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Test reorg, fixed esc's in @define_singleton, added tests.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,115 +1,11 @@ | ||
| import ForwardDiff: derivative | ||
| import Base: rand | ||
| "Test singleton type." | ||
| @define_singleton TestSingleton <: Real | ||
|
|
||
| """ | ||
| Return a random float in an interval (for testing). Do not use | ||
| directly when testing extrema, as these may happen with `0` | ||
| probability. | ||
| """ | ||
| rand(::RealLine, ::Type{Val{true}}) = randn() | ||
| rand(ray::PositiveRay, ::Type{Val{true}}) = ray.left + abs(randn()) | ||
| rand(ray::NegativeRay, ::Type{Val{true}}) = ray.right + abs(randn()) | ||
| rand(seg::Segment, ::Type{Val{true}}) = seg.left + width(seg) * rand() | ||
| @testset "define singleton" begin | ||
|
|
||
| @test TestSingleton <: Real | ||
| @test isa(TESTSINGLETON, TestSingleton) | ||
| @test repr(@doc(TESTSINGLETON)) == repr(@doc(TestSingleton)) == | ||
| repr(doc"Test singleton type.") | ||
|
|
||
| function rand(x::AbstractInterval; left_prob = 0.1, right_prob = 0.1) | ||
| @argcheck left_prob + right_prob ≤ 1 | ||
| z = rand() | ||
| left, right = extrema(x) | ||
| if z < left_prob | ||
| left | ||
| elseif z > 1-right_prob | ||
| right | ||
| else | ||
| rand(x, Val{true}) | ||
| end | ||
| end | ||
|
|
||
|
|
||
| """ | ||
| Return a function that generates random intervals in the given | ||
| interval (for testing). | ||
| """ | ||
| function random_interval(x::AbstractInterval) | ||
| a = rand(x; right_prob = 0) | ||
| b = rand(x; left_prob = 0) | ||
| if a > b | ||
| a, b = b, a | ||
| end | ||
| Interval(a, b) | ||
| end | ||
|
|
||
| """ | ||
| Return a vector of values outside the interval (for testing). | ||
| """ | ||
| scalars_outside_interval(::RealLine) = [] | ||
| scalars_outside_interval(ray::PositiveRay) = ray.left - [0.1, 1.0, 2.0, Inf] | ||
| scalars_outside_interval(ray::NegativeRay) = ray.right + [0.1, 1.0, 2.0, Inf] | ||
| function scalars_outside_interval(seg::Segment) | ||
| vcat(scalars_outside_interval(PositiveRay(seg.left)), | ||
| scalars_outside_interval(NegativeRay(seg.right))) | ||
| end | ||
|
|
||
| """ | ||
| Test univariate transformation `f` with `x`. Tests for: | ||
| 1. type of the transformed value, | ||
| 2. whether it is in the range, | ||
| 3. inverse, | ||
| 4. jacobian determinant and its log using automatic differentiation. | ||
| `forwarddiff_exceptions` is a dictionary handling exceptions that ForwardDiff | ||
| cannot cope with at the moment. See eg | ||
| ## workaround for https://github.com/JuliaDiff/ForwardDiff.jl/issues/209 | ||
| """ | ||
| function test_univariate_scalar{T}(f::UnivariateTransformation, x::T; | ||
| AD_exceptions = Dict()) | ||
| y = f(x) | ||
| @test y ∈ image(f) | ||
| @test inv(f)(y) ≈ x | ||
| (y2, jac) = f(x, JAC) | ||
| @test y == y2 | ||
| expected_jac = get(AD_exceptions, x, abs(derivative(f, x))) | ||
| @test jac ≈ expected_jac | ||
| (y3, logjac) = f(x, LOGJAC) | ||
| @test y == y3 | ||
| @test logjac ≈ log(expected_jac) | ||
| end | ||
|
|
||
| # some exceptions below | ||
| logit_exceptions(t=Multiply(1.0)) = Dict(t(1.0) => Inf) | ||
|
|
||
| logistic_exceptions() = Dict(-Inf => 0.0) | ||
|
|
||
| """ | ||
| Test that univariate transformations map an interval the correct way. | ||
| """ | ||
| function test_univariate_interval(f::UnivariateTransformation, x::AbstractInterval) | ||
| y = f(x) | ||
| left, right = extrema(x) | ||
| f_left, f_right = f(left), f(right) | ||
| if f_right < f_left | ||
| f_right, f_left = f_left, f_right | ||
| end | ||
| y_left, y_right = extrema(y) | ||
| @test y_left ≈ f_left | ||
| @test y_right ≈ f_right | ||
| end | ||
|
|
||
| """ | ||
| Test univariate transformation `f`, called with random numbers and | ||
| intervals generated by `randform`, which should return numbers in the | ||
| domain. | ||
| """ | ||
| function test_univariate(f; N=500, AD_exceptions = Dict()) | ||
| dom = domain(f) | ||
| for i in 1:500 | ||
| test_univariate_scalar(f, rand(dom), AD_exceptions = AD_exceptions) | ||
