Merge pull request #192 from QURIresearch/testing-discipline-two
symbolic dists tests (april 6)
This commit is contained in:
commit
67bcd7052b
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@ -13,6 +13,9 @@ Other:
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yarn start # listens to files and recompiles at every mutation
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yarn test
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yarn test:watch # keeps an active session and runs all tests at every mutation
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# where o := open in osx and o := xdg-open in linux,
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yarn coverage; o _coverage/index.html # produces coverage report and opens it in browser
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```
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# TODO: clean up this README.md
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@ -6,9 +6,8 @@ let env: DistributionOperation.env = {
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xyPointLength: 100,
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}
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let normalDist5: GenericDist_Types.genericDist = Symbolic(#Normal({mean: 5.0, stdev: 2.0}))
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let normalDist10: GenericDist_Types.genericDist = Symbolic(#Normal({mean: 10.0, stdev: 2.0}))
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let normalDist20: GenericDist_Types.genericDist = Symbolic(#Normal({mean: 20.0, stdev: 2.0}))
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let mkNormal = (mean, stdev) => GenericDist_Types.Symbolic(#Normal({mean: mean, stdev: stdev}))
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let normalDist5: GenericDist_Types.genericDist = mkNormal(5.0, 2.0)
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let uniformDist: GenericDist_Types.genericDist = Symbolic(#Uniform({low: 9.0, high: 10.0}))
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let {toFloat, toDist, toString, toError} = module(DistributionOperation.Output)
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@ -20,31 +19,6 @@ let toExt: option<'a> => 'a = E.O.toExt(
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"Should be impossible to reach (This error is in test file)",
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)
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describe("normalize", () => {
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test("has no impact on normal dist", () => {
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let result = run(FromDist(ToDist(Normalize), normalDist5))
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expect(result)->toEqual(Dist(normalDist5))
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})
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})
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describe("mean", () => {
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test("for a normal distribution", () => {
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let result = DistributionOperation.run(~env, FromDist(ToFloat(#Mean), normalDist5))
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expect(result)->toEqual(Float(5.0))
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})
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})
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describe("mixture", () => {
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test("on two normal distributions", () => {
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let result =
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run(Mixture([(normalDist10, 0.5), (normalDist20, 0.5)]))
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->outputMap(FromDist(ToFloat(#Mean)))
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->toFloat
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->toExt
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expect(result)->toBeCloseTo(15.28)
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})
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})
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describe("toPointSet", () => {
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test("on symbolic normal distribution", () => {
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let result =
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@ -52,7 +26,7 @@ describe("toPointSet", () => {
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->outputMap(FromDist(ToFloat(#Mean)))
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->toFloat
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->toExt
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expect(result)->toBeCloseTo(5.09)
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expect(result)->toBeSoCloseTo(5.0, ~digits=0)
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})
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test("on sample set distribution with under 4 points", () => {
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@ -63,7 +37,7 @@ describe("toPointSet", () => {
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expect(result)->toEqual(GenDistError(Other("Converting sampleSet to pointSet failed")))
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})
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Skip.test("on sample set", () => {
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test("on sample set", () => {
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let result =
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run(FromDist(ToDist(ToPointSet), normalDist5))
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->outputMap(FromDist(ToDist(ToSampleSet(1000))))
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@ -71,6 +45,6 @@ describe("toPointSet", () => {
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->outputMap(FromDist(ToFloat(#Mean)))
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->toFloat
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->toExt
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expect(result)->toBeCloseTo(5.09)
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expect(result)->toBeSoCloseTo(5.0, ~digits=-1)
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})
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})
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@ -0,0 +1,70 @@
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open Jest
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open Expect
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open TestHelpers
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// TODO: use Normal.make (etc.), but preferably after the new validation dispatch is in.
