Minor cleanup

This commit is contained in:
Ozzie Gooen 2020-03-20 12:08:26 +00:00
parent 831d1345c5
commit 8d1ab10e06
12 changed files with 275 additions and 645 deletions

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@ -1,21 +0,0 @@
open Jest;
open Expect;
let makeTest = (~only=false, str, item1, item2) =>
only
? Only.test(str, () =>
expect(item1) |> toEqual(item2)
)
: test(str, () =>
expect(item1) |> toEqual(item2)
) /* })*/;
// These fail because of issues with Jest, Babel, and Bucklescript
// describe("XYShapes", () => {
// describe("logScorePoint", () => {
// makeTest(
// "When identical",
// Some(Guesstimator.stringToMixedShape(~string="5 to 20")),
// None,
// )
// })

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@ -10,63 +10,6 @@ let makeTest = (~only=false, str, item1, item2) =>
expect(item1) |> toEqual(item2)
);
module FloatFloatMap = {
module Id =
Belt.Id.MakeComparable({
type t = float;
let cmp: (float, float) => int = Pervasives.compare;
});
type t = Belt.MutableMap.t(Id.t, float, Id.identity);
let fromArray = (ar: array((float, float))) =>
Belt.MutableMap.fromArray(ar, ~id=(module Id));
let toArray = (t: t) => Belt.MutableMap.toArray(t);
let empty = () => Belt.MutableMap.make(~id=(module Id));
let increment = (el, t: t) =>
Belt.MutableMap.update(
t,
el,
fun
| Some(n) => Some(n +. 1.0)
| None => Some(1.0),
);
let get = (el, t: t) => Belt.MutableMap.get(t, el);
let fmap = (fn, t: t) => Belt.MutableMap.map(t, fn);
};
let split = (sortedArray: array(float)) => {
let continuous = [||];
let discrete = FloatFloatMap.empty();
Belt.Array.forEachWithIndex(
sortedArray,
(index, element) => {
let maxIndex = (sortedArray |> Array.length) - 1;
let possiblySimilarElements =
(
switch (index) {
| 0 => [|index + 1|]
| n when n == maxIndex => [|index - 1|]
| _ => [|index - 1, index + 1|]
}
)
|> Belt.Array.map(_, r => sortedArray[r]);
let hasSimilarElement =
Belt.Array.some(possiblySimilarElements, r => r == element);
hasSimilarElement
? FloatFloatMap.increment(element, discrete)
: {
let _ = Js.Array.push(element, continuous);
();
};
();
},
);
(continuous, discrete);
};
describe("Lodash", () => {
describe("Lodash", () => {
makeTest("min", Lodash.min([|1, 3, 4|]), 1);
@ -77,31 +20,5 @@ describe("Lodash", () => {
Lodash.countBy([|1, 3, 4, 4|], r => r),
Js.Dict.fromArray([|("1", 1), ("3", 1), ("4", 2)|]),
);
makeTest(
"split",
split([|1.432, 1.33455, 2.0|]),
([|1.432, 1.33455, 2.0|], FloatFloatMap.empty()),
);
makeTest(
"split",
split([|1.432, 1.33455, 2.0, 2.0, 2.0, 2.0|])
|> (((c, disc)) => (c, disc |> FloatFloatMap.toArray)),
([|1.432, 1.33455|], [|(2.0, 4.0)|]),
);
let makeDuplicatedArray = count => {
let arr = Belt.Array.range(1, count) |> E.A.fmap(float_of_int);
let sorted = arr |> Belt.SortArray.stableSortBy(_, compare);
E.A.concatMany([|sorted, sorted, sorted, sorted|])
|> Belt.SortArray.stableSortBy(_, compare);
};
let (_, discrete) = split(makeDuplicatedArray(10));
let toArr = discrete |> FloatFloatMap.toArray;
makeTest("splitMedium", toArr |> Belt.Array.length, 10);
let (c, discrete) = split(makeDuplicatedArray(500));
let toArr = discrete |> FloatFloatMap.toArray;
makeTest("splitMedium", toArr |> Belt.Array.length, 500);
})
});

