Fix bugs, convenient syntax

brendanjohnharris · Nov 9, 2022 · 3391190 · 3391190
1 parent 6405539
commit 3391190
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Showing 3 changed files with 15 additions and 154 deletions.
diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "Normalization"
 uuid = "be38d6a3-8366-4a42-ad57-222272b5bbe7"
 authors = ["brendanjohnharris <[email protected]>"]
-version = "0.1.0"
+version = "0.2.0"
 
 [deps]
 JuliennedArrays = "5cadff95-7770-533d-a838-a1bf817ee6e0"
@@ -10,4 +10,4 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [compat]
 JuliennedArrays = "0.3.0"
-julia = "1.6 - 1.7"
+julia = "1.6 - 1.8"
diff --git a/src/Normalization.jl b/src/Normalization.jl
@@ -12,19 +12,21 @@ export  fit,
         denormalize!,
         denormalize,
         nansafe,
+        @_Normalization,
         ZScore,
         RobustZScore,
+        MixedSigmoid,
         Sigmoid,
         RobustSigmoid,
+        MixedSigmoid,
         MinMax
 
 abstract type AbstractNormalization end
-(𝒯::Type{<:AbstractNormalization})(dims) = 𝒯(;dims)
 function (𝒯::Type{<:AbstractNormalization})(dims, p)
     isnothing(p) || (all(x->x==p[1], length.(p)) && error("Inconsistent parameter dimensions"))
     𝒯(;dims, p)
 end
-(T::AbstractNormalization)(dims) = dims == () || (T.dims = dims)
+(T::AbstractNormalization)(;dims) = dims == () || (T.dims = dims)
 
 macro _Normalization(name, 𝑝, 𝑓, 𝑓⁻¹)
     :(mutable struct $(esc(name)) <: AbstractNormalization
@@ -68,18 +70,20 @@ _mixedNorm.(common_norms)
 _nansafe(p) = x -> p(filter(!isnan, x))
 nansafe!(T::AbstractNormalization) = (T.𝑝=_nansafe.(T.𝑝); ())
 nansafe(T::AbstractNormalization) = (N = deepcopy(T); nansafe!(N); N)
-nansafe(𝒯::Type{<:AbstractNormalization}; dims=nothing) = dims |> 𝒯 |> nansafe
+nansafe(𝒯::Type{<:AbstractNormalization}; dims=nothing) = 𝒯(; dims) |> nansafe
 
 Base.reshape(x::Number, dims...) = reshape([x], dims...)
 function fit!(T::AbstractNormalization, X::AbstractArray; dims=())
-    T(dims)
+    T(;dims)
     dims = isnothing(T.dims) ? (1:ndims(X)) : T.dims
     psz = size(X) |> collect
     psz[[dims...]] .= 1
     T.p = reshape.(map.(T.𝑝, (JuliennedArrays.Slices(X, dims...),)), psz...)
 end
 fit(T::AbstractNormalization, X::AbstractArray; kw...)=(T=deepcopy(T); fit!(T, X; kw...); T)
-fit(𝒯::Type{<:AbstractNormalization}, X::AbstractArray; dims=nothing) = (T = 𝒯(dims); fit!(T, X); T)
+fit(𝒯::Type{<:AbstractNormalization}, X::AbstractArray; dims=nothing) = (T = 𝒯(; dims); fit!(T, X); T)
+
+(𝒯::Type{<:AbstractNormalization})(X; dims=nothing) = fit(𝒯, X; dims)
 
 function normalize!(X::AbstractArray, T::AbstractNormalization)
     isnothing(T.p) && fit!(T, X)
@@ -89,6 +93,8 @@ NormUnion = Union{AbstractNormalization, Type{<:AbstractNormalization}}
 normalize!(X::AbstractArray, 𝒯::NormUnion; dims=nothing) = normalize!(X, fit(𝒯, X; dims))
 normalize(X::AbstractArray, T::NormUnion; kwargs...) = (Y=copy(X); normalize!(Y, T; kwargs...); Y)
 
