Merge pull request #534 from quantified-uncertainty/add-logistic
Add Logistic Distribution
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commit
339afdb4de
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@ -33,6 +33,7 @@ describe("eval on distribution functions", () => {
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testEval("mean(gamma(5,5))", "Ok(25)")
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testEval("mean(bernoulli(0.2))", "Ok(0.2)")
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testEval("mean(bernoulli(0.8))", "Ok(0.8)")
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testEval("mean(logistic(5,1))", "Ok(5)")
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})
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describe("toString", () => {
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testEval("toString(normal(5,2))", "Ok('Normal(5,2)')")
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@ -216,6 +216,24 @@ module Uniform = {
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}
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}
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module Logistic = {
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type t = logistic
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let make = (location, scale) =>
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scale > 0.0
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? Ok(#Logistic({location: location, scale: scale}))
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: Error("Scale must be positive")
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let pdf = (x, t: t) => Stdlib.Logistic.pdf(x, t.location, t.scale)
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let cdf = (x, t: t) => Stdlib.Logistic.cdf(x, t.location, t.scale)
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let inv = (p, t: t) => Stdlib.Logistic.quantile(p, t.location, t.scale)
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let sample = (t: t) => {
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let s = Uniform.sample({low: 0.0, high: 1.0})
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inv(s, t)
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}
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let mean = (t: t) => Ok(Stdlib.Logistic.mean(t.location, t.scale))
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let toString = ({location, scale}: t) => j`Logistic($location,$scale)`
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}
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module Bernoulli = {
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type t = bernoulli
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let make = p =>
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@ -304,6 +322,7 @@ module T = {
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| #Cauchy(n) => Cauchy.pdf(x, n)
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| #Gamma(n) => Gamma.pdf(x, n)
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| #Lognormal(n) => Lognormal.pdf(x, n)
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| #Logistic(n) => Logistic.pdf(x, n)
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| #Uniform(n) => Uniform.pdf(x, n)
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| #Beta(n) => Beta.pdf(x, n)
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| #Float(n) => Float.pdf(x, n)
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@ -317,6 +336,7 @@ module T = {
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| #Exponential(n) => Exponential.cdf(x, n)
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| #Cauchy(n) => Cauchy.cdf(x, n)
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| #Gamma(n) => Gamma.cdf(x, n)
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| #Logistic(n) => Logistic.cdf(x, n)
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| #Lognormal(n) => Lognormal.cdf(x, n)
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| #Uniform(n) => Uniform.cdf(x, n)
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| #Beta(n) => Beta.cdf(x, n)
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@ -331,6 +351,7 @@ module T = {
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| #Exponential(n) => Exponential.inv(x, n)
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| #Cauchy(n) => Cauchy.inv(x, n)
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| #Gamma(n) => Gamma.inv(x, n)
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| #Logistic(n) => Logistic.inv(x, n)
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| #Lognormal(n) => Lognormal.inv(x, n)
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| #Uniform(n) => Uniform.inv(x, n)
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| #Beta(n) => Beta.inv(x, n)
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@ -345,6 +366,7 @@ module T = {
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| #Exponential(n) => Exponential.sample(n)
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| #Cauchy(n) => Cauchy.sample(n)
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| #Gamma(n) => Gamma.sample(n)
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| #Logistic(n) => Logistic.sample(n)
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| #Lognormal(n) => Lognormal.sample(n)
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| #Uniform(n) => Uniform.sample(n)
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| #Beta(n) => Beta.sample(n)
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@ -369,6 +391,7 @@ module T = {
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| #Cauchy(n) => Cauchy.toString(n)
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| #Normal(n) => Normal.toString(n)
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| #Gamma(n) => Gamma.toString(n)
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| #Logistic(n) => Logistic.toString(n)
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| #Lognormal(n) => Lognormal.toString(n)
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| #Uniform(n) => Uniform.toString(n)
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| #Beta(n) => Beta.toString(n)
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@ -383,6 +406,7 @@ module T = {
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| #Cauchy(n) => Cauchy.inv(minCdfValue, n)
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| #Normal(n) => Normal.inv(minCdfValue, n)
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| #Lognormal(n) => Lognormal.inv(minCdfValue, n)
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| #Logistic(n) => Logistic.inv(minCdfValue, n)
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| #Gamma(n) => Gamma.inv(minCdfValue, n)
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| #Uniform({low}) => low
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| #Bernoulli(n) => Bernoulli.min(n)