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let mkNormal = (mean, stdev) => GenericDist_Types.Symbolic(#Normal({mean: mean, stdev: stdev}))
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let mkBeta = (alpha, beta) => GenericDist_Types.Symbolic(#Beta({alpha: alpha, beta: beta}))
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let mkExponential = rate => GenericDist_Types.Symbolic(#Exponential({rate: rate}))
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let mkUniform = (low, high) => GenericDist_Types.Symbolic(#Uniform({low: low, high: high}))
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let mkCauchy = (local, scale) => GenericDist_Types.Symbolic(#Cauchy({local: local, scale: scale}))
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let mkLognormal = (mu, sigma) => GenericDist_Types.Symbolic(#Lognormal({mu: mu, sigma: sigma}))
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describe("mixture", () => {
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testAll("fair mean of two normal distributions", list{(0.0, 1e2), (-1e1, -1e-4), (-1e1, 1e2), (-1e1, 1e1)}, tup => { // should be property
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let (mean1, mean2) = tup
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let meanValue = {
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run(Mixture([(mkNormal(mean1, 9e-1), 0.5), (mkNormal(mean2, 9e-1), 0.5)]))
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-> outputMap(FromDist(ToFloat(#Mean)))
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}
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meanValue -> unpackFloat -> expect -> toBeSoCloseTo((mean1 +. mean2) /. 2.0, ~digits=-1)
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})
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testAll(
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"weighted mean of a beta and an exponential",
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// This would not survive property testing, it was easy for me to find cases that NaN'd out.
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list{((128.0, 1.0), 2.0), ((2e-1, 64.0), 16.0), ((1e0, 1e0), 64.0)},
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tup => {
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let ((alpha, beta), rate) = tup
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let betaWeight = 0.25
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let exponentialWeight = 0.75
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let meanValue = {
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run(Mixture(
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[
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(mkBeta(alpha, beta), betaWeight),
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(mkExponential(rate), exponentialWeight)
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]
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)) -> outputMap(FromDist(ToFloat(#Mean)))
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}
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let betaMean = 1.0 /. (1.0 +. beta /. alpha)
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let exponentialMean = 1.0 /. rate
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meanValue
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-> unpackFloat
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-> expect
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-> toBeSoCloseTo(
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betaWeight *. betaMean +. exponentialWeight *. exponentialMean,
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~digits=-1
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)
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}
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)
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testAll(
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"weighted mean of lognormal and uniform",
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// Would not survive property tests: very easy to find cases that NaN out.
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list{((-1e2,1e1), (2e0,1e0)), ((-1e-16,1e-16), (1e-8,1e0)), ((0.0,1e0), (1e0,1e-2))},
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tup => {
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let ((low, high), (mu, sigma)) = tup
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let uniformWeight = 0.6
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let lognormalWeight = 0.4
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let meanValue = {
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run(Mixture([(mkUniform(low, high), uniformWeight), (mkLognormal(mu, sigma), lognormalWeight)]))
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-> outputMap(FromDist(ToFloat(#Mean)))
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}
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let uniformMean = (low +. high) /. 2.0
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let lognormalMean = mu +. sigma ** 2.0 /. 2.0
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meanValue
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-> unpackFloat
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-> expect
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-> toBeSoCloseTo(uniformWeight *. uniformMean +. lognormalWeight *. lognormalMean, ~digits=-1)
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}
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)
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})
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@ -0,0 +1,41 @@
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open Jest
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open TestHelpers
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describe("Continuous and discrete splits", () => {
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makeTest(
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"splits (1)",
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SampleSet.Internals.T.splitContinuousAndDiscrete([1.432, 1.33455, 2.0]),
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([1.432, 1.33455, 2.0], E.FloatFloatMap.empty()),
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)
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makeTest(
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"splits (2)",
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SampleSet.Internals.T.splitContinuousAndDiscrete([
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1.432,
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1.33455,
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2.0,
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2.0,
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2.0,
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2.0,
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]) |> (((c, disc)) => (c, disc |> E.FloatFloatMap.toArray)),
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([1.432, 1.33455], [(2.0, 4.0)]),
