => Error("Division by zero")
|
||||
| [|Ok(l), Ok(r)|] => Ok(`Combination(l, r, `DivideOperation))
|
||||
| [|Ok(l), Ok(r)|] => Ok(`Operation(`StandardOperation(`Divide, l, r)))
|
||||
| _ => Error("Division needs two operands"))
|
||||
}
|
||||
| Fn({name: "pow", args}) => {
|
||||
args
|
||||
|> E.A.fmap(functionParser)
|
||||
|> (fun
|
||||
| [|Ok(l), Ok(r)|] => Ok(`Combination(l, r, `ExponentiateOperation))
|
||||
| [|Ok(l), Ok(r)|] => Ok(`Operation(`StandardOperation(`Exponentiate, l, r)))
|
||||
| _ => Error("Division needs two operands")
|
||||
| _ => Error("Exponentiations needs two operands"))
|
||||
}
|
||||
| Fn({name: "leftTruncate", args}) => {
|
||||
args
|
||||
|> E.A.fmap(functionParser)
|
||||
|> (fun
|
||||
| [|Ok(l), Ok(`DistData(`Symbolic(`Float(r))))|] => Ok(`LeftTruncate(l, r))
|
||||
| [|Ok(d), Ok(`DistData(`Symbolic(`Float(lc))))|] => Ok(`Operation(`Truncate(Some(lc), None, d)))
|
||||
| _ => Error("leftTruncate needs two arguments: the expression and the cutoff"))
|
||||
}
|
||||
| Fn({name: "rightTruncate", args}) => {
|
||||
args
|
||||
|> E.A.fmap(functionParser)
|
||||
|> (fun
|
||||
| [|Ok(l), Ok(`DistData(`Symbolic(`Float(r))))|] => Ok(`RightTruncate(l, r))
|
||||
| [|Ok(d), Ok(`DistData(`Symbolic(`Float(rc))))|] => Ok(`Operation(`Truncate(None, Some(rc), d)))
|
||||
| _ => Error("rightTruncate needs two arguments: the expression and the cutoff"))
|
||||
}
|
||||
| Fn({name: "truncate", args}) => {
|
||||
args
|
||||
|> E.A.fmap(functionParser)
|
||||
|> (fun
|
||||
| [|Ok(d), Ok(`DistData(`Symbolic(`Float(lc)))), Ok(`DistData(`Symbolic(`Float(rc))))|] => Ok(`Operation(`Truncate(Some(lc), Some(rc), d)))
|
||||
// TODO: allow on-the-fly evaluations of FloatFromDists to be used as cutoff arguments here.
|
||||
| _ => Error("rightTruncate needs two arguments: the expression and the cutoff"))
|
||||
}
|
||||
| Fn({name}) => Error(name ++ ": function not supported")
|
||||
|
|
|
|||
|
|
@ -1,69 +1,60 @@
|
|||
/* This module represents a tree node. */
|
||||
|
||||
/* TreeNodes are either Data (i.e. symbolic or rendered distributions) or Operations. */
|
||||
type treeNode = [
|
||||
| `DistData(distData)
|
||||
| `Operation(operation)
|
||||
] and distData = [
|
||||
type distData = [
|
||||
| `Symbolic(SymbolicDist.dist)
|
||||
| `RenderedShape(DistTypes.shape)
|
||||
] and operation = [
|
||||
// binary operations
|
||||
| `StandardOperation(standardOperation, treeNode, treeNode)
|
||||
| `PointwiseOperation(pointwiseOperation, treeNode, treeNode)
|
||||
| `ScaleOperation(scaleOperation, treeNode, scaleBy)
|
||||
// unary operations
|
||||
| `Render(treeNode) // always evaluates to `DistData(`RenderedShape(...))
