From 9349930ad89f5a45deb6556bac4323ec41ef0d0e Mon Sep 17 00:00:00 2001 From: Roman Galochkin Date: Tue, 25 Feb 2020 13:13:17 +0300 Subject: [PATCH] Fixes CdfLibrary.js --- src/utility/CdfLibrary.js | 334 +++++++++++++++++++------------------- 1 file changed, 165 insertions(+), 169 deletions(-) diff --git a/src/utility/CdfLibrary.js b/src/utility/CdfLibrary.js index ffe63322..2e23da7e 100644 --- a/src/utility/CdfLibrary.js +++ b/src/utility/CdfLibrary.js @@ -1,174 +1,170 @@ 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 }; - } + Cdf, + Pdf, + ContinuousDistribution, + ContinuousDistributionCombination, + scoringFunctions, +} = require("@foretold/cdf/lib"); +const _ = require("lodash"); - /** - * - * @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 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 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; +/** + * + * @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 integral; + ); } - - module.exports = { - cdfToPdf, - pdfToCdf, - findY, - findX, - convertToNewLength, - mean, - scoreNonMarketCdfCdf, - differentialEntropy, - integral, - }; - \ No newline at end of file + + 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, +};