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)
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let makeDuplicatedArray = count => {
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let arr = Belt.Array.range(1, count) |> E.A.fmap(float_of_int)
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let sorted = arr |> Belt.SortArray.stableSortBy(_, compare)
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E.A.concatMany([sorted, sorted, sorted, sorted]) |> Belt.SortArray.stableSortBy(_, compare)
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}
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let (_, discrete1) = SampleSet.Internals.T.splitContinuousAndDiscrete(
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makeDuplicatedArray(10),
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)
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let toArr1 = discrete1 |> E.FloatFloatMap.toArray
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makeTest("splitMedium at count=10", toArr1 |> Belt.Array.length, 10)
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let (_c, discrete2) = SampleSet.Internals.T.splitContinuousAndDiscrete(
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makeDuplicatedArray(500),
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)
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let toArr2 = discrete2 |> E.FloatFloatMap.toArray
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makeTest("splitMedium at count=500", toArr2 |> Belt.Array.length, 500)
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})
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161
packages/squiggle-lang/__tests__/Distributions/Symbolic_test.res
Normal file
161
packages/squiggle-lang/__tests__/Distributions/Symbolic_test.res
Normal file
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@ -0,0 +1,161 @@
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open Jest
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open Expect
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open TestHelpers
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// TODO: use Normal.make (but preferably after teh new validation dispatch is in)
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let mkNormal = (mean, stdev) => GenericDist_Types.Symbolic(#Normal({mean: mean, stdev: stdev}))
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describe("(Symbolic) normalize", () => {
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testAll("has no impact on normal distributions", list{-1e8, -1e-2, 0.0, 1e-4, 1e16}, mean => {
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let normalValue = mkNormal(mean, 2.0)
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let normalizedValue = run(FromDist(ToDist(Normalize), normalValue))
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normalizedValue
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-> unpackDist
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-> expect
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-> toEqual(normalValue)
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})
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})
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describe("(Symbolic) mean", () => {
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testAll("of normal distributions", list{-1e8, -16.0, -1e-2, 0.0, 1e-4, 32.0, 1e16}, mean => {
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run(FromDist(ToFloat(#Mean), mkNormal(mean, 4.0)))
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-> unpackFloat
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-> expect
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-> toBeCloseTo(mean)
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})
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Skip.test("of normal(0, -1) (it NaNs out)", () => {
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run(FromDist(ToFloat(#Mean), mkNormal(1e1, -1e0)))
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-> unpackFloat
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-> expect
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-> ExpectJs.toBeFalsy
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})
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test("of normal(0, 1e-8) (it doesn't freak out at tiny stdev)", () => {
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run(FromDist(ToFloat(#Mean), mkNormal(0.0, 1e-8)))
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-> unpackFloat
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-> expect
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-> toBeCloseTo(0.0)
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})
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testAll("of exponential distributions", list{1e-7, 2.0, 10.0, 100.0}, rate => {
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let meanValue = run(FromDist(ToFloat(#Mean), GenericDist_Types.Symbolic(#Exponential({rate: rate}))))
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meanValue -> unpackFloat -> expect -> toBeCloseTo(1.0 /. rate) // https://en.wikipedia.org/wiki/Exponential_distribution#Mean,_variance,_moments,_and_median
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})
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test("of a cauchy distribution", () => {
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let meanValue = run(FromDist(ToFloat(#Mean), GenericDist_Types.Symbolic(#Cauchy({local: 1.0, scale: 1.0}))))
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meanValue
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-> unpackFloat
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-> expect
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-> toBeCloseTo(2.01868297874546)
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//-> toBe(GenDistError(Other("Cauchy distributions may have no mean value.")))
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})
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testAll("of triangular distributions", list{(1.0,2.0,3.0), (-1e7,-1e-7,1e-7), (-1e-7,1e0,1e7), (-1e-16,0.0,1e-16)}, tup => {
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let (low, medium, high) = tup
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let meanValue = run(FromDist(
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ToFloat(#Mean),
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GenericDist_Types.Symbolic(#Triangular({low: low, medium: medium, high: high}))
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))
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meanValue
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-> unpackFloat
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-> expect
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-> toBeCloseTo((low +. medium +. high) /. 3.0) // https://www.statology.org/triangular-distribution/
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})
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// TODO: nonpositive inputs are SUPPOSED to crash.
