Implement vararg methods for mapreduce similar to map, fixes #27704.

NEWS.md

@@ -49,6 +49,7 @@ Standard library changes
 * A no-argument construct to `Ptr{T}` has been added which constructs a null pointer ([#30919])
 * `strip` now accepts a function argument in the same manner as `lstrip` and `rstrip` ([#31211])
 * `mktempdir` now accepts a `prefix` keyword argument to customize the file name ([#31230], [#22922])
+* `mapreduce` now accept multiple iterators, similar to `map` ([#?????]).
 
 #### LinearAlgebra
 

base/reduce.jl

@@ -179,9 +179,9 @@ mapreduce_impl(f, op, A::AbstractArray, ifirst::Integer, ilast::Integer) =
     mapreduce_impl(f, op, A, ifirst, ilast, pairwise_blocksize(f, op))
 
 """
-    mapreduce(f, op, itr; [init])
+    mapreduce(f, op, itrs...; [init])
 
-Apply function `f` to each element in `itr`, and then reduce the result using the binary
+Apply function `f` to each element(s) in `itrs`, and then reduce the result using the binary
 function `op`. If provided, `init` must be a neutral element for `op` that will be returned
 for empty collections. It is unspecified whether `init` is used for non-empty collections.
 In general, it will be necessary to provide `init` to work with empty collections.
@@ -191,6 +191,9 @@ In general, it will be necessary to provide `init` to work with empty collection
 intermediate collection needs to be created. See documentation for [`reduce`](@ref) and
 [`map`](@ref).
 
+!!! compat "Julia 1.2"
+    `mapreduce` with multiple iterators requires Julia 1.2 or later.
+
 # Examples
 ```jldoctest
 julia> mapreduce(x->x^2, +, [1:3;]) # == 1 + 4 + 9
@@ -203,6 +206,7 @@ implementations may reuse the return value of `f` for elements that appear multi
 guaranteed left or right associativity and invocation of `f` for every value.
 """
 mapreduce(f, op, itr; kw...) = mapfoldl(f, op, itr; kw...)
+mapreduce(f, op, itrs...; kw...) = reduce(op, Generator(f, itrs...); kw...)
 
 # Note: sum_seq usually uses four or more accumulators after partial
 # unrolling, so each accumulator gets at most 256 numbers

base/reducedim.jl

@@ -278,11 +278,14 @@ reducedim!(op, R::AbstractArray{RT}, A::AbstractArray) where {RT} =
     mapreducedim!(identity, op, R, A)
 
 """
-    mapreduce(f, op, A::AbstractArray; dims=:, [init])
+    mapreduce(f, op, A::AbstractArray...; dims=:, [init])
 
 Evaluates to the same as `reduce(op, map(f, A); dims=dims, init=init)`, but is generally
 faster because the intermediate array is avoided.
 
+!!! compat "Julia 1.2"
+    `mapreduce` with multiple iterators requires Julia 1.2 or later.
+
 # Examples
 ```jldoctest
 julia> a = reshape(Vector(1:16), (4,4))
@@ -302,6 +305,7 @@ julia> mapreduce(isodd, |, a, dims=1)
 ```
 """
 mapreduce(f, op, A::AbstractArray; dims=:, kw...) = _mapreduce_dim(f, op, kw.data, A, dims)
+mapreduce(f, op, A::AbstractArray...; kw...) = reduce(op, map(f, A...); kw...)
 
 _mapreduce_dim(f, op, nt::NamedTuple{(:init,)}, A::AbstractArray, ::Colon) = mapfoldl(f, op, A; nt...)
 

test/reduce.jl

@@ -43,6 +43,27 @@ using .Main.OffsetArrays
 @test mapreduce(-, +, [-10 -9 -3]) == ((10 + 9) + 3)
 @test mapreduce((x)->x[1:3], (x,y)->"($x+$y)", ["abcd", "efgh", "01234"]) == "((abc+efg)+012)"
 
+# mapreduce with multiple iterators
+@test mapreduce(*, +, (i for i in 2:3), (i for i in 4:5)) == 23
+@test mapreduce(*, +, (i for i in 2:3), (i for i in 4:5); init = 2) == 25
+@test mapreduce(*, (x,y)->"($x+$y)", ["a", "b", "c"], ["d", "e", "f"]) == "((ad+be)+cf)"
+@test mapreduce(*, (x,y)->"($x+$y)", ["a", "b", "c"], ["d", "e", "f"]; init = "gh") ==
+    "(((gh+ad)+be)+cf)"
+
+@test mapreduce(*, +, [2, 3], [4, 5]) == 23
+@test mapreduce(*, +, [2, 3], [4, 5]; init = 2) == 25
+@test mapreduce(*, +, [2, 3], [4, 5]; dims = 1) == [23]
+@test mapreduce(*, +, [2, 3], [4, 5]; dims = 1, init = 2) == [25]
+@test mapreduce(*, +, [2, 3], [4, 5]; dims = 2) == [8, 15]
+@test mapreduce(*, +, [2, 3], [4, 5]; dims = 2, init = 2) == [10, 17]
+
+@test mapreduce(*, +, [2 3; 4 5], [6 7; 8 9]) == 110
+@test mapreduce(*, +, [2 3; 4 5], [6 7; 8 9]; init = 2) == 112
+@test mapreduce(*, +, [2 3; 4 5], [6 7; 8 9]; dims = 1) == [44 66]
+@test mapreduce(*, +, [2 3; 4 5], [6 7; 8 9]; dims = 1, init = 2) == [46 68]
+@test mapreduce(*, +, [2 3; 4 5], [6 7; 8 9]; dims = 2) == reshape([33, 77], :, 1)
+@test mapreduce(*, +, [2 3; 4 5], [6 7; 8 9]; dims = 2, init = 2) == reshape([35, 79], :, 1)
+
 # mapreduce() for 1- 2- and n-sized blocks (PR #19325)
 @test mapreduce(-, +, [-10]) == 10
 @test mapreduce(abs2, +, [-9, -3]) == 81 + 9