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@ -0,0 +1,51 @@
open Jest;
open Expect;
let makeTest = (~only=false, str, item1, item2) =>
only
? Only.test(str, () =>
expect(item1) |> toEqual(item2)
)
: test(str, () =>
expect(item1) |> toEqual(item2)
);
describe("Lodash", () => {
describe("Lodash", () => {
makeTest(
"split",
Samples.T.splitContinuousAndDiscrete([|1.432, 1.33455, 2.0|]),
([|1.432, 1.33455, 2.0|], E.FloatFloatMap.empty()),
);
makeTest(
"split",
Samples.T.splitContinuousAndDiscrete([|
1.432,
1.33455,
2.0,
2.0,
2.0,
2.0,
|])
|> (((c, disc)) => (c, disc |> E.FloatFloatMap.toArray)),
([|1.432, 1.33455|], [|(2.0, 4.0)|]),
);
let makeDuplicatedArray = count => {
let arr = Belt.Array.range(1, count) |> E.A.fmap(float_of_int);
let sorted = arr |> Belt.SortArray.stableSortBy(_, compare);
E.A.concatMany([|sorted, sorted, sorted, sorted|])
|> Belt.SortArray.stableSortBy(_, compare);
};
let (_, discrete) =
Samples.T.splitContinuousAndDiscrete(makeDuplicatedArray(10));
let toArr = discrete |> E.FloatFloatMap.toArray;
makeTest("splitMedium", toArr |> Belt.Array.length, 10);
let (c, discrete) =
Samples.T.splitContinuousAndDiscrete(makeDuplicatedArray(500));
let toArr = discrete |> E.FloatFloatMap.toArray;
makeTest("splitMedium", toArr |> Belt.Array.length, 500);
})
});

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@ -26,7 +26,6 @@
"author": "",
"license": "MIT",
"dependencies": {
"@foretold/cdf": "1.0.15",
"@foretold/components": "0.0.3",
"@foretold/guesstimator": "1.0.10",
"@glennsl/bs-jest": "^0.5.0",

112
src/Samples.re Normal file
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@ -0,0 +1,112 @@
module JS = {
[@bs.deriving abstract]
type distJs = {
xs: array(float),
ys: array(float),
};
let jsToDist = (d: distJs): DistTypes.xyShape => {
xs: xsGet(d),
ys: ysGet(d),
};
[@bs.module "./utility/KdeLibrary.js"]
external samplesToContinuousPdf: (array(float), int, int) => distJs =
"samplesToContinuousPdf";
};
module KDE = {
let normalSampling = (samples, outputXYPoints, kernelWidth) => {
samples
|> JS.samplesToContinuousPdf(_, outputXYPoints, kernelWidth)
|> JS.jsToDist;
};
let inGroups = (samples, outputXYPoints, kernelWidth, ~cuttoff=0.9, ()) => {
let partitionAt =
samples
|> E.A.length
|> float_of_int
|> (e => e *. cuttoff)
|> int_of_float;
let part1XYPoints =
outputXYPoints |> float_of_int |> (e => e *. cuttoff) |> int_of_float;
let part2XYPoints = outputXYPoints - part1XYPoints |> Js.Math.max_int(30);
let part1Data =
samples |> Belt.Array.slice(_, ~offset=0, ~len=partitionAt);
let part2DataLength = (samples |> E.A.length) - partitionAt;
let part2Data =
samples
|> Belt.Array.slice(
_,
~offset=(-1) * part2DataLength,
~len=part2DataLength,
);
let part1 =
part1Data
|> JS.samplesToContinuousPdf(_, part1XYPoints, kernelWidth)
|> JS.jsToDist;
let part2 =
part2Data
|> JS.samplesToContinuousPdf(_, part2XYPoints, 3)
|> JS.jsToDist;
let opp = 1.0 -. cuttoff;
part1;
};
};
module T = {
type t = array(float);
let splitContinuousAndDiscrete = (sortedArray: t) => {
let continuous = [||];
let discrete = E.FloatFloatMap.empty();
Belt.Array.forEachWithIndex(
sortedArray,
(index, element) => {
let maxIndex = (sortedArray |> Array.length) - 1;
let possiblySimilarElements =
(
switch (index) {
| 0 => [|index + 1|]
| n when n == maxIndex => [|index - 1|]
| _ => [|index - 1, index + 1|]
}
)
|> Belt.Array.map(_, r => sortedArray[r]);
let hasSimilarElement =
Belt.Array.some(possiblySimilarElements, r => r == element);
hasSimilarElement
? E.FloatFloatMap.increment(element, discrete)
: {
let _ = Js.Array.push(element, continuous);
();
};
();
},
);
(continuous, discrete);
};
// todo: Figure out some way of doing this without having to integrate so many times.
let toShape = (~samples: t, ~outputXYPoints=3000, ~kernelWidth=10, ()) => {
Array.fast_sort(compare, samples);
let (continuousPart, discretePart) = E.A.Floats.split(samples);
let length = samples |> E.A.length;
let lengthFloat = float_of_int(length);
let discrete: DistTypes.xyShape =
discretePart
|> E.FloatFloatMap.fmap(r => r /. lengthFloat)
|> E.FloatFloatMap.toArray
|> XYShape.T.fromZippedArray;
let pdf: DistTypes.xyShape =
continuousPart |> E.A.length > 20
? {
samples |> KDE.normalSampling(_, outputXYPoints, kernelWidth);
}
: {xs: [||], ys: [||]};
let continuous = pdf |> Distributions.Continuous.fromShape;
let shape = MixedShapeBuilder.buildSimple(~continuous, ~discrete);
shape;
};
};

