419 lines
11 KiB
ReasonML
419 lines
11 KiB
ReasonML
open Jest;
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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 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);
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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);
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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|]});
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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|]});
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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.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 = shape;
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makeTest("minX", T.minX(discrete), 1.0);
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makeTest("maxX", T.maxX(discrete), 8.0);
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makeTest(
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"mapY",
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T.mapY(r => r *. 2.0, discrete) |> (r => r.ys),
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[|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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T.scaleBy(~scale=4.0, discrete),
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{xs: [|1., 4., 8.|], ys: [|1.2, 2.0, 0.8|]},
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);
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makeTest(
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"scaleToIntegralSum",
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T.scaleToIntegralSum(~intendedSum=4.0, discrete),
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{xs: [|1., 4., 8.|], ys: [|1.2, 2.0, 0.8|]},
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);
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makeTest(
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"scaleToIntegralSum: back and forth",
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discrete
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|> T.scaleToIntegralSum(~intendedSum=4.0)
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|> T.scaleToIntegralSum(~intendedSum=1.0),
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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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),
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);
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makeTest(
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"integral with 1 element",
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T.Integral.get(~cache=None, {xs: [|0.0|], ys: [|1.0|]}),
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Distributions.Continuous.make(`Stepwise, {xs: [|0.0|], ys: [|1.0|]}),
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);
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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.getMean(discrete), 3.9);
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makeTestCloseEquality(
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"variance",
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T.getVariance(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("Mixed", () => {
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open Distributions.Mixed;
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let discrete: 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 continuous =
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Distributions.Continuous.make(
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`Linear,
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{xs: [|3., 7., 14.|], ys: [|0.058, 0.082, 0.124|]},
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)
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|> Distributions.Continuous.T.scaleToIntegralSum(~intendedSum=1.0);
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let mixed =
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MixedShapeBuilder.build(
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~continuous,
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~discrete,
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~assumptions={
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continuous: ADDS_TO_CORRECT_PROBABILITY,
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discrete: ADDS_TO_CORRECT_PROBABILITY,
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discreteProbabilityMass: Some(0.5),
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},
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)
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|> E.O.toExn("");
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makeTest("minX", T.minX(mixed), 1.0);
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makeTest("maxX", T.maxX(mixed), 14.0);
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makeTest(
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"mapY",
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T.mapY(r => r *. 2.0, mixed),
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Distributions.Mixed.make(
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~continuous=
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Distributions.Continuous.make(
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`Linear,
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{
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xs: [|3., 7., 14.|],
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ys: [|
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0.11588411588411589,
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0.16383616383616384,
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0.24775224775224775,
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|],
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},
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),
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~discrete={xs: [|1., 4., 8.|], ys: [|0.6, 1.0, 0.4|]},
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~discreteProbabilityMassFraction=0.5,
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),
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);
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makeTest(
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"xToY at 4.0",
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T.xToY(4., mixed),
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{discrete: 0.25, continuous: 0.03196803196803197},
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);
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makeTest(
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"xToY at 0.0",
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T.xToY(0., mixed),
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{discrete: 0.0, continuous: 0.028971028971028972},
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);
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makeTest(
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"xToY at 5.0",
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T.xToY(7., mixed),
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{discrete: 0.0, continuous: 0.04095904095904096},
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);
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makeTest("integralEndY", T.Integral.sum(~cache=None, mixed), 1.0);
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makeTest(
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"scaleBy",
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T.scaleBy(~scale=2.0, mixed),
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Distributions.Mixed.make(
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~continuous=
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Distributions.Continuous.make(
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`Linear,
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{
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xs: [|3., 7., 14.|],
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ys: [|
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0.11588411588411589,
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0.16383616383616384,
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0.24775224775224775,
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|],
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},
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),
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~discrete={xs: [|1., 4., 8.|], ys: [|0.6, 1.0, 0.4|]},
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~discreteProbabilityMassFraction=0.5,
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),
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);
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makeTest(
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"integral",
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T.Integral.get(~cache=None, mixed),