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testAll("of beta distributions", list{(1e-4, 6.4e1), (1.28e2, 1e0), (1e-16, 1e-16), (1e16, 1e16), (-1e4, 1e1), (1e1, -1e4)}, tup => {
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let (alpha, beta) = tup
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let meanValue = run(FromDist(
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ToFloat(#Mean),
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GenericDist_Types.Symbolic(#Beta({alpha: alpha, beta: beta}))
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))
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meanValue
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-> unpackFloat
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-> expect
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-> toBeCloseTo(1.0 /. (1.0 +. (beta /. alpha))) // https://en.wikipedia.org/wiki/Beta_distribution#Mean
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})
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// TODO: When we have our theory of validators we won't want this to be NaN but to be an error.
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test("of beta(0, 0)", () => {
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let meanValue = run(FromDist(
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ToFloat(#Mean),
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GenericDist_Types.Symbolic(#Beta({alpha: 0.0, beta: 0.0}))
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))
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meanValue
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-> unpackFloat
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-> expect
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-> ExpectJs.toBeFalsy
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})
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testAll("of lognormal distributions", list{(2.0, 4.0), (1e-7, 1e-2), (-1e6, 10.0), (1e3, -1e2), (-1e8, -1e4), (1e2, 1e-5)}, tup => {
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let (mu, sigma) = tup
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let meanValue = run(FromDist(
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ToFloat(#Mean),
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GenericDist_Types.Symbolic(#Lognormal({mu: mu, sigma: sigma}))
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))
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meanValue
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-> unpackFloat
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-> expect
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-> toBeCloseTo(Js.Math.exp(mu +. sigma ** 2.0 /. 2.0 )) // https://brilliant.org/wiki/log-normal-distribution/
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})
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testAll("of uniform distributions", list{(1e-5, 12.345), (-1e4, 1e4), (-1e16, -1e2), (5.3e3, 9e9)}, tup => {
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let (low, high) = tup
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let meanValue = run(FromDist(
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ToFloat(#Mean),
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GenericDist_Types.Symbolic(#Uniform({low: low, high: high}))
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))
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meanValue
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-> unpackFloat
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-> expect
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-> toBeCloseTo((low +. high) /. 2.0) // https://en.wikipedia.org/wiki/Continuous_uniform_distribution#Moments
