Fixes CdfLibrary.js

src/utility/CdfLibrary.js

@@ -4,57 +4,57 @@ const {
   ContinuousDistribution,
   ContinuousDistributionCombination,
   scoringFunctions,
-  } = require("@foretold/cdf/lib");
-  const _ = require("lodash");
+} = require("@foretold/cdf/lib");
+const _ = require("lodash");
 
-  /**
+/**
  *
  * @param xs
  * @param ys
  * @returns {{ys: *, xs: *}}
  */
-  function cdfToPdf({ xs, ys }) {
+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 }) {
+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) {
+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) {
+function scoreNonMarketCdfCdf(sampleCount, predictionCdf, resolutionCdf, resolutionUniformAdditionWeight = 0) {
   let toCdf = (r) => (new Cdf(r.xs, r.ys));
   let prediction = toCdf(predictionCdf);
-    if (_.isFinite(resolutionUniformAdditionWeight)){
+  if (_.isFinite(resolutionUniformAdditionWeight)) {
     prediction = prediction.combineWithUniformOfCdf(
       {
         cdf: toCdf(resolutionCdf),
@@ -69,75 +69,72 @@ const {
     resultCdf: toCdf(resolutionCdf),
     sampleCount,
   });
-  }
+}
 
-  /**
+/**
  *
  * @param sampleCount
  * @param cdf
  */
-  function differentialEntropy(sampleCount, 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 }) {
+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 }) {
+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 }) {
+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)){
+function integral({ xs, ys }) {
+  if (_.includes(ys, NaN)) {
     return NaN;
-    }
-    else if (_.includes(ys, Infinity) && _.includes(ys, -Infinity)){
+  } else if (_.includes(ys, Infinity) && _.includes(ys, -Infinity)) {
     return NaN;
-    }
-    else if (_.includes(ys, Infinity)){
+  } else if (_.includes(ys, Infinity)) {
     return Infinity;
-    }
-    else if (_.includes(ys, -Infinity)){
+  } else if (_.includes(ys, -Infinity)) {
     return -Infinity;
   }
 
@@ -158,9 +155,9 @@ const {
 
   }
   return integral;
-  }
+}
 
-  module.exports = {
+module.exports = {
   cdfToPdf,
   pdfToCdf,
   findY,
@@ -170,5 +167,4 @@ const {
   scoreNonMarketCdfCdf,
   differentialEntropy,
   integral,
-  };
-  
+};