| end | ||
| for i in 1:500 | ||
| test_univariate_interval(f, random_interval(dom)) | ||
| end | ||
| for x in scalars_outside_interval(dom) | ||
| @test_throws DomainError f(x) | ||
| end | ||
| end | ||
|
|
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,114 @@ | ||
| import ForwardDiff: derivative | ||
| import Base: rand | ||
|
|
||
| """ | ||
| Return a random float in an interval (for testing). Do not use | ||
| directly when testing extrema, as these may happen with `0` | ||
| probability. | ||
| """ | ||
| rand(::RealLine, ::Type{Val{true}}) = randn() | ||
| rand(ray::PositiveRay, ::Type{Val{true}}) = ray.left + abs(randn()) | ||
| rand(ray::NegativeRay, ::Type{Val{true}}) = ray.right + abs(randn()) | ||
| rand(seg::Segment, ::Type{Val{true}}) = seg.left + width(seg) * rand() | ||
|
|
||
| function rand(x::AbstractInterval; left_prob = 0.1, right_prob = 0.1) | ||
| @argcheck left_prob + right_prob ≤ 1 | ||
| z = rand() | ||
| left, right = extrema(x) | ||
| if z < left_prob | ||
| left | ||
| elseif z > 1-right_prob | ||
| right | ||
| else | ||
| rand(x, Val{true}) | ||
| end | ||
| end | ||
|
|
||
|
|
||
| """ | ||
| Return a function that generates random intervals in the given | ||
| interval (for testing). | ||
| """ | ||
| function random_interval(x::AbstractInterval) | ||
| a = rand(x; right_prob = 0) | ||
| b = rand(x; left_prob = 0) | ||
| if a > b | ||
| a, b = b, a | ||
| end | ||
| Interval(a, b) | ||
| end | ||
|
|
||
| """ | ||
| Return a vector of values outside the interval (for testing). | ||
| """ | ||
| scalars_outside_interval(::RealLine) = [] | ||
| scalars_outside_interval(ray::PositiveRay) = ray.left - [0.1, 1.0, 2.0, Inf] | ||
| scalars_outside_interval(ray::NegativeRay) = ray.right + [0.1, 1.0, 2.0, Inf] | ||
| function scalars_outside_interval(seg::Segment) | ||
| vcat(scalars_outside_interval(PositiveRay(seg.left)), | ||
| scalars_outside_interval(NegativeRay(seg.right))) | ||
| end | ||
|
|
||
| """ | ||
| Test univariate transformation `f` with `x`. Tests for: | ||
| 1. type of the transformed value, | ||
| 2. whether it is in the range, | ||
| 3. inverse, | ||
| 4. jacobian determinant and its log using automatic differentiation. | ||
| `forwarddiff_exceptions` is a dictionary handling exceptions that ForwardDiff | ||
| cannot cope with at the moment. See eg | ||
| ## workaround for https://github.com/JuliaDiff/ForwardDiff.jl/issues/209 | ||
| """ | ||
| function test_univariate_scalar{T}(f::UnivariateTransformation, x::T; | ||
| AD_exceptions = Dict()) | ||
| y = f(x) | ||
| @test y ∈ image(f) | ||
| @test inv(f)(y) ≈ x | ||
| (y2, jac) = f(x, JAC) | ||
| @test y == y2 | ||
| expected_jac = get(AD_exceptions, x, abs(derivative(f, x))) | ||
| @test jac ≈ expected_jac | ||
| (y3, logjac) = f(x, LOGJAC) | ||
| @test y == y3 | ||
| @test logjac ≈ log(expected_jac) | ||
| end | ||
|
|
||
| # some exceptions below | ||
| logit_exceptions(t=Multiply(1.0)) = Dict(t(1.0) => Inf) | ||
|
|
||
| logistic_exceptions() = Dict(-Inf => 0.0) | ||
|
|
||
| """ | ||
| Test that univariate transformations map an interval the correct way. | ||
| """ | ||
| function test_univariate_interval(f::UnivariateTransformation, x::AbstractInterval) | ||
| y = f(x) | ||
| left, right = extrema(x) | ||
| f_left, f_right = f(left), f(right) | ||
| if f_right < f_left | ||
| f_right, f_left = f_left, f_right | ||
| end | ||
| y_left, y_right = extrema(y) | ||
| @test y_left ≈ f_left | ||
| @test y_right ≈ f_right | ||
| end | ||
|
|
||
| """ | ||
| Test univariate transformation `f`, called with random numbers and | ||
| intervals generated by `randform`, which should return numbers in the | ||
| domain. | ||
| """ | ||
| function test_univariate(f; N=500, AD_exceptions = Dict()) | ||
| dom = domain(f) | ||
| for i in 1:500 | ||
| test_univariate_scalar(f, rand(dom), AD_exceptions = AD_exceptions) | ||
| end | ||
| for i in 1:500 | ||
| test_univariate_interval(f, random_interval(dom)) | ||
| end | ||
| for x in scalars_outside_interval(dom) | ||
| @test_throws DomainError f(x) | ||
| end | ||
| end |