Merge pull request #30 from NunoSempere/meanAndVariance
Mean and variance
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commit
0fd5e2dcdb
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@ -12,6 +12,15 @@ let makeTest = (~only=false, str, item1, item2) =>
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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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@ -185,6 +194,13 @@ describe("Shape", () => {
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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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@ -291,7 +307,7 @@ describe("Shape", () => {
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);
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});
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describe("Mixed", () => {
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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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@ -362,4 +378,42 @@ describe("Shape", () => {
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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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});
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@ -17,6 +17,9 @@ module type dist = {
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let integralEndY: (~cache: option(integral), t) => float;
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let integralXtoY: (~cache: option(integral), float, t) => float;
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let integralYtoX: (~cache: option(integral), float, t) => float;
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let getMean: t => float;
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let getVariance: t => float;
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};
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module Dist = (T: dist) => {
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@ -35,6 +38,8 @@ module Dist = (T: dist) => {
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let toDiscrete = T.toDiscrete;
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let toScaledContinuous = T.toScaledContinuous;
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let toScaledDiscrete = T.toScaledDiscrete;
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let getMean = T.getMean;
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let getVariance = T.getVariance;
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// TODO: Move this to each class, have use integral to produce integral in DistPlus class.
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let scaleBy = (~scale=1.0, t: t) => t |> mapY((r: float) => r *. scale);
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@ -135,6 +140,23 @@ module Continuous = {
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let toDiscrete = _ => None;
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let toScaledContinuous = t => Some(t);
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let toScaledDiscrete = _ => None;
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let getMean = (t: t) => {
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let indefiniteIntegralStepwise = (p, h1) => h1 *. p ** 2.0 /. 2.0;
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let indefiniteIntegralLinear = (p, a, b) =>
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a *. p ** 2.0 /. 2.0 +. b *. p ** 3.0 /. 3.0;
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XYShape.Analysis.integrateContinuousShape(
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~indefiniteIntegralStepwise,
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~indefiniteIntegralLinear,
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t,
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);
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};
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let getVariance = (t: t): float =>
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XYShape.Analysis.getVarianceDangerously(
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t,
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getMean,
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XYShape.Analysis.getMeanOfSquaresContinuousShape,
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);
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});
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};
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@ -144,11 +166,22 @@ module Discrete = {
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let sortedByX = (t: DistTypes.discreteShape) =>
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t |> XYShape.T.zip |> XYShape.Zipped.sortByX;
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let empty = XYShape.T.empty;
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let combine = (fn, t1: DistTypes.discreteShape, t2: DistTypes.discreteShape): DistTypes.discreteShape => {
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XYShape.Combine.combine(~xsSelection=ALL_XS, ~xToYSelection=XYShape.XtoY.stepwiseIfAtX, ~fn, t1, t2)
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}
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let _default0 = ((fn, a,b) => fn(E.O.default(0.0, a), E.O.default(0.0, b)));
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let reduce = (fn, items) => items |> E.A.fold_left(combine(_default0((fn))), empty);
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let combine =
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(fn, t1: DistTypes.discreteShape, t2: DistTypes.discreteShape)
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: DistTypes.discreteShape => {
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XYShape.Combine.combine(
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~xsSelection=ALL_XS,
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~xToYSelection=XYShape.XtoY.stepwiseIfAtX,
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~fn,
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t1,
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t2,
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);
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};
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let _default0 = (fn, a, b) =>
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fn(E.O.default(0.0, a), E.O.default(0.0, b));
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let reduce = (fn, items) =>
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items |> E.A.fold_left(combine(_default0(fn)), empty);
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module T =
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Dist({
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type t = DistTypes.discreteShape;
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@ -195,6 +228,14 @@ module Discrete = {
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|> integral(~cache)
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|> Continuous.getShape
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|> XYShape.YtoX.linear(f);
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let getMean = (t: t): float =>
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E.A.reducei(t.xs, 0.0, (acc, x, i) => acc +. x *. t.ys[i]);
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let getVariance = (t: t): float => {
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let getMeanOfSquares = t =>
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getMean(XYShape.Analysis.squareXYShape(t));
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XYShape.Analysis.getVarianceDangerously(t, getMean, getMeanOfSquares);
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};
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});
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};
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@ -366,6 +407,41 @@ module Mixed = {
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discreteProbabilityMassFraction,
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};
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};
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let getMean = (t: t): float => {
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let discreteProbabilityMassFraction =
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t.discreteProbabilityMassFraction;
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switch (discreteProbabilityMassFraction) {
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| 1.0 => Discrete.T.getMean(t.discrete)
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| 0.0 => Continuous.T.getMean(t.continuous)
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| _ =>
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Discrete.T.getMean(t.discrete)
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*. discreteProbabilityMassFraction
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+. Continuous.T.getMean(t.continuous)
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*. (1.0 -. discreteProbabilityMassFraction)
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};
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};
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let getVariance = (t: t): float => {
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let discreteProbabilityMassFraction =
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t.discreteProbabilityMassFraction;
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let getMeanOfSquares = (t: t) => {
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Discrete.T.getMean(XYShape.Analysis.squareXYShape(t.discrete))
