Added normal from90PercentCI for distributions that are at 0 or below
This commit is contained in:
parent
77c9ce09be
commit
5129ff20d5
|
@ -111,12 +111,12 @@ module MathAdtToDistDst = {
|
|||
|
||||
let to_: array(arg) => result(SymbolicDist.bigDist, string) =
|
||||
fun
|
||||
| [|Value(low), Value(high)|] when low <= 0.0 && low < high=> {
|
||||
Ok(`Simple(SymbolicDist.Normal.from90PercentCI(low, high)));
|
||||
}
|
||||
| [|Value(low), Value(high)|] when low < high => {
|
||||
Ok(`Simple(SymbolicDist.Lognormal.from90PercentCI(low, high)));
|
||||
}
|
||||
| [|Value(low), _|] when low <= 0.0 => {
|
||||
Error("Low value cannot be less than 0.");
|
||||
}
|
||||
| [|Value(_), Value(_)|] =>
|
||||
Error("Low value must be less than high value.")
|
||||
| _ => Error("Wrong number of variables in lognormal distribution");
|
||||
|
|
|
@ -31,7 +31,10 @@ type triangular = {
|
|||
high: float,
|
||||
};
|
||||
|
||||
type continuousShape = {pdf: DistTypes.continuousShape, cdf: DistTypes.continuousShape}
|
||||
type continuousShape = {
|
||||
pdf: DistTypes.continuousShape,
|
||||
cdf: DistTypes.continuousShape,
|
||||
};
|
||||
|
||||
type contType = [ | `Continuous | `Discrete];
|
||||
|
||||
|
@ -53,12 +56,14 @@ type bigDist = [ | `Simple(dist) | `PointwiseCombination(pointwiseAdd)];
|
|||
|
||||
module ContinuousShape = {
|
||||
type t = continuousShape;
|
||||
let make = (pdf, cdf):t => ({pdf, cdf});
|
||||
let pdf = (x, t: t) => Distributions.Continuous.T.xToY(x,t.pdf).continuous
|
||||
let inv = (p, t: t) => Distributions.Continuous.T.xToY(p,t.pdf).continuous
|
||||
let make = (pdf, cdf): t => {pdf, cdf};
|
||||
let pdf = (x, t: t) =>
|
||||
Distributions.Continuous.T.xToY(x, t.pdf).continuous;
|
||||
let inv = (p, t: t) =>
|
||||
Distributions.Continuous.T.xToY(p, t.pdf).continuous;
|
||||
// TODO: Fix the sampling, to have it work correctly.
|
||||
let sample = (t:t) => 3.0;
|
||||
let toString = (t) => {j|CustomContinuousShape|j};
|
||||
let sample = (t: t) => 3.0;
|
||||
let toString = t => {j|CustomContinuousShape|j};
|
||||
let contType: contType = `Continuous;
|
||||
};
|
||||
|
||||
|
@ -92,6 +97,12 @@ module Triangular = {
|
|||
module Normal = {
|
||||
type t = normal;
|
||||
let pdf = (x, t: t) => Jstat.normal##pdf(x, t.mean, t.stdev);
|
||||
|
||||
let from90PercentCI = (low, high) => {
|
||||
let mean = E.A.Floats.mean([|low, high|]);
|
||||
let stdev = (high -. low) /. 1.645;
|
||||
`Normal({mean, stdev});
|
||||
};
|
||||
let inv = (p, t: t) => Jstat.normal##inv(p, t.mean, t.stdev);
|
||||
let sample = (t: t) => Jstat.normal##sample(t.mean, t.stdev);
|
||||
let toString = ({mean, stdev}: t) => {j|Normal($mean,$stdev)|j};
|
||||
|
@ -167,10 +178,10 @@ module GenericSimple = {
|
|||
| `Uniform(n) => Uniform.pdf(x, n)
|
||||
| `Beta(n) => Beta.pdf(x, n)
|
||||
| `Float(n) => Float.pdf(x, n)
|
||||
| `ContinuousShape(n) => ContinuousShape.pdf(x,n)
|
||||
| `ContinuousShape(n) => ContinuousShape.pdf(x, n)
|
||||
};
|
||||
|
||||
let contType = (dist:dist):contType =>
|
||||
let contType = (dist: dist): contType =>
|
||||
switch (dist) {
|
||||
| `Normal(_) => Normal.contType
|
||||
| `Triangular(_) => Triangular.contType
|
||||
|
@ -193,7 +204,7 @@ module GenericSimple = {
