0.19 (#240)

* First steps toward factoring out TransformVariables * factor out exponential-families and tweedie * AffinePushfwd <: AbstractPushforward * don't export `mydot` * moving things around * drop TV functionality * bugfix * bugfix * format * factor out Chain (let's move it to a new package) * fix type instability * update Bernoulli * import massof * bugfix * add some methods * Revert "First steps toward factoring out TransformVariables" This reverts commit c78c166. * smfinv => invsmf * update MeasureBase version * add some smfs * edits * fix typo * fix laplace * fix bernoulli * fix beta * fix StudentT * Fix Uniform * fix Gumbel * fix Dirichlet * minor Dirichlet update * more Dirichlet * drop as(m::DistributionMeasures.DistributionMeasure) (for now) * drop tests for Chain * drop DistributionMeasures dependency (for now) * fix cauchy * fixing Binomial * small updates * update * updates * drop brokwn measures * fix normal * asparams methods * update tests * smfAD macro * update tests * smf(::Normal) * move import * add ForwardDiff * dep constraint * fix tests * smf for affine pushfwd * update tests * update test name * tests * tests * update StudentT * tests * Some `For` updates * optimize `Normal` * update insupport for Normal * update basemeasure for For measures * some affine updates * some fixes * bump version * formatting * oops didn't mean to add that * format * MeasureBase 0.14 + other updates * compatibility fixes * updates * more fixes * fixes * more fixes * tests passing * bump version * drop breakage CI for now * update Julia versions for CI * Drop commented-out code
JuliaMath · Aug 22, 2023 · 84440f8 · 84440f8 · cscherrer · Aug 22, 2023
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diff --git a/.github/workflows/Breakage.yml b/.github/workflows/Breakage.yml
@@ -15,8 +15,8 @@ jobs:
       fail-fast: false
       matrix:
         pkg: [
-          "cscherrer/Soss.jl",
-          "mschauer/Mitosis.jl"
+          # "cscherrer/Soss.jl",
+          # "mschauer/Mitosis.jl"
         ]
         pkgversion: [latest, stable]
 

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
@@ -22,8 +22,8 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.6'
-          - '1.7'
+          - '1.8'
+          - '1.9'
         os:
           - ubuntu-latest
           - macOS-latest

diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "MeasureTheory"
 uuid = "eadaa1a4-d27c-401d-8699-e962e1bbc33b"
 authors = ["Chad Scherrer <chad.scherrer@gmail.com> and contributors"]
-version = "0.18.4"
+version = "0.19.0"
 
 [deps]
 Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
@@ -10,16 +10,15 @@ Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
 ConcreteStructs = "2569d6c7-a4a2-43d3-a901-331e8e4be471"
 ConstructionBase = "187b0558-2788-49d3-abe0-74a17ed4e7c9"
 DensityInterface = "b429d917-457f-4dbc-8f4c-0cc954292b1d"
-DistributionMeasures = "35643b39-bfd4-4670-843f-16596ca89bf3"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 DynamicIterators = "6c76993d-992e-5bf1-9e63-34920a5a5a38"
 FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
+ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 IfElse = "615f187c-cbe4-4ef1-ba3b-2fcf58d6d173"
 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"
@@ -47,30 +46,29 @@ Compat = "3.42, 4"
 ConcreteStructs = "0.2"
 ConstructionBase = "1.3"
 DensityInterface = "0.4"
-DistributionMeasures = "0.2"
 Distributions = "0.25"
 DynamicIterators = "0.4"
-FillArrays = "0.12, 0.13"
+FillArrays = "1"
+ForwardDiff = "0.10"
 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 = "0.13"
+MeasureBase = "0.14"
 NamedTupleTools = "0.13, 0.14"
 PositiveFactorizations = "0.2"
 PrettyPrinting = "0.3, 0.4"
 Reexport = "1"
 SpecialFunctions = "1, 2"
-Static = "0.5, 0.6"
+Static = "0.8"
 StaticArraysCore = "1"
-StatsBase = "0.32, 0.33"
+StatsBase = "0.34"
 StatsFuns = "0.9, 1"
-TransformVariables = "0.5, 0.6, 0.7"
+TransformVariables = "0.8"
 Tricks = "0.1"
 julia = "1.6"
 

diff --git a/src/MeasureTheory.jl b/src/MeasureTheory.jl
@@ -8,12 +8,9 @@ using MLStyle
 import TransformVariables
 const TV = TransformVariables
 
