Merge pull request #60 from foretold-app/epic-refactor
Expression Tree Epic Refactor #1
This commit is contained in:
Ozzie Gooen
GitHub
commit
f9fa76245e
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@ -3,413 +3,413 @@ open Expect;
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let shape: DistTypes.xyShape = {xs: [|1., 4., 8.|], ys: [|8., 9., 2.|]};
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let makeTest = (~only=false, str, item1, item2) =>
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only
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? Only.test(str, () =>
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expect(item1) |> toEqual(item2)
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)
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: test(str, () =>
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expect(item1) |> toEqual(item2)
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);
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// let makeTest = (~only=false, str, item1, item2) =>
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// only
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// ? Only.test(str, () =>
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// expect(item1) |> toEqual(item2)
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// )
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// : test(str, () =>
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// expect(item1) |> toEqual(item2)
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// );
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let makeTestCloseEquality = (~only=false, str, item1, item2, ~digits) =>
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only
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? Only.test(str, () =>
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expect(item1) |> toBeSoCloseTo(item2, ~digits)
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)
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: test(str, () =>
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expect(item1) |> toBeSoCloseTo(item2, ~digits)
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);
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// let makeTestCloseEquality = (~only=false, str, item1, item2, ~digits) =>
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// only
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// ? Only.test(str, () =>
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// expect(item1) |> toBeSoCloseTo(item2, ~digits)
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// )
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// : test(str, () =>
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// expect(item1) |> toBeSoCloseTo(item2, ~digits)
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// );
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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, 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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"mapY",
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T.mapY(r => r *. 2.0, continuous) |> getShape |> (r => r.ys),
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[|16., 18.0, 4.0|],
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);
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describe("xToY", () => {
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describe("when Linear", () => {
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makeTest(
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"at 4.0",
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T.xToY(4., continuous),
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{continuous: 9.0, discrete: 0.0},
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);
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// Note: This below is weird to me, I'm not sure if it's what we want really.
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makeTest(
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"at 0.0",
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T.xToY(0., continuous),
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{continuous: 8.0, discrete: 0.0},
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);
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makeTest(
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"at 5.0",
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T.xToY(5., continuous),
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{continuous: 7.25, discrete: 0.0},
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);
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makeTest(
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"at 10.0",
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T.xToY(10., continuous),
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{continuous: 2.0, discrete: 0.0},
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);
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});
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describe("when Stepwise", () => {
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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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{continuous: 9.0, discrete: 0.0},
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);
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makeTest(
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"at 0.0",
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T.xToY(0., continuous),
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{continuous: 0.0, discrete: 0.0},
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);
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makeTest(
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"at 5.0",
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T.xToY(5., continuous),
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{continuous: 9.0, discrete: 0.0},
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);
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makeTest(
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"at 10.0",
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T.xToY(10., continuous),
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{continuous: 2.0, discrete: 0.0},
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);
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});
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});
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makeTest(
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"integral",
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T.Integral.get(~cache=None, continuous) |> getShape,
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{xs: [|1.0, 4.0, 8.0|], ys: [|0.0, 25.5, 47.5|]},
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);
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makeTest(
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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|]}, None);
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continuous |> toLinear |> E.O.fmap(getShape);
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},
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Some({
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xs: [|1.00007, 1.00007, 4.0, 4.00007, 8.0, 8.00007|],
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ys: [|0.0, 0.1, 0.1, 5.0, 5.0, 1.0|],
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}),
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);
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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|]}, 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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);
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makeTest(
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"integralXToY",
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T.Integral.xToY(~cache=None, 0.0, continuous),
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0.0,
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);
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makeTest(
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"integralXToY",
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T.Integral.xToY(~cache=None, 2.0, continuous),
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8.5,
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);
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makeTest(
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"integralXToY",
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T.Integral.xToY(~cache=None, 100.0, continuous),
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47.5,
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);
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makeTest(
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"integralEndY",
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continuous
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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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});
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// describe("Shape", () => {
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// describe("Continuous", () => {
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// open Continuous;
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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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// "mapY",
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// T.mapY(r => r *. 2.0, continuous) |> getShape |> (r => r.ys),
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// [|16., 18.0, 4.0|],
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// );
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// describe("xToY", () => {
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// describe("when Linear", () => {
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// makeTest(
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// "at 4.0",
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// T.xToY(4., continuous),
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// {continuous: 9.0, discrete: 0.0},
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// );
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// // Note: This below is weird to me, I'm not sure if it's what we want really.
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// makeTest(
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// "at 0.0",
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// T.xToY(0., continuous),
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// {continuous: 8.0, discrete: 0.0},
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// );
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// makeTest(
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// "at 5.0",
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// T.xToY(5., continuous),
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// {continuous: 7.25, discrete: 0.0},
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// );
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// makeTest(
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// "at 10.0",
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// T.xToY(10., continuous),
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// {continuous: 2.0, discrete: 0.0},
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// );
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// });
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// describe("when Stepwise", () => {
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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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// {continuous: 9.0, discrete: 0.0},
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// );
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// makeTest(
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// "at 0.0",
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// T.xToY(0., continuous),
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// {continuous: 0.0, discrete: 0.0},
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// );
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// makeTest(
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// "at 5.0",
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// T.xToY(5., continuous),
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// {continuous: 9.0, discrete: 0.0},
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// );
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// makeTest(
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// "at 10.0",
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// T.xToY(10., continuous),
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// {continuous: 2.0, discrete: 0.0},
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// );
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// });
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// });
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// makeTest(
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// "integral",
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// T.Integral.get(~cache=None, continuous) |> getShape,
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// {xs: [|1.0, 4.0, 8.0|], ys: [|0.0, 25.5, 47.5|]},
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// );
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// makeTest(
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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|]}, None);
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// continuous |> toLinear |> E.O.fmap(getShape);
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// },
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// Some({
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// xs: [|1.00007, 1.00007, 4.0, 4.00007, 8.0, 8.00007|],
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// ys: [|0.0, 0.1, 0.1, 5.0, 5.0, 1.0|],
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// }),
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// );
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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|]}, 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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// );
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// makeTest(
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// "integralXToY",
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// T.Integral.xToY(~cache=None, 0.0, continuous),
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// 0.0,
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// );
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// makeTest(
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// "integralXToY",
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// T.Integral.xToY(~cache=None, 2.0, continuous),
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// 8.5,
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// );
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// makeTest(
