Dev (#213)

* Dirichlet(k::Integer, α) = Dirichlet(Fill(α, k))

* export TransformVariables as TV

* drop redundant import

* 0.0 => zero(Float64)

* drop outdated Dists.logpdf

* update StudentT

* drop redundant import

* update Uniform

* bump MeasureBase version

* reworking beta

* small update to StudentT

* basemeasure for discrete Distributions

* using LogExpFunctions => import LogExpFunctions

* quoteof(::Chain)

* prettyprinting and chain-mucking

* Some refactoring for Markov chains

* import MeasureBase: ≪

* version bound for PrettyPrinting

* copy(rng) might change its type (e.g. GLOBAL_RNG)

* tests pass

* cleaning up

* more cleanup

* big update

* get tests passing

* formatting

* oops typo

* move affine to MeasureTheory

* updating

* Val => StaticSymbol

* more fixes

* fix fix fix

* more logdesnity => logdensity_def

* more logdesnity fixes

* debugging

* formatting

* bugfixes

* working on tests

* updates

* working on tests

* tests passing!

* refactor

* working on tests

* drop static weight for now

* fix sampling from ProductMeasure{<:Base.Generator}

* tests passing!!

* more stuff

* constructor => constructorof

* constructor =? construtorof

* updates

* working on tests

* fix Dirichlet

* update Bernoulli

* working on tests

* bugfixes for RealizedSamples

* tests passing!!

* tighten down inference

* as(::PowerMeasure)

* drop type-level stuff

* using InverseFunctions.jl

* update license

* affero

* copyright

* update CI to 1.6

* xform => TV.as

* oops missed a conflict

* fix merge corruption

* typo

* fix license

* Update README.md

* merge

* enumerate instead of zip

* bugfix

* inline rand

* drop `static` from `insupport` results

* update proxies

* Move ConditionalMeasure to MeasureBase

* IfElse.ifelse(p::Bernoulli, t, f)

* IfElseMeasure

* update some base measures

* test broken :(

* fix some redundancies

* instance_type => Core.Typeof

* update testvalue for Bernoulli and Binomial

* un-break broken test (now passing)

* Fall-back `For` method for when inference fails

* drop extra spaces

* more whitespace

* bump MeasureBase dependency version

* add newline

* tidy up

* ifelse tests

* OEF newline

* avoid type piracy

* add Julia 1.7 to CI

* make Julia 1.6 happy

* approx instead of ==

* Require at least Julia 1.6

* Try Sebastian's idea test_measures ::Any[]

* Another Any[]

* Drop Likelihood test

* drop 1.7 CI (seems buggy?)

* bump version

* export likelihood

* Snedecor's F

* Gamma distribution

* more gamma stuff

* Beroulli()

* inverse Gaussian

* Getting modifed GLM.jl tests to pass

* drop pdf and logpdf

* Poisson bugfix

* update Normal(μ, σ²)

* Gamma(μ, ϕ) for GLMs

* updates for GLM support

* start on truncated

* update parameterized measures

* drop FactoredBase

* drop old LazyArrays dependency

* insupport(::Distribution)

* Left out"Dists."

* don't export `ifelse` (#192)

* Kleisli => TransitionKernel

* depend on StatsBase

* tests passing

* bump MeasureBase version

* work on truncated and censored

* improve func_string

* Simplify logdensity_def(::For, x)

* Move truncated and censored updates to separate branches

* newline

* comment out in-progress stuff

* newline

* bump version

* update formatting spec

* more formatting

* tweedie docs

* drop redundant exports

* update exports

* omega => lambda

* drop SequentialEx

* get tests passing

* add kernel tests

* gitignore

* better `Pretty.tile` for Affine and AffineTransforms

* formatting

* kleisli => kernel

* update tile(::For)

* update Compat version

* bump MB version

* update gamma

* Let's come back to InverseGaussian

* CI on 1.7

* update IfElse

* formatting

* update product

* @kwstruct Bernoulli(logitp)

* Base.size(r::RealizedSamples)

* cdf(::Affine, x)

* working on Aqua and JET fixes

* formatting

* So many `rand` methods, do we really need all of these?

