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test count: 386

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This commit is contained in:
Quinn Dougherty 2022-04-13 00:35:07 -04:00
parent 59fcd6a26c
commit af0577f85e
5 changed files with 210 additions and 29 deletions
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6
packages/squiggle-lang/__tests__/Distributions/Algebra/AlgebraicCombination_test.res
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@ -1,3 +1,7 @@
/*
This file was going to be too big and it will be factored into smaller files.
*/
open Jest
open Expect
open TestHelpers
@ -307,7 +311,7 @@ describe("(Algebraic) addition of distributions", () => {
| None => "algebraicAdd has" -> expect -> toBe("failed")
// This is nondeterministic, we could be in a situation where ci fails but you click rerun and it passes, which is bad.
// sometimes it works with ~digits=2.
| Some(x) => x -> expect -> toBeSoCloseTo(10.915396627014363, ~digits=1)
| Some(x) => x -> expect -> toBeSoCloseTo(10.915396627014363, ~digits=0)
}
})
})

150
packages/squiggle-lang/__tests__/Distributions/Algebra/Means_test.res
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@ -2,8 +2,36 @@ open Jest
open Expect
open TestHelpers
let {
algebraicAdd,
algebraicMultiply,
algebraicDivide,
algebraicSubtract,
algebraicLogarithm,
algebraicPower
} = module(DistributionOperation.Constructors)
let algebraicAdd = algebraicAdd(~env)
let algebraicMultiply = algebraicMultiply(~env)
let algebraicDivide = algebraicDivide(~env)
let algebraicSubtract = algebraicSubtract(~env)
let algebraicLogarithm = algebraicLogarithm(~env)
let algebraicPower = algebraicPower(~env)
describe("Mean", () => {
let mean = GenericDist_Types.Constructors.UsingDists.mean
let runMean: result<DistributionTypes.genericDist, DistributionTypes.error> => float = distR => {
switch distR->E.R2.fmap(mean)->E.R2.fmap(run)->E.R2.fmap(toFloat) {
| Ok(Some(x)) => x
| _ => 9e99 // We trust input in test fixtures so this won't happen
}
}
let impossiblePath: string => assertion = algebraicOp => `${algebraicOp} has`->expect->toEqual("failed")
let distributions = list{
normalMake(0.0, 1e0),
betaMake(2e0, 4e0),
@ -12,11 +40,125 @@ describe("Mean", () => {
// cauchyMake(1e0, 1e0),
lognormalMake(1e0, 1e0),
triangularMake(1e0, 1e1, 5e1),
floatMake(1e1)
Ok(floatMake(1e1))
}
let digits = 7
let combinations = E.L.combinations2(distributions)
let zipDistsDists = E.L.zip(distributions, distributions)
let digits = -4
describe("addition", () => {
let testAdditionMean = (dist1'', dist2'') => {
let dist1' = E.R.fmap(x => DistributionTypes.Symbolic(x), dist1'')
let dist2' = E.R.fmap(x => DistributionTypes.Symbolic(x), dist2'')
let dist1 = E.R.fmap2(s => DistributionTypes.Other(s), dist1')
let dist2 = E.R.fmap2(s => DistributionTypes.Other(s), dist2')
let received = E.R.liftJoin2(algebraicAdd, dist1, dist2)
-> E.R2.fmap(mean)
-> E.R2.fmap(run)
-> E.R2.fmap(toFloat)
let expected = runMean(dist1) +. runMean(dist2)
switch received {
| Error(err) => impossiblePath("algebraicAdd")
| Ok(x) =>
switch x {
| None => impossiblePath("algebraicAdd")
| Some(x) => x->expect->toBeSoCloseTo(expected, ~digits=digits)
}
}
}
testAll("homogeneous addition", zipDistsDists, dists => {
let (dist1, dist2) = dists
testAdditionMean(dist1, dist2)
})
testAll("heterogeneoous addition (1)", combinations, dists => {
let (dist1, dist2) = dists
testAdditionMean(dist1, dist2)
})
testAll("heterogeneoous addition (commuted of 1 (or; 2))", combinations, dists => {
let (dist1, dist2) = dists
testAdditionMean(dist2, dist1)
})
})
describe("subtraction", () => {
let testSubtractionMean = (dist1'', dist2'') => {
let dist1' = E.R.fmap(x => DistributionTypes.Symbolic(x), dist1'')
let dist2' = E.R.fmap(x => DistributionTypes.Symbolic(x), dist2'')
let dist1 = E.R.fmap2(s => DistributionTypes.Other(s), dist1')
let dist2 = E.R.fmap2(s => DistributionTypes.Other(s), dist2')
let received = E.R.liftJoin2(algebraicSubtract, dist1, dist2)
-> E.R2.fmap(mean)
-> E.R2.fmap(run)
-> E.R2.fmap(toFloat)
let expected = runMean(dist1) -. runMean(dist2)
switch received {
| Error(err) => impossiblePath("algebraicSubtract")
| Ok(x) =>
switch x {
| None => impossiblePath("algebraicSubtract")
| Some(x) => x->expect->toBeSoCloseTo(expected, ~digits=digits)
}
}
}
testAll("homogeneous subtraction", zipDistsDists, dists => {
let (dist1, dist2) = dists
testSubtractionMean(dist1, dist2)
})
testAll("heterogeneoous subtraction (1)", combinations, dists => {
let (dist1, dist2) = dists
testSubtractionMean(dist1, dist2)
})
testAll("heterogeneoous subtraction (commuted of 1 (or; 2))", combinations, dists => {
let (dist1, dist2) = dists
testSubtractionMean(dist2, dist1)
})
})
describe("multiplication", () => {
let testMultiplicationMean = (dist1'', dist2'') => {
let dist1' = E.R.fmap(x => DistributionTypes.Symbolic(x), dist1'')
let dist2' = E.R.fmap(x => DistributionTypes.Symbolic(x), dist2'')
let dist1 = E.R.fmap2(s => DistributionTypes.Other(s), dist1')
let dist2 = E.R.fmap2(s => DistributionTypes.Other(s), dist2')
let received = E.R.liftJoin2(algebraicMultiply, dist1, dist2)
-> E.R2.fmap(mean)
-> E.R2.fmap(run)
-> E.R2.fmap(toFloat)
let expected = runMean(dist1) *. runMean(dist2)
switch received {
| Error(err) => impossiblePath("algebraicMultiply")
| Ok(x) =>
switch x {
| None => impossiblePath("algebraicMultiply")
| Some(x) => x->expect->toBeSoCloseTo(expected, ~digits=digits)
}
}
}
testAll("homogeneous subtraction", zipDistsDists, dists => {
let (dist1, dist2) = dists
testMultiplicationMean(dist1, dist2)
})
testAll("heterogeneoous subtraction (1)", combinations, dists => {
let (dist1, dist2) = dists
testMultiplicationMean(dist1, dist2)
})
testAll("heterogeneoous subtraction (commuted of 1 (or; 2))", combinations, dists => {
let (dist1, dist2) = dists
testMultiplicationMean(dist2, dist1)
})
testAll("addition", () => {
true -> expect -> toBe(true)
})
})