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// "integralXToY",
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// T.Integral.xToY(~cache=None, 100.0, continuous),
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// 47.5,
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// );
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// makeTest(
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// "integralEndY",
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// continuous
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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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// });
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describe("Discrete", () => {
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open Distributions.Discrete;
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let shape: 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 = 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 => 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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"xToY at 4.0",
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T.xToY(4., discrete),
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{discrete: 0.5, continuous: 0.0},
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);
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makeTest(
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"xToY at 0.0",
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T.xToY(0., discrete),
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{discrete: 0.0, continuous: 0.0},
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);
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makeTest(
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"xToY at 5.0",
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T.xToY(5., discrete),
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{discrete: 0.0, continuous: 0.0},
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);
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makeTest(
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"scaleBy",
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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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"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.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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"integral",
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T.Integral.get(~cache=None, discrete),
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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, 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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T.Integral.xToY(~cache=None, 6.0, discrete),
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0.9,
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);
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makeTest("integralEndY", T.Integral.sum(~cache=None, discrete), 1.0);
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makeTest("mean", T.mean(discrete), 3.9);
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makeTestCloseEquality(
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"variance",
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T.variance(discrete),
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5.89,
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~digits=7,
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);
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});
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// describe("Discrete", () => {
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// open Discrete;
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// let shape: 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 = 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 => 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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// "xToY at 4.0",
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// T.xToY(4., discrete),
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// {discrete: 0.5, continuous: 0.0},
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// );
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// makeTest(
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// "xToY at 0.0",
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// T.xToY(0., discrete),
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// {discrete: 0.0, continuous: 0.0},
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// );
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// makeTest(
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// "xToY at 5.0",
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// T.xToY(5., discrete),
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// {discrete: 0.0, continuous: 0.0},
|
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// );
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// makeTest(
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// "scaleBy",
|
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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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// "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.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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// "integral",
|
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// T.Integral.get(~cache=None, discrete),
|
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// Continuous.make(
|
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// `Stepwise,
|
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// {xs: [|1., 4., 8.|], ys: [|0.3, 0.8, 1.0|]},
|
||||
// None
|
||||
// ),
|
||||
// );
|
||||
// makeTest(
|
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// "integral with 1 element",
|
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// T.Integral.get(~cache=None, Discrete.make({xs: [|0.0|], ys: [|1.0|]}, None)),
|
||||
// Continuous.make(`Stepwise, {xs: [|0.0|], ys: [|1.0|]}, None),
|
||||
// );
|
||||
// makeTest(
|
||||
// "integralXToY",
|
||||
// T.Integral.xToY(~cache=None, 6.0, discrete),
|
||||
// 0.9,
|
||||
// );
|
||||
// makeTest("integralEndY", T.Integral.sum(~cache=None, discrete), 1.0);
|
||||
// makeTest("mean", T.mean(discrete), 3.9);
|
||||
// makeTestCloseEquality(
|
||||
// "variance",
|
||||
// T.variance(discrete),
|
||||
// 5.89,
|
||||
// ~digits=7,
|
||||
// );
|
||||
// });
|
||||
|
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describe("Mixed", () => {
|
||||
open Distributions.Mixed;
|
||||
let discreteShape: DistTypes.xyShape = {
|
||||
xs: [|1., 4., 8.|],
|
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ys: [|0.3, 0.5, 0.2|],
|
||||
};
|
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let discrete = Distributions.Discrete.make(discreteShape, None);
|
||||
let continuous =
|
||||
Distributions.Continuous.make(
|
||||
`Linear,
|
||||
{xs: [|3., 7., 14.|], ys: [|0.058, 0.082, 0.124|]},
|
||||
None
|
||||
)
|
||||
|> Distributions.Continuous.T.normalize; //scaleToIntegralSum(~intendedSum=1.0);
|
||||
let mixed = Distributions.Mixed.make(
|
||||
~continuous,
|
||||
~discrete,
|
||||
);
|
||||
makeTest("minX", T.minX(mixed), 1.0);
|
||||
makeTest("maxX", T.maxX(mixed), 14.0);
|
||||
makeTest(
|
||||
"mapY",
|
||||
T.mapY(r => r *. 2.0, mixed),
|
||||
Distributions.Mixed.make(
|
||||
~continuous=
|
||||
Distributions.Continuous.make(
|
||||
`Linear,
|
||||
{
|
||||
xs: [|3., 7., 14.|],
|
||||
ys: [|
|
||||
0.11588411588411589,
|
||||
0.16383616383616384,
|
||||
0.24775224775224775,
|
||||
|],
|
||||
},
|
||||
None
|
||||
),
|
||||
~discrete=Distributions.Discrete.make({xs: [|1., 4., 8.|], ys: [|0.6, 1.0, 0.4|]}, None)
|
||||
),
|
||||
);
|
||||
makeTest(
|
||||
"xToY at 4.0",
|
||||
T.xToY(4., mixed),
|
||||
{discrete: 0.25, continuous: 0.03196803196803197},
|
||||
);
|
||||
makeTest(
|
||||
"xToY at 0.0",
|
||||
T.xToY(0., mixed),
|
||||
{discrete: 0.0, continuous: 0.028971028971028972},
|
||||
);
|
||||
makeTest(
|
||||
"xToY at 5.0",
|
||||
T.xToY(7., mixed),
|
||||
{discrete: 0.0, continuous: 0.04095904095904096},
|
||||
);
|
||||
makeTest("integralEndY", T.Integral.sum(~cache=None, mixed), 1.0);
|
||||
makeTest(
|
||||
"scaleBy",
|
||||
Distributions.Mixed.scaleBy(~scale=2.0, mixed),
|
||||
Distributions.Mixed.make(
|
||||
~continuous=
|
||||
Distributions.Continuous.make(
|
||||
`Linear,
|
||||
{
|
||||
xs: [|3., 7., 14.|],
|
||||
ys: [|
|
||||
0.11588411588411589,
|
||||
0.16383616383616384,
|
||||
0.24775224775224775,
|
||||
|],
|
||||
},
|
||||
None
|
||||
),
|
||||
~discrete=Distributions.Discrete.make({xs: [|1., 4., 8.|], ys: [|0.6, 1.0, 0.4|]}, None),
|
||||
),
|
||||
);
|
||||
makeTest(
|
||||
"integral",
|
||||
T.Integral.get(~cache=None, mixed),
|
||||
Distributions.Continuous.make(
|
||||
`Linear,
|
||||
{
|
||||
xs: [|1.00007, 1.00007, 3., 4., 4.00007, 7., 8., 8.00007, 14.|],
|
||||
ys: [|
|
||||
0.0,
|
||||
0.0,
|
||||
0.15,
|
||||
0.18496503496503497,
|
||||
0.4349674825174825,
|
||||
0.5398601398601399,
|
||||
0.5913086913086913,
|
||||
0.6913122927072927,
|
||||
1.0,
|
||||
|],
|
||||
},
|
||||
None,
|
||||
),
|
||||
);
|
||||
});
|
||||
// describe("Mixed", () => {
|
||||
// open Distributions.Mixed;
|
||||
// let discreteShape: DistTypes.xyShape = {
|
||||
// xs: [|1., 4., 8.|],
|
||||
// ys: [|0.3, 0.5, 0.2|],
|
||||
// };
|
||||
// let discrete = Discrete.make(discreteShape, None);
|
||||
// let continuous =
|
||||
// Continuous.make(
|
||||
// `Linear,
|
||||
// {xs: [|3., 7., 14.|], ys: [|0.058, 0.082, 0.124|]},
|
||||
// None
|
||||
// )
|
||||
// |> Continuous.T.normalize; //scaleToIntegralSum(~intendedSum=1.0);
|
||||
// let mixed = Mixed.make(
|
||||
// ~continuous,
|
||||
// ~discrete,
|
||||
// );
|
||||
// makeTest("minX", T.minX(mixed), 1.0);
|
||||
// makeTest("maxX", T.maxX(mixed), 14.0);
|
||||
// makeTest(
|
||||
// "mapY",
|
||||
// T.mapY(r => r *. 2.0, mixed),
|
||||
// Mixed.make(
|
||||
// ~continuous=
|
||||
// Continuous.make(
|
||||
// `Linear,
|
||||
// {
|
||||
// xs: [|3., 7., 14.|],
|
||||
// ys: [|
|
||||
// 0.11588411588411589,
|
||||
// 0.16383616383616384,
|
||||
// 0.24775224775224775,
|
||||
// |],
|
||||
// },
|
||||
// None
|
||||
// ),
|
||||
// ~discrete=Discrete.make({xs: [|1., 4., 8.|], ys: [|0.6, 1.0, 0.4|]}, None)
|
||||
// ),
|
||||
// );
|
||||
// makeTest(
|
||||
// "xToY at 4.0",
|
||||
// T.xToY(4., mixed),
|
||||
// {discrete: 0.25, continuous: 0.03196803196803197},
|
||||
// );
|
||||
// makeTest(
|
||||
// "xToY at 0.0",
|
||||
// T.xToY(0., mixed),
|
||||
// {discrete: 0.0, continuous: 0.028971028971028972},
|
||||
// );
|
||||
// makeTest(
|
||||
// "xToY at 5.0",
|
||||
// T.xToY(7., mixed),
|
||||
// {discrete: 0.0, continuous: 0.04095904095904096},
|
||||
// );
|
||||
// makeTest("integralEndY", T.Integral.sum(~cache=None, mixed), 1.0);
|
||||
// makeTest(
|
||||
// "scaleBy",
|
||||
// Mixed.scaleBy(~scale=2.0, mixed),
|
||||
// Mixed.make(
|
||||
// ~continuous=
|
||||
// Continuous.make(
|
||||
// `Linear,
|
||||
// {
|
||||
// xs: [|3., 7., 14.|],
|
||||
// ys: [|
|
||||
// 0.11588411588411589,
|
||||
// 0.16383616383616384,
|
||||
// 0.24775224775224775,
|
||||
// |],
|
||||
// },
|
||||
// None
|
||||
// ),
|
||||
// ~discrete=Discrete.make({xs: [|1., 4., 8.|], ys: [|0.6, 1.0, 0.4|]}, None),
|
||||
// ),
|
||||
// );
|
||||
// makeTest(
|
||||
// "integral",
|
||||
// T.Integral.get(~cache=None, mixed),
|
||||
// Continuous.make(
|
||||
// `Linear,
|
||||
// {
|
||||
// xs: [|1.00007, 1.00007, 3., 4., 4.00007, 7., 8., 8.00007, 14.|],
|
||||
// ys: [|
|
||||
// 0.0,
|
||||
// 0.0,
|
||||
// 0.15,
|
||||
// 0.18496503496503497,
|
||||
// 0.4349674825174825,
|
||||
// 0.5398601398601399,
|
||||
// 0.5913086913086913,
|
||||
// 0.6913122927072927,
|
||||
// 1.0,
|
||||
// |],
|
||||
// },
|
||||
// None,
|
||||
// ),
|
||||
// );
|
||||
// });
|
||||
|
||||
describe("Distplus", () => {
|
||||
open Distributions.DistPlus;
|
||||
let discreteShape: DistTypes.xyShape = {
|
||||
xs: [|1., 4., 8.|],
|
||||
ys: [|0.3, 0.5, 0.2|],
|
||||
};
|
||||
let discrete = Distributions.Discrete.make(discreteShape, None);
|
||||
let continuous =
|
||||
Distributions.Continuous.make(
|
||||
`Linear,
|
||||
{xs: [|3., 7., 14.|], ys: [|0.058, 0.082, 0.124|]},
|
||||
None
|
||||
)
|
||||
|> Distributions.Continuous.T.normalize; //scaleToIntegralSum(~intendedSum=1.0);
|
||||
let mixed =
|
||||
Distributions.Mixed.make(
|
||||
~continuous,
|
||||
~discrete,
|
||||
);
|
||||
let distPlus =
|
||||
Distributions.DistPlus.make(
|
||||
~shape=Mixed(mixed),
|
||||
~guesstimatorString=None,
|
||||
(),
|
||||
);
|
||||
makeTest("minX", T.minX(distPlus), 1.0);
|
||||
makeTest("maxX", T.maxX(distPlus), 14.0);
|
||||
makeTest(
|
||||
"xToY at 4.0",
|
||||
T.xToY(4., distPlus),
|
||||
{discrete: 0.25, continuous: 0.03196803196803197},
|
||||
);
|
||||
makeTest(
|
||||
"xToY at 0.0",
|
||||
T.xToY(0., distPlus),
|
||||
{discrete: 0.0, continuous: 0.028971028971028972},
|
||||
);
|
||||
makeTest(
|
||||
"xToY at 5.0",
|
||||
T.xToY(7., distPlus),
|
||||
{discrete: 0.0, continuous: 0.04095904095904096},
|
||||
);
|
||||
makeTest("integralEndY", T.Integral.sum(~cache=None, distPlus), 1.0);
|
||||
makeTest(
|
||||
"integral",
|
||||
T.Integral.get(~cache=None, distPlus) |> T.toContinuous,
|
||||
Some(
|
||||
Distributions.Continuous.make(
|
||||
`Linear,
|
||||
{
|
||||
xs: [|1.00007, 1.00007, 3., 4., 4.00007, 7., 8., 8.00007, 14.|],
|
||||
ys: [|
|
||||
0.0,
|
||||
0.0,
|
||||
0.15,
|
||||
0.18496503496503497,
|
||||
0.4349674825174825,
|
||||
0.5398601398601399,
|
||||
0.5913086913086913,
|
||||
0.6913122927072927,
|
||||
1.0,
|
||||
|],
|
||||
},
|
||||
None,
|
||||
),
|
||||
),
|
||||
);
|
||||
});
|
||||
// describe("Distplus", () => {
|
||||
// open DistPlus;
|
||||
// let discreteShape: DistTypes.xyShape = {
|
||||
// xs: [|1., 4., 8.|],
|
||||
// ys: [|0.3, 0.5, 0.2|],
|
||||
// };
|
||||
// let discrete = Discrete.make(discreteShape, None);
|
||||
// let continuous =
|
||||
// Continuous.make(
|
||||
// `Linear,
|
||||
// {xs: [|3., 7., 14.|], ys: [|0.058, 0.082, 0.124|]},
|
||||
// None
|
||||
// )
|
||||
// |> Continuous.T.normalize; //scaleToIntegralSum(~intendedSum=1.0);
|
||||
// let mixed =
|
||||
// Mixed.make(
|
||||
// ~continuous,
|
||||
// ~discrete,
|
||||
// );
|
||||
// let distPlus =
|
||||
// DistPlus.make(
|
||||
// ~shape=Mixed(mixed),
|
||||
// ~guesstimatorString=None,
|
||||
// (),
|
||||
// );
|
||||
// makeTest("minX", T.minX(distPlus), 1.0);
|
||||
// makeTest("maxX", T.maxX(distPlus), 14.0);
|
||||
// makeTest(
|
||||
// "xToY at 4.0",
|
||||
// T.xToY(4., distPlus),
|
||||
// {discrete: 0.25, continuous: 0.03196803196803197},
|
||||
// );
|
||||
// makeTest(
|
||||
// "xToY at 0.0",
|
||||
// T.xToY(0., distPlus),
|
||||
// {discrete: 0.0, continuous: 0.028971028971028972},
|
||||
// );
|
||||
// makeTest(
|
||||
// "xToY at 5.0",
|
||||
// T.xToY(7., distPlus),
|
||||
// {discrete: 0.0, continuous: 0.04095904095904096},
|
||||
// );
|
||||
// makeTest("integralEndY", T.Integral.sum(~cache=None, distPlus), 1.0);
|
||||
// makeTest(
|
||||
// "integral",
|
||||