* bump version

* bugfix

* bugfix

* loosen constraint

Project.toml

@@ -1,7 +1,7 @@
 name = "MeasureTheory"
 uuid = "eadaa1a4-d27c-401d-8699-e962e1bbc33b"
 authors = ["Chad Scherrer <[email protected]> and contributors"]
-version = "0.16.3"
+version = "0.16.4"
 
 [deps]
 Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
@@ -18,6 +18,7 @@ Infinities = "e1ba4f0e-776d-440f-acd9-e1d2e9742647"
 InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
 InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112"
 KeywordCalls = "4d827475-d3e4-43d6-abe3-9688362ede9f"
+LazyArrays = "5078a376-72f3-5289-bfd5-ec5146d43c02"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LogExpFunctions = "2ab3a3ac-af41-5b50-aa03-7779005ae688"
 MLStyle = "d8e11817-5142-5d16-987a-aa16d5891078"
@@ -53,11 +54,12 @@ IfElse = "0.1"
 Infinities = "0.1"
 InverseFunctions = "0.1"
 KeywordCalls = "0.2"
+LazyArrays = "0.22"
 LogExpFunctions = "0.3.3"
 MLStyle = "0.4"
 MacroTools = "0.5"
 MappedArrays = "0.4"
-MeasureBase = "0.10"
+MeasureBase = "0.12"
 NamedTupleTools = "0.13, 0.14"
 NestedTuples = "0.3"
 PositiveFactorizations = "0.2"

src/combinators/affine.jl

@@ -1,5 +1,6 @@
 export Affine, AffineTransform
 using LinearAlgebra
+import Base
 
 const AFFINEPARS = [
     (:μ, :σ)
@@ -43,8 +44,8 @@ Base.size(f::AffineTransform{(:λ,)}) = size(f.λ)
 
 LinearAlgebra.rank(f::AffineTransform{(:σ,)})    = rank(f.σ)
 LinearAlgebra.rank(f::AffineTransform{(:λ,)})    = rank(f.λ)
-LinearAlgebra.rank(f::AffineTransform{(:μ,:σ,)}) = rank(f.σ)
-LinearAlgebra.rank(f::AffineTransform{(:μ,:λ,)}) = rank(f.λ)
+LinearAlgebra.rank(f::AffineTransform{(:μ, :σ)}) = rank(f.σ)
+LinearAlgebra.rank(f::AffineTransform{(:μ, :λ)}) = rank(f.λ)
 
 function Base.size(f::AffineTransform{(:μ,)})
     (n,) = size(f.μ)
@@ -181,7 +182,7 @@ Affine(nt::NamedTuple, μ::AbstractMeasure) = affine(nt, μ)
 
 Affine(nt::NamedTuple) = affine(nt)
 
-parent(d::Affine) = getfield(d, :parent)
+Base.parent(d::Affine) = getfield(d, :parent)
 
 function params(μ::Affine)
     nt1 = getfield(getfield(μ, :f), :par)
@@ -262,6 +263,12 @@ end
     weightedmeasure(-logjac(d), OrthoLebesgue(params(d)))
 end
 
+@inline function basemeasure(
+    d::MeasureTheory.Affine{N,L,Tuple{A}},
+) where {N,L<:MeasureBase.Lebesgue,A<:AbstractArray}
+    weightedmeasure(-logjac(d), OrthoLebesgue(params(d)))
+end
+
 @inline function basemeasure(
     d::Affine{N,M,Tuple{A1,A2}},
 ) where {N,M,A1<:AbstractArray,A2<:AbstractArray}
@@ -328,3 +335,7 @@ end
 @inline function insupport(d::Affine, x)
     insupport(d.parent, inverse(d.f)(x))
 end
+
+@inline function Distributions.cdf(d::Affine, x)
+    cdf(parent(d), inverse(d.f)(x))
+end

src/combinators/exponential-families.jl

@@ -1,4 +1,5 @@
 export ExponentialFamily
+using LazyArrays
 
 @concrete terse struct ExponentialFamily <: AbstractTransitionKernel
     support_contains
@@ -16,10 +17,10 @@ function ExponentialFamily(support_contains, base, mdim, pdim, t, a)
     return ExponentialFamily(support_contains, base, mdim, pdim, t, I, a)
 end
 