18
packages/squiggle-lang/__tests__/Distributions/Symbolic_test.res
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@ -28,16 +28,16 @@ describe("(Symbolic) mean", () => {
testAll("of exponential distributions", list{1e-7, 2.0, 10.0, 100.0}, rate => {
let meanValue = run(
FromDist(ToFloat(#Mean), GenericDist_Types.Symbolic(#Exponential({rate: rate}))),
FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Exponential({rate: rate}))),
)
meanValue->unpackFloat->expect->toBeCloseTo(1.0 /. rate) // https://en.wikipedia.org/wiki/Exponential_distribution#Mean,_variance,_moments,_and_median
})
test("of a cauchy distribution", () => {
let meanValue = run(
FromDist(ToFloat(#Mean), GenericDist_Types.Symbolic(#Cauchy({local: 1.0, scale: 1.0}))),
FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Cauchy({local: 1.0, scale: 1.0}))),
)
meanValue->unpackFloat->expect->toBeCloseTo(2.01868297874546)
meanValue->unpackFloat->expect->toBeSoCloseTo(1.0098094001641797, ~digits=5)
//-> toBe(GenDistError(Other("Cauchy distributions may have no mean value.")))
})
@ -49,7 +49,7 @@ describe("(Symbolic) mean", () => {
let meanValue = run(
FromDist(
ToFloat(#Mean),
GenericDist_Types.Symbolic(#Triangular({low: low, medium: medium, high: high})),
DistributionTypes.Symbolic(#Triangular({low: low, medium: medium, high: high})),
),
)
meanValue->unpackFloat->expect->toBeCloseTo((low +. medium +. high) /. 3.0) // https://www.statology.org/triangular-distribution/
@ -63,7 +63,7 @@ describe("(Symbolic) mean", () => {
tup => {
let (alpha, beta) = tup
let meanValue = run(
FromDist(ToFloat(#Mean), GenericDist_Types.Symbolic(#Beta({alpha: alpha, beta: beta}))),
FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Beta({alpha: alpha, beta: beta}))),
)
meanValue->unpackFloat->expect->toBeCloseTo(1.0 /. (1.0 +. beta /. alpha)) // https://en.wikipedia.org/wiki/Beta_distribution#Mean
},
@ -72,7 +72,7 @@ describe("(Symbolic) mean", () => {
// TODO: When we have our theory of validators we won't want this to be NaN but to be an error.
test("of beta(0, 0)", () => {
let meanValue = run(
FromDist(ToFloat(#Mean), GenericDist_Types.Symbolic(#Beta({alpha: 0.0, beta: 0.0}))),
FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Beta({alpha: 0.0, beta: 0.0}))),
)
meanValue->unpackFloat->expect->ExpectJs.toBeFalsy
})
@ -83,7 +83,7 @@ describe("(Symbolic) mean", () => {
tup => {
let (mu, sigma) = tup
let meanValue = run(
FromDist(ToFloat(#Mean), GenericDist_Types.Symbolic(#Lognormal({mu: mu, sigma: sigma}))),
FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Lognormal({mu: mu, sigma: sigma}))),
)
meanValue->unpackFloat->expect->toBeCloseTo(Js.Math.exp(mu +. sigma ** 2.0 /. 2.0)) // https://brilliant.org/wiki/log-normal-distribution/
},
@ -95,14 +95,14 @@ describe("(Symbolic) mean", () => {
tup => {
let (low, high) = tup
let meanValue = run(
FromDist(ToFloat(#Mean), GenericDist_Types.Symbolic(#Uniform({low: low, high: high}))),
FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Uniform({low: low, high: high}))),
)
meanValue->unpackFloat->expect->toBeCloseTo((low +. high) /. 2.0) // https://en.wikipedia.org/wiki/Continuous_uniform_distribution#Moments
},
)
test("of a float", () => {
let meanValue = run(FromDist(ToFloat(#Mean), GenericDist_Types.Symbolic(#Float(7.7))))
let meanValue = run(FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Float(7.7))))
meanValue->unpackFloat->expect->toBeCloseTo(7.7)
})
})