// T.Integral.get(~cache=None, distPlus) |> T.toContinuous,
|
||||
// Some(
|
||||
// Continuous.make(
|
||||
// `Linear,
|
||||
// {
|
||||
// xs: [|1.00007, 1.00007, 3., 4., 4.00007, 7., 8., 8.00007, 14.|],
|
||||
// ys: [|
|
||||
// 0.0,
|
||||
// 0.0,
|
||||
// 0.15,
|
||||
// 0.18496503496503497,
|
||||
// 0.4349674825174825,
|
||||
// 0.5398601398601399,
|
||||
// 0.5913086913086913,
|
||||
// 0.6913122927072927,
|
||||
// 1.0,
|
||||
// |],
|
||||
// },
|
||||
// None,
|
||||
// ),
|
||||
// ),
|
||||
// );
|
||||
// });
|
||||
|
||||
describe("Shape", () => {
|
||||
let mean = 10.0;
|
||||
let stdev = 4.0;
|
||||
let variance = stdev ** 2.0;
|
||||
let numSamples = 10000;
|
||||
open Distributions.Shape;
|
||||
let normal: SymbolicTypes.symbolicDist = `Normal({mean, stdev});
|
||||
let normalShape = ExpressionTree.toShape(numSamples, `SymbolicDist(normal));
|
||||
let lognormal = SymbolicDist.Lognormal.fromMeanAndStdev(mean, stdev);
|
||||
let lognormalShape = ExpressionTree.toShape(numSamples, `SymbolicDist(lognormal));
|
||||
// describe("Shape", () => {
|
||||
// let mean = 10.0;
|
||||
// let stdev = 4.0;
|
||||
// let variance = stdev ** 2.0;
|
||||
// let numSamples = 10000;
|
||||
// open Distributions.Shape;
|
||||
// let normal: SymbolicTypes.symbolicDist = `Normal({mean, stdev});
|
||||
// let normalShape = ExpressionTree.toShape(numSamples, `SymbolicDist(normal));
|
||||
// let lognormal = SymbolicDist.Lognormal.fromMeanAndStdev(mean, stdev);
|
||||
// let lognormalShape = ExpressionTree.toShape(numSamples, `SymbolicDist(lognormal));
|
||||
|
||||
makeTestCloseEquality(
|
||||
"Mean of a normal",
|
||||
T.mean(normalShape),
|
||||
mean,
|
||||
~digits=2,
|
||||
);
|
||||
makeTestCloseEquality(
|
||||
"Variance of a normal",
|
||||
T.variance(normalShape),
|
||||
variance,
|
||||
~digits=1,
|
||||
);
|
||||
makeTestCloseEquality(
|
||||
"Mean of a lognormal",
|
||||
T.mean(lognormalShape),
|
||||
mean,
|
||||
~digits=2,
|
||||
);
|
||||
makeTestCloseEquality(
|
||||
"Variance of a lognormal",
|
||||
T.variance(lognormalShape),
|
||||
variance,
|
||||
~digits=0,
|
||||
);
|
||||
});
|
||||
});
|
||||
// makeTestCloseEquality(
|
||||
// "Mean of a normal",
|
||||
// T.mean(normalShape),
|
||||
// mean,
|
||||
// ~digits=2,
|
||||
// );
|
||||
// makeTestCloseEquality(
|
||||
// "Variance of a normal",
|
||||
// T.variance(normalShape),
|
||||
// variance,
|
||||
// ~digits=1,
|
||||
// );
|
||||
// makeTestCloseEquality(
|
||||
// "Mean of a lognormal",
|
||||
// T.mean(lognormalShape),
|
||||
// mean,
|
||||
// ~digits=2,
|
||||
// );
|
||||
// makeTestCloseEquality(
|
||||
// "Variance of a lognormal",
|
||||
// T.variance(lognormalShape),
|
||||
// variance,
|
||||
// ~digits=0,
|
||||
// );
|
||||
// });
|
||||
// });
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
let entries = EntryTypes.[Continuous.entry,ExpressionTreeExamples.entry];
|
||||
let entries = EntryTypes.[Continuous2.entry,ExpressionTreeExamples.entry];
|
||||
|
|
@ -41,17 +41,17 @@ module DemoDist = {
|
|||
? "Nothing to show" |> R.ste
|
||||
: {
|
||||
let distPlus =
|
||||
Distributions.DistPlus.make(
|
||||
DistPlus.make(
|
||||
~shape=
|
||||
Continuous(
|
||||
Distributions.Continuous.make(`Linear, {xs, ys}, None),
|
||||
Continuous.make(`Linear, {xs, ys}, None),
|
||||
),
|
||||
~domain=Complete,
|
||||
~unit=UnspecifiedDistribution,
|
||||
~guesstimatorString=None,
|
||||
(),
|
||||
)
|
||||
|> Distributions.DistPlus.T.normalize;
|
||||
|> DistPlus.T.normalize;
|
||||
<DistPlusPlot distPlus />;
|
||||
};
|
||||
<Antd.Card title={"Distribution" |> R.ste}>
|
||||
|
|
|
|||
|
|
@ -51,14 +51,14 @@ module DemoDist = {
|
|||
shape
|
||||
|> E.O.fmap(shape => {
|
||||
let distPlus =
|
||||
Distributions.DistPlus.make(
|
||||
DistPlus.make(
|
||||
~shape,
|
||||
~domain=Complete,
|
||||
~unit=UnspecifiedDistribution,
|
||||
~guesstimatorString=None,
|
||||
(),
|
||||
)
|
||||
|> Distributions.DistPlus.T.normalize;
|
||||
|> DistPlus.T.normalize;
|
||||
<DistPlusPlot distPlus />;
|
||||
})
|
||||
|> E.O.default(ReasonReact.null);
|
||||
|
|
|
|||
|
|
@ -291,8 +291,8 @@ module Draw = {
|
|||
/*
|
||||
let continuousShape =
|
||||
Convert.canvasShapeToContinuousShape(~canvasShape, ~canvasElement);
|
||||
let mean = Distributions.Continuous.T.mean(continuousShape);
|
||||
let variance = Distributions.Continuous.T.variance(continuousShape);
|
||||
let mean = Continuous.T.mean(continuousShape);
|
||||
let variance = Continuous.T.variance(continuousShape);
|
||||
let meanLocation =
|
||||
Convert.findClosestInOrderedArrayDangerously(mean, canvasShape.xValues);
|
||||
let meanLocationCanvasX = canvasShape.ws[meanLocation];
|
||||
|
|
@ -394,7 +394,7 @@ module Draw = {
|
|||
switch (normalShape) {
|
||||
| Mixed(_) => {xs: [||], ys: [||]}
|
||||
| Discrete(_) => {xs: [||], ys: [||]}
|
||||
| Continuous(m) => Distributions.Continuous.getShape(m)
|
||||
| Continuous(m) => Continuous.getShape(m)
|
||||
};
|
||||
|
||||
/* // To use a lognormal instead:
|
||||
|
|
@ -405,7 +405,7 @@ module Draw = {
|
|||
switch (lognormalShape) {
|
||||
| Mixed(_) => {xs: [||], ys: [||]}
|
||||
| Discrete(_) => {xs: [||], ys: [||]}
|
||||
| Continuous(m) => Distributions.Continuous.getShape(m)
|
||||
| Continuous(m) => Continuous.getShape(m)
|
||||
};
|
||||
*/
|
||||
|
||||
|
|
@ -669,11 +669,11 @@ module State = {
|
|||
|
||||
/* create a cdf from a pdf */
|
||||
let _pdf =
|
||||
Distributions.Continuous.T.normalize(
|
||||
Continuous.T.normalize(
|
||||
pdf,
|
||||
);
|
||||
|
||||
let cdf = Distributions.Continuous.T.integral(~cache=None, _pdf);
|
||||
let cdf = Continuous.T.integral(~cache=None, _pdf);
|
||||
let xs = [||];
|
||||
let ys = [||];
|
||||
for (i in 1 to 999) {
|
||||
|
|
|
|||
|
|
@ -37,27 +37,27 @@ let table = (distPlus, x) => {
|
|||
</td>
|
||||
<td className="px-4 py-2 border ">
|
||||
{distPlus
|
||||
|> Distributions.DistPlus.T.xToY(x)
|
||||
|> DistPlus.T.xToY(x)
|
||||
|> DistTypes.MixedPoint.toDiscreteValue
|
||||
|> Js.Float.toPrecisionWithPrecision(_, ~digits=7)
|
||||
|> ReasonReact.string}
|
||||
</td>
|
||||
<td className="px-4 py-2 border ">
|
||||
{distPlus
|
||||
|> Distributions.DistPlus.T.xToY(x)
|
||||
|> DistPlus.T.xToY(x)
|
||||
|> DistTypes.MixedPoint.toContinuousValue
|
||||
|> Js.Float.toPrecisionWithPrecision(_, ~digits=7)
|
||||
|> ReasonReact.string}
|
||||
</td>
|
||||
<td className="px-4 py-2 border ">
|
||||
{distPlus
|
||||
|> Distributions.DistPlus.T.Integral.xToY(~cache=None, x)
|
||||
|> DistPlus.T.Integral.xToY(~cache=None, x)
|
||||
|> E.Float.with2DigitsPrecision
|
||||
|> ReasonReact.string}
|
||||
</td>
|
||||
<td className="px-4 py-2 border ">
|
||||
{distPlus
|
||||
|> Distributions.DistPlus.T.Integral.sum(~cache=None)
|
||||
|> DistPlus.T.Integral.sum(~cache=None)
|
||||
|> E.Float.with2DigitsPrecision
|
||||
|> ReasonReact.string}
|
||||
</td>
|
||||
|
|
@ -85,9 +85,9 @@ let table = (distPlus, x) => {
|
|||
<tr>
|
||||
<td className="px-4 py-2 border">
|
||||
{distPlus
|
||||
|> Distributions.DistPlus.T.toContinuous
|
||||
|> DistPlus.T.toContinuous
|
||||
|> E.O.fmap(
|
||||
Distributions.Continuous.T.Integral.sum(~cache=None),
|
||||
Continuous.T.Integral.sum(~cache=None),
|
||||
)
|
||||
|> E.O.fmap(E.Float.with2DigitsPrecision)
|
||||
|> E.O.default("")
|
||||
|
|
@ -95,9 +95,9 @@ let table = (distPlus, x) => {
|
|||
</td>
|
||||
<td className="px-4 py-2 border ">
|
||||
{distPlus
|
||||
|> Distributions.DistPlus.T.normalizedToContinuous
|
||||
|> DistPlus.T.normalizedToContinuous
|
||||
|> E.O.fmap(
|
||||
Distributions.Continuous.T.Integral.sum(~cache=None),
|
||||
Continuous.T.Integral.sum(~cache=None),
|
||||
)
|
||||
|> E.O.fmap(E.Float.with2DigitsPrecision)
|
||||
|> E.O.default("")
|
||||
|
|
@ -105,16 +105,16 @@ let table = (distPlus, x) => {
|
|||
</td>
|
||||
<td className="px-4 py-2 border ">
|
||||
{distPlus
|
||||
|> Distributions.DistPlus.T.toDiscrete
|
||||
|> E.O.fmap(Distributions.Discrete.T.Integral.sum(~cache=None))
|
||||
|> DistPlus.T.toDiscrete
|
||||
|> E.O.fmap(Discrete.T.Integral.sum(~cache=None))
|
||||
|> E.O.fmap(E.Float.with2DigitsPrecision)
|
||||
|> E.O.default("")
|
||||
|> ReasonReact.string}
|
||||
</td>
|
||||
<td className="px-4 py-2 border ">
|
||||
{distPlus
|
||||
|> Distributions.DistPlus.T.normalizedToDiscrete
|
||||
|> E.O.fmap(Distributions.Discrete.T.Integral.sum(~cache=None))
|
||||
|> DistPlus.T.normalizedToDiscrete
|
||||
|> E.O.fmap(Discrete.T.Integral.sum(~cache=None))
|
||||
|> E.O.fmap(E.Float.with2DigitsPrecision)
|
||||
|> E.O.default("")
|
||||
|> ReasonReact.string}
|
||||
|
|
@ -143,42 +143,42 @@ let percentiles = distPlus => {
|
|||
<tr>
|
||||
<td className="px-4 py-2 border">
|
||||
{distPlus
|
||||
|> Distributions.DistPlus.T.Integral.yToX(~cache=None, 0.01)
|
||||
|> DistPlus.T.Integral.yToX(~cache=None, 0.01)
|
||||
|> showFloat}
|
||||
</td>
|
||||
<td className="px-4 py-2 border">
|
||||
{distPlus
|
||||
|> Distributions.DistPlus.T.Integral.yToX(~cache=None, 0.05)
|
||||
|> DistPlus.T.Integral.yToX(~cache=None, 0.05)
|
||||
|> showFloat}
|
||||
</td>
|
||||
<td className="px-4 py-2 border">
|
||||
{distPlus
|
||||
|> Distributions.DistPlus.T.Integral.yToX(~cache=None, 0.25)
|
||||
|> DistPlus.T.Integral.yToX(~cache=None, 0.25)
|
||||
|> showFloat}
|
||||
</td>
|
||||
<td className="px-4 py-2 border">
|
||||
{distPlus
|
||||
|> Distributions.DistPlus.T.Integral.yToX(~cache=None, 0.5)
|
||||
|> DistPlus.T.Integral.yToX(~cache=None, 0.5)
|
||||
|> showFloat}
|
||||
</td>
|
||||
<td className="px-4 py-2 border">
|
||||
{distPlus
|
||||
|> Distributions.DistPlus.T.Integral.yToX(~cache=None, 0.75)
|
||||
|> DistPlus.T.Integral.yToX(~cache=None, 0.75)
|
||||
|> showFloat}
|
||||
</td>
|
||||
<td className="px-4 py-2 border">
|
||||
{distPlus
|
||||
|> Distributions.DistPlus.T.Integral.yToX(~cache=None, 0.95)
|
||||
|> DistPlus.T.Integral.yToX(~cache=None, 0.95)
|
||||
|> showFloat}
|
||||
</td>
|
||||
<td className="px-4 py-2 border">
|
||||
{distPlus
|
||||
|> Distributions.DistPlus.T.Integral.yToX(~cache=None, 0.99)
|
||||
|> DistPlus.T.Integral.yToX(~cache=None, 0.99)
|
||||
|> showFloat}
|
||||
</td>
|
||||
<td className="px-4 py-2 border">
|
||||
{distPlus
|
||||
|> Distributions.DistPlus.T.Integral.yToX(~cache=None, 0.99999)
|
||||
|> DistPlus.T.Integral.yToX(~cache=None, 0.99999)
|
||||
|> showFloat}
|
||||
</td>
|
||||
</tr>
|
||||
|
|
@ -197,13 +197,13 @@ let percentiles = distPlus => {
|
|||
<tbody>
|
||||
<tr>
|
||||
<td className="px-4 py-2 border">
|
||||
{distPlus |> Distributions.DistPlus.T.mean |> showFloat}
|
||||
{distPlus |> DistPlus.T.mean |> showFloat}
|
||||
</td>
|
||||
<td className="px-4 py-2 border">
|
||||
{distPlus |> Distributions.DistPlus.T.variance |> (r => r ** 0.5) |> showFloat}
|
||||
{distPlus |> DistPlus.T.variance |> (r => r ** 0.5) |> showFloat}
|
||||
</td>
|
||||
<td className="px-4 py-2 border">
|
||||
{distPlus |> Distributions.DistPlus.T.variance |> showFloat}
|
||||
{distPlus |> DistPlus.T.variance |> showFloat}
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
|
|
@ -224,19 +224,19 @@ let adjustBoth = discreteProbabilityMassFraction => {
|
|||
module DistPlusChart = {
|
||||
[@react.component]
|
||||
let make = (~distPlus: DistTypes.distPlus, ~config: chartConfig, ~onHover) => {
|
||||
open Distributions.DistPlus;
|
||||
let discrete = distPlus |> T.normalizedToDiscrete |> E.O.fmap(Distributions.Discrete.getShape);
|
||||
open DistPlus;
|
||||
let discrete = distPlus |> T.normalizedToDiscrete |> E.O.fmap(Discrete.getShape);
|
||||
let continuous =
|
||||
distPlus
|
||||
|> T.normalizedToContinuous
|
||||
|> E.O.fmap(Distributions.Continuous.getShape);
|
||||
|> E.O.fmap(Continuous.getShape);
|
||||
let range = T.xTotalRange(distPlus);
|
||||
|
||||
// // We subtract a bit from the range to make sure that it fits. Maybe this should be done in d3 instead.
|
||||
// let minX =
|
||||
// switch (
|
||||
// distPlus
|
||||
// |> Distributions.DistPlus.T.Integral.yToX(~cache=None, 0.0001),
|
||||
// |> DistPlus.T.Integral.yToX(~cache=None, 0.0001),
|
||||
// range,
|
||||
// ) {
|
||||
// | (min, Some(range)) => Some(min -. range *. 0.001)
|
||||
|
|
@ -244,16 +244,16 @@ module DistPlusChart = {
|
|||
// };
|
||||
|
||||
let minX = {
|
||||
distPlus |> Distributions.DistPlus.T.Integral.yToX(~cache=None, 0.00001);
|
||||
distPlus |> DistPlus.T.Integral.yToX(~cache=None, 0.00001);
|
||||
};
|
||||
|
||||
let maxX = {
|
||||
distPlus |> Distributions.DistPlus.T.Integral.yToX(~cache=None, 0.99);
|
||||
distPlus |> DistPlus.T.Integral.yToX(~cache=None, 0.99);
|
||||
};
|
||||
|
||||
let timeScale = distPlus.unit |> DistTypes.DistributionUnit.toJson;
|
||||
let discreteProbabilityMassFraction =
|
||||
distPlus |> Distributions.DistPlus.T.toDiscreteProbabilityMassFraction;
|
||||
distPlus |> DistPlus.T.toDiscreteProbabilityMassFraction;
|
||||
let (yMaxDiscreteDomainFactor, yMaxContinuousDomainFactor) =
|
||||
adjustBoth(discreteProbabilityMassFraction);
|
||||
<DistributionPlot
|
||||
|
|
@ -276,18 +276,18 @@ module DistPlusChart = {
|
|||
module IntegralChart = {
|
||||
[@react.component]
|
||||
let make = (~distPlus: DistTypes.distPlus, ~config: chartConfig, ~onHover) => {
|
||||
open Distributions.DistPlus;
|
||||
open DistPlus;
|
||||
let integral = distPlus.integralCache;
|
||||
let continuous =
|
||||
integral
|
||||
|> Distributions.Continuous.toLinear
|
||||
|> E.O.fmap(Distributions.Continuous.getShape);
|
||||
|> Continuous.toLinear
|
||||
|> E.O.fmap(Continuous.getShape);
|
||||
let minX = {
|
||||
distPlus |> Distributions.DistPlus.T.Integral.yToX(~cache=None, 0.00001);
|
||||
distPlus |> DistPlus.T.Integral.yToX(~cache=None, 0.00001);
|
||||
};
|
||||
|
||||
let maxX = {
|
||||
distPlus |> Distributions.DistPlus.T.Integral.yToX(~cache=None, 0.99);
|
||||
distPlus |> DistPlus.T.Integral.yToX(~cache=None, 0.99);
|
||||
};
|
||||
let timeScale = distPlus.unit |> DistTypes.DistributionUnit.toJson;
|
||||
<DistributionPlot
|
||||
|
|
|
|||
275
src/distPlus/distribution/Continuous.re
Normal file
275
src/distPlus/distribution/Continuous.re
Normal file
|
|
@ -0,0 +1,275 @@
|
|||
open Distributions;
|
||||
|
||||
type t = DistTypes.continuousShape;
|
||||
let getShape = (t: t) => t.xyShape;
|
||||
let interpolation = (t: t) => t.interpolation;
|
||||
let make = (interpolation, xyShape, knownIntegralSum): t => {
|
||||
xyShape,
|
||||
interpolation,
|
||||
knownIntegralSum,
|
||||
};
|
||||
let shapeMap = (fn, {xyShape, interpolation, knownIntegralSum}: t): t => {
|
||||
xyShape: fn(xyShape),
|
||||
interpolation,
|
||||
knownIntegralSum,
|
||||
};
|
||||
let lastY = (t: t) => t |> getShape |> XYShape.T.lastY;
|
||||
let oShapeMap =
|
||||
(fn, {xyShape, interpolation, knownIntegralSum}: t)
|
||||
: option(DistTypes.continuousShape) =>
|
||||
fn(xyShape) |> E.O.fmap(make(interpolation, _, knownIntegralSum));
|
||||
|
||||
let empty: DistTypes.continuousShape = {
|
||||
xyShape: XYShape.T.empty,
|
||||
interpolation: `Linear,
|
||||
knownIntegralSum: Some(0.0),
|
||||
};
|
||||
let combinePointwise =
|
||||
(
|
||||
~knownIntegralSumsFn,
|
||||
fn: (float, float) => float,
|
||||
t1: DistTypes.continuousShape,
|
||||
t2: DistTypes.continuousShape,
|
||||
)
|
||||
: DistTypes.continuousShape => {
|
||||
// If we're adding the distributions, and we know the total of each, then we
|
||||
// can just sum them up. Otherwise, all bets are off.