-function MeasureBase.powermeasure(fam::ExponentialFamily, dims::NTuple{N,I}) where {N,I}
+function MeasureBase.powermeasure(fam::ExponentialFamily, dims::NTuple)
     support_contains(x) = all(xj -> fam.support_contains(xj), x)
     t = Tuple((y -> f.(y) for f in fam.t))
-    a(η) = BroadcastArray(fam.a, η)
+    a(η) = LazyArrays.BroadcastArray(fam.a, η)
     p = prod(dims)
     ExponentialFamily(
         support_contains,
@@ -32,6 +33,8 @@ function MeasureBase.powermeasure(fam::ExponentialFamily, dims::NTuple{N,I}) whe
     )
 end
 
+powermeasure(fam::ExponentialFamily, ::Tuple{}) = fam
+
 @concrete terse struct ExpFamMeasure <: AbstractMeasure
     fam
     η # instantiated to a value

src/distproxy.jl

@@ -25,7 +25,23 @@ for m in keys(PROXIES)
         @eval begin
             import $m: $f
             export $f
-            $m.$f(d::AbstractMeasure, args...) = $m.$f(MeasureTheory.proxy(d), args...)
         end
     end
 end
+
+entropy(m::AbstractMeasure, b::Real) = entropy(proxy(m), b)
+mean(m::AbstractMeasure) = mean(proxy(m))
+std(m::AbstractMeasure) = std(proxy(m))
+var(m::AbstractMeasure) = var(proxy(m))
+quantile(m::AbstractMeasure, q) = quantile(proxy(m), q)
+
+for f in [
+    :cdf
+    :ccdf
+    :logcdf
+    :logccdf
+]
+    @eval begin
+        $f(d::AbstractMeasure, args...) = $f(MeasureTheory.proxy(d), args...)
+    end
+end

src/parameterized/inverse-gaussian.jl

@@ -48,6 +48,6 @@ function logdensity_def(d::InverseGaussian{(:μ, :ϕ)}, x)
 end
 
 function basemeasure(d::InverseGaussian{(:μ, :ϕ)})
-    ℓ = static(-0.5) * (static(log2π) + log(d.ϕ))
+    ℓ = static(-0.5) * (static(float(log2π)) + log(d.ϕ))
     weightedmeasure(ℓ, Lebesgue())
 end

src/parameterized/mvnormal.jl

@@ -22,14 +22,14 @@ export MvNormal
 
 as(d::MvNormal{(:μ,)}) = as(Array, length(d.μ))
 
-as(d::MvNormal{(:Σ,),Tuple{C}}) where {C<:Cholesky}    = as(Array, size(d.Σ, 1))
-as(d::MvNormal{(:Λ,),Tuple{C}}) where {C<:Cholesky}    = as(Array, size(d.Λ, 1))
-as(d::MvNormal{(:μ, :Σ),Tuple{C}}) where {C<:Cholesky} = as(Array, size(d.Σ, 1))
-as(d::MvNormal{(:μ, :Λ),Tuple{C}}) where {C<:Cholesky} = as(Array, size(d.Λ, 1))
+as(d::MvNormal{(:Σ,),Tuple{C}}) where {C<:Cholesky} = as(Array, size(d.Σ, 1))
+as(d::MvNormal{(:Λ,),Tuple{C}}) where {C<:Cholesky} = as(Array, size(d.Λ, 1))
+as(d::MvNormal{(:μ, :Σ),<:Tuple{T,C}}) where {T,C<:Cholesky} = as(Array, size(d.Σ, 1))
+as(d::MvNormal{(:μ, :Λ),<:Tuple{T,C}}) where {T,C<:Cholesky} = as(Array, size(d.Λ, 1))
 