|
||||
| `Truncate(leftCutoff, rightCutoff, treeNode)
|
||||
| `Normalize(treeNode)
|
||||
// direct evaluations of dists (e.g. cdf, sample)
|
||||
| `FloatFromDist(distToFloatOperation, treeNode)
|
||||
] and standardOperation = [
|
||||
];
|
||||
|
||||
type standardOperation = [
|
||||
| `Add
|
||||
| `Multiply
|
||||
| `Subtract
|
||||
| `Divide
|
||||
| `Exponentiate
|
||||
] and pointwiseOperation = [
|
||||
| `Add
|
||||
| `Multiply
|
||||
] and scaleOperation = [
|
||||
| `Multiply
|
||||
| `Log
|
||||
];
|
||||
type pointwiseOperation = [ | `Add | `Multiply];
|
||||
type scaleOperation = [ | `Multiply | `Exponentiate | `Log];
|
||||
type distToFloatOperation = [ | `Pdf(float) | `Inv(float) | `Mean | `Sample];
|
||||
|
||||
/* TreeNodes are either Data (i.e. symbolic or rendered distributions) or Operations. */
|
||||
type treeNode = [
|
||||
| `DistData(distData) // a leaf node that describes a distribution
|
||||
| `Operation(operation) // an operation on two child nodes
|
||||
]
|
||||
and scaleBy = treeNode and leftCutoff = option(float) and rightCutoff = option(float)
|
||||
and distToFloatOperation = [
|
||||
| `Pdf(float)
|
||||
| `Cdf(float)
|
||||
| `Inv(float)
|
||||
| `Sample
|
||||
and operation = [
|
||||
| // binary operations
|
||||
`StandardOperation(
|
||||
standardOperation,
|
||||
treeNode,
|
||||
treeNode,
|
||||
)
|
||||
// unary operations
|
||||
| `PointwiseOperation(pointwiseOperation, treeNode, treeNode) // always evaluates to `DistData(`RenderedShape(...))
|
||||
| `ScaleOperation(scaleOperation, treeNode, treeNode) // always evaluates to `DistData(`RenderedShape(...))
|
||||
| `Render(treeNode) // always evaluates to `DistData(`RenderedShape(...))
|
||||
| `Truncate // always evaluates to `DistData(`RenderedShape(...))
|
||||
(
|
||||
option(float),
|
||||
option(float),
|
||||
treeNode,
|
||||
) // leftCutoff and rightCutoff
|
||||
| `Normalize // always evaluates to `DistData(`RenderedShape(...))
|
||||
// leftCutoff and rightCutoff
|
||||
(
|
||||
treeNode,
|
||||
)
|
||||
| `FloatFromDist // always evaluates to `DistData(`RenderedShape(...))
|
||||
// leftCutoff and rightCutoff
|
||||
(
|
||||
distToFloatOperation,
|
||||
treeNode,
|
||||
)
|
||||
];
|
||||
|
||||
module TreeNode = {
|
||||
type t = treeNode;
|
||||
type simplifier = treeNode => result(treeNode, string);
|
||||
|
||||
type renderParams = {
|
||||
operationToDistData: (int, operation) => result(t, string),
|
||||
sampleCount: int,
|
||||
}
|
||||
|
||||
let rec renderToShape = (renderParams, t: t): result(DistTypes.shape, string) => {
|
||||
switch (t) {
|
||||
| `DistData(`RenderedShape(s)) => Ok(s) // already a rendered shape, we're done here
|
||||
| `DistData(`Symbolic(d)) =>
|
||||
switch (d) {
|
||||
| `Float(v) =>
|
||||
Ok(Discrete(Distributions.Discrete.make({xs: [|v|], ys: [|1.0|]}, Some(1.0))));
|
||||
| _ =>
|
||||
let xs = SymbolicDist.GenericDistFunctions.interpolateXs(~xSelection=`ByWeight, d, renderParams.sampleCount);
|
||||
let ys = xs |> E.A.fmap(x => SymbolicDist.GenericDistFunctions.pdf(x, d));
|
||||
Ok(Continuous(Distributions.Continuous.make(`Linear, {xs, ys}, Some(1.0))));
|
||||
}
|
||||
| `Operation(op) => E.R.bind(renderParams.operationToDistData(renderParams.sampleCount, op), renderToShape(renderParams))