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})
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test("of a float", () => {
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let meanValue = run(FromDist(
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ToFloat(#Mean),
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GenericDist_Types.Symbolic(#Float(7.7))
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))
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meanValue -> unpackFloat -> expect -> toBeCloseTo(7.7)
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})
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})
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describe("Normal distribution with sparklines", () => {
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let parameterWiseAdditionPdf = (n1: SymbolicDistTypes.normal, n2: SymbolicDistTypes.normal) => {
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let normalDistAtSumMeanConstr = SymbolicDist.Normal.add(n1, n2)
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let normalDistAtSumMean: SymbolicDistTypes.normal = switch normalDistAtSumMeanConstr {
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| #Normal(params) => params
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}
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x => SymbolicDist.Normal.pdf(x, normalDistAtSumMean)
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}
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let normalDistAtMean5: SymbolicDistTypes.normal = {mean: 5.0, stdev: 2.0}
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let normalDistAtMean10: SymbolicDistTypes.normal = {mean: 10.0, stdev: 2.0}
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let range20Float = E.A.rangeFloat(0, 20) // [0.0,1.0,2.0,3.0,4.0,...19.0,]
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test("mean=5 pdf", () => {
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let pdfNormalDistAtMean5 = x => SymbolicDist.Normal.pdf(x, normalDistAtMean5)
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let sparklineMean5 = fnImage(pdfNormalDistAtMean5, range20Float)
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Sparklines.create(sparklineMean5, ())
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-> expect
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-> toEqual(`▁▂▃▅███▅▃▂▁▁▁▁▁▁▁▁▁▁▁`)
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})
|
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|
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test("parameter-wise addition of two normal distributions", () => {
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let sparklineMean15 = normalDistAtMean5 -> parameterWiseAdditionPdf(normalDistAtMean10) -> fnImage(range20Float)
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Sparklines.create(sparklineMean15, ())
|
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-> expect
|
||||
-> toEqual(`▁▁▁▁▁▁▁▁▁▁▂▃▅▇███▇▅▃▂`)
|
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})
|
||||
|
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test("mean=10 cdf", () => {
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let cdfNormalDistAtMean10 = x => SymbolicDist.Normal.cdf(x, normalDistAtMean10)
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let sparklineMean10 = fnImage(cdfNormalDistAtMean10, range20Float)
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Sparklines.create(sparklineMean10, ())
|
||||
-> expect
|
||||
-> toEqual(`▁▁▁▁▁▁▁▁▂▃▅▆▇████████`)
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||||
})
|
||||
})
|
|
@ -1,47 +0,0 @@
|
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open Jest
|
||||
open Expect
|
||||
|
||||
let makeTest = (~only=false, str, item1, item2) =>
