71 lines
2.9 KiB
Plaintext
71 lines
2.9 KiB
Plaintext
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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