reduce dependencies (#127)

* reduce dependencies

* version bump

* InfiniteArrays => Infinities

* bugfix

* Revert to ∞ from InfiniteArrays

Project.toml

@@ -1,11 +1,10 @@
 name = "MeasureTheory"
 uuid = "eadaa1a4-d27c-401d-8699-e962e1bbc33b"
 authors = ["Chad Scherrer <[email protected]> and contributors"]
-version = "0.9.3"
+version = "0.10.0"
 
 [deps]
 Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
-ArrayInterface = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"
 ConcreteStructs = "2569d6c7-a4a2-43d3-a901-331e8e4be471"
 ConstructionBase = "187b0558-2788-49d3-abe0-74a17ed4e7c9"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
@@ -17,43 +16,34 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MLStyle = "d8e11817-5142-5d16-987a-aa16d5891078"
 MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
 MappedArrays = "dbb5928d-eab1-5f90-85c2-b9b0edb7c900"
-MonteCarloMeasurements = "0987c9cc-fe09-11e8-30f0-b96dd679fdca"
 NamedTupleTools = "d9ec5142-1e00-5aa0-9d6a-321866360f50"
 NestedTuples = "a734d2a7-8d68-409b-9419-626914d4061d"
 PositiveFactorizations = "85a6dd25-e78a-55b7-8502-1745935b8125"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
-SimpleTraits = "699a6c99-e7fa-54fc-8d76-47d257e15c1d"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
-StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
 TransformVariables = "84d833dd-6860-57f9-a1a7-6da5db126cff"
 Tricks = "410a4b4d-49e4-4fbc-ab6d-cb71b17b3775"
-Tullio = "bc48ee85-29a4-5162-ae0b-a64e1601d4bc"
 
 [compat]
 Accessors = "0.1"
-ArrayInterface = "2,3"
 ConcreteStructs = "0.2"
 ConstructionBase = "1.3"
 Distributions = "0.23, 0.24, 0.25"
 DynamicIterators = "0.4.2"
 FillArrays = "0.11, 0.12"
-InfiniteArrays = "0.9, 0.10, 0.11"
+InfiniteArrays = "0.11"
 KeywordCalls = "0.1, 0.2"
 MLStyle = "0.4"
 MacroTools = "0.5"
 MappedArrays = "0.3, 0.4"
-MonteCarloMeasurements = "0.10"
 NamedTupleTools = "0.13"
 NestedTuples = "0.3"
 PositiveFactorizations = "0.2"
-SimpleTraits = "0.9"
 SpecialFunctions = "0.10, 1"
-StaticArrays = "0.12, 1"
 StatsFuns = "0.9"
 TransformVariables = "0.4"
 Tricks = "0.1.4"
-Tullio = "0.2, 0.3"
 julia = "1.5"
 
 [extras]

src/MeasureTheory.jl

@@ -26,6 +26,7 @@ using Accessors
 const ∞ = InfiniteArrays.∞
 
 export ∞
+export basemeasure
 
 export as
 
@@ -48,7 +49,7 @@ include("const.jl")
 include("exp.jl")
 include("domains.jl")
 include("utils.jl")
-include("traits.jl")
+# include("traits.jl")
 include("absolutecontinuity.jl")
 include("parameterized.jl")
 include("macros.jl")

src/combinators/power.jl

@@ -1,5 +1,5 @@
 import Base
-
+using FillArrays: Fill
 # """
 # A power measure is a product of a measure with itself. The number of elements in
 # the product determines the dimensionality of the resulting support.

src/combinators/product.jl

@@ -142,7 +142,6 @@ end
 end
 
 
-using Tullio
 
 
 

src/distproxy.jl

@@ -1,7 +1,7 @@
 export distproxy
 function distproxy end
 
-import MonteCarloMeasurements
+# import MonteCarloMeasurements
 
 PROXIES = Dict(
     :Distributions => [
@@ -10,9 +10,9 @@ PROXIES = Dict(
         :entropy
         :cdf
         ],
-    :MonteCarloMeasurements => [
-        :Particles
-    ]
+    # :MonteCarloMeasurements => [
+    #     :Particles
+    # ]
 )
 
 for m in keys(PROXIES)
@@ -25,7 +25,7 @@ for m in keys(PROXIES)
     end
 end
 
-MonteCarloMeasurements.Particles(N::Int, d::AbstractMeasure) = MonteCarloMeasurements.Particles(N, distproxy(d))
+# MonteCarloMeasurements.Particles(N::Int, d::AbstractMeasure) = MonteCarloMeasurements.Particles(N, distproxy(d))
 
