Fixes CdfLibrary.js
This commit is contained in:
Roman Galochkin
parent
4fc7723e32
commit
9349930ad8
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@ -1,174 +1,170 @@
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const {
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Cdf,
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Pdf,
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ContinuousDistribution,
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ContinuousDistributionCombination,
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scoringFunctions,
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} = require("@foretold/cdf/lib");
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const _ = require("lodash");
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/**
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*
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* @param xs
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* @param ys
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* @returns {{ys: *, xs: *}}
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*/
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function cdfToPdf({ xs, ys }) {
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let cdf = new Cdf(xs, ys);
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let pdf = cdf.toPdf();
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return { xs: pdf.xs, ys: pdf.ys };
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}
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Cdf,
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Pdf,
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ContinuousDistribution,
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ContinuousDistributionCombination,
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scoringFunctions,
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} = require("@foretold/cdf/lib");
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const _ = require("lodash");
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/**
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*
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* @param xs
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* @param ys
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* @returns {{ys: *, xs: *}}
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*/
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function pdfToCdf({ xs, ys }) {
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let cdf = new Pdf(xs, ys);
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let pdf = cdf.toCdf();
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return { xs: pdf.xs, ys: pdf.ys };
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}
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/**
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*
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* @param sampleCount
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* @param vars
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* @returns {{ys: *, xs: *}}
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*/
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function mean(sampleCount, vars) {
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let cdfs = vars.map(r => new Cdf(r.xs, r.ys));
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let comb = new ContinuousDistributionCombination(cdfs);
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let newCdf = comb.combineYsWithMean(sampleCount);
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return { xs: newCdf.xs, ys: newCdf.ys };
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}
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/**
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*
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* @param sampleCount
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* @param predictionCdf
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* @param resolutionCdf
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*/
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function scoreNonMarketCdfCdf(sampleCount, predictionCdf, resolutionCdf, resolutionUniformAdditionWeight=0) {
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let toCdf = (r) => (new Cdf(r.xs, r.ys));
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let prediction = toCdf(predictionCdf);
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if (_.isFinite(resolutionUniformAdditionWeight)){
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prediction = prediction.combineWithUniformOfCdf(
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{
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cdf: toCdf(resolutionCdf),
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uniformWeight: resolutionUniformAdditionWeight,
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sampleCount
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}
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);
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}
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return scoringFunctions.distributionInputDistributionOutputMarketless({
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predictionCdf: prediction,
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resultCdf: toCdf(resolutionCdf),
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sampleCount,
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});
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}
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/**
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*
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* @param sampleCount
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* @param cdf
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*/
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function differentialEntropy(sampleCount, cdf) {
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let toCdf = (r) => (new Cdf(r.xs, r.ys));
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return scoringFunctions.differentialEntropy({
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cdf: toCdf(cdf),
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sampleCount: sampleCount
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});
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}
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/**
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*
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* @param x
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* @param xs
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* @param ys
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* @returns {number}
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*/
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function findY(x, { xs, ys }) {
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let cdf = new Cdf(xs, ys);
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return cdf.findY(x);
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}
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/**
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*
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* @param xs
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* @param ys
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* @returns {{ys: *, xs: *}}
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*/
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function cdfToPdf({ xs, ys }) {
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let cdf = new Cdf(xs, ys);
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let pdf = cdf.toPdf();
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return { xs: pdf.xs, ys: pdf.ys };
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}
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/**
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*
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* @param x
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* @param xs
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* @param ys
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* @returns {number[]}
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*/
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function convertToNewLength(n, { xs, ys }) {
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let dist = new ContinuousDistribution(xs, ys);
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return dist.convertToNewLength(n);
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}
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/**
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*
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* @param y
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* @param xs
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* @param ys
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* @returns {number}
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*/
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function findX(y, { xs, ys }) {
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let cdf = new Cdf(xs, ys);
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return cdf.findX(y);
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}
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/**
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*
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* @param xs
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* @param ys
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* @returns {number[]}
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*/
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function integral({ xs, ys }) {
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if (_.includes(ys, NaN)){
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return NaN;
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}
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else if (_.includes(ys, Infinity) && _.includes(ys, -Infinity)){
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return NaN;
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}
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else if (_.includes(ys, Infinity)){
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return Infinity;
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}
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else if (_.includes(ys, -Infinity)){
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return -Infinity;
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}
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let integral = 0;
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for (let i = 1; i < ys.length; i++) {
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let thisY = ys[i];
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let lastY = ys[i - 1];
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let thisX = xs[i];