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@ -8,6 +8,13 @@ let make =
unit,
};
let applyTruncation = (truncateTo, distPlus) =>
switch (truncateTo, distPlus) {
| (Some(t), Some(d)) => Some(d |> Distributions.DistPlus.T.truncate(t))
| (None, Some(d)) => Some(d)
| _ => None
};
let toDistPlus =
(
~sampleCount=2000,
@ -17,18 +24,9 @@ let toDistPlus =
t: distPlusIngredients,
)
: option(distPlus) => {
let shape =
Guesstimator.toMixed(
~string=t.guesstimatorString,
~sampleCount,
~outputXYPoints,
~kernelWidth,
(),
);
// TODO:
// Truncate before resizing.
// Faster sort
let distPlus =
let samples =
Guesstimator.stringToSamples(t.guesstimatorString, sampleCount);
let shape = Samples.T.toShape(~samples, ~outputXYPoints, ~kernelWidth, ());
shape
|> E.O.fmap(
Distributions.DistPlus.make(
@ -39,12 +37,6 @@ let toDistPlus =
(),
),
)
|> E.O.fmap(
Distributions.DistPlus.T.scaleToIntegralSum(~intendedSum=1.0),
);
switch (truncateTo, distPlus) {
| (Some(t), Some(d)) => Some(d |> Distributions.DistPlus.T.truncate(t))
| (None, Some(d)) => Some(d)
| _ => None
};
|> E.O.fmap(Distributions.DistPlus.T.scaleToIntegralSum(~intendedSum=1.0))
|> applyTruncation(truncateTo);
};