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*. t.discreteProbabilityMassFraction
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+. XYShape.Analysis.getMeanOfSquaresContinuousShape(t.continuous)
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*. (1.0 -. t.discreteProbabilityMassFraction);
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};
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switch (discreteProbabilityMassFraction) {
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| 1.0 => Discrete.T.getVariance(t.discrete)
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| 0.0 => Continuous.T.getVariance(t.continuous)
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| _ =>
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XYShape.Analysis.getVarianceDangerously(
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t,
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getMean,
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getMeanOfSquares,
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)
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};
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};
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});
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};
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@ -470,6 +546,20 @@ module Shape = {
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Discrete.T.mapY(fn),
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Continuous.T.mapY(fn),
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));
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let getMean = (t: t): float =>
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switch (t) {
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| Mixed(m) => Mixed.T.getMean(m)
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| Discrete(m) => Discrete.T.getMean(m)
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| Continuous(m) => Continuous.T.getMean(m)
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};
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let getVariance = (t: t): float =>
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switch (t) {
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| Mixed(m) => Mixed.T.getVariance(m)
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| Discrete(m) => Discrete.T.getVariance(m)
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| Continuous(m) => Continuous.T.getVariance(m)
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};
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});
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};
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@ -584,6 +674,8 @@ module DistPlus = {
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let integralYtoX = (~cache as _, f, t: t) => {
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Shape.T.Integral.yToX(~cache=Some(t.integralCache), f, toShape(t));
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};
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let getMean = (t: t) => Shape.T.getMean(t.shape);
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let getVariance = (t: t) => Shape.T.getVariance(t.shape);
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});
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};
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@ -17,7 +17,7 @@ module T = {
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type ts = array(xyShape);
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let xs = (t: t) => t.xs;
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let ys = (t: t) => t.ys;
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let empty = ({xs: [||], ys: [||]});
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let empty = {xs: [||], ys: [||]};
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let minX = (t: t) => t |> xs |> E.A.Sorted.min |> extImp;
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let maxX = (t: t) => t |> xs |> E.A.Sorted.max |> extImp;
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let firstY = (t: t) => t |> ys |> E.A.first |> extImp;
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@ -297,4 +297,64 @@ let logScorePoint = (sampleCount, t1, t2) =>
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|> Range.integrateWithTriangles
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|> E.O.fmap(T.accumulateYs((+.)))
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|> E.O.fmap(Pairs.last)
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|> E.O.fmap(Pairs.y);
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|> E.O.fmap(Pairs.y);
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module Analysis = {
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let integrateContinuousShape =
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(
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~indefiniteIntegralStepwise=(p, h1) => h1 *. p,
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~indefiniteIntegralLinear=(p, a, b) => a *. p +. b *. p ** 2.0 /. 2.0,
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t: DistTypes.continuousShape,
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)
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: float => {
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let xs = t.xyShape.xs;
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let ys = t.xyShape.ys;
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E.A.reducei(
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xs,
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0.0,
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(acc, _x, i) => {
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let areaUnderIntegral =
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switch (t.interpolation, i) {
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| (_, 0) => 0.0
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| (`Stepwise, _) =>
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indefiniteIntegralStepwise(xs[i], ys[i - 1])
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-. indefiniteIntegralStepwise(xs[i - 1], ys[i - 1])
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| (`Linear, _) =>
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let x1 = xs[i - 1];
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let x2 = xs[i];
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let h1 = ys[i - 1];
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let h2 = ys[i];
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let b = (h1 -. h2) /. (x1 -. x2);
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let a = h1 -. b *. x1;
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indefiniteIntegralLinear(x2, a, b)
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-. indefiniteIntegralLinear(x1, a, b);
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};
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acc +. areaUnderIntegral;
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},
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);
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};
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let getMeanOfSquaresContinuousShape = (t: DistTypes.continuousShape) => {
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let indefiniteIntegralLinear = (p, a, b) =>
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a *. p ** 3.0 /. 3.0 +. b *. p ** 4.0 /. 4.0;
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let indefiniteIntegralStepwise = (p, h1) => h1 *. p ** 3.0 /. 3.0;
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integrateContinuousShape(
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~indefiniteIntegralStepwise,
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~indefiniteIntegralLinear,
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t,
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);
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};
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let getVarianceDangerously =
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(t: 't, getMean: 't => float, getMeanOfSquares: 't => float): float => {
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let meanSquared = getMean(t) ** 2.0;
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let meanOfSquares = getMeanOfSquares(t);
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meanOfSquares -. meanSquared;
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};
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let squareXYShape = (t): DistTypes.xyShape => {
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...t,
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xs: E.A.fmap(x => x ** 2.0, t.xs),
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};
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};
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@ -259,6 +259,9 @@ module A = {
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let fold_right = Array.fold_right;
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let concatMany = Belt.Array.concatMany;
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let keepMap = Belt.Array.keepMap;
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let init = Array.init;
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let reduce = Belt.Array.reduce;
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let reducei = Belt.Array.reduceWithIndex;
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let min = a =>
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get(a, 0)
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|> O.fmap(first => Belt.Array.reduce(a, first, (i, j) => i < j ? i : j));
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