Merge 6fc966d into 2181649

Project.toml

@@ -1,6 +1,6 @@
 name = "KernelFunctions"
 uuid = "ec8451be-7e33-11e9-00cf-bbf324bd1392"
-version = "0.10.7"
+version = "0.10.8"
 
 [deps]
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"

docs/Manifest.toml

@@ -119,7 +119,7 @@ version = "0.21.1"
 deps = ["ChainRulesCore", "Compat", "CompositionsBase", "Distances", "FillArrays", "Functors", "LinearAlgebra", "Random", "Requires", "SpecialFunctions", "StatsBase", "StatsFuns", "TensorCore", "Test", "ZygoteRules"]
 path = ".."
 uuid = "ec8451be-7e33-11e9-00cf-bbf324bd1392"
-version = "0.10.6"
+version = "0.10.7"
 
 [[Kronecker]]
 deps = ["FillArrays", "LinearAlgebra", "NamedDims", "SparseArrays", "StatsBase"]

src/mokernels/moinput.jl

@@ -1,15 +1,44 @@
 """
-    MOInput(x::AbstractVector, out_dim::Integer)
+    MOInputIsotopicByFeatures(x::AbstractVector, out_dim::Integer)
 
-A data type to accomodate modelling multi-dimensional output data.
+`MOInputIsotopicByFeatures(x, out_dim)` has length `out_dim * length(x)`.
 
-`MOInput(x, out_dim)` has length `length(x) * out_dim`.
+```jldoctest
+julia> x = [1, 2, 3];
+
+julia> KernelFunctions.MOInputIsotopicByFeatures(x, 2)
+6-element KernelFunctions.MOInputIsotopicByFeatures{Int64, Vector{Int64}}:
+ (1, 1)
+ (1, 2)
+ (2, 1)
+ (2, 2)
+ (3, 1)
+ (3, 2)
+```
+
+Accommodates modelling multi-dimensional output data where all outputs are always observed.
+
+As shown above, an `MOInputIsotopicByFeatures` represents a vector of tuples.
+The first `out_dim` elements represent all outputs for the first input, the second
+`out_dim` elements represent the outputs for the second input, etc.
+
+See [Inputs for Multiple Outputs](@ref) in the docs for more info.
+"""
+struct MOInputIsotopicByFeatures{S,T<:AbstractVector{S}} <: AbstractVector{Tuple{S,Int}}
+    x::T
+    out_dim::Integer
+end
+
+"""
+    MOInputIsotopicByOutputs(x::AbstractVector, out_dim::Integer)
+
+`MOInputIsotopicByOutputs(x, out_dim)` has length `length(x) * out_dim`.
 
 ```jldoctest
 julia> x = [1, 2, 3];
 
-julia> MOInput(x, 2)
-6-element MOInput{Vector{Int64}}:
+julia> KernelFunctions.MOInputIsotopicByOutputs(x, 2)
+6-element KernelFunctions.MOInputIsotopicByOutputs{Int64, Vector{Int64}}:
  (1, 1)
  (2, 1)
  (3, 1)
@@ -18,40 +47,59 @@ julia> MOInput(x, 2)
  (3, 2)
 ```
 
-As shown above, an `MOInput` represents a vector of tuples.
+Accommodates modelling multi-dimensional output data where all outputs are always observed.
+
+As shown above, an `MOInputIsotopicByOutputs` represents a vector of tuples.
 The first `length(x)` elements represent the inputs for the first output, the second
 `length(x)` elements represent the inputs for the second output, etc.
-
-See [Inputs for Multiple Outputs](@ref) in the docs for more info.
 """
-struct MOInput{T<:AbstractVector} <: AbstractVector{Tuple{Any,Int}}
+struct MOInputIsotopicByOutputs{S,T<:AbstractVector{S}} <: AbstractVector{Tuple{S,Int}}
     x::T
     out_dim::Integer
 end
 
-Base.length(inp::MOInput) = inp.out_dim * length(inp.x)
-
-Base.size(inp::MOInput, d) = d::Integer == 1 ? inp.out_dim * size(inp.x, 1) : 1
-Base.size(inp::MOInput) = (inp.out_dim * size(inp.x, 1),)
-
-Base.lastindex(inp::MOInput) = length(inp)
-Base.firstindex(inp::MOInput) = 1
-
-function Base.getindex(inp::MOInput, ind::Integer)
-    if ind > 0
-        out_dim = ind ÷ length(inp.x) + 1
-        ind = ind % length(inp.x)
-        if ind == 0
-            ind = length(inp.x)
-            out_dim -= 1
-        end
-        return (inp.x[ind], out_dim::Int)
-    else
-        throw(BoundsError(string("Trying to access at ", ind)))
-    end
+const IsotopicMOInputs = Union{MOInputIsotopicByFeatures,MOInputIsotopicByOutputs}
+
+function Base.getindex(inp::MOInputIsotopicByOutputs, ind::Integer)
+    @boundscheck checkbounds(inp, ind)
+    output_index, feature_index = fldmod1(ind, length(inp.x))
+    feature = @inbounds inp.x[feature_index]
+    return feature, output_index
 end
 
