Merge pull request #533 from quantified-uncertainty/add-bernoulli

Add Bernoulli distribution
This commit is contained in:
Ozzie Gooen 2022-05-16 10:30:19 -04:00 committed by GitHub
commit 72b3643c6a
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7 changed files with 395 additions and 8 deletions

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@ -31,6 +31,8 @@ describe("eval on distribution functions", () => {
testEval("mean(normal(5,2))", "Ok(5)") testEval("mean(normal(5,2))", "Ok(5)")
testEval("mean(lognormal(1,2))", "Ok(20.085536923187668)") testEval("mean(lognormal(1,2))", "Ok(20.085536923187668)")
testEval("mean(gamma(5,5))", "Ok(25)") testEval("mean(gamma(5,5))", "Ok(25)")
testEval("mean(bernoulli(0.2))", "Ok(0.2)")
testEval("mean(bernoulli(0.8))", "Ok(0.8)")
}) })
describe("toString", () => { describe("toString", () => {
testEval("toString(normal(5,2))", "Ok('Normal(5,2)')") testEval("toString(normal(5,2))", "Ok('Normal(5,2)')")

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@ -34,27 +34,28 @@
], ],
"author": "Quantified Uncertainty Research Institute", "author": "Quantified Uncertainty Research Institute",
"dependencies": { "dependencies": {
"rescript": "^9.1.4", "@stdlib/stats": "^0.0.13",
"jstat": "^1.9.5", "jstat": "^1.9.5",
"mathjs": "^10.5.2",
"pdfast": "^0.2.0", "pdfast": "^0.2.0",
"mathjs": "^10.5.2" "rescript": "^9.1.4"
}, },
"devDependencies": { "devDependencies": {
"bisect_ppx": "^2.7.1",
"lodash": "^4.17.21",
"rescript-fast-check": "^1.1.1",
"@glennsl/rescript-jest": "^0.9.0", "@glennsl/rescript-jest": "^0.9.0",
"@istanbuljs/nyc-config-typescript": "^1.0.2", "@istanbuljs/nyc-config-typescript": "^1.0.2",
"@types/jest": "^27.5.0", "@types/jest": "^27.5.0",
"babel-plugin-transform-es2015-modules-commonjs": "^6.26.2", "babel-plugin-transform-es2015-modules-commonjs": "^6.26.2",
"bisect_ppx": "^2.7.1",
"chalk": "^5.0.1", "chalk": "^5.0.1",
"codecov": "^3.8.3", "codecov": "^3.8.3",
"fast-check": "^2.25.0", "fast-check": "^2.25.0",
"gentype": "^4.3.0", "gentype": "^4.3.0",
"jest": "^27.5.1", "jest": "^27.5.1",
"lodash": "^4.17.21",
"moduleserve": "^0.9.1", "moduleserve": "^0.9.1",
"nyc": "^15.1.0", "nyc": "^15.1.0",
"reanalyze": "^2.19.0", "reanalyze": "^2.19.0",
"rescript-fast-check": "^1.1.1",
"ts-jest": "^27.1.4", "ts-jest": "^27.1.4",
"ts-loader": "^9.3.0", "ts-loader": "^9.3.0",
"ts-node": "^10.7.0", "ts-node": "^10.7.0",