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@ -1,170 +0,0 @@
const {
Cdf,
Pdf,
ContinuousDistribution,
ContinuousDistributionCombination,
scoringFunctions,
} = require("@foretold/cdf/lib");
const _ = require("lodash");
/**
*
* @param xs
* @param ys
* @returns {{ys: *, xs: *}}
*/
function cdfToPdf({ xs, ys }) {
let cdf = new Cdf(xs, ys);
let pdf = cdf.toPdf();
return { xs: pdf.xs, ys: pdf.ys };
}
/**
*
* @param xs
* @param ys
* @returns {{ys: *, xs: *}}
*/
function pdfToCdf({ xs, ys }) {
let cdf = new Pdf(xs, ys);
let pdf = cdf.toCdf();
return { xs: pdf.xs, ys: pdf.ys };
}
/**
*
* @param sampleCount
* @param vars
* @returns {{ys: *, xs: *}}
*/
function mean(sampleCount, vars) {
let cdfs = vars.map(r => new Cdf(r.xs, r.ys));
let comb = new ContinuousDistributionCombination(cdfs);
let newCdf = comb.combineYsWithMean(sampleCount);
return { xs: newCdf.xs, ys: newCdf.ys };
}
/**
*
* @param sampleCount
* @param predictionCdf
* @param resolutionCdf
*/
function scoreNonMarketCdfCdf(sampleCount, predictionCdf, resolutionCdf, resolutionUniformAdditionWeight = 0) {
let toCdf = (r) => (new Cdf(r.xs, r.ys));
let prediction = toCdf(predictionCdf);
if (_.isFinite(resolutionUniformAdditionWeight)) {
prediction = prediction.combineWithUniformOfCdf(
{
cdf: toCdf(resolutionCdf),
uniformWeight: resolutionUniformAdditionWeight,
sampleCount
}
);
}
return scoringFunctions.distributionInputDistributionOutputMarketless({
predictionCdf: prediction,
resultCdf: toCdf(resolutionCdf),
sampleCount,
});
}
/**
*
* @param sampleCount
* @param cdf
*/
function differentialEntropy(sampleCount, cdf) {
let toCdf = (r) => (new Cdf(r.xs, r.ys));
return scoringFunctions.differentialEntropy({
cdf: toCdf(cdf),
sampleCount: sampleCount
});
}
/**
*
* @param x
* @param xs
* @param ys
* @returns {number}
*/
function findY(x, { xs, ys }) {
let cdf = new Cdf(xs, ys);
return cdf.findY(x);
}
/**
*
* @param x
* @param xs
* @param ys
* @returns {number[]}
*/
function convertToNewLength(n, { xs, ys }) {
let dist = new ContinuousDistribution(xs, ys);
return dist.convertToNewLength(n);
}
/**
*
* @param y
* @param xs
* @param ys
* @returns {number}
*/
function findX(y, { xs, ys }) {
let cdf = new Cdf(xs, ys);
return cdf.findX(y);
}
/**
*
* @param xs
* @param ys
* @returns {number[]}
*/
function integral({ xs, ys }) {
if (_.includes(ys, NaN)) {
return NaN;
} else if (_.includes(ys, Infinity) && _.includes(ys, -Infinity)) {
return NaN;
} else if (_.includes(ys, Infinity)) {
return Infinity;
} else if (_.includes(ys, -Infinity)) {
return -Infinity;
}
let integral = 0;
for (let i = 1; i < ys.length; i++) {
let thisY = ys[i];
let lastY = ys[i - 1];
let thisX = xs[i];
let lastX = xs[i - 1];
if (
_.isFinite(thisY) && _.isFinite(lastY) &&
_.isFinite(thisX) && _.isFinite(lastX)
) {
let sectionInterval = ((thisY + lastY) / 2) * (thisX - lastX);
integral = integral + sectionInterval;
}
}
return integral;
}
module.exports = {
cdfToPdf,
pdfToCdf,
findY,
findX,
convertToNewLength,
mean,
scoreNonMarketCdfCdf,
differentialEntropy,
integral,
};

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@ -1,56 +0,0 @@
module JS = {
[@bs.deriving abstract]
type distJs = {
xs: array(float),
ys: array(float),
};
let distToJs = (d: DistTypes.xyShape) => distJs(~xs=d.xs, ~ys=d.ys);
let jsToDist = (d: distJs): DistTypes.xyShape => {
xs: xsGet(d),
ys: ysGet(d),
};
let doAsDist = (f, d: DistTypes.xyShape) => d |> distToJs |> f |> jsToDist;
[@bs.module "./CdfLibrary.js"]
external cdfToPdf: distJs => distJs = "cdfToPdf";
[@bs.module "./CdfLibrary.js"]
external pdfToCdf: distJs => distJs = "pdfToCdf";
[@bs.module "./CdfLibrary.js"]
external findY: (float, distJs) => float = "findY";
[@bs.module "./CdfLibrary.js"]
external findX: (float, distJs) => float = "findX";
[@bs.module "./CdfLibrary.js"]
external integral: distJs => float = "integral";
[@bs.module "./CdfLibrary.js"]
external differentialEntropy: (int, distJs) => distJs =
"differentialEntropy";
[@bs.module "./CdfLibrary.js"]
external convertToNewLength: (int, distJs) => distJs = "convertToNewLength";
};
module Distribution = {
let convertToNewLength = (int, {xs, _} as dist: DistTypes.xyShape) =>
switch (E.A.length(xs)) {
| 0
| 1 => dist
| _ => dist |> JS.doAsDist(JS.convertToNewLength(int))
};
let toPdf = dist => dist |> JS.doAsDist(JS.cdfToPdf);
let toCdf = dist => dist |> JS.doAsDist(JS.pdfToCdf);
let findX = (y, dist) => dist |> JS.distToJs |> JS.findX(y);
let findY = (x, dist) => dist |> JS.distToJs |> JS.findY(x);
let integral = dist => dist |> JS.distToJs |> JS.integral;
let differentialEntropy = (maxCalculationLength, dist) =>
dist
|> JS.doAsDist(JS.differentialEntropy(maxCalculationLength))
|> integral;
};

