Added additional distributions
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@ -77,7 +77,7 @@ let make = () => {
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~validationStrategy=OnDemand,
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~schema,
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~onSubmit=({state}) => {None},
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~initialState={guesstimatorString: "mm(1 to 10000)"},
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~initialState={guesstimatorString: "mm(1 to 1000)"},
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(),
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);
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@ -27,7 +27,31 @@ type beta = {
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[@bs.meth] "inv": (float, float, float) => float,
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[@bs.meth] "sample": (float, float) => float,
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};
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type exponential = {
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.
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[@bs.meth] "pdf": (float, float) => float,
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[@bs.meth] "cdf": (float, float) => float,
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[@bs.meth] "inv": (float, float) => float,
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[@bs.meth] "sample": float => float,
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};
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type cauchy = {
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.
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[@bs.meth] "pdf": (float, float, float) => float,
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[@bs.meth] "cdf": (float, float, float) => float,
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[@bs.meth] "inv": (float, float, float) => float,
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[@bs.meth] "sample": (float, float) => float,
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};
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type triangular = {
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.
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[@bs.meth] "pdf": (float, float, float, float) => float,
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[@bs.meth] "cdf": (float, float, float, float) => float,
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[@bs.meth] "inv": (float, float, float, float) => float,
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[@bs.meth] "sample": (float, float, float) => float,
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};
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[@bs.module "jStat"] external normal: normal = "normal";
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[@bs.module "jStat"] external lognormal: lognormal = "lognormal";
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[@bs.module "jStat"] external uniform: uniform = "uniform";
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[@bs.module "jStat"] external beta: beta = "beta";
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[@bs.module "jStat"] external beta: beta = "beta";
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[@bs.module "jStat"] external exponential: exponential = "exponential";
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[@bs.module "jStat"] external cauchy: cauchy = "cauchy";
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[@bs.module "jStat"] external triangular: triangular = "triangular";
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@ -124,6 +124,23 @@ module MathAdtToDistDst = {
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| [|Value(alpha), Value(beta)|] => Ok(`Simple(`Beta({alpha, beta})))
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| _ => Error("Wrong number of variables in lognormal distribution");
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let exponential: array(arg) => result(SymbolicDist.bigDist, string) =
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fun
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| [|Value(rate)|] => Ok(`Simple(`Exponential({rate: rate})))
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| _ => Error("Wrong number of variables in Exponential distribution");
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let cauchy: array(arg) => result(SymbolicDist.bigDist, string) =
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fun
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| [|Value(local), Value(scale)|] =>
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Ok(`Simple(`Cauchy({local, scale})))
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| _ => Error("Wrong number of variables in cauchy distribution");
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let triangular: array(arg) => result(SymbolicDist.bigDist, string) =
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fun
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| [|Value(low), Value(medium), Value(high)|] =>
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Ok(`Simple(`Triangular({low, medium, high})))
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| _ => Error("Wrong number of variables in triangle distribution");
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let multiModal =
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(
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args: array(result(SymbolicDist.bigDist, string)),
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@ -157,6 +174,9 @@ module MathAdtToDistDst = {
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| Fn({name: "uniform", args}) => uniform(args)
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| Fn({name: "beta", args}) => beta(args)
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| Fn({name: "to", args}) => to_(args)
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| Fn({name: "exponential", args}) => exponential(args)
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| Fn({name: "cauchy", args}) => cauchy(args)
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| Fn({name: "triangular", args}) => triangular(args)
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| Fn({name: "mm", args}) => {
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let dists = args |> E.A.fmap(functionParser);
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let weights =
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@ -18,17 +18,57 @@ type beta = {
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beta: float,
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};
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type exponential = {rate: float};
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type cauchy = {
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local: float,
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scale: float,
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};
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type triangular = {
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low: float,
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medium: float,
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high: float,
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};
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type dist = [
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| `Normal(normal)
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| `Beta(beta)
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| `Lognormal(lognormal)
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| `Uniform(uniform)
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| `Exponential(exponential)
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| `Cauchy(cauchy)