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@ -216,6 +216,32 @@ module Uniform = {
} }
} }
module Bernoulli = {
type t = bernoulli
let make = p =>
p >= 0.0 && p <= 1.0
? Ok(#Bernoulli({p: p}))
: Error("Bernoulli parameter must be between 0 and 1")
let pmf = (x, t: t) => Stdlib.Bernoulli.pmf(x, t.p)
//Bernoulli is a discrete distribution, so it doesn't really have a pdf().
//We fake this for now with the pmf function, but this should be fixed at some point.
let pdf = (x, t: t) => Stdlib.Bernoulli.pmf(x, t.p)
let cdf = (x, t: t) => Stdlib.Bernoulli.cdf(x, t.p)
let inv = (p, t: t) => Stdlib.Bernoulli.quantile(p, t.p)
let mean = (t: t) => Ok(Stdlib.Bernoulli.mean(t.p))
let min = (t: t) => t.p == 1.0 ? 1.0 : 0.0
let max = (t: t) => t.p == 0.0 ? 0.0 : 1.0
let sample = (t: t) => {
let s = Uniform.sample({low: 0.0, high: 1.0})
inv(s, t)
}
let toString = ({p}: t) => j`Bernoulli($p)`
let toPointSetDist = ({p}: t): PointSetTypes.pointSetDist => Discrete(
Discrete.make(~integralSumCache=Some(1.0), {xs: [0.0, 1.0], ys: [1.0 -. p, p]}),
)
}
module Gamma = { module Gamma = {
type t = gamma type t = gamma
let make = (shape: float, scale: float) => { let make = (shape: float, scale: float) => {
@ -252,6 +278,9 @@ module Float = {
let mean = (t: t) => Ok(t) let mean = (t: t) => Ok(t)
let sample = (t: t) => t let sample = (t: t) => t
let toString = (t: t) => j`Delta($t)` let toString = (t: t) => j`Delta($t)`
let toPointSetDist = (t: t): PointSetTypes.pointSetDist => Discrete(
Discrete.make(~integralSumCache=Some(1.0), {xs: [t], ys: [1.0]}),
)
} }
module From90thPercentile = { module From90thPercentile = {
@ -278,6 +307,7 @@ module T = {
| #Uniform(n) => Uniform.pdf(x, n) | #Uniform(n) => Uniform.pdf(x, n)
| #Beta(n) => Beta.pdf(x, n) | #Beta(n) => Beta.pdf(x, n)
| #Float(n) => Float.pdf(x, n) | #Float(n) => Float.pdf(x, n)
| #Bernoulli(n) => Bernoulli.pdf(x, n)
} }
let cdf = (x, dist) => let cdf = (x, dist) =>
@ -291,6 +321,7 @@ module T = {
| #Uniform(n) => Uniform.cdf(x, n) | #Uniform(n) => Uniform.cdf(x, n)
| #Beta(n) => Beta.cdf(x, n) | #Beta(n) => Beta.cdf(x, n)
| #Float(n) => Float.cdf(x, n) | #Float(n) => Float.cdf(x, n)
| #Bernoulli(n) => Bernoulli.cdf(x, n)
} }
let inv = (x, dist) => let inv = (x, dist) =>
@ -304,6 +335,7 @@ module T = {
| #Uniform(n) => Uniform.inv(x, n) | #Uniform(n) => Uniform.inv(x, n)
| #Beta(n) => Beta.inv(x, n) | #Beta(n) => Beta.inv(x, n)
| #Float(n) => Float.inv(x, n) | #Float(n) => Float.inv(x, n)
| #Bernoulli(n) => Bernoulli.inv(x, n)
} }
let sample: symbolicDist => float = x => let sample: symbolicDist => float = x =>
@ -317,6 +349,7 @@ module T = {
| #Uniform(n) => Uniform.sample(n) | #Uniform(n) => Uniform.sample(n)
| #Beta(n) => Beta.sample(n) | #Beta(n) => Beta.sample(n)
| #Float(n) => Float.sample(n) | #Float(n) => Float.sample(n)
| #Bernoulli(n) => Bernoulli.sample(n)
} }
let doN = (n, fn) => { let doN = (n, fn) => {
@ -340,6 +373,7 @@ module T = {
| #Uniform(n) => Uniform.toString(n) | #Uniform(n) => Uniform.toString(n)
| #Beta(n) => Beta.toString(n) | #Beta(n) => Beta.toString(n)
| #Float(n) => Float.toString(n) | #Float(n) => Float.toString(n)
| #Bernoulli(n) => Bernoulli.toString(n)
} }
let min: symbolicDist => float = x => let min: symbolicDist => float = x =>
@ -351,6 +385,7 @@ module T = {
| #Lognormal(n) => Lognormal.inv(minCdfValue, n) | #Lognormal(n) => Lognormal.inv(minCdfValue, n)
| #Gamma(n) => Gamma.inv(minCdfValue, n) | #Gamma(n) => Gamma.inv(minCdfValue, n)
| #Uniform({low}) => low | #Uniform({low}) => low
| #Bernoulli(n) => Bernoulli.min(n)
| #Beta(n) => Beta.inv(minCdfValue, n) | #Beta(n) => Beta.inv(minCdfValue, n)
| #Float(n) => n | #Float(n) => n
} }
@ -364,6 +399,7 @@ module T = {
| #Gamma(n) => Gamma.inv(maxCdfValue, n) | #Gamma(n) => Gamma.inv(maxCdfValue, n)
| #Lognormal(n) => Lognormal.inv(maxCdfValue, n) | #Lognormal(n) => Lognormal.inv(maxCdfValue, n)
| #Beta(n) => Beta.inv(maxCdfValue, n) | #Beta(n) => Beta.inv(maxCdfValue, n)
| #Bernoulli(n) => Bernoulli.max(n)
| #Uniform({high}) => high | #Uniform({high}) => high
| #Float(n) => n | #Float(n) => n
} }
@ -378,6 +414,7 @@ module T = {
| #Beta(n) => Beta.mean(n) | #Beta(n) => Beta.mean(n)
| #Uniform(n) => Uniform.mean(n) | #Uniform(n) => Uniform.mean(n)
| #Gamma(n) => Gamma.mean(n) | #Gamma(n) => Gamma.mean(n)
| #Bernoulli(n) => Bernoulli.mean(n)
| #Float(n) => Float.mean(n) | #Float(n) => Float.mean(n)
} }
@ -453,7 +490,8 @@ module T = {
d: symbolicDist, d: symbolicDist,
): PointSetTypes.pointSetDist => ): PointSetTypes.pointSetDist =>
switch d { switch d {
| #Float(v) => Discrete(Discrete.make(~integralSumCache=Some(1.0), {xs: [v], ys: [1.0]})) | #Float(v) => Float.toPointSetDist(v)
| #Bernoulli(v) => Bernoulli.toPointSetDist(v)
| _ => | _ =>
let xs = interpolateXs(~xSelection, d, sampleCount) let xs = interpolateXs(~xSelection, d, sampleCount)
let ys = xs |> E.A.fmap(x => pdf(x, d)) let ys = xs |> E.A.fmap(x => pdf(x, d))