+(T::AbstractNormalization)(X) = normalize(X, T)
+
 function denormalize!(X::AbstractArray, T::AbstractNormalization)
     isnothing(T.p) && error("Cannot denormalize with an unfit normalization")
     mapdims!(T.𝑓⁻¹, X, T.p...; T.dims)

diff --git a/test/runtests.jl b/test/runtests.jl
@@ -30,6 +30,8 @@ end
     # * ZScore a 2D array over the first dim.
     T = fit(ZScore, X, dims=1)
     Y = normalize(X, T)
+    N = ZScore(X; dims=1) # Alternate syntax
+    @test N(X) == Y
     @test !isnothing(T.p)
     @test length(T.p) == 2
     @test length(T.p[1]) == length(T.p[2]) == size(X, 2)
@@ -39,153 +41,6 @@ end
     @test X == Y
     @test_nowarn denormalize!(Y, T)
     @test Y ≈ _X
-
-    # X = copy(_X)
-    # t = fit(ZScoreTransform, X, dims=1, scale=false)
-    # Y = transform(t, X)
-    # @test length(t.mean) == 8
-    # @test isempty(t.scale)
-    # @test Y ≈ X .- mean(X, dims=1)
-    # @test reconstruct(t, Y) ≈ X
-    # @test transform!(t, X) === X
-    # @test isequal(X, Y)
-    # @test reconstruct!(t, Y) === Y
-    # @test Y ≈ _X
-
-    # X = copy(_X)
-    # t = fit(ZScoreTransform, X, dims=1)
-    # Y = transform(t, X)
-    # @test length(t.mean) == 8
-    # @test length(t.scale) == 8
-    # @test Y ≈ (X .- mean(X, dims=1)) ./ std(X, dims=1)
-    # @test reconstruct(t, Y) ≈ X
-    # @test Y ≈ standardize(ZScoreTransform, X, dims=1)
-    # @test transform!(t, X) === X
-    # @test isequal(X, Y)
-    # @test reconstruct!(t, Y) === Y
-    # @test Y ≈ _X
-
-    # X = copy(_X)
-    # t = fit(ZScoreTransform, X, dims=2)
-    # Y = transform(t, X)
-    # @test length(t.mean) == 5
-    # @test length(t.scale) == 5
-    # @test Y ≈ (X .- mean(X, dims=2)) ./ std(X, dims=2)
-    # @test reconstruct(t, Y) ≈ X
-    # @test Y ≈ standardize(ZScoreTransform, X, dims=2)
-    # @test transform!(t, X) === X
-    # @test isequal(X, Y)
-    # @test reconstruct!(t, Y) === Y
-    # @test Y ≈ _X
-
-    # X = copy(_X)
-    # t = fit(UnitRangeTransform, X, dims=1, unit=false)
-    # Y = transform(t, X)
-    # @test length(t.min) == 8
-    # @test length(t.scale) == 8
-    # @test Y ≈ X ./ (maximum(X, dims=1) .- minimum(X, dims=1))
-    # @test reconstruct(t, Y) ≈ X
-    # @test transform!(t, X) === X
-    # @test isequal(X, Y)
-    # @test reconstruct!(t, Y) === Y
-    # @test Y ≈ _X
-
-    # X = copy(_X)
-    # t = fit(UnitRangeTransform, X, dims=1)
-    # Y = transform(t, X)
-    # @test isa(t, AbstractDataTransform)
-    # @test length(t.min) == 8
-    # @test length(t.scale) == 8
-    # @test Y ≈ (X .- minimum(X, dims=1)) ./ (maximum(X, dims=1) .- minimum(X, dims=1))
-    # @test reconstruct(t, Y) ≈ X
-    # @test Y ≈ standardize(UnitRangeTransform, X, dims=1)
-    # @test transform!(t, X) === X
-    # @test isequal(X, Y)
