commented out tests are now explained
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Quinn Dougherty
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4f5a1ff946
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3ff810ee1b
49
packages/squiggle-lang/__tests__/TS/PointSet_test.ts
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49
packages/squiggle-lang/__tests__/TS/PointSet_test.ts
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@ -0,0 +1,49 @@
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import { errorValueToString } from "../../src/js/index";
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import { testRun, failDefault, expectErrorToBeBounded } from "./TestHelpers";
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import * as fc from "fast-check";
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describe("Mean of mixture is weighted average of means", () => {
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test("mx(beta(a,b), lognormal(m,s), [x,y])", () => {
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fc.assert(
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fc.property(
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fc.float({ min: 1e-1 }), // alpha
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fc.float({ min: 1 }), // beta
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fc.float(), // mu
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fc.float({ min: 1e-1 }), // sigma
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fc.float({ min: 1e-7 }),
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fc.float({ min: 1e-7 }),
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(a, b, m, s, x, y) => {
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let squiggleString = `mean(mx(beta(${a},${b}), lognormal(${m},${s}), [${x}, ${y}]))`;
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let res = testRun(squiggleString);
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switch (res.tag) {
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case "Error":
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expect(errorValueToString(res.value)).toEqual(
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"<I wonder if test cases will find this>"
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);
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break;
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case "Ok":
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let weightDenom = x + y;
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let betaWeight = x / weightDenom;
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let lognormalWeight = y / weightDenom;
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let betaMean = 1 / (1 + b / a);
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let lognormalMean = m + s ** 2 / 2;
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if (res.value.tag == "number") {
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expectErrorToBeBounded(
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res.value.value,
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betaWeight * betaMean + lognormalWeight * lognormalMean,
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1,
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-1
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);
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} else {
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expect(res.value.value).toEqual("some error message");
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}
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break;
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default:
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failDefault();
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break;
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}
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}
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)
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);
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});
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});
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@ -1,4 +1,5 @@
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import { Distribution } from "../../src/js/index";
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import { expectErrorToBeBounded, failDefault } from "./TestHelpers";
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import * as fc from "fast-check";
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// Beware: float64Array makes it appear in an infinite loop.
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@ -126,80 +127,49 @@ describe("SampleSet: cdf", () => {
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// });
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// });
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// This should be true, but I can't get it to work.
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// describe("SampleSet: mean is mean", () => {
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// test("mean(samples(xs)) sampling twice as widely as the input", () => {
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// fc.assert(
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// fc.property(
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// fc.float64Array({ minLength: 10, maxLength: 100000 }),
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// (xs) => {
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// let ys = Array.from(xs);
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// let n = ys.length;
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// let dist = new Distribution(
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// { tag: "SampleSet", value: ys },
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// { sampleCount: 2 * n, xyPointLength: 4 * n }
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// );
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//
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// expect(dist.mean().value).toBeCloseTo(
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// ys.reduce((a, b) => a + b, 0.0) / n
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// );
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// }
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// )
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// );
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// });
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//
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// test("mean(samples(xs)) sampling half as widely as the input", () => {
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// fc.assert(
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// fc.property(
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// fc.float64Array({ minLength: 10, maxLength: 100000 }),
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// (xs) => {
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// let ys = Array.from(xs);
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// let n = ys.length;
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// let dist = new Distribution(
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// { tag: "SampleSet", value: ys },
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// { sampleCount: Math.floor(5 / 2), xyPointLength: 4 * n }
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// );
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//
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// expect(dist.mean().value).toBeCloseTo(
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// ys.reduce((a, b) => a + b, 0.0) / n
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// );
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// }
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// )
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// );
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// });
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// test("mean(samples(xs)) sampling twice as widely as the input", () => {
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// fc.assert(
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// fc.property(
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// fc.float64Array({ minLength: 10, maxLength: 100000 }),
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// (xs) => {
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// let ys = Array.from(xs);
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// let n = ys.length;
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// let dist = new Distribution(
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// { tag: "SampleSet", value: ys },
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// { sampleCount: 2 * n, xyPointLength: 4 * n }
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// );
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// let mean = dist.mean()
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// if (typeof mean.value == "number") {
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// expectErrorToBeBounded(mean.value, ys.reduce((a, b) => a + b, 0.0) / n, 5e-1, 1)
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// } else {
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// failDefault()
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// }
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// }
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// )
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// );
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// });
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//
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// test("mean(samples(xs)) sampling half as widely as the input", () => {
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// fc.assert(
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// fc.property(
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// fc.float64Array({ minLength: 10, maxLength: 100000 }),
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// (xs) => {
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// let ys = Array.from(xs);
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// let n = ys.length;
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// let dist = new Distribution(
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// { tag: "SampleSet", value: ys },
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// { sampleCount: Math.floor(n / 2), xyPointLength: 4 * n }
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// );
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// let mean = dist.mean()
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// if (typeof mean.value == "number") {
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// expectErrorToBeBounded(mean.value, ys.reduce((a, b) => a + b, 0.0) / n, 5e-1, 1)
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// } else {
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// failDefault()
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// }
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// }
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// )
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// );
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// });
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// describe("Mean of mixture is weighted average of means", () => {
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// test("mx(beta(a,b), lognormal(m,s), [x,y])", () => {
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// fc.assert(
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// fc.property(
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// fc.float({ min: 1e-1 }), // alpha
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// fc.float({ min: 1 }), // beta
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// fc.float(), // mu
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// fc.float({ min: 1e-1 }), // sigma
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// fc.float({ min: 1e-7 }),
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// fc.float({ min: 1e-7 }),
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// (a, b, m, s, x, y) => {
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// let squiggleString = `mean(mx(beta(${a},${b}), lognormal(${m},${s}), [${x}, ${y}]))`;
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// let res = testRun(squiggleString);
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// switch (res.tag) {
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// case "Error":
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// expect(errorValueToString(res.value)).toEqual(
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// "<I wonder if test cases will find this>"
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// );
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// case "Ok":
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// let betaWeight = x / (x + y);
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// let lognormalWeight = y / (x + y);
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// let betaMean = 1 / (1 + b / a);
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// let lognormalMean = m + s ** 2 / 2;
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// expect(res.value).toEqual({
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// tag: "number",
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// value: betaWeight * betaMean + lognormalWeight * lognormalMean,
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// });
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// default:
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// expect("mean returned").toBe(`something other than a number`);
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// }
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// }
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// )
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// );
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// });
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// });
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@ -1,10 +1,4 @@
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import {
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run,
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squiggleExpression,
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errorValueToString,
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errorValue,
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result,
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} from "../../src/js/index";
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import { errorValueToString } from "../../src/js/index";
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import { testRun } from "./TestHelpers";
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import * as fc from "fast-check";
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@ -13,5 +13,24 @@ export function testRun(x: string): any {
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}
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export function failDefault() {
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expect("be reached").toBe("codepath should never be");
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expect("be reached").toBe("codepath should never");
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}
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/**
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* This appears also in `TestHelpers.res`. According to https://www.math.net/percent-error, it computes
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* absolute error when numerical stability concerns make me not want to compute relative error.
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* */
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export function expectErrorToBeBounded(
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received: number,
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expected: number,
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epsilon: number,
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digits: number
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) {
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let distance = Math.abs(received - expected);
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let expectedAbs = Math.abs(expected);
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let normalizingDenom = Math.max(expectedAbs, 1);
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let error = distance / normalizingDenom;
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expect(Math.round(10 ** digits * error) / 10 ** digits).toBeLessThanOrEqual(
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epsilon
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);
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}
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