|
||||
};
|
||||
};
|
||||
|
||||
/* The following modules encapsulate everything we can do with
|
||||
* different kinds of operations. */
|
||||
|
||||
|
|
@ -154,105 +145,136 @@ module TreeNode = {
|
|||
};
|
||||
};
|
||||
|
||||
let evaluateNumerically = (standardOp, renderParams, t1, t2) => {
|
||||
let evaluateNumerically = (standardOp, operationToDistData, t1, t2) => {
|
||||
let func = funcFromOp(standardOp);
|
||||
|
||||
// TODO: downsample the two shapes
|
||||
let renderedShape1 = t1 |> renderToShape(renderParams);
|
||||
let renderedShape2 = t2 |> renderToShape(renderParams);
|
||||
// force rendering into shapes
|
||||
let renderedShape1 = operationToDistData(`Render(t1));
|
||||
let renderedShape2 = operationToDistData(`Render(t2));
|
||||
|
||||
// This will most likely require a mixed
|
||||
|
||||
switch ((renderedShape1, renderedShape2)) {
|
||||
switch (renderedShape1, renderedShape2) {
|
||||
| (
|
||||
Ok(`DistData(`RenderedShape(s1))),
|
||||
Ok(`DistData(`RenderedShape(s2))),
|
||||
) =>
|
||||
Ok(
|
||||
`DistData(
|
||||
`RenderedShape(Distributions.Shape.convolve(func, s1, s2)),
|
||||
),
|
||||
)
|
||||
| (Error(e1), _) => Error(e1)
|
||||
| (_, Error(e2)) => Error(e2)
|
||||
| (Ok(s1), Ok(s2)) => Ok(`DistData(`RenderedShape(Distributions.Shape.convolve(func, s1, s2))))
|
||||
| _ => Error("Could not render shapes.")
|
||||
};
|
||||
};
|
||||
|
||||
let evaluateToDistData =
|
||||
(standardOp: standardOperation, renderParams, t1: t, t2: t): result(treeNode, string) =>
|
||||
(standardOp: standardOperation, operationToDistData, t1: t, t2: t)
|
||||
: result(treeNode, string) =>
|
||||
standardOp
|
||||
|> Simplify.attempt(_, t1, t2)
|
||||
|> E.R.bind(
|
||||
_,
|
||||
fun
|
||||
| `DistData(d) => Ok(`DistData(d)) // the analytical simplifaction worked, nice!
|
||||
| `Operation(_) => // if not, run the convolution
|
||||
evaluateNumerically(standardOp, renderParams, t1, t2),
|
||||
| `Operation(_) =>
|
||||
// if not, run the convolution
|
||||
evaluateNumerically(standardOp, operationToDistData, t1, t2),
|
||||
);
|
||||
};
|
||||
|
||||
module ScaleOperation = {
|
||||
let rec mean = (renderParams, t: t): result(float, string) => {
|
||||
switch (t) {
|
||||
| `DistData(`RenderedShape(s)) => Ok(Distributions.Shape.T.mean(s))
|
||||
| `DistData(`Symbolic(s)) => SymbolicDist.GenericDistFunctions.mean(s)
|
||||
// evaluating the operation returns result(treeNode(distData)). We then want to make sure
|
||||
| `Operation(op) => E.R.bind(renderParams.operationToDistData(renderParams.sampleCount, op), mean(renderParams))
|
||||
}
|
||||
};
|
||||
|
||||
let fnFromOp =
|
||||
fun
|
||||
| `Multiply => ( *. )
|
||||
| `Log => ((a, b) => ( log(a) /. log(b) ));
|
||||
| `Exponentiate => ( ** )
|
||||
| `Log => ((a, b) => log(a) /. log(b));
|
||||
|
||||
let knownIntegralSumFnFromOp =
|
||||
fun
|
||||
| `Multiply => (a, b) => Some(a *. b)
|
||||
| `Multiply => ((a, b) => Some(a *. b))
|
||||
| `Exponentiate => ((_, _) => None)
|
||||
| `Log => ((_, _) => None);
|
||||
|
||||
let evaluateToDistData = (scaleOp, renderParams, t, scaleBy) => {
|
||||
let evaluateToDistData = (scaleOp, operationToDistData, t, scaleBy) => {
|
||||
// scaleBy has to be a single float, otherwise we'll return an error.