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@ -1,5 +1,31 @@
open Rationale.Function.Infix;
module FloatFloatMap = {
module Id =
Belt.Id.MakeComparable({
type t = float;
let cmp: (float, float) => int = Pervasives.compare;
});
type t = Belt.MutableMap.t(Id.t, float, Id.identity);
let fromArray = (ar: array((float, float))) =>
Belt.MutableMap.fromArray(ar, ~id=(module Id));
let toArray = (t: t) => Belt.MutableMap.toArray(t);
let empty = () => Belt.MutableMap.make(~id=(module Id));
let increment = (el, t: t) =>
Belt.MutableMap.update(
t,
el,
fun
| Some(n) => Some(n +. 1.0)
| None => Some(1.0),
);
let get = (el, t: t) => Belt.MutableMap.get(t, el);
let fmap = (fn, t: t) => Belt.MutableMap.map(t, fn);
};
/* Utils */
module U = {
let isEqual = (a, b) => a == b;
@ -298,6 +324,39 @@ module A = {
};
};
};
module Floats = {
let split = (sortedArray: array(float)) => {
let continuous = [||];
let discrete = FloatFloatMap.empty();
Belt.Array.forEachWithIndex(
sortedArray,
(index, element) => {
let maxIndex = (sortedArray |> Array.length) - 1;
let possiblySimilarElements =
(
switch (index) {
| 0 => [|index + 1|]
| n when n == maxIndex => [|index - 1|]
| _ => [|index - 1, index + 1|]
}
)
|> Belt.Array.map(_, r => sortedArray[r]);
let hasSimilarElement =
Belt.Array.some(possiblySimilarElements, r => r == element);
hasSimilarElement
? FloatFloatMap.increment(element, discrete)
: {
let _ = Js.Array.push(element, continuous);
();
};
();
},
);
(continuous, discrete);
};
};
};
module JsArray = {