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@ -398,6 +422,7 @@ module T = {
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| #Normal(n) => Normal.inv(maxCdfValue, n)
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| #Gamma(n) => Gamma.inv(maxCdfValue, n)
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| #Lognormal(n) => Lognormal.inv(maxCdfValue, n)
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| #Logistic(n) => Logistic.inv(maxCdfValue, n)
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| #Beta(n) => Beta.inv(maxCdfValue, n)
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| #Bernoulli(n) => Bernoulli.max(n)
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| #Uniform({high}) => high
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@ -412,6 +437,7 @@ module T = {
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| #Normal(n) => Normal.mean(n)
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| #Lognormal(n) => Lognormal.mean(n)
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| #Beta(n) => Beta.mean(n)
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| #Logistic(n) => Logistic.mean(n)
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| #Uniform(n) => Uniform.mean(n)
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| #Gamma(n) => Gamma.mean(n)
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| #Bernoulli(n) => Bernoulli.mean(n)
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@ -36,6 +36,11 @@ type gamma = {
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scale: float,
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}
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type logistic = {
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location: float,
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scale: float,
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}
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type bernoulli = {p: float}
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@genType
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@ -50,6 +55,7 @@ type symbolicDist = [
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| #Gamma(gamma)
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| #Float(float)
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| #Bernoulli(bernoulli)
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| #Logistic(logistic)
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]
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type analyticalSimplificationResult = [
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@ -178,6 +178,7 @@ module SymbolicConstructors = {
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| "uniform" => Ok(SymbolicDist.Uniform.make)
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| "beta" => Ok(SymbolicDist.Beta.make)
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| "lognormal" => Ok(SymbolicDist.Lognormal.make)
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| "logistic" => Ok(SymbolicDist.Logistic.make)
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| "cauchy" => Ok(SymbolicDist.Cauchy.make)
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| "gamma" => Ok(SymbolicDist.Gamma.make)
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| "to" => Ok(SymbolicDist.From90thPercentile.make)
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@ -212,7 +213,14 @@ let dispatchToGenericOutput = (
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| ("delta", [EvNumber(f)]) =>
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SymbolicDist.Float.makeSafe(f)->SymbolicConstructors.symbolicResultToOutput
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| (
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("normal" | "uniform" | "beta" | "lognormal" | "cauchy" | "gamma" | "to") as fnName,
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("normal"
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| "uniform"
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| "beta"
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| "lognormal"
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| "cauchy"
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| "gamma"
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| "to"
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| "logistic") as fnName,
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[EvNumber(f1), EvNumber(f2)],
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) =>
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SymbolicConstructors.twoFloat(fnName)
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@ -10,4 +10,31 @@ module Bernoulli = {
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@module external mean: float => float = "@stdlib/stats/base/dists/bernoulli/mean"
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let mean = mean
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@module external stdev: float => float = "@stdlib/stats/base/dists/bernoulli/stdev"
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let stdev = stdev
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@module external variance: float => float = "@stdlib/stats/base/dists/bernoulli/variance"
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let variance = variance
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}
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module Logistic = {
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@module external cdf: (float, float, float) => float = "@stdlib/stats/base/dists/logistic/cdf"
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let cdf = cdf
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@module external pdf: (float, float, float) => float = "@stdlib/stats/base/dists/logistic/pdf"
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let pdf = pdf
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@module
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external quantile: (float, float, float) => float = "@stdlib/stats/base/dists/logistic/quantile"
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let quantile = quantile
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@module external mean: (float, float) => float = "@stdlib/stats/base/dists/logistic/mean"
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let mean = mean
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@module external stdev: (float, float) => float = "@stdlib/stats/base/dists/logistic/stdev"
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let stdev = stdev
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@module external variance: (float, float) => float = "@stdlib/stats/base/dists/logistic/variance"
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let variance = variance
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}
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