|
||||
let fn = fnFromOp(scaleOp);
|
||||
let knownIntegralSumFn = knownIntegralSumFnFromOp(scaleOp);
|
||||
let renderedShape = t |> renderToShape(renderParams);
|
||||
let scaleByMeanValue = mean(renderParams, scaleBy);
|
||||
|
||||
switch ((renderedShape, scaleByMeanValue)) {
|
||||
let renderedShape = operationToDistData(`Render(t));
|
||||
|
||||
switch (renderedShape, scaleBy) {
|
||||
| (Error(e1), _) => Error(e1)
|
||||
| (_, Error(e2)) => Error(e2)
|
||||
| (Ok(rs), Ok(sm)) =>
|
||||
Ok(`DistData(`RenderedShape(Distributions.Shape.T.mapY(~knownIntegralSumFn=knownIntegralSumFn(sm), fn(sm), rs))))
|
||||
}
|
||||
| (
|
||||
Ok(`DistData(`RenderedShape(rs))),
|
||||
`DistData(`Symbolic(`Float(sm))),
|
||||
) =>
|
||||
Ok(
|
||||
`DistData(
|
||||
`RenderedShape(
|
||||
Distributions.Shape.T.mapY(
|
||||
~knownIntegralSumFn=knownIntegralSumFn(sm),
|
||||
fn(sm),
|
||||
rs,
|
||||
),
|
||||
),
|
||||
),
|
||||
)
|
||||
| (_, _) => Error("Can only scale by float values.")
|
||||
};
|
||||
};
|
||||
};
|
||||
|
||||
module PointwiseOperation = {
|
||||
let funcFromOp: (pointwiseOperation => ((float, float) => float)) =
|
||||
fun
|
||||
| `Add => (+.)
|
||||
| `Multiply => ( *. );
|
||||
let pointwiseAdd = (operationToDistData, t1, t2) => {
|
||||
let renderedShape1 = operationToDistData(`Render(t1));
|
||||
let renderedShape2 = operationToDistData(`Render(t2));
|
||||
|
||||
let evaluateToDistData = (pointwiseOp, renderParams, t1, t2) => {
|
||||
let func = funcFromOp(pointwiseOp);
|
||||
let renderedShape1 = t1 |> renderToShape(renderParams);
|
||||
let renderedShape2 = t2 |> renderToShape(renderParams);
|
||||
switch ((renderedShape1, renderedShape2)) {
|
||||
| (Error(e1), _) => Error(e1)
|
||||
| (_, Error(e2)) => Error(e2)
|
||||
| (Ok(`DistData(`RenderedShape(rs1))), Ok(`DistData(`RenderedShape(rs2)))) => Ok(`DistData(`RenderedShape(Distributions.Shape.combine(~knownIntegralSumsFn=(a, b) => Some(a +. b), (+.), rs1, rs2))))
|
||||
| _ => Error("Could not perform pointwise addition.")
|
||||
};
|
||||
};
|
||||
|
||||
// TODO: figure out integral, diff between pointwiseAdd and pointwiseProduct and other stuff
|
||||
// Distributions.Shape.reduce(func, renderedShape1, renderedShape2);
|
||||
let pointwiseMultiply = (operationToDistData, t1, t2) => {
|
||||
// TODO: construct a function that we can easily sample from, to construct
|
||||
// a RenderedShape. Use the xMin and xMax of the rendered shapes to tell the sampling function where to look.
|
||||
Error("Pointwise multiplication not yet supported.");
|
||||
};
|
||||
|
||||
Error("Pointwise operations currently not supported.")
