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additionally filter specific non-relevant warning messages with JET.test_package
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| Original file line number | Diff line number | Diff line change |
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| ```@meta | ||
| CurrentModule = DistributionFits | ||
| ``` | ||
|
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| # Multivariate LogNormal distribution | ||
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| Can be fitted to a given mean, provided the Covariance of the underlying | ||
| normal distribution. | ||
|
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| ```@docs | ||
| fit_mean_Σ(::Type{MvLogNormal}, mean::AbstractVector{T1}, Σ::AbstractMatrix{T2}) where {T1 <:Real,T2 <:Real} | ||
| ``` | ||
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| ```jldoctest; output = false, setup = :(using DistributionFits) | ||
| Σ = hcat([0.6,0.02],[0.02,0.7]) | ||
| μ = [1.2,1.3] | ||
| d = MvLogNormal(μ, Σ) | ||
| d2 = fit_mean_Σ(MvLogNormal, mean(d), Σ) | ||
| isapprox(d2, d, rtol = 1e6) | ||
| # output | ||
| true | ||
| ``` |
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| """ | ||
| fit_mean_Σ(::Type{<:Distribution}, mean, Σ) | ||
| Fit a Distribution to mean and uncertainty quantificator Σ. | ||
| The meaning of `Σ` depends on the type of distribution: | ||
| - `MvLogNormal`: the Covariancematrix of the associated normal distribution | ||
| - `LogNormal`: the scale parameter, i.e. the standard deviation at log-scale, `σ` | ||
| """ | ||
| function fit_mean_Σ(::Type{MvLogNormal}, mean::AbstractVector{T1}, Σ::AbstractMatrix{T2}) where {T1 <:Real,T2 <:Real} | ||
| _T = promote_type(T1, T2) | ||
| fit_mean_Σ(MvLogNormal{_T}, mean, Σ) | ||
| end | ||
| function fit_mean_Σ(::Type{MvLogNormal{T}}, mean::AbstractVector{T1}, Σ::AbstractMatrix{T2}) where {T, T1 <:Real,T2 <:Real} | ||
| meanT = T1 == T ? mean : begin | ||
| meanT = similar(mean, T) | ||
| meanT .= mean | ||
| end | ||
| ΣT = T2 == T ? Σ : begin | ||
| ΣT = similar(Σ, T) | ||
| ΣT .= Σ | ||
| end | ||
| σ2 = diag(ΣT) | ||
| μ = log.(meanT) .- σ2 ./ 2 | ||
| MvLogNormal(μ, ΣT) | ||
| end |
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| ##### Specific distributions ##### | ||
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| for fname in [ | ||
| # "dirichlet.jl", | ||
| # "multinomial.jl", | ||
| # "dirichletmultinomial.jl", | ||
| # "jointorderstatistics.jl", | ||
| # "mvnormal.jl", | ||
| # "mvnormalcanon.jl", | ||
| # "mvlogitnormal.jl", | ||
| "mvlognormal.jl", | ||
| # "mvtdist.jl", | ||
| # "vonmisesfisher.jl" | ||
| ] | ||
| include(joinpath("multivariate", fname)) | ||
| end |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,17 @@ | ||
| using PDMats | ||
| using DistributionFits | ||
| using Test | ||
|
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| @testset "fit_mean_Σ" begin | ||
| Σ = PDiagMat([0.6,0.7]) | ||
| μ = [1.2,1.2] | ||
| d = MvLogNormal(μ, Σ) | ||
| mean(d) | ||
| d2 = fit_mean_Σ(MvLogNormal, mean(d), params(d)[2]) | ||
| @test d2 ≈ d rtol = 1e6 | ||
| # | ||
| # Float32 | ||
| d2_f32 = fit_mean_Σ(MvLogNormal, Float32.(mean(d)), Float32.(params(d)[2])) | ||
| @test d2 ≈ d rtol = 1e6 | ||
| @test partype(d2_f32) === Float32 | ||
| end; |
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| using DistributionFits | ||
| using Test | ||
| using Random: Random | ||
| using LoggingExtras | ||
| using Optim | ||
|
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| pkgdir = dirname(dirname(pathof(DistributionFits))) | ||
| testdir = joinpath(pkgdir, "test") | ||
| include(joinpath(testdir,"testutils.jl")) | ||
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| function test_univariate_fits(d, D = typeof(d)) | ||
| @testset "fit moments" begin | ||
| if !occursin("fit(::Type{D}", | ||
| string(first(methods(fit, (Type{typeof(d)}, AbstractMoments))))) | ||
| m = Moments(mean(d), var(d)) | ||
| d_fit = fit(D, m) | ||
| @test d ≈ d_fit | ||
| @test partype(d_fit) == partype(d) | ||
| end | ||
| end | ||
| @testset "fit two quantiles" begin | ||
| qpl = @qp_l(quantile(d, 0.05)) | ||
| qpu = @qp_u(quantile(d, 0.95)) | ||
