quantified.uncertainty/squiggle
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

last CR comments

Browse Source
This commit is contained in:
Quinn Dougherty 2022-04-13 20:58:16 -04:00
parent 99d1c345c5
commit b3c0adcd30
2 changed files with 16 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
packages/squiggle-lang/__tests__/Distributions/Invariants/Means_test.res
View File

@ -13,7 +13,7 @@ open Expect
open TestHelpers open TestHelpers
module Internals = { module Internals = {
let epsilon = 1e1 let epsilon = 5e1
let mean = GenericDist_Types.Constructors.UsingDists.mean let mean = GenericDist_Types.Constructors.UsingDists.mean
@ -21,12 +21,12 @@ module Internals = {
`${algebraicOp} has`->expect->toEqual("failed") `${algebraicOp} has`->expect->toEqual("failed")
let distributions = list{ let distributions = list{
normalMake(0.0, 1e0), normalMake(4e0, 1e0),
betaMake(2e0, 4e0), betaMake(2e0, 4e0),
exponentialMake(1.234e0), exponentialMake(1.234e0),
uniformMake(7e0, 1e1), uniformMake(7e0, 1e1),
// cauchyMake(1e0, 1e0), // cauchyMake(1e0, 1e0),
lognormalMake(1e0, 1e0), lognormalMake(2e0, 1e0),
triangularMake(1e0, 1e1, 5e1), triangularMake(1e0, 1e1, 5e1),
Ok(floatMake(1e1)), Ok(floatMake(1e1)),
} }
@ -77,7 +77,7 @@ let algebraicPower = algebraicPower(~env)
let {testOperationMean, distributions, pairsOfDifferentDistributions, epsilon} = module(Internals) let {testOperationMean, distributions, pairsOfDifferentDistributions, epsilon} = module(Internals)
describe("Means invariant", () => { describe("Means are invariant", () => {
describe("for addition", () => { describe("for addition", () => {
let testAdditionMean = testOperationMean(algebraicAdd, "algebraicAdd", \"+.", ~epsilon) let testAdditionMean = testOperationMean(algebraicAdd, "algebraicAdd", \"+.", ~epsilon)

16
packages/squiggle-lang/__tests__/TestHelpers.res
View File

@ -1,13 +1,21 @@
open Jest open Jest
open Expect open Expect
/*
This encodes the expression for percent error
The test says "the percent error of received against expected is bounded by epsilon"
However, the semantics are degraded by catching some numerical instability:
when expected is too small, the return of this function might blow up to infinity.
So we capture that by taking the max of abs(expected) against a 1.
A sanity check of this function would be welcome, in general it is a better way of approaching
squiggle-lang tests than toBeSoCloseTo.
*/
let expectErrorToBeBounded = (received, expected, ~epsilon) => { let expectErrorToBeBounded = (received, expected, ~epsilon) => {
let distance = Js.Math.abs_float(received -. expected) let distance = Js.Math.abs_float(received -. expected)
let expectedAbs = Js.Math.abs_float(expected) let expectedAbs = Js.Math.abs_float(expected)
let normalizingDenom = Js.Math.max_float( let normalizingDenom = Js.Math.max_float(expectedAbs, 1e0)
expectedAbs,
epsilon < 1.0 ? epsilon ** 2.0 : epsilon ** -2.0,
)
let error = distance /. normalizingDenom let error = distance /. normalizingDenom
error->expect->toBeLessThan(epsilon) error->expect->toBeLessThan(epsilon)
} }