-    # @test reconstruct!(t, Y) === Y
-    # @test Y ≈ _X
-
-    # X = copy(_X)
-    # t = fit(UnitRangeTransform, X, dims=2)
-    # Y = transform(t, X)
-    # @test isa(t, AbstractDataTransform)
-    # @test length(t.min) == 5
-    # @test length(t.scale) == 5
-    # @test Y ≈ (X .- minimum(X, dims=2)) ./ (maximum(X, dims=2) .- minimum(X, dims=2))
-    # @test reconstruct(t, Y) ≈ X
-    # @test transform!(t, X) === X
-    # @test isequal(X, Y)
-    # @test reconstruct!(t, Y) === Y
-    # @test Y ≈ _X
-
-    # # vector
-    # X = rand(10)
-    # _X = copy(X)
-
-    # t = fit(ZScoreTransform, X, dims=1, center=false, scale=false)
-    # Y = transform(t, X)
-    # @test transform(t, X) ≈ Y
-    # @test reconstruct(t, Y) ≈ X
-    # @test transform!(t, X) === X
-    # @test isequal(X, Y)
-    # @test reconstruct!(t, Y) === Y
-    # @test Y ≈ _X
-
-    # X = copy(_X)
-    # t = fit(ZScoreTransform, X, dims=1, center=false)
-    # Y = transform(t, X)
-    # @test Y ≈ X ./ std(X, dims=1)
-    # @test transform(t, X) ≈ Y
-    # @test reconstruct(t, Y) ≈ X
-    # @test transform!(t, X) === X
-    # @test isequal(X, Y)
-    # @test reconstruct!(t, Y) === Y
-    # @test Y ≈ _X
-
-    # X = copy(_X)
-    # t = fit(ZScoreTransform, X, dims=1, scale=false)
-    # Y = transform(t, X)
-    # @test Y ≈ X .- mean(X, dims=1)
-    # @test transform(t, X) ≈ Y
-    # @test reconstruct(t, Y) ≈ X
-    # @test transform!(t, X) === X
-    # @test isequal(X, Y)
-    # @test reconstruct!(t, Y) === Y
-    # @test Y ≈ _X
-
-    # X = copy(_X)
-    # t = fit(ZScoreTransform, X, dims=1)
-    # Y = transform(t, X)
-    # @test Y ≈ (X .- mean(X, dims=1)) ./ std(X, dims=1)
-    # @test transform(t, X) ≈ Y
-    # @test reconstruct(t, Y) ≈ X
-    # @test Y ≈ standardize(ZScoreTransform, X, dims=1)
-    # @test transform!(t, X) === X
-    # @test isequal(X, Y)
-    # @test reconstruct!(t, Y) === Y
-    # @test Y ≈ _X
-
-    # X = copy(_X)
-    # t = fit(UnitRangeTransform, X, dims=1)
-    # Y = transform(t, X)
-    # @test Y ≈ (X .- minimum(X, dims=1)) ./ (maximum(X, dims=1) .- minimum(X, dims=1))
-    # @test transform(t, X) ≈ Y
-    # @test reconstruct(t, Y) ≈ X
-    # @test transform!(t, X) === X
-    # @test isequal(X, Y)
-    # @test reconstruct!(t, Y) === Y
-    # @test Y ≈ _X
-
-    # X = copy(_X)
-    # t = fit(UnitRangeTransform, X, dims=1, unit=false)
-    # Y = transform(t, X)
-    # @test Y ≈ X ./ (maximum(X, dims=1) .- minimum(X, dims=1))
-    # @test transform(t, X) ≈ Y
-    # @test reconstruct(t, Y) ≈ X
-    # @test Y ≈ standardize(UnitRangeTransform, X, dims=1, unit=false)
-    # @test transform!(t, X) === X
-    # @test isequal(X, Y)
-    # @test reconstruct!(t, Y) === Y
-    # @test Y ≈ _X
 end
 
 @testset "3D normalization" begin