 function as(d::MvNormal{(:σ,),Tuple{M}}) where {M<:Triangular}
     σ = d.σ
-    if @inbounds all(i -> σ[i] > 0, diagind(σ))
+    if @inbounds all(i -> σ[i] ≠ 0, diagind(σ))
         return as(Array, size(σ, 1))
     else
         @error "Not implemented yet"
@@ -49,7 +49,7 @@ for N in setdiff(AFFINEPARS, [(:μ,)])
     @eval begin
         function as(d::MvNormal{$N})
             p = proxy(d)
-            if rank(getfield(p,:f)) == only(supportdim(d))
+            if rank(getfield(p, :f)) == only(supportdim(d))
                 return as(Array, supportdim(d))
             else
                 @error "Not yet implemented"
@@ -61,13 +61,13 @@ end
 supportdim(d::MvNormal) = supportdim(params(d))
 
 supportdim(nt::NamedTuple{(:Σ,)}) = size(nt.Σ, 1)
-supportdim(nt::NamedTuple{(:μ,:Σ)}) = size(nt.Σ, 1)
+supportdim(nt::NamedTuple{(:μ, :Σ)}) = size(nt.Σ, 1)
 supportdim(nt::NamedTuple{(:Λ,)}) = size(nt.Λ, 1)
-supportdim(nt::NamedTuple{(:μ,:Λ)}) = size(nt.Λ, 1)
+supportdim(nt::NamedTuple{(:μ, :Λ)}) = size(nt.Λ, 1)
 
 @useproxy MvNormal
 
-for N in [(:Σ,), (:μ,:Σ), (:Λ,), (:μ,:Λ)]
+for N in [(:Σ,), (:μ, :Σ), (:Λ,), (:μ, :Λ)]
     @eval basemeasure_depth(d::MvNormal{$N}) = static(2)
 end
 
@@ -78,7 +78,15 @@ rand(rng::AbstractRNG, ::Type{T}, d::MvNormal) where {T} = rand(rng, T, proxy(d)
 insupport(d::MvNormal, x) = insupport(proxy(d), x)
 
 # Note: (C::Cholesky).L may or may not make a copy, depending on C.uplo, which is not included in the type
-@inline proxy(d::MvNormal{(:Σ,),Tuple{C}}) where {C<:Cholesky}    = affine((σ = d.Σ.L,), Normal()^supportdim(d))
-@inline proxy(d::MvNormal{(:Λ,),Tuple{C}}) where {C<:Cholesky}    = affine((λ = d.Λ.L,), Normal()^supportdim(d))
-@inline proxy(d::MvNormal{(:μ, :Σ),Tuple{C}}) where {C<:Cholesky} = affine((μ = d.μ, σ = d.Σ.L), Normal()^supportdim(d))
-@inline proxy(d::MvNormal{(:μ, :Λ),Tuple{C}}) where {C<:Cholesky} = affine((μ = d.μ, λ = d.Λ.L), Normal()^supportdim(d))
+@inline function proxy(d::MvNormal{(:Σ,),Tuple{C}}) where {C<:Cholesky}
+    affine((σ = d.Σ.L,), Normal()^supportdim(d))
+end
+@inline function proxy(d::MvNormal{(:Λ,),Tuple{C}}) where {C<:Cholesky}
+    affine((λ = d.Λ.L,), Normal()^supportdim(d))
+end
+@inline function proxy(d::MvNormal{(:μ, :Σ),Tuple{T,C}}) where {T,C<:Cholesky}
+    affine((μ = d.μ, σ = d.Σ.L), Normal()^supportdim(d))
+end
+@inline function proxy(d::MvNormal{(:μ, :Λ),Tuple{T,C}}) where {T,C<:Cholesky}
+    affine((μ = d.μ, λ = d.Λ.L), Normal()^supportdim(d))
+end

src/parameterized/normal.jl

@@ -146,7 +146,7 @@ HalfNormal(σ) = HalfNormal((σ = σ,))
 end
 
 @inline function basemeasure(d::Normal{(:σ²,)})
-    ℓ = static(-0.5) * (static(log2π) + log(d.σ²))
+    ℓ = static(-0.5) * (static(float(log2π)) + log(d.σ²))
     weightedmeasure(ℓ, Lebesgue())
 end
 

src/resettable-rng.jl

@@ -78,6 +78,57 @@ function Random.Sampler(
     return Random.Sampler(r.rng, s, r)
 end
 