-Base.iterate(inp::MOInput) = (inp[1], 1)
-function Base.iterate(inp::MOInput, state)
-    return (state < length(inp)) ? (inp[state + 1], state + 1) : nothing
+function Base.getindex(inp::MOInputIsotopicByFeatures, ind::Integer)
+    @boundscheck checkbounds(inp, ind)
+    feature_index, output_index = fldmod1(ind, inp.out_dim)
+    feature = @inbounds inp.x[feature_index]
+    return feature, output_index
 end
+
+Base.size(inp::IsotopicMOInputs) = (inp.out_dim * length(inp.x),)
+
+"""
+    MOInput(x::AbstractVector, out_dim::Integer)
+
+A data type to accommodate modelling multi-dimensional output data.
+`MOInput(x, out_dim)` has length `length(x) * out_dim`.
+
+```jldoctest
+julia> x = [1, 2, 3];
+
+julia> MOInput(x, 2)
+6-element KernelFunctions.MOInputIsotopicByOutputs{Int64, Vector{Int64}}:
+ (1, 1)
+ (2, 1)
+ (3, 1)
+ (1, 2)
+ (2, 2)
+ (3, 2)
+```
+As shown above, an `MOInput` represents a vector of tuples.
+The first `length(x)` elements represent the inputs for the first output, the second
+`length(x)` elements represent the inputs for the second output, etc.
+See [Inputs for Multiple Outputs](@ref) in the docs for more info.
+
+`MOInput` will be deprecated in version 0.11 in favour of `MOInputIsotopicByOutputs`,
+and removed in version 0.12.
+"""
+const MOInput = MOInputIsotopicByOutputs

test/mokernels/moinput.jl

@@ -1,19 +1,47 @@
 @testset "moinput" begin
     x = [rand(5) for _ in 1:4]
-    mgpi = MOInput(x, 3)
-
-    @test length(mgpi) == 12
-    @test size(mgpi) == (12,)
-    @test size(mgpi, 1) == 12
-    @test size(mgpi, 2) == 1
-    @test lastindex(mgpi) == 12
-    @test firstindex(mgpi) == 1
-    @test iterate(mgpi) == (mgpi[1], 1)
-    @test iterate(mgpi, 2) == (mgpi[3], 3)
-    @test_throws BoundsError mgpi[0]
-
-    @test mgpi[2] == (x[2], 1)
-    @test mgpi[5] == (x[1], 2)
-    @test mgpi[7] == (x[3], 2)
-    @test all([(x_, i) for i in 1:3 for x_ in x] .== mgpi)
+    type_1 = AbstractVector{Tuple{Vector{Float64},Int}}
+    type_2 = AbstractVector{Tuple{AbstractVector{Vector{Float64}},Int}}
+
+    @testset "isotopicbyoutputs" begin
+        ibo = MOInput(x, 3)
+
+        @test ibo == KernelFunctions.MOInputIsotopicByOutputs(x, 3)
+
+        @test isa(ibo, type_1) == true
+        @test isa(ibo, type_2) == false
+
+        @test length(ibo) == 12
+        @test size(ibo) == (12,)
+        @test size(ibo, 1) == 12
+        @test size(ibo, 2) == 1
+        @test lastindex(ibo) == 12
+        @test firstindex(ibo) == 1
+        @test_throws BoundsError ibo[0]
+
+        @test ibo[2] == (x[2], 1)
+        @test ibo[5] == (x[1], 2)
+        @test ibo[7] == (x[3], 2)
+        @test all([(x_, i) for i in 1:3 for x_ in x] .== ibo)
+    end
+
+    @testset "isotopicbyfeatures" begin
+        ibf = KernelFunctions.MOInputIsotopicByFeatures(x, 3)
+
+        @test isa(ibf, type_1) == true
+        @test isa(ibf, type_2) == false
+
+        @test length(ibf) == 12
+        @test size(ibf) == (12,)
+        @test size(ibf, 1) == 12
+        @test size(ibf, 2) == 1
+        @test lastindex(ibf) == 12
+        @test firstindex(ibf) == 1
+        @test_throws BoundsError ibf[0]
+
+        @test ibf[2] == (x[1], 2)
+        @test ibf[5] == (x[2], 2)
+        @test ibf[7] == (x[3], 1)
+        @test all([(x_, i) for x_ in x for i in 1:3] .== ibf)
+    end
 end

test/runtests.jl

@@ -183,8 +183,8 @@ include("test_utils.jl")
                 KernelFunctions;
                 doctestfilters=[
                     r"{([a-zA-Z0-9]+,\s?)+[a-zA-Z0-9]+}",
-                    r"(Array{[a-zA-Z0-9]+,\s?1}|Vector{[a-zA-Z0-9]+})",
-                    r"(Array{[a-zA-Z0-9]+,\s?2}|Matrix{[a-zA-Z0-9]+})",
+                    r"(Array{[a-zA-Z0-9]+,\s?1}|\s?Vector{[a-zA-Z0-9]+})",
+                    r"(Array{[a-zA-Z0-9]+,\s?2}|\s?Matrix{[a-zA-Z0-9]+})",
                 ],
             )
         end