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@ -1,4 +1,3 @@
module Internals = {
[@bs.deriving abstract]
type discrete = {
xs: array(float),
@ -10,177 +9,5 @@ module Internals = {
ys: ysGet(d),
};
[@bs.deriving abstract]
type combined = {
continuous: CdfLibrary.JS.distJs,
discrete,
};
// todo: Force to be fewer samples
let toContinous = (r: combined) =>
continuousGet(r)
|> CdfLibrary.JS.jsToDist
|> Distributions.Continuous.fromShape;
let toDiscrete = (r: combined): DistTypes.xyShape =>
discreteGet(r) |> jsToDistDiscrete;
[@bs.module "./GuesstimatorLibrary.js"]
external toCombinedFormat: (string, int, int, int) => combined = "run";
[@bs.module "./GuesstimatorLibrary.js"]
external stringToSamples: (string, int) => array(float) = "stringToSamples";
[@bs.module "./GuesstimatorLibrary.js"]
external samplesToContinuousPdf:
(array(float), int, int) => CdfLibrary.JS.distJs =
"samplesToContinuousPdf";
};
module KDE = {
let normalSampling = (samples, outputXYPoints, kernelWidth) => {
samples
|> Internals.samplesToContinuousPdf(_, outputXYPoints, kernelWidth)
|> CdfLibrary.JS.jsToDist;
};
let inGroups = (samples, outputXYPoints, kernelWidth, ~cuttoff=0.9, ()) => {
let partitionAt =
samples
|> E.A.length
|> float_of_int
|> (e => e *. cuttoff)
|> int_of_float;
let part1XYPoints =
outputXYPoints |> float_of_int |> (e => e *. cuttoff) |> int_of_float;
let part2XYPoints = outputXYPoints - part1XYPoints |> Js.Math.max_int(30);
let part1Data =
samples |> Belt.Array.slice(_, ~offset=0, ~len=partitionAt);
let part2DataLength = (samples |> E.A.length) - partitionAt;
let part2Data =
samples
|> Belt.Array.slice(
_,
~offset=(-1) * part2DataLength,
~len=part2DataLength,
);
let part1 =
part1Data
|> Internals.samplesToContinuousPdf(_, part1XYPoints, kernelWidth)
|> CdfLibrary.JS.jsToDist;
let part2 =
part2Data
|> Internals.samplesToContinuousPdf(_, part2XYPoints, 3)
|> CdfLibrary.JS.jsToDist;
let opp = 1.0 -. cuttoff;
// let result =
// XYShape.T.Combine.combineLinear(
// part1,
// part2,
// (a, b) => {
// let aa = a *. cuttoff;
// let bb = b *. opp;
// aa +. bb;
// },
// );
// Js.log2("HI", result);
// result;
part1;
};
};
module FloatFloatMap = {
module Id =
Belt.Id.MakeComparable({
type t = float;
let cmp: (float, float) => int = Pervasives.compare;
});
type t = Belt.MutableMap.t(Id.t, float, Id.identity);
let fromArray = (ar: array((float, float))) =>
Belt.MutableMap.fromArray(ar, ~id=(module Id));
let toArray = (t: t) => Belt.MutableMap.toArray(t);
let empty = () => Belt.MutableMap.make(~id=(module Id));
let increment = (el, t: t) =>
Belt.MutableMap.update(
t,
el,
fun
| Some(n) => Some(n +. 1.0)
| None => Some(1.0),
);
let get = (el, t: t) => Belt.MutableMap.get(t, el);
let fmap = (fn, t: t) => Belt.MutableMap.map(t, fn);
};
// todo: Figure out some way of doing this without creating a new array.
let split = (sortedArray: array(float)) => {
let continuous = [||];
let discrete = FloatFloatMap.empty();
Belt.Array.forEachWithIndex(
sortedArray,
(index, element) => {
let maxIndex = (sortedArray |> Array.length) - 1;
let possiblySimilarElements =
(
switch (index) {
| 0 => [|index + 1|]
| n when n == maxIndex => [|index - 1|]
| _ => [|index - 1, index + 1|]
}
)
|> Belt.Array.map(_, r => sortedArray[r]);
let hasSimilarElement =
Belt.Array.some(possiblySimilarElements, r => r == element);
hasSimilarElement
? FloatFloatMap.increment(element, discrete)
: {
let _ = Js.Array.push(element, continuous);
();
};
();
},
);
(continuous, discrete);
};
let toMixed =
(
~string,
~sampleCount=3000,
~outputXYPoints=3000,
~kernelWidth=10,
~cuttoff=0.995,
(),
) => {
let start = Js.Date.now();
let timeMessage = message => Js.log2(message, Js.Date.now() -. start);
timeMessage("Starting");
let samples = Internals.stringToSamples(string, sampleCount);
timeMessage("Finished sampling");
let length = samples |> E.A.length;
Array.fast_sort(compare, samples);
let (continuousPart, disc) = split(samples);
let lengthFloat = float_of_int(length);
let discrete: DistTypes.xyShape =
disc
|> FloatFloatMap.fmap(r => r /. lengthFloat)
|> FloatFloatMap.toArray
|> XYShape.T.fromZippedArray;
let pdf: DistTypes.xyShape =
continuousPart |> E.A.length > 20
? {
// samples |> KDE.inGroups(_, outputXYPoints, kernelWidth, ~cuttoff, ());
samples |> KDE.normalSampling(_, outputXYPoints, kernelWidth);
}
: {xs: [||], ys: [||]};
timeMessage("Finished pdf");
let continuous = pdf |> Distributions.Continuous.fromShape;
let shape = MixedShapeBuilder.buildSimple(~continuous, ~discrete);
timeMessage("Finished shape");
shape;
};