|
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only
|
||||
? Only.test(str, () => expect(item1) -> toEqual(item2))
|
||||
: test(str, () => expect(item1) -> toEqual(item2))
|
||||
|
||||
describe("Lodash", () =>
|
||||
describe("Lodash", () => {
|
||||
makeTest(
|
||||
"split",
|
||||
SampleSet.Internals.T.splitContinuousAndDiscrete([1.432, 1.33455, 2.0]),
|
||||
([1.432, 1.33455, 2.0], E.FloatFloatMap.empty()),
|
||||
)
|
||||
makeTest(
|
||||
"split",
|
||||
SampleSet.Internals.T.splitContinuousAndDiscrete([
|
||||
1.432,
|
||||
1.33455,
|
||||
2.0,
|
||||
2.0,
|
||||
2.0,
|
||||
2.0,
|
||||
]) |> (((c, disc)) => (c, disc |> E.FloatFloatMap.toArray)),
|
||||
([1.432, 1.33455], [(2.0, 4.0)]),
|
||||
)
|
||||
|
||||
let makeDuplicatedArray = count => {
|
||||
let arr = Belt.Array.range(1, count) |> E.A.fmap(float_of_int)
|
||||
let sorted = arr |> Belt.SortArray.stableSortBy(_, compare)
|
||||
E.A.concatMany([sorted, sorted, sorted, sorted]) |> Belt.SortArray.stableSortBy(_, compare)
|
||||
}
|
||||
|
||||
let (_, discrete) = SampleSet.Internals.T.splitContinuousAndDiscrete(
|
||||
makeDuplicatedArray(10),
|
||||
)
|
||||
let toArr = discrete |> E.FloatFloatMap.toArray
|
||||
makeTest("splitMedium", toArr |> Belt.Array.length, 10)
|
||||
|
||||
let (_c, discrete) = SampleSet.Internals.T.splitContinuousAndDiscrete(
|
||||
makeDuplicatedArray(500),
|
||||
)
|
||||
let toArr = discrete |> E.FloatFloatMap.toArray
|
||||
makeTest("splitMedium", toArr |> Belt.Array.length, 500)
|
||||
})
|
||||
)
|
|
@ -1,33 +0,0 @@
|
|||
open Jest
|
||||
open Expect
|
||||
open Js.Array
|
||||
open SymbolicDist
|
||||
|
||||
let makeTest = (~only=false, str, item1, item2) =>
|
||||
only
|
||||
? Only.test(str, () => expect(item1) -> toEqual(item2))
|
||||
: test(str, () => expect(item1) -> toEqual(item2))
|
||||
|
||||
let pdfImage = (thePdf, inps) => map(thePdf, inps)
|
||||
|
||||
let parameterWiseAdditionHelper = (n1: SymbolicDistTypes.normal, n2: SymbolicDistTypes.normal) => {
|
||||
let normalDistAtSumMeanConstr = Normal.add(n1, n2)
|
||||
let normalDistAtSumMean: SymbolicDistTypes.normal = switch normalDistAtSumMeanConstr {
|
||||
| #Normal(params) => params
|
||||
}
|
||||
x => Normal.pdf(x, normalDistAtSumMean)
|
||||
}
|
||||
|
||||
describe("Normal distribution with sparklines", () => {
|
||||
|
||||
let normalDistAtMean5: SymbolicDistTypes.normal = {mean: 5.0, stdev: 2.0}
|
||||
let normalDistAtMean10: SymbolicDistTypes.normal = {mean: 10.0, stdev: 2.0}
|
||||
let range20Float = E.A.rangeFloat(0, 20) // [0.0,1.0,2.0,3.0,4.0,...19.0,]
|
||||
|
||||
let pdfNormalDistAtMean5 = x => Normal.pdf(x, normalDistAtMean5)
|
||||
let sparklineMean5 = pdfImage(pdfNormalDistAtMean5, range20Float)
|
||||
makeTest("mean=5", Sparklines.create(sparklineMean5, ()), `▁▂▃▅███▅▃▂▁▁▁▁▁▁▁▁▁▁▁`)
|
||||
|
||||
let sparklineMean15 = normalDistAtMean5 -> parameterWiseAdditionHelper(normalDistAtMean10) -> pdfImage(range20Float)
|
||||
makeTest("parameter-wise addition of two normal distributions", Sparklines.create(sparklineMean15, ()), `▁▁▁▁▁▁▁▁▁▁▂▃▅▇███▇▅▃▂`)
|
||||
})
|
26
packages/squiggle-lang/__tests__/TestHelpers.res
Normal file
26
packages/squiggle-lang/__tests__/TestHelpers.res
Normal file
|
@ -0,0 +1,26 @@
|
|||
open Jest
|
||||
open Expect
|
||||
|
||||
let makeTest = (~only=false, str, item1, item2) =>
|
||||
only
|
||||
? Only.test(str, () => expect(item1) -> toEqual(item2))
|
||||
: test(str, () => expect(item1) -> toEqual(item2))
|
||||
|
||||
|
||||
let {toFloat, toDist, toString, toError, fmap} = module(DistributionOperation.Output)
|
||||
|
||||
let fnImage = (theFn, inps) => Js.Array.map(theFn, inps)
|
||||
|
||||
let env: DistributionOperation.env = {
|
||||
sampleCount: 100,
|
||||
xyPointLength: 100,
|
||||
}
|
||||
|