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Distributions.Continuous.make(
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`Linear,
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{
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xs: [|1.00007, 1.00007, 3., 4., 4.00007, 7., 8., 8.00007, 14.|],
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ys: [|
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0.0,
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0.0,
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0.15,
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0.18496503496503497,
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0.4349674825174825,
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0.5398601398601399,
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0.5913086913086913,
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0.6913122927072927,
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1.0,
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},
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),
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);
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});
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describe("Distplus", () => {
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open Distributions.DistPlus;
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let discrete: DistTypes.xyShape = {
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xs: [|1., 4., 8.|],
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ys: [|0.3, 0.5, 0.2|],
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};
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let continuous =
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Distributions.Continuous.make(
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`Linear,
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{xs: [|3., 7., 14.|], ys: [|0.058, 0.082, 0.124|]},
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)
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|> Distributions.Continuous.T.scaleToIntegralSum(~intendedSum=1.0);
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let mixed =
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MixedShapeBuilder.build(
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~continuous,
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~discrete,
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~assumptions={
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continuous: ADDS_TO_CORRECT_PROBABILITY,
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discrete: ADDS_TO_CORRECT_PROBABILITY,
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discreteProbabilityMass: Some(0.5),
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},
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)
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|> E.O.toExn("");
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let distPlus =
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Distributions.DistPlus.make(
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~shape=Mixed(mixed),
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~guesstimatorString=None,
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(),
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);
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makeTest("minX", T.minX(distPlus), 1.0);
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makeTest("maxX", T.maxX(distPlus), 14.0);
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makeTest(
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"xToY at 4.0",
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T.xToY(4., distPlus),
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{discrete: 0.25, continuous: 0.03196803196803197},
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);
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makeTest(
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"xToY at 0.0",
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T.xToY(0., distPlus),
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{discrete: 0.0, continuous: 0.028971028971028972},
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);
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makeTest(
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"xToY at 5.0",
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T.xToY(7., distPlus),
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{discrete: 0.0, continuous: 0.04095904095904096},
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);
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makeTest("integralEndY", T.Integral.sum(~cache=None, distPlus), 1.0);
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makeTest(
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"integral",
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T.Integral.get(~cache=None, distPlus) |> T.toContinuous,
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Some(
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Distributions.Continuous.make(
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`Linear,
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{
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xs: [|1.00007, 1.00007, 3., 4., 4.00007, 7., 8., 8.00007, 14.|],
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ys: [|
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0.0,
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0.0,
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0.15,
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0.18496503496503497,
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0.4349674825174825,
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0.5398601398601399,
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0.5913086913086913,
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0.6913122927072927,
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1.0,
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|],
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},
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),
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),
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);
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});
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describe("Shape", () => {
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let mean = 10.0;
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let stdev = 4.0;
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let variance = stdev ** 2.0;
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let numSamples = 10000;
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open Distributions.Shape;
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let normal: SymbolicDist.dist = `Normal({mean, stdev});
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let normalShape = SymbolicDist.GenericSimple.toShape(normal, numSamples);
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let lognormal = SymbolicDist.Lognormal.fromMeanAndStdev(mean, stdev);
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let lognormalShape =
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SymbolicDist.GenericSimple.toShape(lognormal, numSamples);
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makeTestCloseEquality(
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"Mean of a normal",
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T.getMean(normalShape),
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mean,
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~digits=2,
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);
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makeTestCloseEquality(
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"Variance of a normal",
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T.getVariance(normalShape),
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variance,
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~digits=1,
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);
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makeTestCloseEquality(
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"Mean of a lognormal",
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T.getMean(lognormalShape),
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mean,
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~digits=2,
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);
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makeTestCloseEquality(
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"Variance of a lognormal",
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T.getVariance(lognormalShape),
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variance,
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~digits=0,
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);
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});
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}); |