Simplify weights

src/deprecates.jl

@@ -35,3 +35,7 @@ end
 @deprecate wmedian(v::RealVector, w::RealVector) median(v, weights(w))
 
 @deprecate quantile(v::AbstractArray{<:Real}) quantile(v, [.0, .25, .5, .75, 1.0])
+
+### Deprecated September 2019
+@deprecate sum(A::AbstractArray, w::AbstractWeights, dims::Int) sum(A, w, dims=dims)
+@deprecate values(wv::AbstractWeights) convert(Vector, wv)

src/weights.jl

@@ -1,4 +1,4 @@
-###### Weight vector #####
+##### Weight vector #####
 abstract type AbstractWeights{S<:Real, T<:Real, V<:AbstractVector{T}} <: AbstractVector{T} end
 
 """
@@ -18,12 +18,24 @@ macro weights(name)
 end
 
 length(wv::AbstractWeights) = length(wv.values)
-values(wv::AbstractWeights) = wv.values
 sum(wv::AbstractWeights) = wv.sum
 isempty(wv::AbstractWeights) = isempty(wv.values)
 size(wv::AbstractWeights) = size(wv.values)
 
-Base.getindex(wv::AbstractWeights, i) = getindex(wv.values, i)
+Base.convert(::Type{Vector}, wv::AbstractWeights) = convert(Vector, wv.values)
+
+@propagate_inbounds function Base.getindex(wv::AbstractWeights, i::Integer)
+    @boundscheck checkbounds(wv, i)
+    @inbounds wv.values[i]
+end
+
+@propagate_inbounds function Base.getindex(wv::W, i::AbstractArray) where W <: AbstractWeights
+    @boundscheck checkbounds(wv, i)
+    @inbounds v = wv.values[i]
+    W(v, sum(v))
+end
+
+Base.getindex(wv::W, ::Colon) where {W <: AbstractWeights} = W(copy(wv.values), sum(wv))
 
 @propagate_inbounds function Base.setindex!(wv::AbstractWeights, v::Real, i::Int)
     s = v - wv[i]
@@ -247,7 +259,7 @@ eweights(n::Integer, λ::Real) = eweights(1:n, λ)
 eweights(t::AbstractVector, r::AbstractRange, λ::Real) =
     eweights(something.(indexin(t, r)), λ)
 
-# NOTE: No variance correction is implemented for exponential weights
+# NOTE: no variance correction is implemented for exponential weights
 
 struct UnitWeights{T<:Real} <: AbstractWeights{Int, T, V where V<:Vector{T}}
     len::Int
@@ -260,23 +272,24 @@ Construct a `UnitWeights` vector with length `s` and weight elements of type `T`
 All weight elements are identically one.
 """ UnitWeights
 
-values(wv::UnitWeights{T}) where T = fill(one(T), length(wv))
 sum(wv::UnitWeights{T}) where T = convert(T, length(wv))
 isempty(wv::UnitWeights) = iszero(wv.len)
 length(wv::UnitWeights) = wv.len
 size(wv::UnitWeights) = Tuple(length(wv))
 
+Base.convert(::Type{Vector}, wv::UnitWeights{T}) where {T} = ones(T, length(wv))
+
 @propagate_inbounds function Base.getindex(wv::UnitWeights{T}, i::Integer) where T
     @boundscheck checkbounds(wv, i)
     one(T)
 end
 
 @propagate_inbounds function Base.getindex(wv::UnitWeights{T}, i::AbstractArray{<:Int}) where T
     @boundscheck checkbounds(wv, i)
-    fill(one(T), size(i))
+    UnitWeights{T}(length(i))
 end
 
-Base.getindex(wv::UnitWeights{T}, ::Colon) where T = fill(one(T), length(wv))
+Base.getindex(wv::UnitWeights{T}, ::Colon) where {T} = UnitWeights{T}(wv.len)
 
 """
     uweights(s::Integer)
@@ -315,7 +328,7 @@ This definition is equivalent to the correction applied to unweighted data.
     corrected ? (1 / (w.len - 1)) : (1 / w.len)
 end
 
-##### Equality tests #####
+#### Equality tests #####
 
 for w in (AnalyticWeights, FrequencyWeights, ProbabilityWeights, Weights)
     @eval begin
@@ -341,22 +354,7 @@ Compute the weighted sum of an array `v` with weights `w`, optionally over the d
 """
 wsum(v::AbstractVector, w::AbstractVector) = dot(v, w)
 wsum(v::AbstractArray, w::AbstractVector) = dot(vec(v), w)
-
-# Note: the methods for BitArray and SparseMatrixCSC are to avoid ambiguities
-Base.sum(v::BitArray, w::AbstractWeights) = wsum(v, values(w))
-Base.sum(v::SparseArrays.SparseMatrixCSC, w::AbstractWeights) = wsum(v, values(w))
-Base.sum(v::AbstractArray, w::AbstractWeights) = dot(v, values(w))
-
-for v in (AbstractArray{<:Number}, BitArray, SparseArrays.SparseMatrixCSC, AbstractArray)
-    @eval begin
-        function Base.sum(v::$v, w::UnitWeights)
-            if length(v) != length(w)
-                throw(DimensionMismatch("Inconsistent array dimension."))
-            end
-            return sum(v)
-        end
-    end
-end
+wsum(v::AbstractArray, w::AbstractVector, dims::Colon) = wsum(v, w)
 
 ## wsum along dimension
 #
@@ -392,7 +390,6 @@ end
 #     (d) A is a general dense array with eltype <: BlasReal:
 #         dim <= 2: delegate to (a) and (b)
 #         otherwise, decompose A into multiple pages
-#
 
 function _wsum1!(R::AbstractArray, A::AbstractVector, w::AbstractVector, init::Bool)
     r = wsum(A, w)
@@ -455,7 +452,8 @@ function _wsumN!(R::StridedArray{T}, A::DenseArray{T,N}, w::StridedVector{T}, di
     return R
 end
 