|
||||
let evaluateToDistData = (pointwiseOp, operationToDistData, t1, t2) => {
|
||||
switch (pointwiseOp) {
|
||||
| `Add => pointwiseAdd(operationToDistData, t1, t2)
|
||||
| `Multiply => pointwiseMultiply(operationToDistData, t1, t2)
|
||||
}
|
||||
};
|
||||
};
|
||||
|
||||
module Truncate = {
|
||||
module Simplify = {
|
||||
let tryTruncatingNothing: simplifier = fun
|
||||
| `Operation(`Truncate(None, None, `DistData(d))) => Ok(`DistData(d))
|
||||
let tryTruncatingNothing: simplifier =
|
||||
fun
|
||||
| `Operation(`Truncate(None, None, `DistData(d))) =>
|
||||
Ok(`DistData(d))
|
||||
| t => Ok(t);
|
||||
|
||||
let tryTruncatingUniform: simplifier = fun
|
||||
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}))));
|
||||
Ok(
|
||||
`DistData(`Symbolic(`Uniform({low: newLow, high: newHigh}))),
|
||||
);
|
||||
}
|
||||
| t => Ok(t);
|
||||
|
||||
let attempt = (leftCutoff, rightCutoff, t): result(treeNode, string) => {
|
||||
let originalTreeNode = `Operation(`Truncate(leftCutoff, rightCutoff, t));
|
||||
let originalTreeNode =
|
||||
`Operation(`Truncate((leftCutoff, rightCutoff, t)));
|
||||
|
||||
originalTreeNode
|
||||
|> tryTruncatingNothing
|
||||
|
|
@ -260,101 +282,191 @@ module TreeNode = {
|
|||
};
|
||||
};
|
||||
|
||||
let evaluateNumerically = (leftCutoff, rightCutoff, renderParams, t) => {
|
||||
let evaluateNumerically =
|
||||
(leftCutoff, rightCutoff, operationToDistData, t) => {
|
||||
// TODO: use named args in renderToShape; if we're lucky we can at least get the tail
|
||||
// of a distribution we otherwise wouldn't get at all
|
||||
let renderedShape = t |> renderToShape(renderParams);
|
||||
let renderedShape = operationToDistData(`Render(t));
|
||||
|
||||
E.R.bind(renderedShape, rs => {
|
||||
let truncatedShape = rs |> Distributions.Shape.truncate(leftCutoff, rightCutoff);
|
||||
switch (renderedShape) {
|
||||
| Ok(`DistData(`RenderedShape(rs))) =>
|
||||
let truncatedShape =
|
||||
rs |> Distributions.Shape.T.truncate(leftCutoff, rightCutoff);
|
||||
Ok(`DistData(`RenderedShape(rs)));
|
||||
});
|
||||
| Error(e1) => Error(e1)
|
||||
| _ => Error("Could not truncate distribution.")
|
||||
};
|
||||
};
|
||||
|
||||
let evaluateToDistData = (leftCutoff: option(float), rightCutoff: option(float), renderParams, t: treeNode): result(treeNode, string) => {
|
||||
let evaluateToDistData =
|
||||
(
|
||||
leftCutoff: option(float),
|
||||
rightCutoff: option(float),
|
||||
operationToDistData,
|
||||
t: treeNode,
|
||||
)
|
||||
: result(treeNode, string) => {
|
||||
t
|
||||
|> Simplify.attempt(leftCutoff, rightCutoff)
|
||||
|> E.R.bind(
|
||||
_,
|
||||
fun
|
||||
| `DistData(d) => Ok(`DistData(d)) // the analytical simplifaction worked, nice!
|
||||
| `Operation(_) => evaluateNumerically(leftCutoff, rightCutoff, renderParams, t),
|
||||
| `Operation(_) =>
|
||||
evaluateNumerically(
|
||||
leftCutoff,
|
||||
rightCutoff,
|
||||
operationToDistData,
|
||||
t,
|
||||
),
|
||||
); // if not, run the convolution
|
||||
};
|
||||
};
|
||||
|
||||
module Normalize = {
|
||||
let rec evaluateToDistData = (renderParams, t: treeNode): result(treeNode, string) => {
|
||||
let rec evaluateToDistData =
|
||||
(operationToDistData, t: treeNode): result(treeNode, string) => {
|
||||
switch (t) {
|
||||
| `DistData(`Symbolic(_)) => Ok(t)
|
||||
| `DistData(`RenderedShape(s)) => {
|
||||
let normalized = Distributions.Shape.normalize(s);
|
||||
| `DistData(`RenderedShape(s)) =>
|
||||
let normalized = Distributions.Shape.T.normalize(s);
|
||||
Ok(`DistData(`RenderedShape(normalized)));
|
||||
}
|
||||
| `Operation(op) => E.R.bind(renderParams.operationToDistData(renderParams.sampleCount, op), evaluateToDistData(renderParams))
|
||||
}
|
||||
}
|
||||
| `Operation(op) =>
|
||||
E.R.bind(
|
||||
operationToDistData(op),
|
||||
evaluateToDistData(operationToDistData),
|
||||
)
|
||||
};
|
||||
};