||||
let run = DistributionOperation.run(~env)
|
||||
let outputMap = fmap(~env)
|
||||
let unreachableInTestFileMessage = "Should be impossible to reach (This error is in test file)"
|
||||
let toExtFloat: option<float> => float = E.O.toExt(unreachableInTestFileMessage)
|
||||
let toExtDist: option<GenericDist_Types.genericDist> => GenericDist_Types.genericDist = E.O.toExt(unreachableInTestFileMessage)
|
||||
// let toExt: option<'a> => 'a = E.O.toExt(unreachableInTestFileMessage)
|
||||
let unpackFloat = x => x -> toFloat -> toExtFloat
|
||||
let unpackDist = y => y -> toDist -> toExtDist
|
|
@ -47,7 +47,7 @@
|
|||
[
|
||||
"../../node_modules/bisect_ppx/ppx",
|
||||
"--exclude-files",
|
||||
".*_Test\\.res$$"
|
||||
".*_test\\.res$$"
|
||||
]
|
||||
]
|
||||
}
|
||||
|
|
|
@ -5,4 +5,7 @@ module.exports = {
|
|||
setupFilesAfterEnv: [
|
||||
"<rootdir>/../../node_modules/bisect_ppx/src/runtime/js/jest.bs.js"
|
||||
],
|
||||
testPathIgnorePatterns: [
|
||||
"__tests__/TestHelpers.bs.js"
|
||||
],
|
||||
};
|
||||
|
|
|
@ -10,7 +10,7 @@
|
|||
"test:reducer": "jest --testPathPattern '.*__tests__/Reducer.*'",
|
||||
"test": "jest",
|
||||
"test:watch": "jest --watchAll",
|
||||
"coverage": "rm -f *.coverage; yarn clean; BISECT_ENABLE=yes yarn build; yarn test; ./node_modules/.bin/bisect-ppx-report html",
|
||||
"coverage": "rm -f *.coverage; yarn clean; BISECT_ENABLE=yes yarn build; yarn test; bisect-ppx-report html",
|
||||
"all": "yarn build && yarn bundle && yarn test"
|
||||
},
|
||||
"keywords": [
|
||||
|
|
|
@ -10,10 +10,10 @@ type env = {
|
|||
}
|
||||
|
||||
type outputType =
|
||||
| Dist(GenericDist_Types.genericDist)
|
||||
| Dist(genericDist)
|
||||
| Float(float)
|
||||
| String(string)
|
||||
| GenDistError(GenericDist_Types.error)
|
||||
| GenDistError(error)
|
||||
|
||||
/*
|
||||
We're going to add another function to this module later, so first define a
|
||||
|
|
|
@ -1,6 +1,6 @@
|
|||
type genericDist =
|
||||
| PointSet(PointSetTypes.pointSetDist)
|
||||
| SampleSet(array<float>)
|
||||
| SampleSet(SampleSet.t)
|
||||
| Symbolic(SymbolicDistTypes.symbolicDist)
|
||||
|
||||
type error =
|
||||
|
|
|
@ -1,3 +1,5 @@
|
|||
type t = array<float>
|
||||
|
||||
// TODO: Refactor to raise correct error when not enough samples
|
||||
|
||||
module Internals = {
|
||||
|
|
|
@ -55,7 +55,7 @@ module Exponential = {
|
|||
rate: rate,
|
||||
}),
|
||||
)
|
||||
: Error("Exponential distributions mean must be larger than 0")
|
||||
: Error("Exponential distributions rate must be larger than 0.")
|
||||
let pdf = (x, t: t) => Jstat.Exponential.pdf(x, t.rate)
|
||||
let cdf = (x, t: t) => Jstat.Exponential.cdf(x, t.rate)
|
||||
let inv = (p, t: t) => Jstat.Exponential.inv(p, t.rate)
|
||||
|
@ -71,7 +71,7 @@ module Cauchy = {
|
|||
let cdf = (x, t: t) => Jstat.Cauchy.cdf(x, t.local, t.scale)
|
||||
let inv = (p, t: t) => Jstat.Cauchy.inv(p, t.local, t.scale)
|
||||
let sample = (t: t) => Jstat.Cauchy.sample(t.local, t.scale)
|
||||
let mean = (_: t) => Error("Cauchy distributions have no mean value.")
|
||||
let mean = (_: t) => Error("Cauchy distributions may have no mean value.")
|
||||
let toString = ({local, scale}: t) => j`Cauchy($local, $scale)`
|
||||
}
|
||||
|
||||
|
@ -80,8 +80,8 @@ module Triangular = {
|
|||
let make = (low, medium, high): result<symbolicDist, string> =>
|
||||
low < medium && medium < high
|
||||
? Ok(#Triangular({low: low, medium: medium, high: high}))
|
||||
: Error("Triangular values must be increasing order")
|
||||
let pdf = (x, t: t) => Jstat.Triangular.pdf(x, t.low, t.high, t.medium)
|
||||
: Error("Triangular values must be increasing order.")
|
||||
let pdf = (x, t: t) => Jstat.Triangular.pdf(x, t.low, t.high, t.medium) // not obvious in jstat docs that high comes before medium?