|
||||
let combinedIntegralSum =
|
||||
Common.combineIntegralSums(
|
||||
knownIntegralSumsFn,
|
||||
t1.knownIntegralSum,
|
||||
t2.knownIntegralSum,
|
||||
);
|
||||
|
||||
make(
|
||||
`Linear,
|
||||
XYShape.PointwiseCombination.combineLinear(
|
||||
~fn=(+.),
|
||||
t1.xyShape,
|
||||
t2.xyShape,
|
||||
),
|
||||
combinedIntegralSum,
|
||||
);
|
||||
};
|
||||
|
||||
let toLinear = (t: t): option(t) => {
|
||||
switch (t) {
|
||||
| {interpolation: `Stepwise, xyShape, knownIntegralSum} =>
|
||||
xyShape
|
||||
|> XYShape.Range.stepsToContinuous
|
||||
|> E.O.fmap(make(`Linear, _, knownIntegralSum))
|
||||
| {interpolation: `Linear} => Some(t)
|
||||
};
|
||||
};
|
||||
let shapeFn = (fn, t: t) => t |> getShape |> fn;
|
||||
let updateKnownIntegralSum = (knownIntegralSum, t: t): t => {
|
||||
...t,
|
||||
knownIntegralSum,
|
||||
};
|
||||
|
||||
let reduce =
|
||||
(
|
||||
~knownIntegralSumsFn: (float, float) => option(float)=(_, _) => None,
|
||||
fn,
|
||||
continuousShapes,
|
||||
) =>
|
||||
continuousShapes
|
||||
|> E.A.fold_left(combinePointwise(~knownIntegralSumsFn, fn), empty);
|
||||
|
||||
let mapY = (~knownIntegralSumFn=_ => 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, t: t): t => {
|
||||
t
|
||||
|> mapY((r: float) => r *. scale)
|
||||
|> updateKnownIntegralSum(
|
||||
E.O.bind(t.knownIntegralSum, v => Some(scale *. v)),
|
||||
);
|
||||
};
|
||||
|
||||
module T =
|
||||
Dist({
|
||||
type t = DistTypes.continuousShape;
|
||||
type integral = DistTypes.continuousShape;
|
||||
let minX = shapeFn(XYShape.T.minX);
|
||||
let maxX = shapeFn(XYShape.T.maxX);
|
||||
let mapY = mapY;
|
||||
let toDiscreteProbabilityMassFraction = _ => 0.0;
|
||||
let toShape = (t: t): DistTypes.shape => Continuous(t);
|
||||
let xToY = (f, {interpolation, xyShape}: t) => {
|
||||
(
|
||||
switch (interpolation) {
|
||||
| `Stepwise =>
|
||||
xyShape |> XYShape.XtoY.stepwiseIncremental(f) |> E.O.default(0.0)
|
||||
| `Linear => xyShape |> XYShape.XtoY.linear(f)
|
||||
}
|
||||
)
|
||||
|> DistTypes.MixedPoint.makeContinuous;
|
||||
};
|
||||
|
||||
let truncate =
|
||||
(leftCutoff: option(float), rightCutoff: option(float), t: t) => {
|
||||
let lc = E.O.default(neg_infinity, leftCutoff);
|
||||
let rc = E.O.default(infinity, rightCutoff);
|
||||
let truncatedZippedPairs =
|
||||
t
|
||||
|> getShape
|
||||
|> XYShape.T.zip
|
||||
|> XYShape.Zipped.filterByX(x => x >= lc && x <= rc);
|
||||
|
||||
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) =>
|
||||
if (t |> getShape |> XYShape.T.length > 0) {
|
||||
switch (cache) {
|
||||
| Some(cache) => cache
|
||||
| None =>
|
||||
t
|
||||
|> getShape
|
||||
|> XYShape.Range.integrateWithTriangles
|
||||
|> E.O.toExt("This should not have happened")
|
||||
|> make(`Linear, _, None)
|
||||
};
|
||||
} else {
|
||||
make(`Linear, {xs: [|neg_infinity|], ys: [|0.0|]}, None);
|
||||
};
|
||||
|
||||
let downsample = (~cache=None, length, t): t =>
|
||||
t
|
||||
|> shapeMap(
|
||||
XYShape.XsConversion.proportionByProbabilityMass(
|
||||
length,
|
||||
integral(~cache, t).xyShape,
|
||||
),
|
||||
);
|
||||
let integralEndY = (~cache, t: t) =>
|
||||
t.knownIntegralSum |> E.O.default(t |> integral(~cache) |> lastY);
|
||||
let integralXtoY = (~cache, f, t: t) =>
|
||||
t |> integral(~cache) |> shapeFn(XYShape.XtoY.linear(f));
|
||||
let integralYtoX = (~cache, f, t: t) =>
|
||||
t |> integral(~cache) |> shapeFn(XYShape.YtoX.linear(f));
|
||||
let toContinuous = t => Some(t);
|
||||
let toDiscrete = _ => None;
|
||||
|
||||
let normalize = (t: t): t => {
|
||||
t
|
||||
|> scaleBy(~scale=1. /. integralEndY(~cache=None, t))
|
||||
|> updateKnownIntegralSum(Some(1.0));
|
||||
};
|
||||
|
||||
let normalizedToContinuous = t => Some(t |> normalize);
|
||||
let normalizedToDiscrete = _ => None;
|
||||
|
||||
let mean = (t: t) => {
|
||||
let indefiniteIntegralStepwise = (p, h1) => h1 *. p ** 2.0 /. 2.0;
|
||||
let indefiniteIntegralLinear = (p, a, b) =>
|
||||
a *. p ** 2.0 /. 2.0 +. b *. p ** 3.0 /. 3.0;
|
||||
|
||||
XYShape.Analysis.integrateContinuousShape(
|
||||
~indefiniteIntegralStepwise,
|
||||
~indefiniteIntegralLinear,
|
||||
t,
|
||||
);
|
||||
};
|
||||
let variance = (t: t): float =>
|
||||
XYShape.Analysis.getVarianceDangerously(
|
||||
t,
|
||||
mean,
|
||||
XYShape.Analysis.getMeanOfSquaresContinuousShape,
|
||||
);
|
||||
});
|
||||
|
||||
/* This simply creates multiple copies of the continuous distribution, scaled and shifted according to
|
||||
each discrete data point, and then adds them all together. */
|
||||
let combineAlgebraicallyWithDiscrete =
|
||||
(
|
||||
~downsample=false,
|
||||
op: ExpressionTypes.algebraicOperation,
|
||||
t1: t,
|
||||
t2: DistTypes.discreteShape,
|
||||
) => {
|
||||
let t1s = t1 |> getShape;
|
||||
let t2s = t2.xyShape; // would like to use Discrete.getShape here, but current file structure doesn't allow for that
|
||||
let t1n = t1s |> XYShape.T.length;
|
||||
let t2n = t2s |> XYShape.T.length;
|
||||
|
||||
let fn = Operation.Algebraic.toFn(op);
|
||||
|
||||
let outXYShapes: array(array((float, float))) =
|
||||
Belt.Array.makeUninitializedUnsafe(t2n);
|
||||
|
||||
for (j in 0 to t2n - 1) {
|
||||
// for each one of the discrete points
|
||||
// create a new distribution, as long as the original continuous one
|
||||
|
||||
let dxyShape: array((float, float)) =
|
||||
Belt.Array.makeUninitializedUnsafe(t1n);
|
||||
for (i in 0 to t1n - 1) {
|
||||
let _ =
|
||||
Belt.Array.set(
|
||||
dxyShape,
|
||||
i,
|
||||
(fn(t1s.xs[i], t2s.xs[j]), t1s.ys[i] *. t2s.ys[j]),
|
||||
);
|
||||
();
|
||||
};
|
||||
|
||||
let _ = Belt.Array.set(outXYShapes, j, dxyShape);
|
||||
();
|
||||
};
|
||||
|
||||
let combinedIntegralSum =
|
||||
Common.combineIntegralSums(
|
||||
(a, b) => Some(a *. b),
|
||||
t1.knownIntegralSum,
|
||||
t2.knownIntegralSum,
|
||||
);
|
||||
|
||||
outXYShapes
|
||||
|> E.A.fmap(s => {
|
||||
let xyShape = XYShape.T.fromZippedArray(s);
|
||||
make(`Linear, xyShape, None);
|
||||
})
|
||||
|> reduce((+.))
|
||||
|> updateKnownIntegralSum(combinedIntegralSum);
|
||||
};
|
||||
|
||||
let combineAlgebraically =
|
||||
(~downsample=false, op: ExpressionTypes.algebraicOperation, t1: t, t2: t) => {
|
||||
let s1 = t1 |> getShape;
|
||||
let s2 = t2 |> getShape;
|
||||
let t1n = s1 |> XYShape.T.length;
|
||||
let t2n = s2 |> XYShape.T.length;
|
||||
if (t1n == 0 || t2n == 0) {
|
||||
empty;
|
||||
} else {
|
||||
let combinedShape =
|
||||
AlgebraicShapeCombination.combineShapesContinuousContinuous(op, s1, s2);
|
||||
let combinedIntegralSum =
|
||||
Common.combineIntegralSums(
|
||||
(a, b) => Some(a *. b),
|
||||
t1.knownIntegralSum,
|
||||
t2.knownIntegralSum,
|
||||
);
|
||||
// return a new Continuous distribution
|
||||
make(`Linear, combinedShape, combinedIntegralSum);
|
||||
};
|
||||
};
|
||||
210
src/distPlus/distribution/Discrete.re
Normal file
210
src/distPlus/distribution/Discrete.re
Normal file
|
|
@ -0,0 +1,210 @@
|
|||
open Distributions;
|
||||
|
||||
type t = DistTypes.discreteShape;
|
||||
|
||||
let make = (xyShape, knownIntegralSum): t => {xyShape, knownIntegralSum};
|
||||
let shapeMap = (fn, {xyShape, knownIntegralSum}: t): t => {
|
||||
xyShape: fn(xyShape),
|
||||
knownIntegralSum,
|
||||
};
|
||||
let getShape = (t: t) => t.xyShape;
|
||||
let oShapeMap = (fn, {xyShape, knownIntegralSum}: t): option(t) =>
|
||||
fn(xyShape) |> E.O.fmap(make(_, knownIntegralSum));
|
||||
|
||||
let empty: t = {xyShape: XYShape.T.empty, knownIntegralSum: Some(0.0)};
|
||||
let shapeFn = (fn, t: t) => t |> getShape |> fn;
|
||||
|
||||
let lastY = (t: t) => t |> getShape |> XYShape.T.lastY;
|
||||
|
||||
let combinePointwise =
|
||||
(
|
||||
~knownIntegralSumsFn,
|
||||
fn,
|
||||
t1: DistTypes.discreteShape,
|
||||
t2: DistTypes.discreteShape,
|
||||
)
|
||||
: DistTypes.discreteShape => {
|
||||
let combinedIntegralSum =
|
||||
Common.combineIntegralSums(
|
||||
knownIntegralSumsFn,
|
||||
t1.knownIntegralSum,
|
||||
t2.knownIntegralSum,
|
||||
);
|
||||
|
||||
make(
|
||||
XYShape.PointwiseCombination.combine(
|
||||
~xsSelection=ALL_XS,
|
||||
~xToYSelection=XYShape.XtoY.stepwiseIfAtX,
|
||||
~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 reduce =
|
||||
(~knownIntegralSumsFn=(_, _) => None, fn, discreteShapes)
|
||||
: DistTypes.discreteShape =>
|
||||
discreteShapes
|
||||
|> E.A.fold_left(combinePointwise(~knownIntegralSumsFn, fn), empty);
|
||||
|
||||
let updateKnownIntegralSum = (knownIntegralSum, t: t): t => {
|
||||
...t,
|
||||
knownIntegralSum,
|
||||
};
|
||||
|
||||
/* This multiples all of the data points together and creates a new discrete distribution from the results.
|
||||
Data points at the same xs get added together. It may be a good idea to downsample t1 and t2 before and/or the result after. */
|
||||
let combineAlgebraically =
|
||||
(op: ExpressionTypes.algebraicOperation, t1: t, t2: t) => {
|
||||
let t1s = t1 |> getShape;
|
||||
let t2s = t2 |> getShape;
|
||||
let t1n = t1s |> XYShape.T.length;
|
||||
let t2n = t2s |> XYShape.T.length;
|
||||
|
||||
let combinedIntegralSum =
|
||||
Common.combineIntegralSums(
|
||||
(s1, s2) => Some(s1 *. s2),
|
||||
t1.knownIntegralSum,
|
||||
t2.knownIntegralSum,
|
||||
);
|
||||
|
||||
let fn = Operation.Algebraic.toFn(op);
|
||||
let xToYMap = E.FloatFloatMap.empty();
|
||||
|
||||
for (i in 0 to t1n - 1) {
|
||||
for (j in 0 to t2n - 1) {
|
||||
let x = fn(t1s.xs[i], t2s.xs[j]);
|
||||
let cv = xToYMap |> E.FloatFloatMap.get(x) |> E.O.default(0.);
|
||||
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;
|
||||
|
||||
let combinedShape = XYShape.T.fromZippedArray(rxys);
|
||||
|
||||
make(combinedShape, combinedIntegralSum);
|
||||
};
|
||||
|
||||
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, t: t): t => {
|
||||
t
|
||||
|> mapY((r: float) => r *. scale)
|
||||
|> updateKnownIntegralSum(
|
||||
E.O.bind(t.knownIntegralSum, v => Some(scale *. v)),
|
||||
);
|
||||
};
|
||||
|
||||
module T =
|
||||
Dist({
|
||||
type t = DistTypes.discreteShape;
|
||||
type integral = DistTypes.continuousShape;
|
||||
let integral = (~cache, t) =>
|
||||
if (t |> getShape |> XYShape.T.length > 0) {
|
||||
switch (cache) {
|
||||
| Some(c) => c
|
||||
| None =>
|
||||
Continuous.make(
|
||||
`Stepwise,
|
||||
XYShape.T.accumulateYs((+.), getShape(t)),
|
||||
None,
|
||||
)
|
||||
};
|
||||
} else {
|
||||
Continuous.make(
|
||||
`Stepwise,
|
||||
{xs: [|neg_infinity|], ys: [|0.0|]},
|
||||
None,
|
||||
);
|
||||
};
|
||||
|
||||
let integralEndY = (~cache, t: t) =>
|
||||
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;
|
||||
let mapY = mapY;
|
||||
let toShape = (t: t): DistTypes.shape => Discrete(t);
|
||||
let toContinuous = _ => None;
|
||||
let toDiscrete = t => Some(t);
|
||||
|
||||
let normalize = (t: t): t => {
|
||||
t
|
||||
|> scaleBy(~scale=1. /. integralEndY(~cache=None, t))
|
||||
|> updateKnownIntegralSum(Some(1.0));
|
||||
};
|
||||
|
||||
let normalizedToContinuous = _ => None;
|
||||
let normalizedToDiscrete = t => Some(t); // TODO: this should be normalized!
