cleanup; CR
Value: [1e-9 to 1e-5]
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@ -9,7 +9,7 @@ describe("dotSubtract", () => {
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test("mean of normal minus exponential (unit)", () => {
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test("mean of normal minus exponential (unit)", () => {
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let mean = 0.0
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let mean = 0.0
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let rate = 10.0
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let rate = 10.0
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exception MeanFailed
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let dotDifference = DistributionOperation.Constructors.pointwiseSubtract(
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let dotDifference = DistributionOperation.Constructors.pointwiseSubtract(
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~env,
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~env,
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mkNormal(mean, 1.0),
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mkNormal(mean, 1.0),
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@ -19,7 +19,7 @@ describe("dotSubtract", () => {
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let meanAnalytical = mean -. 1.0 /. rate
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let meanAnalytical = mean -. 1.0 /. rate
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switch meanResult {
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switch meanResult {
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| Ok(meanValue) => meanValue->expect->toBeCloseTo(meanAnalytical)
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| Ok(meanValue) => meanValue->expect->toBeCloseTo(meanAnalytical)
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| Error(err) => err->expect->toBe(DistributionTypes.OperationError(DivisionByZeroError))
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| Error(_) => raise(MeanFailed)
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}
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}
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})
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})
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Skip.test("mean of normal minus exponential (property)", () => {
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Skip.test("mean of normal minus exponential (property)", () => {
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@ -1,11 +0,0 @@
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/*
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This test case comes via Nuño https://github.com/quantified-uncertainty/squiggle/issues/433
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*/
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open Jest
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open Expect
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describe("KL divergence", () => {
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test("our's agrees with analytical", () => {
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true->expect->toBe(true)
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})
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})
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@ -1,19 +0,0 @@
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import { testRun } from "./TestHelpers";
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describe("KL divergence", () => {
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test.skip("by integral solver agrees with analytical", () => {
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let squiggleStringKL = `prediction=normal(4, 1)
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answer=normal(1,1)
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logSubtraction=dotSubtract(scaleLog(answer),scaleLog(prediction))
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klintegrand=dotMultiply(logSubtraction, answer)
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klintegral = integralSum(klintegrand)
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analyticalKl = log(1 / 1) + 1 ^ 2 / (2 * 1 ^ 2) + ((4 - 1) * (1 - 4) / (2 * 1 * 1)) - 1 / 2
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klintegral - analyticalKl`;
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let squiggleResultKL = testRun(squiggleStringKL);
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expect(squiggleResultKL.value).toBeCloseTo(0);
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});
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});
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let squiggleStringLS = `prediction=normal(4,1)
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answer=normal(1,1)
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logScore(prediction, answer)`;
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@ -70,4 +70,3 @@ let cauchyMakeR = (local, rate) => fmapGenDist(SymbolicDist.Cauchy.make(local, r
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let lognormalMakeR = (mu, sigma) => fmapGenDist(SymbolicDist.Lognormal.make(mu, sigma))
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let lognormalMakeR = (mu, sigma) => fmapGenDist(SymbolicDist.Lognormal.make(mu, sigma))
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let triangularMakeR = (low, mode, high) =>
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let triangularMakeR = (low, mode, high) =>
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fmapGenDist(SymbolicDist.Triangular.make(low, mode, high))
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fmapGenDist(SymbolicDist.Triangular.make(low, mode, high))
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// let floatMakeR = x =>E.R.fmap(s => DistributionTypes.Symbolic(s), SymbolicDist.Float.make(x))
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