-using DistributionMeasures
 using TransformVariables: asℝ₊, as𝕀, asℝ, transform
 
 import Base
-import Distributions
-const Dists = Distributions
 
 export TV
 export transform
@@ -27,14 +24,12 @@ export Lebesgue
 export ℝ, ℝ₊, 𝕀
 export ⊙
 export SpikeMixture
-export CountingMeasure
 export TrivialMeasure
 export Likelihood
 export testvalue
 export basekernel
 
 using Infinities
-using DynamicIterators
 using KeywordCalls
 using ConstructionBase
 using Accessors
@@ -58,9 +53,12 @@ import MeasureBase:
     paramnames,
     ∫,
     𝒹,
-    ∫exp
+    ∫exp,
+    smf,
+    invsmf,
+    massof
 import MeasureBase: ≪
-using MeasureBase: BoundedInts, BoundedReals, CountingMeasure, IntegerDomain, IntegerNumbers
+using MeasureBase: BoundedInts, BoundedReals, CountingBase, IntegerDomain, IntegerNumbers
 using MeasureBase: weightedmeasure, restrict
 using MeasureBase: AbstractTransitionKernel
 
@@ -97,13 +95,16 @@ using MeasureBase: kernel
 using MeasureBase: Returns
 import MeasureBase: proxy, @useproxy
 import MeasureBase: basemeasure_depth
-using MeasureBase: LebesgueMeasure
+using MeasureBase: LebesgueBase
 
 import DensityInterface: logdensityof
 import DensityInterface: densityof
 import DensityInterface: DensityKind
 using DensityInterface
 
+using ForwardDiff
+using ForwardDiff: Dual
+
 gentype(μ::AbstractMeasure) = typeof(testvalue(μ))
 
 # gentype(μ::AbstractMeasure) = gentype(basemeasure(μ))
@@ -117,20 +118,22 @@ xlogy(x, y) = x * log(y)
 xlog1py(x::Number, y::Number) = LogExpFunctions.xlog1py(x, y)
 xlog1py(x, y) = x * log(1 + y)
 
+using MeasureBase: Φ, Φinv
 as(args...; kwargs...) = TV.as(args...; kwargs...)
 
+# Type piracy until https://github.com/JuliaMath/MeasureBase.jl/issues/127 is fixed
+MeasureBase.rand(::FixedRNG, ::Type{Bool}) = true
+
 include("utils.jl")
 include("const.jl")
 include("combinators/for.jl")
-# include("traits.jl")
-include("parameterized.jl")
 
 include("macros.jl")
 include("combinators/affine.jl")
 include("combinators/weighted.jl")
 include("combinators/product.jl")
 include("combinators/transforms.jl")
-include("combinators/exponential-families.jl")
+# include("combinators/exponential-families.jl")
 include("resettable-rng.jl")
 include("realized.jl")
 include("combinators/chain.jl")
@@ -165,5 +168,7 @@ include("combinators/ifelse.jl")
 include("transforms/corrcholesky.jl")
 include("transforms/ordered.jl")
 
+include("parameterized.jl")
+
 include("distproxy.jl")
 end # module
diff --git a/src/combinators/affine.jl b/src/combinators/affine.jl
@@ -128,7 +128,7 @@ logjac(f::AffineTransform{(:μ, :σ)}) = logjac(f.σ)
 logjac(f::AffineTransform{(:μ, :λ)}) = -logjac(f.λ)
 logjac(f::AffineTransform{(:σ,)}) = logjac(f.σ)
 logjac(f::AffineTransform{(:λ,)}) = -logjac(f.λ)
-logjac(f::AffineTransform{(:μ,)}) = 0.0
+logjac(f::AffineTransform{(:μ,)}) = static(0.0)
 
 ###############################################################################
 
@@ -161,7 +161,7 @@ basemeasure(d::OrthoLebesgue) = d
 
 logdensity_def(::OrthoLebesgue, x) = static(0.0)
 
-struct AffinePushfwd{N,M,T} <: AbstractMeasure
+struct AffinePushfwd{N,M,T} <: MeasureBase.AbstractPushforward
     f::AffineTransform{N,T}
     parent::M
 end
@@ -172,6 +172,10 @@ function Pretty.tile(d::AffinePushfwd)
     Pretty.list_layout([pars, Pretty.tile(d.parent)]; prefix = :AffinePushfwd)
 end
 
+@inline MeasureBase.transport_origin(d::AffinePushfwd) = d.parent
+@inline MeasureBase.to_origin(d::AffinePushfwd, y) = inverse(getfield(d, :f))(y)
+@inline MeasureBase.from_origin(d::AffinePushfwd, x) = getfield(d, :f)(x)
+
 @inline function testvalue(d::AffinePushfwd)
     f = getfield(d, :f)
     z = testvalue(parent(d))
@@ -282,7 +286,7 @@ end
 @inline function basemeasure(d::AffinePushfwd{N,L}) where {N,L<:Lebesgue}
     weightedmeasure(-logjac(d), d.parent)
 end
-@inline function basemeasure(d::AffinePushfwd{N,L}) where {N,L<:LebesgueMeasure}
+@inline function basemeasure(d::AffinePushfwd{N,L}) where {N,L<:LebesgueBase}
     weightedmeasure(-logjac(d), d.parent)
 end
 