|
||||
};
|
||||
|
||||
module FloatFromDist = {
|
||||
let evaluateFromSymbolic = (distToFloatOp: distToFloatOperation, s) => {
|
||||
let value = switch (distToFloatOp) {
|
||||
| `Pdf(f) => SymbolicDist.GenericDistFunctions.pdf(f, s)
|
||||
| `Cdf(f) => 0.0
|
||||
| `Inv(f) => SymbolicDist.GenericDistFunctions.inv(f, s)
|
||||
| `Sample => SymbolicDist.GenericDistFunctions.sample(s)
|
||||
}
|
||||
Ok(`DistData(`Symbolic(`Float(value))));
|
||||
let value =
|
||||
switch (distToFloatOp) {
|
||||
| `Pdf(f) => Ok(SymbolicDist.GenericDistFunctions.pdf(f, s))
|
||||
| `Inv(f) => Ok(SymbolicDist.GenericDistFunctions.inv(f, s))
|
||||
| `Sample => Ok(SymbolicDist.GenericDistFunctions.sample(s))
|
||||
| `Mean => SymbolicDist.GenericDistFunctions.mean(s)
|
||||
};
|
||||
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.");
|
||||
E.R.bind(value, v => Ok(`DistData(`Symbolic(`Float(v)))));
|
||||
};
|
||||
let rec evaluateToDistData = (distToFloatOp: distToFloatOperation, renderParams, t: treeNode): result(treeNode, string) => {
|
||||
let evaluateFromRenderedShape =
|
||||
(distToFloatOp: distToFloatOperation, rs: DistTypes.shape)
|
||||
: result(treeNode, string) => {
|
||||
Ok(`DistData(`Symbolic(`Float(Distributions.Shape.T.mean(rs)))));
|
||||
};
|
||||
let rec evaluateToDistData =
|
||||
(
|
||||
distToFloatOp: distToFloatOperation,
|
||||
operationToDistData,
|
||||
t: treeNode,
|
||||
)
|
||||
: result(treeNode, string) => {
|
||||
switch (t) {
|
||||
| `DistData(`Symbolic(s)) => evaluateFromSymbolic(distToFloatOp, s) // we want to evaluate the distToFloatOp on the symbolic dist
|
||||
| `DistData(`RenderedShape(rs)) => evaluateFromRenderedShape(distToFloatOp, rs)
|
||||
| `Operation(op) => E.R.bind(renderParams.operationToDistData(renderParams.sampleCount, op), evaluateToDistData(distToFloatOp, renderParams))
|
||||
}
|
||||
}
|
||||
| `DistData(`RenderedShape(rs)) =>
|
||||
evaluateFromRenderedShape(distToFloatOp, rs)
|
||||
| `Operation(op) =>
|
||||
E.R.bind(
|
||||
operationToDistData(op),
|
||||
evaluateToDistData(distToFloatOp, operationToDistData),
|
||||
)
|
||||
};
|
||||
};
|
||||
};
|
||||
|
||||
module Render = {
|
||||
let evaluateToRenderedShape = (renderParams, t: treeNode): result(t, string) => {
|
||||
E.R.bind(renderToShape(renderParams, t), rs => Ok(`DistData(`RenderedShape(rs))));
|
||||
let rec evaluateToRenderedShape =
|
||||
(operationToDistData: operation => result(t, string), sampleCount: int, t: treeNode)
|
||||
: result(t, string) => {
|
||||
switch (t) {
|
||||
| `DistData(`RenderedShape(s)) => Ok(`DistData(`RenderedShape(s))) // already a rendered shape, we're done here
|
||||
| `DistData(`Symbolic(d)) =>
|
||||
switch (d) {
|
||||
| `Float(v) =>
|
||||
Ok(
|
||||
`DistData(
|
||||
`RenderedShape(
|
||||
Discrete(
|
||||
Distributions.Discrete.make(
|
||||
{xs: [|v|], ys: [|1.0|]},
|
||||
Some(1.0),
|
||||
),
|
||||
),
|
||||
),
|
||||
),
|
||||
)
|
||||
| _ =>
|
||||
let xs =
|
||||
SymbolicDist.GenericDistFunctions.interpolateXs(
|
||||
~xSelection=`ByWeight,
|
||||
d,
|
||||
sampleCount,
|
||||
);
|
||||
let ys =
|
||||
xs |> E.A.fmap(x => SymbolicDist.GenericDistFunctions.pdf(x, d));
|
||||
Ok(
|
||||
`DistData(
|
||||
`RenderedShape(
|
||||
Continuous(
|
||||
Distributions.Continuous.make(
|
||||
`Linear,
|
||||
{xs, ys},
|
||||
Some(1.0),
|
||||
),
|
||||
),
|
||||
),
|
||||
),
|
||||
);
|
||||
}
|
||||
| `Operation(op) =>
|
||||
E.R.bind(
|
||||
operationToDistData(op),
|
||||
evaluateToRenderedShape(operationToDistData, sampleCount),
|
||||
)
|
||||
};
|
||||
};
|
||||
};
|
||||
|
||||
let rec operationToDistData =
|
||||
(sampleCount: int, op: operation): result(t, string) => {
|
||||
|
||||
// the functions that convert the Operation nodes to DistData nodes need to
|
||||
// have a way to call this function on their children, if their children are themselves Operation nodes.