 # using MonteCaroMeasurements
 

src/parameterized/lkj-cholesky.jl

@@ -48,7 +48,6 @@ asparams(::Type{<:LKJCholesky}, ::Val{:logη}) = asℝ
 
 
 using LinearAlgebra
-using Tullio
 
 logdensity(d::LKJCholesky, C::Cholesky) = logdensity(d, C.UL)
 
@@ -59,13 +58,19 @@ function logdensity(d::LKJCholesky{(:k, :η,)}, L::Union{LinearAlgebra.AbstractT
 
     # Note: https://github.com/cscherrer/MeasureTheory.jl/issues/100#issuecomment-852428192
     c = d.k + 2(η - 1)
-    @tullio s = (c - i) * log(L[i,i])
+    n = size(L,1)
+    s = sum(1:n) do i 
+        (c - i) * @inbounds log(L[i,i]) 
+    end
     return s
 end
 
 function logdensity(d::LKJCholesky{(:k, :logη)}, L::Union{LinearAlgebra.AbstractTriangular, Diagonal})
     c = d.k + 2 * expm1(d.logη)
-    @tullio s = (c - i) * log(L[i,i])
+    n = size(L,1)
+    s = sum(1:n) do i
+        (c - i) * @inbounds log(L[i,i])
+    end
     return s
 end
 

src/parameterized/mvnormal.jl

@@ -6,7 +6,6 @@ using LinearAlgebra
 export MvNormal
 using Random
 import Base
-using Tullio
 
 
 struct MvNormal{N, T, I, J} <: ParameterizedMeasure{N}
@@ -55,9 +54,9 @@ end
 
 ≪(::MvNormal, ::Lebesgue{ℝ}) = true
 
-function logdensity(d::MvNormal{(:Σ⁻¹,)}, x)
-    @tullio ℓ = -0.5 * x[i] * d.Σ⁻¹[i,j] * x[j]
-    return ℓ
-end
+# function logdensity(d::MvNormal{(:Σ⁻¹,)}, x)
+#     @tullio ℓ = -0.5 * x[i] * d.Σ⁻¹[i,j] * x[j]
+#     return ℓ
+# end
 
 mvnormaldims(nt::NamedTuple{(:Σ⁻¹,)}) = size(nt.Σ⁻¹)

src/primitives/counting.jl

@@ -2,8 +2,6 @@ export CountingMeasure
 
 struct CountingMeasure{X} <: PrimitiveMeasure end
 
-@primitive CountingMeasure
-
 function Base.show(io::IO, ::MIME"text/plain", μ::CountingMeasure{X}) where {X}
     io = IOContext(io, :compact => true)
     print(io, "CountingMeasure(", X, ")")

src/primitives/dirac.jl

@@ -5,8 +5,6 @@ struct Dirac{X} <: PrimitiveMeasure
     x::X
 end
 
-@primitive Dirac
-
 sampletype(μ::Dirac{X}) where {X} = X
 
 function (μ::Dirac{X})(s) where {X}

src/primitives/lebesgue.jl

@@ -4,8 +4,6 @@ export Lebesgue
 
 struct Lebesgue{X} <: PrimitiveMeasure end
 
-@primitive Lebesgue
-
 function Base.show(io::IO, ::MIME"text/plain", μ::Lebesgue{X}) where X
     io = IOContext(io, :compact => true)
     print(io, "Lebesgue(", X, ")")

src/primitives/trivial.jl

@@ -2,6 +2,4 @@ export TrivialMeasure
 
 struct TrivialMeasure <: PrimitiveMeasure end
 
-@primitive TrivialMeasure
-
 sampletype(::TrivialMeasure) = Nothing