-function Base.rand(r::ResettableRNG, sp::Random.Sampler)
-    rand(r.rng, sp)
-end
+# UIntBitsTypes = [UInt128, UInt16, UInt32, UInt64, UInt8]
+# IntBitsTypes = [Int128, Int16, Int32, Int64, Int8, UInt128, UInt16, UInt32, UInt64, UInt8]
+# FloatBitsTypes = [Float16, Float32, Float64]
+
+# for I in IntBitsTypes
+#     for T in [
+#         Random.SamplerTrivial{Random.UInt104Raw{I}}
+#         Random.SamplerTrivial{Random.UInt10Raw{I}}
+#     ]
+#         @eval begin
+#             function Base.rand(r::ResettableRNG, sp::$T)
+#                 rand(r.rng, sp)
+#             end
+#         end
+#     end
+# end
+
+# for U in UIntBitsTypes
+#     for I in IntBitsTypes
+#         for T in [
+#             Random.SamplerRangeInt{T,U} where {T<:Union{IntBitsTypes...}}
+#             Random.SamplerRangeFast{U,I}
+#         ]
+#             @eval begin
+#                 function Base.rand(r::ResettableRNG, sp::$T)
+#                     rand(r.rng, sp)
+#                 end
+#             end
+#         end
+#     end
+# end
+
+# for T in [
+#     Random.Sampler
+#     Random.SamplerBigInt
+#     Random.SamplerTag{<:Set,<:Random.Sampler}
+#     # Random.SamplerTrivial{Random.CloseOpen01{T}} where {T<:FloatBitsTypes}
+#     # Random.SamplerTrivial{Random.UInt23Raw{UInt32}}
+#     Random.UniformT
+#     Random.SamplerSimple{T,S,E} where {E,S,T<:Tuple}
+#     Random.SamplerType{T} where {T<:AbstractChar}
+#     Random.SamplerTrivial{Tuple{A}} where {A}
+#     Random.SamplerSimple{Tuple{A,B,C},S,E} where {E,S,A,B,C}
+#     Random.SamplerSimple{<:AbstractArray,<:Random.Sampler}
+#     Random.Masked
+#     Random.SamplerSimple{BitSet,<:Random.Sampler}
+#     Random.SamplerTrivial{<:Random.UniformBits{T},E} where {E,T}
+# ]
+#     @eval begin
+#         function Base.rand(r::ResettableRNG, sp::$T)
+#             rand(r.rng, sp)
+#         end
+#     end
+# end

test/runtests.jl

@@ -12,7 +12,18 @@ using MeasureBase.Interface
 using MeasureTheory: kernel
 using Aqua
 using IfElse
-Aqua.test_all(MeasureTheory; ambiguities = false, unbound_args = false)
+
+# Aqua._test_ambiguities(
+#     Aqua.aspkgids(MeasureTheory);
+#     exclude = [Random.AbstractRNG],
+#     # packages::Vector{PkgId};
+#     # color::Union{Bool, Nothing} = nothing,
+#     # exclude::AbstractArray = [],
+#     # # Options to be passed to `Test.detect_ambiguities`:
+#     # detect_ambiguities_options...,
+# )
+
+Aqua.test_all(MeasureBase; ambiguities = false)
 
 function draw2(μ)
     x = rand(μ)
@@ -23,8 +34,8 @@ function draw2(μ)
     return (x, y)
 end
 
-x = randn(10,3)
-Σ = cholesky(x'*x)
+x = randn(10, 3)
+Σ = cholesky(x' * x)
 Λ = cholesky(inv(Σ))
 σ = MeasureTheory.getL(Σ)
 λ = MeasureTheory.getL(Λ)
@@ -611,7 +622,7 @@ end
 @testset "IfElseMeasure" begin
     p = rand()
     x = randn()
-    
+
     @test let
         a = logdensityof(IfElse.ifelse(Bernoulli(p), Normal(), Normal()), x)
         b = logdensityof(Normal(), x)