|
||||
|
||||
let renderParams: renderParams = {
|
||||
operationToDistData: operationToDistData,
|
||||
sampleCount: sampleCount,
|
||||
};
|
||||
|
||||
switch (op) {
|
||||
| `StandardOperation(standardOp, t1, t2) =>
|
||||
StandardOperation.evaluateToDistData(
|
||||
standardOp, renderParams, t1, t2 // we want to give it the option to render or simply leave it as is
|
||||
standardOp,
|
||||
operationToDistData(sampleCount),
|
||||
t1,
|
||||
t2 // we want to give it the option to render or simply leave it as is
|
||||
)
|
||||
| `PointwiseOperation(pointwiseOp, t1, t2) =>
|
||||
PointwiseOperation.evaluateToDistData(
|
||||
pointwiseOp,
|
||||
renderParams,
|
||||
operationToDistData(sampleCount),
|
||||
t1,
|
||||
t2,
|
||||
)
|
||||
| `ScaleOperation(scaleOp, t, scaleBy) =>
|
||||
ScaleOperation.evaluateToDistData(scaleOp, renderParams, t, scaleBy)
|
||||
| `Truncate(leftCutoff, rightCutoff, t) => Truncate.evaluateToDistData(leftCutoff, rightCutoff, renderParams, t)
|
||||
| `FloatFromDist(distToFloatOp, t) => FloatFromDist.evaluateToDistData(distToFloatOp, renderParams, t)
|
||||
| `Normalize(t) => Normalize.evaluateToDistData(renderParams, t)
|
||||
| `Render(t) => Render.evaluateToRenderedShape(renderParams, t)
|
||||
ScaleOperation.evaluateToDistData(
|
||||
scaleOp,
|
||||
operationToDistData(sampleCount),
|
||||
t,
|
||||
scaleBy,
|
||||
)
|
||||
| `Truncate(leftCutoff, rightCutoff, t) =>
|
||||
Truncate.evaluateToDistData(
|
||||
leftCutoff,
|
||||
rightCutoff,
|
||||
operationToDistData(sampleCount),
|
||||
t,
|
||||
)
|
||||
| `FloatFromDist(distToFloatOp, t) =>
|
||||
FloatFromDist.evaluateToDistData(distToFloatOp, operationToDistData(sampleCount), t)
|
||||
| `Normalize(t) => Normalize.evaluateToDistData(operationToDistData(sampleCount), t)
|
||||
| `Render(t) =>
|
||||
Render.evaluateToRenderedShape(operationToDistData(sampleCount), sampleCount, t)
|
||||
};
|
||||
};
|
||||
|
||||
|
|
@ -372,7 +484,8 @@ module TreeNode = {
|
|||
};
|
||||
|
||||
let rec toString = (t: t): string => {
|
||||
let stringFromStandardOperation = fun
|
||||
let stringFromStandardOperation =
|
||||
fun
|
||||
| `Add => " + "
|
||||
| `Subtract => " - "
|
||||
| `Multiply => " * "
|
||||
|
|
@ -384,31 +497,53 @@ module TreeNode = {
|
|||
| `Add => " .+ "
|
||||
| `Multiply => " .* ";
|
||||
|
||||
let stringFromFloatFromDistOperation =
|
||||
fun
|
||||
| `Pdf(f) => "pdf(x=$f, "
|
||||
| `Inv(f) => "inv(c=$f, "
|
||||
| `Sample => "sample("
|
||||
| `Mean => "mean(";
|
||||
|
||||
|
||||
switch (t) {
|
||||
| `DistData(`Symbolic(d)) => SymbolicDist.GenericDistFunctions.toString(d)
|
||||
| `DistData(`Symbolic(d)) =>
|
||||
SymbolicDist.GenericDistFunctions.toString(d)
|
||||
| `DistData(`RenderedShape(s)) => "[shape]"