|
|||
| `Uniform(n) => Uniform.inv(x, n)
|
||||
| `Beta(n) => Beta.inv(x, n)
|
||||
| `Float(n) => Float.inv(x, n)
|
||||
| `ContinuousShape(n) => ContinuousShape.inv(x,n)
|
||||
| `ContinuousShape(n) => ContinuousShape.inv(x, n)
|
||||
};
|
||||
|
||||
let sample: dist => float =
|
||||
|
@ -206,7 +217,7 @@ module GenericSimple = {
|
|||
| `Uniform(n) => Uniform.sample(n)
|
||||
| `Beta(n) => Beta.sample(n)
|
||||
| `Float(n) => Float.sample(n)
|
||||
| `ContinuousShape(n) => ContinuousShape.sample(n)
|
||||
| `ContinuousShape(n) => ContinuousShape.sample(n);
|
||||
|
||||
let toString: dist => string =
|
||||
fun
|
||||
|
@ -218,7 +229,7 @@ module GenericSimple = {
|
|||
| `Uniform(n) => Uniform.toString(n)
|
||||
| `Beta(n) => Beta.toString(n)
|
||||
| `Float(n) => Float.toString(n)
|
||||
| `ContinuousShape(n) => ContinuousShape.toString(n)
|
||||
| `ContinuousShape(n) => ContinuousShape.toString(n);
|
||||
|
||||
let min: dist => float =
|
||||
fun
|
||||
|
@ -229,7 +240,7 @@ module GenericSimple = {
|
|||
| `Lognormal(n) => Lognormal.inv(minCdfValue, n)
|
||||
| `Uniform({low}) => low
|
||||
| `Beta(n) => Beta.inv(minCdfValue, n)
|
||||
| `ContinuousShape(n) => ContinuousShape.inv(minCdfValue,n)
|
||||
| `ContinuousShape(n) => ContinuousShape.inv(minCdfValue, n)
|
||||
| `Float(n) => n;
|
||||
|
||||
let max: dist => float =
|
||||
|
@ -240,7 +251,7 @@ module GenericSimple = {
|
|||
| `Normal(n) => Normal.inv(maxCdfValue, n)
|
||||
| `Lognormal(n) => Lognormal.inv(maxCdfValue, n)
|
||||
| `Beta(n) => Beta.inv(maxCdfValue, n)
|
||||
| `ContinuousShape(n) => ContinuousShape.inv(maxCdfValue,n)
|
||||
| `ContinuousShape(n) => ContinuousShape.inv(maxCdfValue, n)
|
||||
| `Uniform({high}) => high
|
||||
| `Float(n) => n;
|
||||
|
||||
|
@ -257,16 +268,15 @@ module GenericSimple = {
|
|||
let toShape =
|
||||
(~xSelection: [ | `Linear | `ByWeight]=`Linear, dist: dist, sampleCount)
|
||||
: DistTypes.shape => {
|
||||
switch(dist){
|
||||
| `ContinuousShape(n) => n.pdf |> Distributions.Continuous.T.toShape
|
||||
| dist => {
|
||||
let xs = interpolateXs(~xSelection, dist, sampleCount);
|
||||
let ys = xs |> E.A.fmap(r => pdf(r, dist));
|
||||
XYShape.T.fromArrays(xs, ys)
|
||||
|> Distributions.Continuous.make(`Linear, _)
|
||||
|> Distributions.Continuous.T.toShape;
|
||||
}
|
||||
}
|
||||
switch (dist) {
|
||||
| `ContinuousShape(n) => n.pdf |> Distributions.Continuous.T.toShape
|
||||
| dist =>
|
||||
let xs = interpolateXs(~xSelection, dist, sampleCount);
|
||||
let ys = xs |> E.A.fmap(r => pdf(r, dist));
|
||||
XYShape.T.fromArrays(xs, ys)
|
||||
|> Distributions.Continuous.make(`Linear, _)
|
||||
|> Distributions.Continuous.T.toShape;
|
||||
};
|
||||
};
|
||||
};
|
||||
|
||||
|
@ -289,17 +299,26 @@ module PointwiseAddDistributionsWeighted = {
|
|||
let max = (dists: t) =>
|
||||
dists |> E.A.fmap(d => d |> fst |> GenericSimple.max) |> E.A.max;
|
||||
|
||||
let discreteShape = (dists:t, sampleCount: int) => {
|
||||
let discrete = dists |> E.A.fmap((((r,e)) => r |> fun
|
||||
| `Float(r) => Some((r,e))
|
||||
| _ => None
|
||||
)) |> E.A.O.concatSomes
|
||||
|> E.A.fmap(((x, y)):DistTypes.xyShape => ({xs: [|x|], ys: [|y|]}))
|
||||
|> Distributions.Discrete.reduce((+.))