|
||||
|
||||
let downsample = (~cache=None, i, t: t): t => {
|
||||
// It's not clear how to downsample a set of discrete points in a meaningful way.
|
||||
// The best we can do is to clip off the smallest values.
|
||||
let currentLength = t |> getShape |> XYShape.T.length;
|
||||
|
||||
if (i < currentLength && i >= 1 && currentLength > 1) {
|
||||
let clippedShape =
|
||||
t
|
||||
|> getShape
|
||||
|> XYShape.T.zip
|
||||
|> XYShape.Zipped.sortByY
|
||||
|> Belt.Array.reverse
|
||||
|> Belt.Array.slice(_, ~offset=0, ~len=i)
|
||||
|> XYShape.Zipped.sortByX
|
||||
|> XYShape.T.fromZippedArray;
|
||||
|
||||
make(clippedShape, None); // if someone needs the sum, they'll have to recompute it
|
||||
} else {
|
||||
t;
|
||||
};
|
||||
};
|
||||
|
||||
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
|
||||
|> XYShape.XtoY.stepwiseIfAtX(f)
|
||||
|> E.O.default(0.0)
|
||||
|> DistTypes.MixedPoint.makeDiscrete;
|
||||
|
||||
let integralXtoY = (~cache, f, t) =>
|
||||
t |> integral(~cache) |> Continuous.getShape |> XYShape.XtoY.linear(f);
|
||||
|
||||
let integralYtoX = (~cache, f, t) =>
|
||||
t |> integral(~cache) |> Continuous.getShape |> XYShape.YtoX.linear(f);
|
||||
|
||||
let mean = (t: t): float => {
|
||||
let s = getShape(t);
|
||||
E.A.reducei(s.xs, 0.0, (acc, x, i) => acc +. x *. s.ys[i]);
|
||||
};
|
||||
let variance = (t: t): float => {
|
||||
let getMeanOfSquares = t =>
|
||||
t |> shapeMap(XYShape.Analysis.squareXYShape) |> mean;
|
||||
XYShape.Analysis.getVarianceDangerously(t, mean, getMeanOfSquares);
|
||||
};
|
||||
});
|
||||
151
src/distPlus/distribution/DistPlus.re
Normal file
151
src/distPlus/distribution/DistPlus.re
Normal file
|
|
@ -0,0 +1,151 @@
|
|||
open DistTypes;
|
||||
|
||||
type t = DistTypes.distPlus;
|
||||
|
||||
let shapeIntegral = shape =>
|
||||
Shape.T.Integral.get(~cache=None, shape);
|
||||
let make =
|
||||
(
|
||||
~shape,
|
||||
~guesstimatorString,
|
||||
~domain=Complete,
|
||||
~unit=UnspecifiedDistribution,
|
||||
(),
|
||||
)
|
||||
: t => {
|
||||
let integral = shapeIntegral(shape);
|
||||
{shape, domain, integralCache: integral, unit, guesstimatorString};
|
||||
};
|
||||
|
||||
let update =
|
||||
(
|
||||
~shape=?,
|
||||
~integralCache=?,
|
||||
~domain=?,
|
||||
~unit=?,
|
||||
~guesstimatorString=?,
|
||||
t: t,
|
||||
) => {
|
||||
shape: E.O.default(t.shape, shape),
|
||||
integralCache: E.O.default(t.integralCache, integralCache),
|
||||
domain: E.O.default(t.domain, domain),
|
||||
unit: E.O.default(t.unit, unit),
|
||||
guesstimatorString: E.O.default(t.guesstimatorString, guesstimatorString),
|
||||
};
|
||||
|
||||
let updateShape = (shape, t) => {
|
||||
let integralCache = shapeIntegral(shape);
|
||||
update(~shape, ~integralCache, t);
|
||||
};
|
||||
|
||||
let domainIncludedProbabilityMass = (t: t) =>
|
||||
Domain.includedProbabilityMass(t.domain);
|
||||
|
||||
let domainIncludedProbabilityMassAdjustment = (t: t, f) =>
|
||||
f *. Domain.includedProbabilityMass(t.domain);
|
||||
|
||||
let toShape = ({shape, _}: t) => shape;
|
||||
|
||||
let shapeFn = (fn, {shape}: t) => fn(shape);
|
||||
|
||||
module T =
|
||||
Distributions.Dist({
|
||||
type t = DistTypes.distPlus;
|
||||
type integral = DistTypes.distPlus;
|
||||
let toShape = toShape;
|
||||
let toContinuous = shapeFn(Shape.T.toContinuous);
|
||||
let toDiscrete = shapeFn(Shape.T.toDiscrete);
|
||||
|
||||
let normalize = (t: t): t => {
|
||||
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);
|
||||
};
|
||||
|
||||
let normalizedToContinuous = (t: t) => {
|
||||
t
|
||||
|> toShape
|
||||
|> Shape.T.normalizedToContinuous
|
||||
|> E.O.fmap(
|
||||
Continuous.T.mapY(
|
||||
domainIncludedProbabilityMassAdjustment(t),
|
||||
),
|
||||
);
|
||||
};
|
||||
|
||||
let normalizedToDiscrete = (t: t) => {
|
||||
t
|
||||
|> toShape
|
||||
|> Shape.T.normalizedToDiscrete
|
||||
|> E.O.fmap(
|
||||
Discrete.T.mapY(
|
||||
domainIncludedProbabilityMassAdjustment(t),
|
||||
),
|
||||
);
|
||||
};
|
||||
|
||||
let xToY = (f, t: t) =>
|
||||
t
|
||||
|> toShape
|
||||
|> Shape.T.xToY(f)
|
||||
|> MixedPoint.fmap(domainIncludedProbabilityMassAdjustment(t));
|
||||
|
||||
let minX = shapeFn(Shape.T.minX);
|
||||
let maxX = shapeFn(Shape.T.maxX);
|
||||
let toDiscreteProbabilityMassFraction =
|
||||
shapeFn(Shape.T.toDiscreteProbabilityMassFraction);
|
||||
|
||||
// This bit is kind of awkward, could probably use rethinking.
|
||||
let integral = (~cache, t: t) =>
|
||||
updateShape(Continuous(t.integralCache), t);
|
||||
|
||||
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 =>
|
||||
Shape.T.mapY(~knownIntegralSumFn, fn, shape)
|
||||
|> updateShape(_, t);
|
||||
|
||||
// get the total of everything
|
||||
let integralEndY = (~cache as _, t: t) => {
|
||||
Shape.T.Integral.sum(
|
||||
~cache=Some(t.integralCache),
|
||||
toShape(t),
|
||||
);
|
||||
};
|
||||
|
||||
// TODO: Fix this below, obviously. Adjust for limits
|
||||
let integralXtoY = (~cache as _, f, t: t) => {
|
||||
Shape.T.Integral.xToY(
|
||||
~cache=Some(t.integralCache),
|
||||
f,
|
||||
toShape(t),
|
||||
)
|
||||
|> domainIncludedProbabilityMassAdjustment(t);
|
||||
};
|
||||
|
||||
// TODO: This part is broken when there is a limit, if this is supposed to be taken into account.
|
||||
let integralYtoX = (~cache as _, f, t: t) => {
|
||||
Shape.T.Integral.yToX(~cache=None, f, toShape(t));
|
||||
};
|
||||
|
||||
let mean = (t: t) => {
|
||||
Shape.T.mean(t.shape);
|
||||
};
|
||||
let variance = (t: t) => Shape.T.variance(t.shape);
|
||||
});
|
||||
28
src/distPlus/distribution/DistPlusTime.re
Normal file
28
src/distPlus/distribution/DistPlusTime.re
Normal file
|
|
@ -0,0 +1,28 @@
|
|||
open DistTypes;
|
||||
|
||||
type t = DistTypes.distPlus;
|
||||
|
||||
let unitToJson = ({unit}: t) => unit |> DistTypes.DistributionUnit.toJson;
|
||||
|
||||
let timeVector = ({unit}: t) =>
|
||||
switch (unit) {
|
||||
| TimeDistribution(timeVector) => Some(timeVector)
|
||||
| UnspecifiedDistribution => None
|
||||
};
|
||||
|
||||
let timeInVectorToX = (f: TimeTypes.timeInVector, t: t) => {
|
||||
let timeVector = t |> timeVector;
|
||||
timeVector |> E.O.fmap(TimeTypes.RelativeTimePoint.toXValue(_, f));
|
||||
};
|
||||
|
||||
let xToY = (f: TimeTypes.timeInVector, t: t) => {
|
||||
timeInVectorToX(f, t) |> E.O.fmap(DistPlus.T.xToY(_, t));
|
||||
};
|
||||
|
||||
module Integral = {
|
||||
include DistPlus.T.Integral;
|
||||
let xToY = (f: TimeTypes.timeInVector, t: t) => {
|
||||
timeInVectorToX(f, t)
|
||||
|> E.O.fmap(x => DistPlus.T.Integral.xToY(~cache=None, x, t));
|
||||
};
|
||||
};
|
||||
|
|
@ -28,7 +28,6 @@ type discreteShape = {
|
|||
type mixedShape = {
|
||||
continuous: continuousShape,
|
||||
discrete: discreteShape,
|
||||
// discreteProbabilityMassFraction: float,
|
||||
};
|
||||
|
||||
type shapeMonad('a, 'b, 'c) =
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
307
src/distPlus/distribution/Mixed.re
Normal file
307
src/distPlus/distribution/Mixed.re
Normal file
|
|
@ -0,0 +1,307 @@
|
|||
open Distributions;
|
||||
|
||||
type t = DistTypes.mixedShape;
|
||||
let make = (~continuous, ~discrete): t => {continuous, discrete};
|
||||
|
||||
let totalLength = (t: t): int => {
|
||||
let continuousLength =
|
||||
t.continuous |> Continuous.getShape |> XYShape.T.length;
|
||||
let discreteLength = t.discrete |> Discrete.getShape |> XYShape.T.length;
|
||||
|
||||
continuousLength + discreteLength;
|
||||
};
|
||||
|
||||
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);
|
||||
};
|
||||
|
||||
let toContinuous = ({continuous}: t) => Some(continuous);
|
||||
let toDiscrete = ({discrete}: t) => Some(discrete);
|
||||
|
||||
let combinePointwise = (~knownIntegralSumsFn, fn, t1: t, t2: t) => {
|
||||
let reducedDiscrete =
|
||||
[|t1, t2|]
|
||||
|> E.A.fmap(toDiscrete)
|
||||
|> E.A.O.concatSomes
|
||||
|> Discrete.reduce(~knownIntegralSumsFn, fn);
|
||||
|
||||
let reducedContinuous =
|
||||
[|t1, t2|]
|
||||
|> E.A.fmap(toContinuous)
|
||||
|> E.A.O.concatSomes
|
||||
|> Continuous.reduce(~knownIntegralSumsFn, fn);
|
||||
|
||||
make(~discrete=reducedDiscrete, ~continuous=reducedContinuous);
|
||||
};
|
||||
|
||||
module T =
|
||||
Dist({
|
||||
type t = DistTypes.mixedShape;
|
||||
type integral = DistTypes.continuousShape;
|
||||
let minX = ({continuous, discrete}: t) => {
|
||||
min(Continuous.T.minX(continuous), Discrete.T.minX(discrete));
|
||||
};
|
||||
let maxX = ({continuous, discrete}: t) =>
|
||||
max(Continuous.T.maxX(continuous), Discrete.T.maxX(discrete));
|
||||
let toShape = (t: t): DistTypes.shape => Mixed(t);
|
||||
|
||||
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 totalIntegralSum = continuousIntegralSum +. discreteIntegralSum;
|
||||
|
||||
let newContinuousSum = continuousIntegralSum /. totalIntegralSum;
|
||||
let newDiscreteSum = discreteIntegralSum /. totalIntegralSum;
|
||||
|
||||
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);
|
||||
};
|
||||
|
||||
let xToY = (x, t: t) => {
|
||||
// This evaluates the mixedShape at x, interpolating if necessary.
|
||||
// Note that we normalize entire mixedShape first.
|
||||
let {continuous, discrete}: t = normalize(t);
|
||||
let c = Continuous.T.xToY(x, continuous);
|
||||
let d = Discrete.T.xToY(x, discrete);
|
||||
DistTypes.MixedPoint.add(c, d); // "add" here just combines the two values into a single MixedPoint.
|
||||
};
|
||||
|
||||
let toDiscreteProbabilityMassFraction = ({discrete, continuous}: t) => {
|
||||
let discreteIntegralSum =
|
||||
Discrete.T.Integral.sum(~cache=None, discrete);
|
||||
let continuousIntegralSum =
|
||||
Continuous.T.Integral.sum(~cache=None, continuous);
|
||||
let totalIntegralSum = discreteIntegralSum +. continuousIntegralSum;
|
||||
|
||||
discreteIntegralSum /. totalIntegralSum;
|
||||
};
|
||||
|
||||
let downsample = (~cache=None, count, {discrete, continuous}: t): t => {
|
||||
// We will need to distribute the new xs fairly between the discrete and continuous shapes.
|
||||
// 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 totalIntegralSum = discreteIntegralSum +. continuousIntegralSum;
|
||||
|
||||
// TODO: figure out what to do when the totalIntegralSum is zero.