|
||||
| `Operation(`StandardOperation(op, t1, t2)) => toString(t1) ++ stringFromStandardOperation(op) ++ toString(t2)
|
||||
| `Operation(`PointwiseOperation(op, t1, t2)) => toString(t1) ++ stringFromPointwiseOperation(op) ++ toString(t2)
|
||||
| `Operation(`ScaleOperation(_scaleOp, t, scaleBy)) => toString(t) ++ " @ " ++ toString(scaleBy)
|
||||
| `Operation(`StandardOperation(op, t1, t2)) =>
|
||||
toString(t1) ++ stringFromStandardOperation(op) ++ toString(t2)
|
||||
| `Operation(`PointwiseOperation(op, t1, t2)) =>
|
||||
toString(t1) ++ stringFromPointwiseOperation(op) ++ toString(t2)
|
||||
| `Operation(`ScaleOperation(_scaleOp, t, scaleBy)) =>
|
||||
toString(t) ++ " @ " ++ toString(scaleBy)
|
||||
| `Operation(`Normalize(t)) => "normalize(" ++ toString(t) ++ ")"
|
||||
| `Operation(`Truncate(lc, rc, t)) => "truncate(" ++ toString(t) ++ ", " ++ E.O.dimap(string_of_float, () => "-inf", lc) ++ ", " ++ E.O.dimap(string_of_float, () => "inf", rc) ++ ")"
|
||||
| `Operation(`FloatFromDist(floatFromDistOp, t)) => stringFromFloatFromDistOperation(floatFromDistOp) ++ toString(t) ++ ")"
|
||||
| `Operation(`Truncate(lc, rc, t)) =>
|
||||
"truncate("
|
||||
++ toString(t)
|
||||
++ ", "
|
||||
++ E.O.dimap(Js.Float.toString, () => "-inf", lc)
|
||||
++ ", "
|
||||
++ E.O.dimap(Js.Float.toString, () => "inf", rc)
|
||||
++ ")"
|
||||
| `Operation(`Render(t)) => toString(t)
|
||||
}
|
||||
};
|
||||
};
|
||||
};
|
||||
|
||||
let toShape = (sampleCount: int, treeNode: treeNode) => {
|
||||
let renderResult = TreeNode.toDistData(`Operation(`Render(treeNode)), sampleCount);
|
||||
|
||||
let renderResult =
|
||||
TreeNode.toDistData(`Operation(`Render(treeNode)), sampleCount);
|
||||
|
||||
switch (renderResult) {
|
||||
| Ok(`DistData(`RenderedShape(rs))) => {
|
||||
| Ok(`DistData(`RenderedShape(rs))) =>
|
||||
let continuous = Distributions.Shape.T.toContinuous(rs);
|
||||
let discrete = Distributions.Shape.T.toDiscrete(rs);
|
||||
let shape = MixedShapeBuilder.buildSimple(~continuous, ~discrete);
|
||||
shape |> E.O.toExt("");
|
||||
}
|
||||
| Ok(_) => E.O.toExn("Rendering failed.", None)
|
||||
| Error(message) => E.O.toExn("No shape found!", None)
|
||||
}
|
||||
| Error(message) => E.O.toExn("No shape found, error: " ++ message, None)
|
||||
};
|
||||
};
|
||||
|
||||
let toString = (treeNode: treeNode) =>
|
||||
TreeNode.toString(treeNode);
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@ let propValue = (t: Prop.Value.t) => {
|
|||
RenderTypes.DistPlusRenderer.make(
|
||||
~distPlusIngredients=r,
|
||||
~recommendedLength=10000,
|
||||
~shouldTruncate=true,
|
||||
~shouldDownsample=true,
|
||||
(),
|
||||
)
|
||||
|> DistPlusRenderer.run
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user