First attempt at adding dist functionality

package.json

@@ -22,6 +22,8 @@
   "author": "",
   "license": "MIT",
   "dependencies": {
+    "@foretold/cdf": "^1.0.14",
+    "@foretold/guesstimator": "^1.0.10",
     "antd": "3.17.0",
     "autoprefixer": "^9.7.4",
     "bs-ant-design-alt": "2.0.0-alpha.31",
@@ -46,4 +48,4 @@
     "moduleserve": "^0.9.0",
     "tailwindcss": "^1.2.0"
   }
-}
+}

src/lib/Prop.re

@@ -120,7 +120,7 @@ module TypeWithMetadata = {
   let currentYear =
     make(
       ~id="currentYear",
-      ~name="Current Year",
+      ~name="Current Day",
       ~description=None,
       ~type_=
         DateTime({

src/lib/Types.re

@@ -0,0 +1,4 @@
+type distribution = {
+  xs: array(float),
+  ys: array(float),
+};

src/models/EAFunds.re

@@ -166,8 +166,8 @@ module Interface = {
 
   let model: Prop.Model.t =
     Prop.{
-      name: "EA Funds: Donations & Payouts",
+      name: "CEA Funds: Donations & Payouts",
-      description: "Calculate the payments and payouts of EA Funds based on existing data.",
+      description: "Calculate the payments and payouts of CEA Funds based on existing data.",
       version: "1.0.0",
       author: "Ozzie Gooen",
       inputTypes: [|

src/utility/CdfLibrary.js

@@ -0,0 +1,160 @@
+const {
+    Cdf,
+    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 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,
+    mean,
+    scoreNonMarketCdfCdf,
+    differentialEntropy,
+    integral,
+  };
+  

src/utility/CdfLibrary.re

@@ -0,0 +1,56 @@
+module JS = {
+  [@bs.deriving abstract]
+  type distJs = {
+    xs: array(float),
+    ys: array(float),
+  };
+
+  let distToJs = (d: Types.distribution) => distJs(~xs=d.xs, ~ys=d.ys);
+
+  let jsToDist = (d: distJs): Types.distribution => {
+    xs: xsGet(d),
+    ys: ysGet(d),
+  };
+
+  let doAsDist = (f, d: Types.distribution) => d |> distToJs |> f |> jsToDist;
+
+  [@bs.module "./CdfLibraryImporter.js"]
+  external cdfToPdf: distJs => distJs = "cdfToPdf";
+
+  [@bs.module "./CdfLibraryImporter.js"]
+  external pdfToCdf: distJs => distJs = "pdfToCdf";
+
+  [@bs.module "./CdfLibraryImporter.js"]
+  external findY: (float, distJs) => float = "findY";
+
+  [@bs.module "./CdfLibraryImporter.js"]
+  external findX: (float, distJs) => float = "findX";
+
+  [@bs.module "./CdfLibraryImporter.js"]
+  external integral: distJs => float = "integral";
+
+  [@bs.module "./CdfLibraryImporter.js"]
+  external differentialEntropy: (int, distJs) => distJs =
+    "differentialEntropy";
+
+  [@bs.module "./CdfLibraryImporter.js"]
+  external scoreNonMarketCdfCdf: (int, distJs, distJs, float) => distJs =
+    "scoreNonMarketCdfCdf";
+
+  [@bs.module "./GuesstimatorLibrary.js"]
+  external toGuesstimator: (string, int) => distJs = "run";
+};
+
+module Distribution = {
+  let toPdf = dist => dist |> JS.doAsDist(JS.cdfToPdf);
+  let toCdf = dist => dist |> JS.doAsDist(JS.cdfToPdf);
+  let findX = (y, dist) => dist |> JS.distToJs |> JS.findX(y);
+  let findY = (x, dist) => dist |> JS.distToJs |> JS.findY(x);
+  let fromString = (str: string, sampleCount: int) =>
+    JS.toGuesstimator(str, sampleCount) |> JS.jsToDist;
+  let integral = dist => dist |> JS.distToJs |> JS.integral;
+  let differentialEntropy = (maxCalculationLength, dist) =>
+    dist
+    |> JS.doAsDist(JS.differentialEntropy(maxCalculationLength))
+    |> integral;
+};

src/utility/GuesstimatorLibrary.js

@@ -0,0 +1,46 @@
+import { Guesstimator } from '@foretold/guesstimator';
+import { Samples } from '@foretold/cdf';
+
+const toPdf = (values, min, max) => {
+  const samples = new Samples(values);
+
+  const ratioSize$ = ratioSize(samples);
+  const width = ratioSize$ === 'SMALL' ? 20 : 1;
+
+  const pdf = samples.toPdf({ size: 1000, width, min, max });
+  return {ys:pdf.ys, xs:pdf.xs};
+};
+
+let run = (text, sampleCount, inputs=[], min=false, max=false) => {
+    let [_error, item] = Guesstimator.parse({ text });
+    const { parsedInput } = item;
+    const { guesstimateType } = parsedInput;
+
+    const guesstimator = new Guesstimator({ parsedInput });
+    const value = guesstimator.sample(
+      sampleCount,
+      inputs,
+    );
+    const samplerType = guesstimator.samplerType();
+
+    const values = _.filter(value.values, _.isFinite);
+
+    this.setState({
+      value: event.target.value,
+      items: values,
+    });
+
+    let update;
+    if (values.length === 0) {
+      update = {xs: [], ys: []};
+    } else if (values.length === 1) {
+      update = {xs: [], ys: []};
+    } else {
+      update = toPdf(values, min, max);
+    }
+    return update;
+}
+
+module.exports = {
+  run,
+};

src/utility/GuesstimatorLibrary3.re

@@ -0,0 +1,2 @@
+// [@bs.module "./GuesstimatorLibrary.js"]
+ /* external toGuesstimator: (string, int) => CdfLibrary.JS.distJs = "run"*/