|
||||
|
||||
let downsampledDiscrete =
|
||||
Discrete.T.downsample(
|
||||
int_of_float(
|
||||
float_of_int(count) *. (discreteIntegralSum /. totalIntegralSum),
|
||||
),
|
||||
discrete,
|
||||
);
|
||||
|
||||
let downsampledContinuous =
|
||||
Continuous.T.downsample(
|
||||
int_of_float(
|
||||
float_of_int(count) *. (continuousIntegralSum /. totalIntegralSum),
|
||||
),
|
||||
continuous,
|
||||
);
|
||||
|
||||
{discrete: downsampledDiscrete, continuous: downsampledContinuous};
|
||||
};
|
||||
|
||||
let normalizedToContinuous = (t: t) => Some(normalize(t).continuous);
|
||||
|
||||
let normalizedToDiscrete = ({discrete} as t: t) =>
|
||||
Some(normalize(t).discrete);
|
||||
|
||||
let integral = (~cache, {continuous, discrete}: t) => {
|
||||
switch (cache) {
|
||||
| Some(cache) => cache
|
||||
| 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);
|
||||
|
||||
Continuous.make(
|
||||
`Linear,
|
||||
XYShape.PointwiseCombination.combineLinear(
|
||||
~fn=(+.),
|
||||
Continuous.getShape(continuousIntegral),
|
||||
Continuous.getShape(discreteIntegral),
|
||||
),
|
||||
None,
|
||||
);
|
||||
};
|
||||
};
|
||||
|
||||
let integralEndY = (~cache, t: t) => {
|
||||
integral(~cache, t) |> Continuous.lastY;
|
||||
};
|
||||
|
||||
let integralXtoY = (~cache, f, t) => {
|
||||
t |> integral(~cache) |> Continuous.getShape |> XYShape.XtoY.linear(f);
|
||||
};
|
||||
|
||||
let integralYtoX = (~cache, f, t) => {
|
||||
t |> integral(~cache) |> Continuous.getShape |> XYShape.YtoX.linear(f);
|
||||
};
|
||||
|
||||
// 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 u = E.O.bind(_, knownIntegralSumFn);
|
||||
|
||||
let yMappedDiscrete =
|
||||
discrete
|
||||
|> Discrete.T.mapY(fn)
|
||||
|> Discrete.updateKnownIntegralSum(u(discrete.knownIntegralSum));
|
||||
|
||||
let yMappedContinuous =
|
||||
continuous
|
||||
|> Continuous.T.mapY(fn)
|
||||
|> Continuous.updateKnownIntegralSum(u(continuous.knownIntegralSum));
|
||||
|
||||
{
|
||||
discrete: yMappedDiscrete,
|
||||
continuous: Continuous.T.mapY(fn, continuous),
|
||||
};
|
||||
};
|
||||
|
||||
let mean = ({discrete, continuous}: t): float => {
|
||||
let discreteMean = Discrete.T.mean(discrete);
|
||||
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 totalIntegralSum = discreteIntegralSum +. continuousIntegralSum;
|
||||
|
||||
(
|
||||
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 totalIntegralSum = discreteIntegralSum +. continuousIntegralSum;
|
||||
|
||||
let getMeanOfSquares = ({discrete, continuous}: t) => {
|
||||
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)
|
||||
};
|
||||
};
|
||||
});
|
||||
|
||||
let combineAlgebraically =
|
||||
(~downsample=false, op: ExpressionTypes.algebraicOperation, t1: t, t2: t)
|
||||
: t => {
|
||||
// Discrete convolution can cause a huge increase in the number of samples,
|
||||
// so we'll first downsample.
|
||||
|
||||
// An alternative (to be explored in the future) may be to first perform the full convolution and then to downsample the result;
|
||||
// to use non-uniform fast Fourier transforms (for addition only), add web workers or gpu.js, etc. ...
|
||||
|
||||
let downsampleIfTooLarge = (t: t) => {
|
||||
let sqtl = sqrt(float_of_int(totalLength(t)));
|
||||
sqtl > 10. && downsample ? T.downsample(int_of_float(sqtl), t) : t;
|
||||
};
|
||||
|
||||
let t1d = downsampleIfTooLarge(t1);
|
||||
let t2d = downsampleIfTooLarge(t2);
|
||||
|
||||
// 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.combineAlgebraically(
|
||||
~downsample=false,
|
||||
op,
|
||||
t1d.continuous,
|
||||
t2d.continuous,
|
||||
);
|
||||
let dcConvResult =
|
||||
Continuous.combineAlgebraicallyWithDiscrete(
|
||||
~downsample=false,
|
||||
op,
|
||||
t2d.continuous,
|
||||
t1d.discrete,
|
||||
);
|
||||
let cdConvResult =
|
||||
Continuous.combineAlgebraicallyWithDiscrete(
|
||||
~downsample=false,
|
||||
op,
|
||||
t1d.continuous,
|
||||
t2d.discrete,
|
||||
);
|
||||
let continuousConvResult =
|
||||
Continuous.reduce((+.), [|ccConvResult, dcConvResult, cdConvResult|]);
|
||||
|
||||
// ... finally, discrete (*) discrete => discrete, obviously:
|
||||
let discreteConvResult =
|
||||
Discrete.combineAlgebraically(op, t1d.discrete, t2d.discrete);
|
||||
|
||||
{discrete: discreteConvResult, continuous: continuousConvResult};
|
||||
};
|
||||
|
|
@ -9,25 +9,25 @@ type assumptions = {
|
|||
};
|
||||
|
||||
let buildSimple = (~continuous: option(DistTypes.continuousShape), ~discrete: option(DistTypes.discreteShape)): option(DistTypes.shape) => {
|
||||
let continuous = continuous |> E.O.default(Distributions.Continuous.make(`Linear, {xs: [||], ys: [||]}, Some(0.0)));
|
||||
let discrete = discrete |> E.O.default(Distributions.Discrete.make({xs: [||], ys: [||]}, Some(0.0)));
|
||||
let continuous = continuous |> E.O.default(Continuous.make(`Linear, {xs: [||], ys: [||]}, Some(0.0)));
|
||||
let discrete = discrete |> E.O.default(Discrete.make({xs: [||], ys: [||]}, Some(0.0)));
|
||||
let cLength =
|
||||
continuous
|
||||
|> Distributions.Continuous.getShape
|
||||
|> Continuous.getShape
|
||||
|> XYShape.T.xs
|
||||
|> E.A.length;
|
||||
let dLength = discrete |> Distributions.Discrete.getShape |> XYShape.T.xs |> E.A.length;
|
||||
let dLength = discrete |> Discrete.getShape |> XYShape.T.xs |> E.A.length;
|
||||
switch (cLength, dLength) {
|
||||
| (0 | 1, 0) => None
|
||||
| (0 | 1, _) => Some(Discrete(discrete))
|
||||
| (_, 0) => Some(Continuous(continuous))
|
||||
| (_, _) =>
|
||||
let discreteProbabilityMassFraction =
|
||||
Distributions.Discrete.T.Integral.sum(~cache=None, discrete);
|
||||
let discrete = Distributions.Discrete.T.normalize(discrete);
|
||||
let continuous = Distributions.Continuous.T.normalize(continuous);
|
||||
Discrete.T.Integral.sum(~cache=None, discrete);
|
||||
let discrete = Discrete.T.normalize(discrete);
|
||||
let continuous = Continuous.T.normalize(continuous);
|
||||
let mixedDist =
|
||||
Distributions.Mixed.make(
|
||||
Mixed.make(
|
||||
~continuous,
|
||||
~discrete
|
||||
);
|
||||
|
|
|
|||
209
src/distPlus/distribution/Shape.re
Normal file
209
src/distPlus/distribution/Shape.re
Normal file
|
|
@ -0,0 +1,209 @@
|
|||
open Distributions;
|
||||
|
||||
type t = DistTypes.shape;
|
||||
let mapToAll = ((fn1, fn2, fn3), t: t) =>
|
||||
switch (t) {
|
||||
| Mixed(m) => fn1(m)
|
||||
| Discrete(m) => fn2(m)
|
||||
| Continuous(m) => fn3(m)
|
||||
};
|
||||
|
||||
let fmap = ((fn1, fn2, fn3), t: t): t =>
|
||||
switch (t) {
|
||||
| Mixed(m) => Mixed(fn1(m))
|
||||
| Discrete(m) => Discrete(fn2(m))
|
||||
| Continuous(m) => Continuous(fn3(m))
|
||||
};
|
||||
|
||||
let toMixed =
|
||||
mapToAll((
|
||||
m => m,
|
||||
d => Mixed.make(~discrete=d, ~continuous=Continuous.empty),
|
||||
c => Mixed.make(~discrete=Discrete.empty, ~continuous=c),
|
||||
));
|
||||
|
||||
let combineAlgebraically =
|
||||
(op: ExpressionTypes.algebraicOperation, t1: t, t2: t): t => {
|
||||
switch (t1, t2) {
|
||||
| (Continuous(m1), Continuous(m2)) =>
|
||||
DistTypes.Continuous(
|
||||
Continuous.combineAlgebraically(~downsample=true, op, m1, m2),
|
||||
)
|
||||
| (Discrete(m1), Discrete(m2)) =>
|
||||
DistTypes.Discrete(Discrete.combineAlgebraically(op, m1, m2))
|
||||
| (m1, m2) =>
|
||||
DistTypes.Mixed(
|
||||
Mixed.combineAlgebraically(
|
||||
~downsample=true,
|
||||
op,
|
||||
toMixed(m1),
|
||||
toMixed(m2),
|
||||
),
|
||||
)
|
||||
};
|
||||
};
|
||||
|
||||
let combinePointwise =
|
||||
(~knownIntegralSumsFn=(_, _) => None, fn, t1: t, t2: t) =>
|
||||
switch (t1, t2) {
|
||||
| (Continuous(m1), Continuous(m2)) =>
|
||||
DistTypes.Continuous(
|
||||
Continuous.combinePointwise(~knownIntegralSumsFn, fn, m1, m2),
|
||||
)
|
||||
| (Discrete(m1), Discrete(m2)) =>
|
||||
DistTypes.Discrete(
|
||||
Discrete.combinePointwise(~knownIntegralSumsFn, fn, m1, m2),
|
||||
)
|
||||
| (m1, m2) =>
|
||||
DistTypes.Mixed(
|
||||
Mixed.combinePointwise(
|
||||
~knownIntegralSumsFn,
|
||||
fn,
|
||||
toMixed(m1),
|
||||
toMixed(m2),
|
||||
),
|
||||
)
|
||||
};
|
||||
|
||||
// TODO: implement these functions
|
||||
let pdf = (f: float, t: t): float => {
|
||||
0.0;
|
||||
};
|
||||
|
||||
let inv = (f: float, t: t): float => {
|
||||
0.0;
|
||||
};
|
||||
|
||||
let sample = (t: t): float => {
|
||||
0.0;
|
||||
};
|
||||
|
||||
module T =
|
||||
Dist({
|
||||
type t = DistTypes.shape;
|
||||
type integral = DistTypes.continuousShape;
|
||||
|
||||
let xToY = (f: float) =>
|
||||
mapToAll((
|
||||
Mixed.T.xToY(f),
|
||||
Discrete.T.xToY(f),
|
||||
Continuous.T.xToY(f),
|
||||
));
|
||||
|
||||
let toShape = (t: t) => t;
|
||||
|
||||
let toContinuous = t => None;
|
||||
let toDiscrete = t => None;
|
||||
|
||||
let downsample = (~cache=None, i, t) =>
|
||||
fmap(
|
||||
(
|
||||
Mixed.T.downsample(i),
|
||||
Discrete.T.downsample(i),
|
||||
Continuous.T.downsample(i),
|
||||
),
|
||||
t,
|
||||
);
|
||||
|
||||
let truncate = (leftCutoff, rightCutoff, t): t =>
|
||||
fmap(
|
||||
(
|
||||
Mixed.T.truncate(leftCutoff, rightCutoff),
|
||||
Discrete.T.truncate(leftCutoff, rightCutoff),
|
||||
Continuous.T.truncate(leftCutoff, rightCutoff),
|
||||
),
|
||||
t,
|
||||
);
|
||||
|
||||
let toDiscreteProbabilityMassFraction = t => 0.0;
|
||||
let normalize =
|
||||
fmap((Mixed.T.normalize, Discrete.T.normalize, Continuous.T.normalize));
|
||||
let toContinuous =
|
||||
mapToAll((
|
||||
Mixed.T.toContinuous,
|
||||
Discrete.T.toContinuous,
|
||||
Continuous.T.toContinuous,
|
||||
));
|
||||
let toDiscrete =
|
||||
mapToAll((
|
||||
Mixed.T.toDiscrete,
|
||||
Discrete.T.toDiscrete,
|
||||
Continuous.T.toDiscrete,
|
||||
));
|
||||
|
||||
let toDiscreteProbabilityMassFraction =
|
||||
mapToAll((
|
||||
Mixed.T.toDiscreteProbabilityMassFraction,
|
||||
Discrete.T.toDiscreteProbabilityMassFraction,
|
||||
Continuous.T.toDiscreteProbabilityMassFraction,
|
||||
));
|
||||
|
||||
let normalizedToDiscrete =
|
||||
mapToAll((
|
||||
Mixed.T.normalizedToDiscrete,
|
||||
Discrete.T.normalizedToDiscrete,
|
||||
Continuous.T.normalizedToDiscrete,
|
||||
));
|
||||
let normalizedToContinuous =
|
||||
mapToAll((
|
||||
Mixed.T.normalizedToContinuous,
|
||||
Discrete.T.normalizedToContinuous,
|
||||
Continuous.T.normalizedToContinuous,
|
||||
));
|
||||
let minX = mapToAll((Mixed.T.minX, Discrete.T.minX, Continuous.T.minX));
|
||||
let integral = (~cache) =>
|
||||
mapToAll((
|
||||
Mixed.T.Integral.get(~cache=None),
|
||||
Discrete.T.Integral.get(~cache=None),
|
||||
Continuous.T.Integral.get(~cache=None),
|
||||
));
|
||||
let integralEndY = (~cache) =>
|
||||
mapToAll((
|
||||
Mixed.T.Integral.sum(~cache=None),
|
||||
Discrete.T.Integral.sum(~cache),
|
||||
Continuous.T.Integral.sum(~cache=None),
|
||||
));
|
||||
let integralXtoY = (~cache, f) => {
|
||||
mapToAll((
|
||||
Mixed.T.Integral.xToY(~cache, f),
|
||||
Discrete.T.Integral.xToY(~cache, f),
|
||||
Continuous.T.Integral.xToY(~cache, f),
|
||||
));
|
||||
};
|
||||
let integralYtoX = (~cache, f) => {
|
||||
mapToAll((
|
||||
Mixed.T.Integral.yToX(~cache, f),
|
||||
Discrete.T.Integral.yToX(~cache, f),
|
||||
Continuous.T.Integral.yToX(~cache, f),
|
||||
));
|
||||
};
|
||||
let maxX = mapToAll((Mixed.T.maxX, Discrete.T.maxX, Continuous.T.maxX));
|
||||
let mapY = (~knownIntegralSumFn=previousIntegralSum => None, fn) =>
|
||||
fmap((
|
||||
Mixed.T.mapY(~knownIntegralSumFn, fn),
|
||||
Discrete.T.mapY(~knownIntegralSumFn, fn),
|
||||
Continuous.T.mapY(~knownIntegralSumFn, fn),
|
||||
));
|
||||
|
||||
let mean = (t: t): float =>
|
||||
switch (t) {
|
||||
| Mixed(m) => Mixed.T.mean(m)
|
||||
| Discrete(m) => Discrete.T.mean(m)
|
||||
| Continuous(m) => Continuous.T.mean(m)
|
||||
};
|
||||
|
||||
let variance = (t: t): float =>
|
||||
switch (t) {
|
||||
| Mixed(m) => Mixed.T.variance(m)
|
||||
| Discrete(m) => Discrete.T.variance(m)
|
||||
| Continuous(m) => Continuous.T.variance(m)
|
||||
};
|
||||
});
|
||||
|
||||
let operate = (distToFloatOp: ExpressionTypes.distToFloatOperation, s) =>
|
||||
switch (distToFloatOp) {
|
||||
| `Pdf(f) => pdf(f, s)
|
||||
| `Inv(f) => inv(f, s)
|
||||
| `Sample => sample(s)
|
||||
| `Mean => T.mean(s)
|
||||
};
|
||||
|
|
@ -3,13 +3,13 @@ open ExpressionTypes.ExpressionTree;
|
|||
let toShape = (sampleCount: int, node: node) => {
|
||||
let renderResult =
|
||||
`Render(`Normalize(node))
|
||||
|> ExpressionTreeEvaluator.toLeaf({sampleCount: sampleCount});
|
||||
|> ExpressionTreeEvaluator.toLeaf({sampleCount: sampleCount, evaluateNode: ExpressionTreeEvaluator.toLeaf});
|
||||
|
||||
switch (renderResult) {
|
||||
| Ok(`RenderedDist(rs)) =>
|
||||
// todo: Why is this here? It converts a mixed shape to a mixed shape.