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@ -1,89 +1,5 @@
const {
Samples,
} = require("@foretold/cdf/lib/samples");
const _ = require("lodash");
const { Guesstimator } = require('@foretold/guesstimator/src');
const pdfast = require('pdfast');
/**
* @param values
* @param outputResolutionCount
* @param min
* @param max
* @returns {{discrete: {ys: *, xs: *}, continuous: {ys: [], xs: []}}}
*/
const toPdf = (values, outputResolutionCount, width, min, max) => {
let duplicateSamples = _(values).groupBy().pickBy(x => x.length > 1).keys().value();
let totalLength = _.size(values);
let frequencies = duplicateSamples.map(s => ({
value: parseFloat(s),
percentage: _(values).filter(x => x == s).size() / totalLength
}));
let continuousSamples = _.difference(values, frequencies.map(f => f.value));
let discrete = {
xs: frequencies.map(f => f.value),
ys: frequencies.map(f => f.percentage)
};
let continuous = { ys: [], xs: [] };
if (continuousSamples.length > 20) {
// let c = continuousSamples.map( r => (Math.log2(r)) * 1000);
let c = continuousSamples;
const samples = new Samples(c);
const pdf = samples.toPdf({ size: outputResolutionCount, width, min, max });
// continuous = {xs: pdf.xs.map(r => Math.pow(2,r/1000)), ys: pdf.ys};
continuous = pdf;
}
return { continuous, discrete };
};
/**
* @param text
* @param sampleCount
* @param outputResolutionCount
* @param inputs
* @param min
* @param max
* @returns {{discrete: {ys: *, xs: *}, continuous: {ys: *[], xs: *[]}}}
*/
const run = (
text,
sampleCount,
outputResolutionCount,
width,
inputs = [],
min = false,
max = false,
) => {
const [_error, item] = Guesstimator.parse({ text: "=" + text });
const { parsedInput } = item;
const guesstimator = new Guesstimator({ parsedInput });
const value = guesstimator.sample(
sampleCount,
inputs,
);
const values = _.filter(value.values, _.isFinite);
let update;
let blankResponse = {
continuous: { ys: [], xs: [] },
discrete: { ys: [], xs: [] }
};
if (values.length === 0) {
update = blankResponse;
} else if (values.length === 1) {
update = blankResponse;
} else {
update = toPdf(values, outputResolutionCount, width, min, max);
}
return update;
};
const stringToSamples = (
text,
@ -104,23 +20,6 @@ const stringToSamples = (
}
};
const samplesToContinuousPdf = (
samples,
size,
width,
min = false,
max = false,
) => {
let _samples = _.filter(samples, _.isFinite);
if (_.isFinite(min)) { _samples = _.filter(_samples, r => r > min) };
if (_.isFinite(max)) { _samples = _.filter(_samples, r => r < max) };
let pdf = pdfast.create(_samples, { size, width });
return {xs: pdf.map(r => r.x), ys: pdf.map(r => r.y)};
};
module.exports = {
run,
stringToSamples,
samplesToContinuousPdf
};

21
src/utility/KdeLibrary.js Normal file
View File

@ -0,0 +1,21 @@
const pdfast = require('pdfast');
const _ = require("lodash");
const samplesToContinuousPdf = (
samples,
size,
width,
min = false,
max = false,
) => {
let _samples = _.filter(samples, _.isFinite);
if (_.isFinite(min)) { _samples = _.filter(_samples, r => r > min) };
if (_.isFinite(max)) { _samples = _.filter(_samples, r => r < max) };
let pdf = pdfast.create(_samples, { size, width });
return {xs: pdf.map(r => r.x), ys: pdf.map(r => r.y)};
};
module.exports = {
samplesToContinuousPdf,
};