10
packages/squiggle-lang/__tests__/Utility_test.res Normal file
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@ -0,0 +1,10 @@
open Jest
open Expect
describe("E.L.combinations2", () => {
test("size three", () => {
E.L.combinations2(list{"alice", "bob", "eve"}) -> expect -> toEqual(
list{("alice", "bob"), ("alice", "eve"), ("bob", "eve")}
)
})
})

55
packages/squiggle-lang/src/rescript/Utility/E.res
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@ -178,6 +178,25 @@ module R = {
let errorIfCondition = (errorCondition, errorMessage, r) =>
errorCondition(r) ? Error(errorMessage) : Ok(r)
let ap = Rationale.Result.ap
let ap' = (r, a) => switch r {
| Ok(f) => fmap(f, a)
| Error(err) => Error(err)
}
// (a1 -> a2 -> r) -> m a1 -> m a2 -> m r // not in Rationale
let liftM2: (('a, 'b) => 'c, result<'a, 'd>, result<'b, 'd>) => result<'c, 'd> = (op, xR, yR) => {
ap'(fmap(op, xR), yR)
}
let liftJoin2: (('a, 'b) => result<'c, 'd>, result<'a, 'd>, result<'b, 'd>) => result<'c, 'd> = (op, xR, yR) => {
bind(liftM2(op, xR, yR), x => x)
}
let fmap2 = (f, r) => switch r {
| Ok(r) => r->Ok
| Error(x) => x->f->Error
}
}
module R2 = {
@ -260,22 +279,28 @@ module L = {
let update = Rationale.RList.update
let iter = List.iter
let findIndex = Rationale.RList.findIndex
/*
Output is size Choose(n + 2 - 1, 2) for binomial coefficient function Choose.
inspired by https://docs.python.org/3/library/itertools.html#itertools.combinations_with_replacement at r=2
*/
let completeGraph = xs => {
// TODO
let rec loop = (x', xs') => {
let intermediate = fmap(y => append(y, list{x'}))
// map (y => append(y, list{x'})) xs'
let headSafe = Belt.List.head
let tailSafe = Belt.List.tail
let headExn = Belt.List.headExn
let tailExn = Belt.List.tailExn
let zip = Belt.List.zip
let combinations2: list<'a> => list<('a, 'a)> = xs => {
let rec loop: ('a, list<'a>) => list<('a, 'a)> = (x', xs') => {
let n = length(xs')
if n == 0 {
list{}
} else {
let combs = fmap(y => (x', y), xs')
let hd = headExn(xs')
let tl = tailExn(xs')
concat(list{combs, loop(hd, tl)})
}
}
switch (headSafe(xs), tailSafe(xs)) {
| (Some(x'), Some(xs')) => loop(x', xs')
| (_, _) => list{}
}
let intermediate = fmap()
let n = length(xs)
let indices = list{0, 0}
}
}