-# General Cartesian-based weighted sum across dimensions
+## general Cartesian-based weighted sum across dimensions
+
 @generated function _wsum_general!(R::AbstractArray{RT}, f::supertype(typeof(abs)),
                                    A::AbstractArray{T,N}, w::AbstractVector{WT}, dim::Int, init::Bool) where {T,RT,WT,N}
     quote
@@ -512,7 +510,6 @@ end
     end
 end
 
-
 # N = 1
 _wsum!(R::StridedArray{T}, A::DenseArray{T,1}, w::StridedVector{T}, dim::Int, init::Bool) where {T<:BlasReal} =
     _wsum1!(R, A, w, init)
@@ -533,7 +530,6 @@ _wsum!(R::AbstractArray, A::AbstractArray, w::AbstractVector, dim::Int, init::Bo
 wsumtype(::Type{T}, ::Type{W}) where {T,W} = typeof(zero(T) * zero(W) + zero(T) * zero(W))
 wsumtype(::Type{T}, ::Type{T}) where {T<:BlasReal} = T
 
-
 """
     wsum!(R, A, w, dim; init=true)
 
@@ -559,19 +555,21 @@ function wsum(A::AbstractArray{<:Number}, w::UnitWeights, dim::Int)
     return sum(A, dims=dim)
 end
 
-# extended sum! and wsum
+## extended sum! and wsum
 
 Base.sum!(R::AbstractArray, A::AbstractArray, w::AbstractWeights{<:Real}, dim::Int; init::Bool=true) =
-    wsum!(R, A, values(w), dim; init=init)
+    wsum!(R, A, w, dim; init=init)
 
-Base.sum(A::AbstractArray{<:Number}, w::AbstractWeights{<:Real}, dim::Int) = wsum(A, values(w), dim)
+Base.sum(A::AbstractArray, w::AbstractWeights{<:Real}; dims::Union{Colon,Int}=:) =
+    wsum(A, w, dims)
 
-function Base.sum(A::AbstractArray{<:Number}, w::UnitWeights, dim::Int)
-    size(A, dim) != length(w) && throw(DimensionMismatch("Inconsistent array dimension."))
-    return sum(A, dims=dim)
+function Base.sum(A::AbstractArray, w::UnitWeights; dims::Union{Colon,Int}=:)
+    a = (dims === :) ? length(A) : size(A, dims)
+    a != length(w) && throw(DimensionMismatch("Inconsistent array dimension."))
+    return sum(A, dims=dims)
 end
 
-###### Weighted means #####
+##### Weighted means #####
 
 """
     wmean(v, w::AbstractVector)
@@ -589,9 +587,10 @@ end
 Compute the weighted mean of array `A` with weight vector `w`
 (of type `AbstractWeights`) along dimension `dims`, and write results to `R`.
 """
-mean!(R::AbstractArray, A::AbstractArray, w::AbstractWeights;
-      dims::Union{Nothing,Int}=nothing) = _mean!(R, A, w, dims)
-_mean!(R::AbstractArray, A::AbstractArray, w::AbstractWeights, dims::Nothing) = throw(ArgumentError("dims argument must be provided"))
+mean!(R::AbstractArray, A::AbstractArray, w::AbstractWeights; dims::Union{Nothing,Int}=nothing) =
+    _mean!(R, A, w, dims)
+_mean!(R::AbstractArray, A::AbstractArray, w::AbstractWeights, dims::Nothing) =
+    throw(ArgumentError("dims argument must be provided"))
 _mean!(R::AbstractArray, A::AbstractArray, w::AbstractWeights, dims::Int) =
     rmul!(Base.sum!(R, A, w, dims), inv(sum(w)))
 
@@ -611,24 +610,21 @@ w = rand(n)
 mean(x, weights(w))
 ```
 """
-mean(A::AbstractArray, w::AbstractWeights; dims::Union{Nothing,Int}=nothing) =
+mean(A::AbstractArray, w::AbstractWeights; dims::Union{Colon,Int}=:) =
     _mean(A, w, dims)
-_mean(A::AbstractArray, w::AbstractWeights, dims::Nothing) =
+_mean(A::AbstractArray, w::AbstractWeights, dims::Colon) =
     sum(A, w) / sum(w)
 _mean(A::AbstractArray{T}, w::AbstractWeights{W}, dims::Int) where {T,W} =
     _mean!(similar(A, wmeantype(T, W), Base.reduced_indices(axes(A), dims)), A, w, dims)
 
-function _mean(A::AbstractArray, w::UnitWeights, dims::Nothing)
-    length(A) != length(w) && throw(DimensionMismatch("Inconsistent array dimension."))
-    return mean(A)
-end
-
-function _mean(A::AbstractArray, w::UnitWeights, dims::Int)
-    size(A, dims) != length(w) && throw(DimensionMismatch("Inconsistent array dimension."))
+function mean(A::AbstractArray, w::UnitWeights; dims::Union{Colon,Int}=:)
+    a = (dims === :) ? length(A) : size(A, dims)
+    a != length(w) && throw(DimensionMismatch("Inconsistent array dimension."))
     return mean(A, dims=dims)
 end
 
-###### Weighted quantile #####
+##### Weighted quantile #####
+
 """
     quantile(v, w::AbstractWeights, p)
 
@@ -723,9 +719,8 @@ end
 
 quantile(v::RealVector, w::AbstractWeights{<:Real}, p::Number) = quantile(v, w, [p])[1]
 
+##### Weighted median #####
 
-
-###### Weighted median #####
 """
     median(v::RealVector, w::AbstractWeights)