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@ -36,6 +36,8 @@ type gamma = {
scale: float, scale: float,
} }
type bernoulli = {p: float}
@genType @genType
type symbolicDist = [ type symbolicDist = [
| #Normal(normal) | #Normal(normal)
@ -47,6 +49,7 @@ type symbolicDist = [
| #Triangular(triangular) | #Triangular(triangular)
| #Gamma(gamma) | #Gamma(gamma)
| #Float(float) | #Float(float)
| #Bernoulli(bernoulli)
] ]
type analyticalSimplificationResult = [ type analyticalSimplificationResult = [

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@ -168,6 +168,7 @@ module SymbolicConstructors = {
let oneFloat = name => let oneFloat = name =>
switch name { switch name {
| "exponential" => Ok(SymbolicDist.Exponential.make) | "exponential" => Ok(SymbolicDist.Exponential.make)
| "bernoulli" => Ok(SymbolicDist.Bernoulli.make)
| _ => Error("Unreachable state") | _ => Error("Unreachable state")
} }
@ -204,7 +205,7 @@ let dispatchToGenericOutput = (
): option<DistributionOperation.outputType> => { ): option<DistributionOperation.outputType> => {
let (fnName, args) = call let (fnName, args) = call
switch (fnName, args) { switch (fnName, args) {
| ("exponential" as fnName, [EvNumber(f)]) => | (("exponential" | "bernoulli") as fnName, [EvNumber(f)]) =>
SymbolicConstructors.oneFloat(fnName) SymbolicConstructors.oneFloat(fnName)
->E.R.bind(r => r(f)) ->E.R.bind(r => r(f))
->SymbolicConstructors.symbolicResultToOutput ->SymbolicConstructors.symbolicResultToOutput

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@ -0,0 +1,13 @@
module Bernoulli = {
@module external cdf: (float, float) => float = "@stdlib/stats/base/dists/bernoulli/cdf"
let cdf = cdf
@module external pmf: (float, float) => float = "@stdlib/stats/base/dists/bernoulli/pmf"
let pmf = pmf
@module external quantile: (float, float) => float = "@stdlib/stats/base/dists/bernoulli/quantile"
let quantile = quantile
@module external mean: float => float = "@stdlib/stats/base/dists/bernoulli/mean"
let mean = mean
}

331
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@ -14375,7 +14704,7 @@ read-pkg@^5.2.0:
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type-fest "^0.6.0" type-fest "^0.6.0"
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