@@ -313,19 +317,6 @@ supportdim(nt::NamedTuple{(:σ,),T}) where {T}    = colsize(nt.σ)
 supportdim(nt::NamedTuple{(:λ,),T}) where {T}    = rowsize(nt.λ)
 supportdim(nt::NamedTuple{(:μ,),T}) where {T}    = size(nt.μ)
 
-asparams(::AffinePushfwd, ::StaticSymbol{:μ}) = asℝ
-asparams(::AffinePushfwd, ::StaticSymbol{:σ}) = asℝ₊
-asparams(::Type{A}, ::StaticSymbol{:μ}) where {A<:AffinePushfwd} = asℝ
-asparams(::Type{A}, ::StaticSymbol{:σ}) where {A<:AffinePushfwd} = asℝ₊
-
-function asparams(d::AffinePushfwd{N,M,T}, ::StaticSymbol{:μ}) where {N,M,T<:AbstractArray}
-    as(Array, asℝ, size(d.μ))
-end
-
-function asparams(d::AffinePushfwd{N,M,T}, ::StaticSymbol{:σ}) where {N,M,T<:AbstractArray}
-    as(Array, asℝ, size(d.σ))
-end
-
 function Base.rand(rng::Random.AbstractRNG, ::Type{T}, d::AffinePushfwd) where {T}
     z = rand(rng, T, parent(d))
     f = getfield(d, :f)
@@ -336,8 +327,8 @@ end
     insupport(d.parent, inverse(d.f)(x))
 end
 
-@inline function Distributions.cdf(d::AffinePushfwd, x)
-    cdf(parent(d), inverse(d.f)(x))
+@inline function MeasureBase.smf(d::AffinePushfwd, x)
+    smf(parent(d), inverse(d.f)(x))
 end
 
 @inline function mean(d::AffinePushfwd)
@@ -364,3 +355,67 @@ end
 @inline function std(d::AffinePushfwd{(:μ, :λ)})
     std(parent(d)) / d.λ
 end
+
+###############################################################################
+# smf
+
+@inline function smf(d::AffinePushfwd{(:μ,)}, x)
+    f = getfield(d, :f)
+    smf(parent(d), inverse(f)(x))
+end
+
+@inline function smf(d::AffinePushfwd{(:μ, :σ)}, x)
+    f = getfield(d, :f)
+    p = smf(parent(d), inverse(f)(x))
+    d.σ > 0 ? p : one(p) - p
+end
+
+@inline function smf(d::AffinePushfwd{(:σ,)}, x)
+    f = getfield(d, :f)
+    p = smf(parent(d), inverse(f)(x))
+    d.σ > 0 ? p : one(p) - p
+end
+
+@inline function smf(d::AffinePushfwd{(:λ,)}, x)
+    f = getfield(d, :f)
+    p = smf(parent(d), inverse(f)(x))
+    d.λ > 0 ? p : one(p) - p
+end
+
+@inline function smf(d::AffinePushfwd{(:μ, :λ)}, x)
+    f = getfield(d, :f)
+    p = smf(parent(d), inverse(f)(x))
+    d.λ > 0 ? p : one(p) - p
+end
+
+###############################################################################
+# invsmf
+
+@inline function invsmf(d::AffinePushfwd{(:μ,)}, p)
+    f = getfield(d, :f)
+    f(invsmf(parent(d), p))
+end
+
+@inline function invsmf(d::AffinePushfwd{(:μ, :σ)}, p)
+    p = d.σ > 0 ? p : one(p) - p
+    f = getfield(d, :f)
+    f(invsmf(parent(d), p))
+end
+
+@inline function invsmf(d::AffinePushfwd{(:σ,)}, p)
+    p = d.σ > 0 ? p : one(p) - p
+    f = getfield(d, :f)
+    f(invsmf(parent(d), p))
+end
+
+@inline function invsmf(d::AffinePushfwd{(:λ,)}, p)
+    p = d.λ > 0 ? p : one(p) - p
+    f = getfield(d, :f)
+    f(invsmf(parent(d), p))
+end
+
+@inline function invsmf(d::AffinePushfwd{(:μ, :λ)}, p)
+    p = d.λ > 0 ? p : one(p) - p
+    f = getfield(d, :f)
+    f(invsmf(parent(d), p))
+end