|
||||
discrete
|
||||
}
|
||||
let discreteShape = (dists: t, sampleCount: int) => {
|
||||
let discrete =
|
||||
dists
|
||||
|> E.A.fmap(((r, e)) =>
|
||||
r
|
||||
|> (
|
||||
fun
|
||||
| `Float(r) => Some((r, e))
|
||||
| _ => None
|
||||
)
|
||||
)
|
||||
|> E.A.O.concatSomes
|
||||
|> E.A.fmap(((x, y)) =>
|
||||
({xs: [|x|], ys: [|y|]}: DistTypes.xyShape)
|
||||
)
|
||||
|> Distributions.Discrete.reduce((+.));
|
||||
discrete;
|
||||
};
|
||||
|
||||
let continuousShape = (dists:t, sampleCount: int) => {
|
||||
let continuousShape = (dists: t, sampleCount: int) => {
|
||||
let xs =
|
||||
dists
|
||||
|> E.A.fmap(r =>
|
||||
|
@ -314,16 +333,22 @@ module PointwiseAddDistributionsWeighted = {
|
|||
|> E.A.concatMany;
|
||||
xs |> Array.fast_sort(compare);
|
||||
let ys = xs |> E.A.fmap(pdf(_, dists));
|
||||
XYShape.T.fromArrays(xs, ys)
|
||||
|> Distributions.Continuous.make(`Linear, _)
|
||||
}
|
||||
XYShape.T.fromArrays(xs, ys) |> Distributions.Continuous.make(`Linear, _);
|
||||
};
|
||||
|
||||
let toShape = (dists: t, sampleCount: int) => {
|
||||
let normalized = normalizeWeights(dists);
|
||||
let continuous = normalized |> E.A.filter(((r,_)) => GenericSimple.contType(r) == `Continuous) |> continuousShape(_, sampleCount);
|
||||
let discrete = normalized |> E.A.filter(((r,_)) => GenericSimple.contType(r) == `Discrete) |> discreteShape(_, sampleCount);
|
||||
let shape = MixedShapeBuilder.buildSimple(~continuous=Some(continuous), ~discrete);
|
||||
shape |> E.O.toExt("")
|
||||
let continuous =
|
||||
normalized
|
||||
|> E.A.filter(((r, _)) => GenericSimple.contType(r) == `Continuous)
|
||||
|> continuousShape(_, sampleCount);
|
||||
let discrete =
|
||||
normalized
|
||||
|> E.A.filter(((r, _)) => GenericSimple.contType(r) == `Discrete)
|
||||
|> discreteShape(_, sampleCount);
|
||||
let shape =
|
||||
MixedShapeBuilder.buildSimple(~continuous=Some(continuous), ~discrete);
|
||||
shape |> E.O.toExt("");
|
||||
};
|
||||
|
||||
let toString = (dists: t) => {
|
||||
|
|
Loading…
Reference in New Issue
Block a user