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let lastX = xs[i - 1];
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if (
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_.isFinite(thisY) && _.isFinite(lastY) &&
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_.isFinite(thisX) && _.isFinite(lastX)
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) {
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let sectionInterval = ((thisY + lastY) / 2) * (thisX - lastX);
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integral = integral + sectionInterval;
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/**
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*
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* @param xs
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* @param ys
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* @returns {{ys: *, xs: *}}
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*/
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function pdfToCdf({ xs, ys }) {
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let cdf = new Pdf(xs, ys);
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let pdf = cdf.toCdf();
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return { xs: pdf.xs, ys: pdf.ys };
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}
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/**
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*
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* @param sampleCount
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* @param vars
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* @returns {{ys: *, xs: *}}
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*/
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function mean(sampleCount, vars) {
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let cdfs = vars.map(r => new Cdf(r.xs, r.ys));
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let comb = new ContinuousDistributionCombination(cdfs);
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let newCdf = comb.combineYsWithMean(sampleCount);
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return { xs: newCdf.xs, ys: newCdf.ys };
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}
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/**
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*
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* @param sampleCount
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* @param predictionCdf
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* @param resolutionCdf
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*/
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function scoreNonMarketCdfCdf(sampleCount, predictionCdf, resolutionCdf, resolutionUniformAdditionWeight = 0) {
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let toCdf = (r) => (new Cdf(r.xs, r.ys));
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let prediction = toCdf(predictionCdf);
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if (_.isFinite(resolutionUniformAdditionWeight)) {
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prediction = prediction.combineWithUniformOfCdf(
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{
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cdf: toCdf(resolutionCdf),
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uniformWeight: resolutionUniformAdditionWeight,
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sampleCount
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}
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}
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return integral;
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);
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}
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module.exports = {
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cdfToPdf,
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pdfToCdf,
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findY,
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findX,
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convertToNewLength,
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mean,
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scoreNonMarketCdfCdf,
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differentialEntropy,
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integral,
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};
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return scoringFunctions.distributionInputDistributionOutputMarketless({
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predictionCdf: prediction,
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resultCdf: toCdf(resolutionCdf),
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sampleCount,
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});
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}
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/**
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*
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* @param sampleCount
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* @param cdf
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*/
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function differentialEntropy(sampleCount, cdf) {
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let toCdf = (r) => (new Cdf(r.xs, r.ys));
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return scoringFunctions.differentialEntropy({
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cdf: toCdf(cdf),
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sampleCount: sampleCount
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});
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}
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/**
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*
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* @param x
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* @param xs
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* @param ys
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* @returns {number}
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*/
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function findY(x, { xs, ys }) {
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let cdf = new Cdf(xs, ys);
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return cdf.findY(x);
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}
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/**
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*
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* @param x
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* @param xs
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* @param ys
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* @returns {number[]}
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*/
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function convertToNewLength(n, { xs, ys }) {
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let dist = new ContinuousDistribution(xs, ys);
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return dist.convertToNewLength(n);
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}
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/**
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*
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* @param y
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* @param xs
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* @param ys
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* @returns {number}
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*/
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function findX(y, { xs, ys }) {
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let cdf = new Cdf(xs, ys);
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return cdf.findX(y);
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}
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/**
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*
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* @param xs
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* @param ys
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* @returns {number[]}
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*/
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function integral({ xs, ys }) {
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if (_.includes(ys, NaN)) {
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return NaN;
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} else if (_.includes(ys, Infinity) && _.includes(ys, -Infinity)) {
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return NaN;
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} else if (_.includes(ys, Infinity)) {
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return Infinity;
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} else if (_.includes(ys, -Infinity)) {
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return -Infinity;
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}
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let integral = 0;
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for (let i = 1; i < ys.length; i++) {
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let thisY = ys[i];
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let lastY = ys[i - 1];
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let thisX = xs[i];
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let lastX = xs[i - 1];
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if (
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_.isFinite(thisY) && _.isFinite(lastY) &&
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_.isFinite(thisX) && _.isFinite(lastX)
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) {
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let sectionInterval = ((thisY + lastY) / 2) * (thisX - lastX);
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integral = integral + sectionInterval;
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}
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}
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return integral;
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}
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module.exports = {
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cdfToPdf,
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pdfToCdf,
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findY,
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findX,
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convertToNewLength,
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mean,
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scoreNonMarketCdfCdf,
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differentialEntropy,
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integral,
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};
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