|
||||
let continuous = Distributions.Shape.T.toContinuous(rs);
|
||||
let discrete = Distributions.Shape.T.toDiscrete(rs);
|
||||
let continuous = Shape.T.toContinuous(rs);
|
||||
let discrete = Shape.T.toDiscrete(rs);
|
||||
let shape = MixedShapeBuilder.buildSimple(~continuous, ~discrete);
|
||||
shape |> E.O.toExt("Could not build final shape.");
|
||||
| Ok(_) => E.O.toExn("Rendering failed.", None)
|
||||
|
|
|
|||
|
|
@ -22,13 +22,14 @@ module AlgebraicCombination = {
|
|||
| _ => Ok(`AlgebraicCombination((operation, t1, t2)))
|
||||
};
|
||||
|
||||
let combineAsShapes = (toLeaf, renderParams, algebraicOp, t1, t2) => {
|
||||
let renderShape = r => toLeaf(renderParams, `Render(r));
|
||||
let combineAsShapes =
|
||||
(evaluationParams: evaluationParams, algebraicOp, t1, t2) => {
|
||||
let renderShape = render(evaluationParams);
|
||||
switch (renderShape(t1), renderShape(t2)) {
|
||||
| (Ok(`RenderedDist(s1)), Ok(`RenderedDist(s2))) =>
|
||||
Ok(
|
||||
`RenderedDist(
|
||||
Distributions.Shape.combineAlgebraically(algebraicOp, s1, s2),
|
||||
Shape.combineAlgebraically(algebraicOp, s1, s2),
|
||||
),
|
||||
)
|
||||
| (Error(e1), _) => Error(e1)
|
||||
|
|
@ -39,8 +40,7 @@ module AlgebraicCombination = {
|
|||
|
||||
let operationToLeaf =
|
||||
(
|
||||
toLeaf,
|
||||
renderParams: renderParams,
|
||||
evaluationParams: evaluationParams,
|
||||
algebraicOp: ExpressionTypes.algebraicOperation,
|
||||
t1: t,
|
||||
t2: t,
|
||||
|
|
@ -52,22 +52,23 @@ module AlgebraicCombination = {
|
|||
_,
|
||||
fun
|
||||
| `SymbolicDist(d) as t => Ok(t)
|
||||
| _ => combineAsShapes(toLeaf, renderParams, algebraicOp, t1, t2),
|
||||
| _ => combineAsShapes(evaluationParams, algebraicOp, t1, t2),
|
||||
);
|
||||
};
|
||||
|
||||
module VerticalScaling = {
|
||||
let operationToLeaf = (toLeaf, renderParams, scaleOp, t, scaleBy) => {
|
||||
let operationToLeaf =
|
||||
(evaluationParams: evaluationParams, scaleOp, t, scaleBy) => {
|
||||
// scaleBy has to be a single float, otherwise we'll return an error.
|
||||
let fn = Operation.Scale.toFn(scaleOp);
|
||||
let knownIntegralSumFn = Operation.Scale.toKnownIntegralSumFn(scaleOp);
|
||||
let renderedShape = toLeaf(renderParams, `Render(t));
|
||||
let renderedShape = render(evaluationParams, t);
|
||||
|
||||
switch (renderedShape, scaleBy) {
|
||||
| (Ok(`RenderedDist(rs)), `SymbolicDist(`Float(sm))) =>
|
||||
Ok(
|
||||
`RenderedDist(
|
||||
Distributions.Shape.T.mapY(
|
||||
Shape.T.mapY(
|
||||
~knownIntegralSumFn=knownIntegralSumFn(sm),
|
||||
fn(sm),
|
||||
rs,
|
||||
|
|
@ -81,13 +82,12 @@ module VerticalScaling = {
|
|||
};
|
||||
|
||||
module PointwiseCombination = {
|
||||
let pointwiseAdd = (toLeaf, renderParams, t1, t2) => {
|
||||
let renderShape = r => toLeaf(renderParams, `Render(r));
|
||||
switch (renderShape(t1), renderShape(t2)) {
|
||||
let pointwiseAdd = (evaluationParams: evaluationParams, t1, t2) => {
|
||||
switch (render(evaluationParams, t1), render(evaluationParams, t2)) {
|
||||
| (Ok(`RenderedDist(rs1)), Ok(`RenderedDist(rs2))) =>
|
||||
Ok(
|
||||
`RenderedDist(
|
||||
Distributions.Shape.combinePointwise(
|
||||
Shape.combinePointwise(
|
||||
~knownIntegralSumsFn=(a, b) => Some(a +. b),
|
||||
(+.),
|
||||
rs1,
|
||||
|
|
@ -101,7 +101,7 @@ module PointwiseCombination = {
|
|||
};
|
||||
};
|
||||
|
||||
let pointwiseMultiply = (toLeaf, renderParams, t1, t2) => {
|
||||
let pointwiseMultiply = (evaluationParams: evaluationParams, t1, t2) => {
|
||||
// TODO: construct a function that we can easily sample from, to construct
|
||||
// a RenderedDist. Use the xMin and xMax of the rendered shapes to tell the sampling function where to look.
|
||||
Error(
|
||||
|
|
@ -109,10 +109,11 @@ module PointwiseCombination = {
|
|||
);
|
||||
};
|
||||
|
||||
let operationToLeaf = (toLeaf, renderParams, pointwiseOp, t1, t2) => {
|
||||
let operationToLeaf =
|
||||
(evaluationParams: evaluationParams, pointwiseOp, t1, t2) => {
|
||||
switch (pointwiseOp) {
|
||||
| `Add => pointwiseAdd(toLeaf, renderParams, t1, t2)
|
||||
| `Multiply => pointwiseMultiply(toLeaf, renderParams, t1, t2)
|
||||
| `Add => pointwiseAdd(evaluationParams, t1, t2)
|
||||
| `Multiply => pointwiseMultiply(evaluationParams, t1, t2)
|
||||
};
|
||||
};
|
||||
};
|
||||
|
|
@ -133,24 +134,23 @@ module Truncate = {
|
|||
};
|
||||
};
|
||||
|
||||
let truncateAsShape = (toLeaf, renderParams, leftCutoff, rightCutoff, t) => {
|
||||
let truncateAsShape =
|
||||
(evaluationParams: evaluationParams, leftCutoff, rightCutoff, 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 = toLeaf(renderParams, `Render(t));
|
||||
switch (renderedShape) {
|
||||
switch (render(evaluationParams, t)) {
|
||||
| Ok(`RenderedDist(rs)) =>
|
||||
let truncatedShape =
|
||||
rs |> Distributions.Shape.T.truncate(leftCutoff, rightCutoff);
|
||||
rs |> Shape.T.truncate(leftCutoff, rightCutoff);
|
||||
Ok(`RenderedDist(truncatedShape));
|
||||
| Error(e1) => Error(e1)
|
||||
| Error(e) => Error(e)
|
||||
| _ => Error("Could not truncate distribution.")
|
||||
};
|
||||
};
|
||||
|
||||
let operationToLeaf =
|
||||
(
|
||||
toLeaf,
|
||||
renderParams,
|
||||
evaluationParams,
|
||||
leftCutoff: option(float),
|
||||
rightCutoff: option(float),
|
||||
t: node,
|
||||
|
|
@ -163,62 +163,54 @@ module Truncate = {
|
|||
| `Solution(t) => Ok(t)
|
||||
| `Error(e) => Error(e)
|
||||
| `NoSolution =>
|
||||
truncateAsShape(toLeaf, renderParams, leftCutoff, rightCutoff, t)
|
||||
truncateAsShape(evaluationParams, leftCutoff, rightCutoff, t)
|
||||
);
|
||||
};
|
||||
};
|
||||
|
||||
module Normalize = {
|
||||
let rec operationToLeaf =
|
||||
(toLeaf, renderParams, t: node): result(node, string) => {
|
||||
let rec operationToLeaf = (evaluationParams, t: node): result(node, string) => {
|
||||
switch (t) {
|
||||
| `RenderedDist(s) =>
|
||||
Ok(`RenderedDist(Distributions.Shape.T.normalize(s)))
|
||||
Ok(`RenderedDist(Shape.T.normalize(s)))
|
||||
| `SymbolicDist(_) => Ok(t)
|
||||
| _ =>
|
||||
t
|
||||
|> toLeaf(renderParams)
|
||||
|> E.R.bind(_, operationToLeaf(toLeaf, renderParams))
|
||||
| _ => evaluateAndRetry(evaluationParams, operationToLeaf, t)
|
||||
};
|
||||
};
|
||||
};
|
||||
|
||||
module FloatFromDist = {
|
||||
let symbolicToLeaf = (distToFloatOp: distToFloatOperation, s) => {
|
||||
SymbolicDist.T.operate(distToFloatOp, s)
|
||||
|> E.R.bind(_, v => Ok(`SymbolicDist(`Float(v))));
|
||||
};
|
||||
let renderedToLeaf =
|
||||
(distToFloatOp: distToFloatOperation, rs: DistTypes.shape)
|
||||
: result(node, string) => {
|
||||
Distributions.Shape.operate(distToFloatOp, rs)
|
||||
|> (v => Ok(`SymbolicDist(`Float(v))));
|
||||
};
|
||||
let rec operationToLeaf =
|
||||
(toLeaf, renderParams, distToFloatOp: distToFloatOperation, t: node)
|
||||
(evaluationParams, distToFloatOp: distToFloatOperation, t: node)
|
||||
: result(node, string) => {
|
||||
switch (t) {
|
||||
| `SymbolicDist(s) => symbolicToLeaf(distToFloatOp, s)
|
||||
| `RenderedDist(rs) => renderedToLeaf(distToFloatOp, rs)
|
||||
| `SymbolicDist(s) =>
|
||||
SymbolicDist.T.operate(distToFloatOp, s)
|
||||
|> E.R.bind(_, v => Ok(`SymbolicDist(`Float(v))))
|
||||
| `RenderedDist(rs) =>
|
||||
Shape.operate(distToFloatOp, rs)
|
||||
|> (v => Ok(`SymbolicDist(`Float(v))))
|
||||
| _ =>
|
||||
t
|
||||
|> toLeaf(renderParams)
|
||||
|> E.R.bind(_, operationToLeaf(toLeaf, renderParams, distToFloatOp))
|
||||
|> evaluateAndRetry(evaluationParams, r =>
|
||||
operationToLeaf(r, distToFloatOp)
|
||||
)
|
||||
};
|
||||
};
|
||||
};
|
||||
|
||||
module Render = {
|
||||
let rec operationToLeaf =
|
||||
(toLeaf, renderParams, t: node): result(t, string) => {
|
||||
(evaluationParams: evaluationParams, t: node): result(t, string) => {
|
||||
switch (t) {
|
||||
| `SymbolicDist(d) =>
|
||||
Ok(`RenderedDist(SymbolicDist.T.toShape(renderParams.sampleCount, d)))
|
||||
Ok(
|
||||
`RenderedDist(
|
||||
SymbolicDist.T.toShape(evaluationParams.sampleCount, d),
|
||||
),
|
||||
)
|
||||
| `RenderedDist(_) as t => Ok(t) // already a rendered shape, we're done here
|
||||
| _ =>
|
||||
t
|
||||
|> toLeaf(renderParams)
|
||||
|> E.R.bind(_, operationToLeaf(toLeaf, renderParams))
|
||||
| _ => evaluateAndRetry(evaluationParams, operationToLeaf, t)
|
||||
};
|
||||
};
|
||||
};
|
||||
|
|
@ -229,35 +221,38 @@ module Render = {
|
|||
but most often it will produce a RenderedDist.