|
||||
let cdf = (x, t: t) => Jstat.Triangular.cdf(x, t.low, t.high, t.medium)
|
||||
let inv = (p, t: t) => Jstat.Triangular.inv(p, t.low, t.high, t.medium)
|
||||
let sample = (t: t) => Jstat.Triangular.sample(t.low, t.high, t.medium)
|
||||
|
|
19
yarn.lock
19
yarn.lock
|
@ -8434,6 +8434,13 @@ extglob@^2.0.4:
|
|||
snapdragon "^0.8.1"
|
||||
to-regex "^3.0.1"
|
||||
|
||||
fast-check@^2.17.0:
|
||||
version "2.24.0"
|
||||
resolved "https://registry.yarnpkg.com/fast-check/-/fast-check-2.24.0.tgz#39f85586862108a4de6394c5196ebcf8b76b6c8b"
|
||||
integrity sha512-iNXbN90lbabaCUfnW5jyXYPwMJLFYl09eJDkXA9ZoidFlBK63gNRvcKxv+8D1OJ1kIYjwBef4bO/K3qesUeWLQ==
|
||||
dependencies:
|
||||
pure-rand "^5.0.1"
|
||||
|
||||
fast-deep-equal@^3.1.1, fast-deep-equal@^3.1.3, fast-deep-equal@~3.1.3:
|
||||
version "3.1.3"
|
||||
resolved "https://registry.npmjs.org/fast-deep-equal/-/fast-deep-equal-3.1.3.tgz"
|
||||
|
@ -13566,6 +13573,11 @@ pure-color@^1.2.0:
|
|||
resolved "https://registry.yarnpkg.com/pure-color/-/pure-color-1.3.0.tgz#1fe064fb0ac851f0de61320a8bf796836422f33e"
|
||||
integrity sha1-H+Bk+wrIUfDeYTIKi/eWg2Qi8z4=
|
||||
|
||||
pure-rand@^5.0.1:
|
||||
version "5.0.1"
|
||||
resolved "https://registry.yarnpkg.com/pure-rand/-/pure-rand-5.0.1.tgz#97a287b4b4960b2a3448c0932bf28f2405cac51d"
|
||||
integrity sha512-ksWccjmXOHU2gJBnH0cK1lSYdvSZ0zLoCMSz/nTGh6hDvCSgcRxDyIcOBD6KNxFz3xhMPm/T267Tbe2JRymKEQ==
|
||||
|
||||
q@^1.1.2:
|
||||
version "1.5.1"
|
||||
resolved "https://registry.yarnpkg.com/q/-/q-1.5.1.tgz#7e32f75b41381291d04611f1bf14109ac00651d7"
|
||||
|
@ -14754,6 +14766,13 @@ requires-port@^1.0.0:
|
|||
resolved "https://registry.yarnpkg.com/requires-port/-/requires-port-1.0.0.tgz#925d2601d39ac485e091cf0da5c6e694dc3dcaff"
|
||||
integrity sha1-kl0mAdOaxIXgkc8NpcbmlNw9yv8=
|
||||
|
||||
rescript-fast-check@^1.1.1:
|
||||
version "1.1.1"
|
||||
resolved "https://registry.yarnpkg.com/rescript-fast-check/-/rescript-fast-check-1.1.1.tgz#ef153cb01254b2f01a738faf85b73327d423d5e1"
|
||||
integrity sha512-wxeW0TsL/prkRvEYGbhEiLaLKmYJaECyDcKEWh65hDqP2i76lARzVW3QmYujSYK4OnjAC70dln3X6UC/2m2Huw==
|
||||
dependencies:
|
||||
fast-check "^2.17.0"
|
||||
|
||||
rescript@^9.1.4:
|
||||
version "9.1.4"
|
||||
resolved "https://registry.npmjs.org/rescript/-/rescript-9.1.4.tgz"
|
||||
|
|
Loading…
Reference in New Issue
Block a user