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| `Triangular(triangular)
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];
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type pointwiseAdd = array((dist, float));
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type bigDist = [ | `Simple(dist) | `PointwiseCombination(pointwiseAdd)];
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module Exponential = {
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type t = exponential;
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let pdf = (x, t: t) => Jstat.exponential##pdf(x, t.rate);
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let inv = (p, t: t) => Jstat.exponential##inv(p, t.rate);
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let sample = (t: t) => Jstat.exponential##sample(t.rate);
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let toString = ({rate}: t) => {j|Exponential($rate)|j};
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};
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module Cauchy = {
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type t = cauchy;
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let pdf = (x, t: t) => Jstat.cauchy##pdf(x, t.local, t.scale);
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let inv = (p, t: t) => Jstat.cauchy##inv(p, t.local, t.scale);
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let sample = (t: t) => Jstat.cauchy##sample(t.local, t.scale);
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let toString = ({local, scale}: t) => {j|Cauchy($local, $scale)|j};
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};
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module Triangular = {
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type t = triangular;
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let pdf = (x, t: t) => Jstat.triangular##pdf(x, t.low, t.high, t.medium);
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let inv = (p, t: t) => Jstat.triangular##inv(p, t.low, t.high, t.medium);
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let sample = (t: t) => Jstat.triangular##sample(t.low, t.high, t.medium);
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let toString = ({low, medium, high}: t) => {j|Triangular($low, $medium, $high)|j};
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};
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module Normal = {
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type t = normal;
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let pdf = (x, t: t) => Jstat.normal##pdf(x, t.mean, t.stdev);
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@ -87,6 +127,9 @@ module GenericSimple = {
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let pdf = (x, dist) =>
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switch (dist) {
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| `Normal(n) => Normal.pdf(x, n)
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| `Triangular(n) => Triangular.pdf(x, n)
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| `Exponential(n) => Exponential.pdf(x, n)
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| `Cauchy(n) => Cauchy.pdf(x, n)
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| `Lognormal(n) => Lognormal.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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@ -95,42 +138,53 @@ module GenericSimple = {
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let inv = (x, dist) =>
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switch (dist) {
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| `Normal(n) => Normal.inv(x, n)
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| `Triangular(n) => Triangular.inv(x, n)
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| `Exponential(n) => Exponential.inv(x, n)
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| `Cauchy(n) => Cauchy.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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};
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let sample = dist =>
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switch (dist) {
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let sample: dist => float =
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fun
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| `Normal(n) => Normal.sample(n)
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| `Triangular(n) => Triangular.sample(n)
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| `Exponential(n) => Exponential.sample(n)
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| `Cauchy(n) => Cauchy.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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};
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| `Beta(n) => Beta.sample(n);
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let toString = dist =>
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switch (dist) {
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let toString: dist => string =
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fun
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| `Triangular(n) => Triangular.toString(n)
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| `Exponential(n) => Exponential.toString(n)
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| `Cauchy(n) => Cauchy.toString(n)
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| `Normal(n) => Normal.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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};
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| `Beta(n) => Beta.toString(n);
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let min = dist =>
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switch (dist) {
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let min: dist => float =
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fun
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| `Triangular({low}) => low
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| `Exponential(n) => Exponential.inv(minCdfValue, n)
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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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| `Uniform({low}) => low
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| `Beta(n) => Beta.inv(minCdfValue, n)
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};
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| `Beta(n) => Beta.inv(minCdfValue, n);
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let max = dist =>
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switch (dist) {
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let max: dist => float =
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fun
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| `Triangular(n) => n.high
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| `Exponential(n) => Exponential.inv(maxCdfValue, n)
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| `Cauchy(n) => Cauchy.inv(maxCdfValue, n)
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| `Normal(n) => Normal.inv(maxCdfValue, n)
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| `Lognormal(n) => Lognormal.inv(maxCdfValue, n)
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| `Beta(n) => Beta.inv(maxCdfValue, n)
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| `Uniform({high}) => high
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};
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| `Uniform({high}) => high;
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let interpolateXs =
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(~xSelection: [ | `Linear | `ByWeight]=`Linear, dist: dist, sampleCount) => {
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