| d_fit = fit(D, qpl, qpu) | ||
| @test quantile.(d, [qpl.p, qpu.p]) ≈ [qpl.q, qpu.q] | ||
| d_fit = fit(D, qpl, qpu) | ||
| @test quantile.(d, [qpl.p, qpu.p]) ≈ [qpl.q, qpu.q] | ||
| d_fit = fit(D, qpu, qpl) # sort | ||
| @test quantile.(d, [qpl.p, qpu.p]) ≈ [qpl.q, qpu.q] | ||
| @test partype(d_fit) == partype(d) | ||
| end | ||
| @testset "fit two quantiles, function version" begin | ||
| P = partype(d) | ||
| qpl = qp_l(P(quantile(d, 0.05))) | ||
| qpu = qp_u(P(quantile(d, 0.95))) | ||
| d_fit = fit(D, qpl, qpu) | ||
| @test quantile.(d, [qpl.p, qpu.p]) ≈ [qpl.q, qpu.q] | ||
| d_fit = fit(D, qpl, qpu) | ||
| @test quantile.(d, [qpl.p, qpu.p]) ≈ [qpl.q, qpu.q] | ||
| d_fit = fit(D, qpu, qpl) # sort | ||
| @test quantile.(d, [qpl.p, qpu.p]) ≈ [qpl.q, qpu.q] | ||
| @test partype(d_fit) == partype(d) | ||
| end | ||
| @testset "typeof mean, mode equals partype" begin | ||
| if !(d isa Gamma && first(params(d)) < 1) | ||
| @test mean(d) isa partype(d) | ||
| @test mode(d) isa partype(d) | ||
| end | ||
| end | ||
| @testset "quantile is of eltype" begin | ||
| # quantile still Float64 for Normal of eltype Float32 | ||
| if d isa Normal && eltype(d) != Float64 | ||
| @test_broken quantile(d, 0.1) isa eltype(d) | ||
| else | ||
| @test quantile(d, 0.1) isa eltype(d) | ||
| end | ||
| # quantile is sample-like: stick to eltype - special of normal | ||
| # broken, because quantile Normal{Float32} returns Float32 | ||
| # but eltype(D{Float32}) is Float64 | ||
| if d isa Union{LogNormal, LogitNormal, Exponential, Laplace, Weibull} && | ||
| partype(d) != eltype(d) | ||
| @test_broken quantile(d, 0.1f0) isa eltype(d) | ||
| else | ||
| @test quantile(d, 0.1f0) isa eltype(d) | ||
| end | ||
| end | ||
| @testset "fit to quantilepoint and mean" begin | ||
| if !occursin("fit_mean_quantile(::Type{D}", | ||
| string(first(methods(fit_mean_quantile, | ||
| (Type{typeof(d)}, partype(d), QuantilePoint))))) | ||
| m = log(mean(d)) | ||
| qp = @qp_u(quantile(d, 0.95)) | ||
| logger = d isa Exponential ? MinLevelLogger(current_logger(), Logging.Error) : | ||
| current_logger() | ||
| with_logger(logger) do | ||
| d_fit = fit_mean_quantile(D, mean(d), qp) | ||
| @test d_fit ≈ d | ||
| @test partype(d_fit) == partype(d) | ||
| d_fit = fit(D, mean(d), qp, Val(:mean)) | ||
| @test d_fit ≈ d | ||
| @test partype(d_fit) == partype(d) | ||
| # with lower quantile | ||
| qp = @qp_l(quantile(d, 0.05)) | ||
| d_fit = fit_mean_quantile(D, mean(d), qp) | ||
| @test d_fit ≈ d | ||
| @test partype(d_fit) == partype(d) | ||
| end | ||
| # very close to mean can give very different results: | ||
| # qp = @qp(mean(d)-1e-4,0.95) | ||
| # d_fit = fit_mean_quantile(D, mean(d), qp) | ||
| # @test mean(d_fit) ≈ mean(d) && quantile(d_fit, qp.p) ≈ qp.q | ||
| end | ||
| end | ||
| @testset "fit to quantilepoint and mode" begin | ||
| if !(d isa Gamma && first(params(d)) < 1) && | ||
| !(d isa Weibull) | ||
| qp = qp_u(quantile(d, 0.95)) | ||
| d_fit = fit_mode_quantile(D, mode(d), qp) | ||
| @test d_fit≈d atol=0.1 | ||
| d_fit = fit(D, mode(d), qp, Val(:mode)) | ||
| @test d_fit≈d atol=0.1 | ||
| @test partype(d_fit) == partype(d) | ||
| # with lower quantile | ||
| qp = qp_ll(quantile(d, 0.025)) | ||
| d_fit = fit(D, mode(d), qp, Val(:mode)) | ||
| @test mode(d_fit) ≈ mode(d) | ||
| @test quantile(d_fit, qp.p)≈qp.q atol=0.01 | ||
| @test partype(d_fit) == partype(d) | ||
| end | ||
| end | ||
| @testset "fit to quantilepoint and median" begin | ||
| qp = @qp_u(quantile(d, 0.95)) | ||
| logger = d isa Exponential ? MinLevelLogger(current_logger(), Logging.Error) : | ||
| current_logger() | ||
| with_logger(logger) do | ||
| d_fit = fit(D, median(d), qp, Val(:median)) | ||
| @test d_fit ≈ d | ||
| @test partype(d_fit) == partype(d) | ||
| end | ||
| end | ||
| end | ||
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| const tests = [ | ||
| "mvlognormal", | ||
| ] | ||
| #tests = ["mvlognormal"] | ||
|
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| for t in tests | ||
| @testset "Test $t" begin | ||
| Random.seed!(345679) | ||
| include(joinpath(testdir,"multivariate","$t.jl")) | ||
| end | ||
| end |
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