|
||||
This function is used mainly to turn a parse tree into a single RenderedDist
|
||||
that can then be displayed to the user. */
|
||||
let rec toLeaf = (renderParams, node: t): result(t, string) => {
|
||||
let toLeaf =
|
||||
(
|
||||
evaluationParams: ExpressionTypes.ExpressionTree.evaluationParams,
|
||||
node: t,
|
||||
)
|
||||
: result(t, string) => {
|
||||
switch (node) {
|
||||
// Leaf nodes just stay leaf nodes
|
||||
| `SymbolicDist(_)
|
||||
| `RenderedDist(_) => Ok(node)
|
||||
// Operations need to be turned into leaves
|
||||
// Operations nevaluationParamsd to be turned into leaves
|
||||
| `AlgebraicCombination(algebraicOp, t1, t2) =>
|
||||
AlgebraicCombination.operationToLeaf(
|
||||
toLeaf,
|
||||
renderParams,
|
||||
evaluationParams,
|
||||
algebraicOp,
|
||||
t1,
|
||||
t2,
|
||||
)
|
||||
| `PointwiseCombination(pointwiseOp, t1, t2) =>
|
||||
PointwiseCombination.operationToLeaf(
|
||||
toLeaf,
|
||||
renderParams,
|
||||
evaluationParams,
|
||||
pointwiseOp,
|
||||
t1,
|
||||
t2,
|
||||
)
|
||||
| `VerticalScaling(scaleOp, t, scaleBy) =>
|
||||
VerticalScaling.operationToLeaf(toLeaf, renderParams, scaleOp, t, scaleBy)
|
||||
VerticalScaling.operationToLeaf(evaluationParams, scaleOp, t, scaleBy)
|
||||
| `Truncate(leftCutoff, rightCutoff, t) =>
|
||||
Truncate.operationToLeaf(toLeaf, renderParams, leftCutoff, rightCutoff, t)
|
||||
Truncate.operationToLeaf(evaluationParams, leftCutoff, rightCutoff, t)
|
||||
| `FloatFromDist(distToFloatOp, t) =>
|
||||
FloatFromDist.operationToLeaf(toLeaf, renderParams, distToFloatOp, t)
|
||||
| `Normalize(t) => Normalize.operationToLeaf(toLeaf, renderParams, t)
|
||||
| `Render(t) => Render.operationToLeaf(toLeaf, renderParams, t)
|
||||
FloatFromDist.operationToLeaf(evaluationParams, distToFloatOp, t)
|
||||
| `Normalize(t) => Normalize.operationToLeaf(evaluationParams, t)
|
||||
| `Render(t) => Render.operationToLeaf(evaluationParams, t)
|
||||
};
|
||||
};
|
||||
|
|
|
|||
|
|
@ -5,10 +5,8 @@ type distToFloatOperation = [ | `Pdf(float) | `Inv(float) | `Mean | `Sample];
|
|||
|
||||
module ExpressionTree = {
|
||||
type node = [
|
||||
// leaf nodes:
|
||||
| `SymbolicDist(SymbolicTypes.symbolicDist)
|
||||
| `RenderedDist(DistTypes.shape)
|
||||
// operations:
|
||||
| `AlgebraicCombination(algebraicOperation, node, node)
|
||||
| `PointwiseCombination(pointwiseOperation, node, node)
|
||||
| `VerticalScaling(scaleOperation, node, node)
|
||||
|
|
@ -17,6 +15,20 @@ module ExpressionTree = {
|
|||
| `Normalize(node)
|
||||
| `FloatFromDist(distToFloatOperation, node)
|
||||
];
|
||||
|
||||
type evaluationParams = {
|
||||
sampleCount: int,
|
||||
evaluateNode: (evaluationParams, node) => Belt.Result.t(node, string),
|
||||
};
|
||||
|
||||
let evaluateNode = (evaluationParams: evaluationParams) =>
|
||||
evaluationParams.evaluateNode(evaluationParams);
|
||||
|
||||
let render = (evaluationParams: evaluationParams, r) =>
|
||||
evaluateNode(evaluationParams, `Render(r));
|
||||
|
||||
let evaluateAndRetry = (evaluationParams, fn, node) =>
|
||||
node |> evaluationParams.evaluateNode(evaluationParams) |> E.R.bind(_, fn(evaluationParams));
|
||||
};
|
||||
|
||||
type simplificationResult = [
|
||||
|
|
|
|||
|
|
@ -204,32 +204,32 @@ module MathAdtToDistDst = {
|
|||
};
|
||||
};
|
||||
|
||||
let arrayParser =
|
||||
(args: array(arg))
|
||||
: result(ExpressionTypes.ExpressionTree.node, string) => {
|
||||
let samples =
|
||||
args
|
||||
|> E.A.fmap(
|
||||
fun
|
||||
| Value(n) => Some(n)
|
||||
| _ => None,
|
||||
)
|
||||
|> E.A.O.concatSomes;
|
||||
let outputs = Samples.T.fromSamples(samples);
|
||||
let pdf =
|
||||
outputs.shape |> E.O.bind(_, Distributions.Shape.T.toContinuous);
|
||||
let shape =
|
||||
pdf
|
||||
|> E.O.fmap(pdf => {
|
||||
let _pdf = Distributions.Continuous.T.normalize(pdf);
|
||||
let cdf = Distributions.Continuous.T.integral(~cache=None, _pdf);
|
||||
SymbolicDist.ContinuousShape.make(_pdf, cdf);
|
||||
});
|
||||
switch (shape) {
|
||||
| Some(s) => Ok(`SymbolicDist(`ContinuousShape(s)))
|
||||
| None => Error("Rendering did not work")
|
||||
};
|
||||
};
|
||||
// let arrayParser =
|
||||
// (args: array(arg))
|
||||
// : result(ExpressionTypes.ExpressionTree.node, string) => {
|
||||
// let samples =
|
||||
// args
|
||||
// |> E.A.fmap(
|
||||
// fun
|
||||
// | Value(n) => Some(n)
|
||||
// | _ => None,
|
||||
// )
|
||||
// |> E.A.O.concatSomes;
|
||||
// let outputs = Samples.T.fromSamples(samples);
|
||||
// let pdf =
|
||||
// outputs.shape |> E.O.bind(_, Shape.T.toContinuous);
|
||||
// let shape =
|
||||
// pdf
|
||||
// |> E.O.fmap(pdf => {
|
||||
// let _pdf = Continuous.T.normalize(pdf);
|
||||
// let cdf = Continuous.T.integral(~cache=None, _pdf);
|
||||
// SymbolicDist.ContinuousShape.make(_pdf, cdf);
|
||||
// });
|
||||
// switch (shape) {
|
||||
// | Some(s) => Ok(`SymbolicDist(`ContinuousShape(s)))
|
||||
// | None => Error("Rendering did not work")
|
||||
// };
|
||||
// };
|
||||
|
||||
let operationParser =
|
||||
(
|
||||
|
|
@ -335,9 +335,9 @@ module MathAdtToDistDst = {
|
|||
|
||||
let topLevel =
|
||||
fun
|
||||
| Array(r) => arrayParser(r)
|
||||
| Value(_) as r => nodeParser(r)
|
||||
| Fn(_) as r => nodeParser(r)
|
||||
| Array(_) => Error("Array not valid as top level")
|
||||
| Symbol(_) => Error("Symbol not valid as top level")
|
||||
| Object(_) => Error("Object not valid as top level");
|
||||
|
||||
|
|
|
|||
|
|
@ -7,21 +7,21 @@ let downsampleIfShould =
|
|||
let willDownsample =
|
||||
shouldDownsample
|
||||
&& RenderTypes.ShapeRenderer.Combined.methodUsed(outputs) == `Sampling;
|
||||
willDownsample ? dist |> Distributions.DistPlus.T.downsample(recommendedLength) : dist;
|
||||
willDownsample ? dist |> DistPlus.T.downsample(recommendedLength) : dist;
|
||||
};
|
||||
|
||||
let run =
|
||||
(inputs: RenderTypes.DistPlusRenderer.inputs)
|
||||
: RenderTypes.DistPlusRenderer.outputs => {
|
||||
let toDist = shape =>
|
||||
Distributions.DistPlus.make(
|
||||
DistPlus.make(
|
||||
~shape,
|
||||
~domain=inputs.distPlusIngredients.domain,
|
||||
~unit=inputs.distPlusIngredients.unit,
|
||||
~guesstimatorString=Some(inputs.distPlusIngredients.guesstimatorString),
|
||||
(),
|
||||
)
|
||||
|> Distributions.DistPlus.T.normalize;
|
||||
|> DistPlus.T.normalize;
|
||||
let outputs =
|
||||
ShapeRenderer.run({
|
||||
samplingInputs: inputs.samplingInputs,
|
||||
|
|
|
|||
|
|
@ -120,7 +120,7 @@ module T = {
|
|||
|> E.FloatFloatMap.fmap(r => r /. length)
|
||||
|> E.FloatFloatMap.toArray
|
||||
|> XYShape.T.fromZippedArray
|
||||
|> Distributions.Discrete.make(_, None);
|
||||
|> Discrete.make(_, None);
|
||||
|
||||
let pdf =
|
||||
continuousPart |> E.A.length > 5
|
||||
|
|
@ -150,7 +150,7 @@ module T = {
|
|||
~outputXYPoints=samplingInputs.outputXYPoints,
|
||||
formatUnitWidth(usedUnitWidth),
|
||||
)
|
||||
|> Distributions.Continuous.make(`Linear, _, None)
|
||||
|> Continuous.make(`Linear, _, None)
|
||||
|> (r => Some((r, foo)));
|
||||
}
|
||||
: None;
|
||||
|
|
|
|||
|
|
@ -1,20 +1,5 @@
|
|||
open SymbolicTypes;
|
||||
|
||||
module ContinuousShape = {
|
||||
type t = continuousShape;
|
||||
let make = (pdf, cdf): t => {pdf, cdf};
|
||||
let pdf = (x, t: t) =>
|
||||
Distributions.Continuous.T.xToY(x, t.pdf).continuous;
|
||||
// TODO: pdf and inv are currently the same, this seems broken.
|
||||
let inv = (p, t: t) =>
|
||||
Distributions.Continuous.T.xToY(p, t.pdf).continuous;
|
||||
// TODO: Fix the sampling, to have it work correctly.
|
||||
let sample = (t: t) => 3.0;
|
||||
// TODO: Fix the mean, to have it work correctly.
|
||||
let mean = (t: t) => Ok(0.0);
|
||||
let toString = t => {j|CustomContinuousShape|j};
|
||||
};
|
||||
|
||||
module Exponential = {
|
||||
type t = exponential;
|
||||
let pdf = (x, t: t) => Jstat.exponential##pdf(x, t.rate);
|
||||
|
|
@ -170,7 +155,6 @@ module T = {
|
|||
| `Uniform(n) => Uniform.pdf(x, n)
|
||||
| `Beta(n) => Beta.pdf(x, n)
|
||||
| `Float(n) => Float.pdf(x, n)
|
||||
| `ContinuousShape(n) => ContinuousShape.pdf(x, n)
|
||||
};
|
||||
|
||||
let inv = (x, dist) =>
|
||||
|
|
@ -183,7 +167,6 @@ module T = {
|
|||
| `Uniform(n) => Uniform.inv(x, n)
|
||||
| `Beta(n) => Beta.inv(x, n)
|
||||
| `Float(n) => Float.inv(x, n)
|
||||
| `ContinuousShape(n) => ContinuousShape.inv(x, n)
|
||||
};
|
||||
|
||||
let sample: symbolicDist => float =
|
||||
|
|
@ -196,7 +179,6 @@ module T = {
|
|||
| `Uniform(n) => Uniform.sample(n)
|
||||
| `Beta(n) => Beta.sample(n)
|
||||
| `Float(n) => Float.sample(n)
|
||||
| `ContinuousShape(n) => ContinuousShape.sample(n);
|
||||
|
||||
let toString: symbolicDist => string =
|
||||
fun
|
||||
|
|
@ -208,7 +190,6 @@ module T = {
|
|||
| `Uniform(n) => Uniform.toString(n)
|
||||
| `Beta(n) => Beta.toString(n)
|
||||
| `Float(n) => Float.toString(n)
|
||||
| `ContinuousShape(n) => ContinuousShape.toString(n);
|
||||
|
||||
let min: symbolicDist => float =
|
||||
fun
|
||||
|
|
@ -219,7 +200,6 @@ module T = {
|
|||
| `Lognormal(n) => Lognormal.inv(minCdfValue, n)
|
||||
| `Uniform({low}) => low
|
||||
| `Beta(n) => Beta.inv(minCdfValue, n)
|
||||
| `ContinuousShape(n) => ContinuousShape.inv(minCdfValue, n)
|
||||
| `Float(n) => n;
|
||||
|
||||
let max: symbolicDist => float =
|
||||
|
|
@ -230,7 +210,6 @@ module T = {
|
|||
| `Normal(n) => Normal.inv(maxCdfValue, n)
|
||||
| `Lognormal(n) => Lognormal.inv(maxCdfValue, n)
|
||||
| `Beta(n) => Beta.inv(maxCdfValue, n)
|
||||
| `ContinuousShape(n) => ContinuousShape.inv(maxCdfValue, n)
|
||||
| `Uniform({high}) => high
|
||||
| `Float(n) => n;
|
||||
|
||||
|
|
@ -242,7 +221,6 @@ module T = {
|
|||
| `Normal(n) => Normal.mean(n)
|
||||
| `Lognormal(n) => Lognormal.mean(n)
|
||||
| `Beta(n) => Beta.mean(n)
|
||||
| `ContinuousShape(n) => ContinuousShape.mean(n)
|
||||
| `Uniform(n) => Uniform.mean(n)
|
||||
| `Float(n) => Float.mean(n);
|
||||
|
||||
|
|
@ -300,13 +278,13 @@ module T = {
|
|||
switch (d) {
|
||||
| `Float(v) =>
|
||||
Discrete(
|
||||
Distributions.Discrete.make({xs: [|v|], ys: [|1.0|]}, Some(1.0)),
|
||||
Discrete.make({xs: [|v|], ys: [|1.0|]}, Some(1.0)),
|
||||
)
|
||||
| _ =>
|
||||
let xs = interpolateXs(~xSelection=`ByWeight, d, sampleCount);
|
||||
let ys = xs |> E.A.fmap(x => pdf(x, d));
|
||||
Continuous(
|
||||
Distributions.Continuous.make(`Linear, {xs, ys}, Some(1.0)),
|
||||
Continuous.make(`Linear, {xs, ys}, Some(1.0)),
|
||||
);
|
||||
};
|
||||
};
|
||||
|
|
|
|||
|
|
@ -31,11 +31,6 @@ type triangular = {
|
|||
high: float,
|
||||
};
|
||||
|
||||
type continuousShape = {
|
||||
pdf: DistTypes.continuousShape,
|
||||
cdf: DistTypes.continuousShape,
|
||||
};
|
||||
|
||||
type symbolicDist = [
|
||||
| `Normal(normal)
|
||||
| `Beta(beta)
|
||||
|
|
@ -44,7 +39,6 @@ type symbolicDist = [
|
|||
| `Exponential(exponential)
|
||||
| `Cauchy(cauchy)
|
||||
| `Triangular(triangular)
|
||||
| `ContinuousShape(continuousShape)
|
||||
| `Float(float) // Dirac delta at x. Practically useful only in the context of multimodals.
|
||||
];
|
||||
|
||||
|
|
|
|||
|
|
@ -113,7 +113,7 @@ module Model = {
|
|||
|> RenderTypes.DistPlusRenderer.make(~distPlusIngredients=_, ())
|
||||
|> DistPlusRenderer.run
|
||||
|> RenderTypes.DistPlusRenderer.Outputs.distplus
|
||||
|> E.O.bind(_, Distributions.DistPlusTime.Integral.xToY(Time(dateTime)));
|
||||
|> E.O.bind(_, DistPlusTime.Integral.xToY(Time(dateTime)));
|
||||
};
|
||||
|
||||
let make =
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user