swh:1:snp:a72e953ecd624a7df6e6196bbdd05851996c5e40
Tip revision: 53725f70d597ec585154b298d61c7b00e4dde416 authored by Martin Holters on 03 March 2017, 08:10:56 UTC
patch rdims
patch rdims
Tip revision: 53725f7
broadcast.jl
# This file is a part of Julia. License is MIT: http://julialang.org/license
module TestBroadcastInternals
using Base.Broadcast: broadcast_indices, check_broadcast_indices,
check_broadcast_shape, newindex, _bcs, _bcsm
using Base: Test, OneTo
@test @inferred(_bcs((), (3,5), (3,5))) == (3,5)
@test @inferred(_bcs((), (3,1), (3,5))) == (3,5)
@test @inferred(_bcs((), (3,), (3,5))) == (3,5)
@test @inferred(_bcs((), (3,5), (3,))) == (3,5)
@test_throws DimensionMismatch _bcs((), (3,5), (4,5))
@test_throws DimensionMismatch _bcs((), (3,5), (3,4))
@test @inferred(_bcs((), (-1:1, 2:5), (-1:1, 2:5))) == (-1:1, 2:5)
@test @inferred(_bcs((), (-1:1, 2:5), (1, 2:5))) == (-1:1, 2:5)
@test @inferred(_bcs((), (-1:1, 1), (1, 2:5))) == (-1:1, 2:5)
@test @inferred(_bcs((), (-1:1,), (-1:1, 2:5))) == (-1:1, 2:5)
@test_throws DimensionMismatch _bcs((), (-1:1, 2:6), (-1:1, 2:5))
@test_throws DimensionMismatch _bcs((), (-1:1, 2:5), (2, 2:5))
@test @inferred(broadcast_indices(zeros(3,4), zeros(3,4))) == (OneTo(3),OneTo(4))
@test @inferred(broadcast_indices(zeros(3,4), zeros(3))) == (OneTo(3),OneTo(4))
@test @inferred(broadcast_indices(zeros(3), zeros(3,4))) == (OneTo(3),OneTo(4))
@test @inferred(broadcast_indices(zeros(3), zeros(1,4), zeros(1))) == (OneTo(3),OneTo(4))
check_broadcast_indices((OneTo(3),OneTo(5)), zeros(3,5))
check_broadcast_indices((OneTo(3),OneTo(5)), zeros(3,1))
check_broadcast_indices((OneTo(3),OneTo(5)), zeros(3))
check_broadcast_indices((OneTo(3),OneTo(5)), zeros(3,5), zeros(3))
check_broadcast_indices((OneTo(3),OneTo(5)), zeros(3,5), 1)
check_broadcast_indices((OneTo(3),OneTo(5)), 5, 2)
@test_throws DimensionMismatch check_broadcast_indices((OneTo(3),OneTo(5)), zeros(2,5))
@test_throws DimensionMismatch check_broadcast_indices((OneTo(3),OneTo(5)), zeros(3,4))
@test_throws DimensionMismatch check_broadcast_indices((OneTo(3),OneTo(5)), zeros(3,4,2))
@test_throws DimensionMismatch check_broadcast_indices((OneTo(3),OneTo(5)), zeros(3,5), zeros(2))
check_broadcast_indices((-1:1, 6:9), 1)
check_broadcast_shape((-1:1, 6:9), (-1:1, 6:9))
check_broadcast_shape((-1:1, 6:9), (-1:1, 1))
check_broadcast_shape((-1:1, 6:9), (1, 6:9))
@test_throws DimensionMismatch check_broadcast_shape((-1:1, 6:9), (-1, 6:9))
@test_throws DimensionMismatch check_broadcast_shape((-1:1, 6:9), (-1:1, 6))
ci(x) = CartesianIndex(x)
@test @inferred(newindex(ci((2,2)), (true, true), (-1,-1))) == ci((2,2))
@test @inferred(newindex(ci((2,2)), (true, false), (-1,-1))) == ci((2,-1))
@test @inferred(newindex(ci((2,2)), (false, true), (-1,-1))) == ci((-1,2))
@test @inferred(newindex(ci((2,2)), (false, false), (-1,-1))) == ci((-1,-1))
@test @inferred(newindex(ci((2,2)), (true,), (-1,-1))) == ci((2,))
@test @inferred(newindex(ci((2,2)), (true,), (-1,))) == ci((2,))
@test @inferred(newindex(ci((2,2)), (false,), (-1,))) == ci((-1,))
@test @inferred(newindex(ci((2,2)), (), ())) == ci(())
end
function as_sub(x::AbstractVector)
y = similar(x, eltype(x), tuple(([size(x)...]*2)...))
y = view(y, 2:2:length(y))
y[:] = x[:]
y
end
function as_sub(x::AbstractMatrix)
y = similar(x, eltype(x), tuple(([size(x)...]*2)...))
y = view(y, 2:2:size(y,1), 2:2:size(y,2))
for j=1:size(x,2)
for i=1:size(x,1)
y[i,j] = x[i,j]
end
end
y
end
function as_sub{T}(x::AbstractArray{T,3})
y = similar(x, eltype(x), tuple(([size(x)...]*2)...))
y = view(y, 2:2:size(y,1), 2:2:size(y,2), 2:2:size(y,3))
for k=1:size(x,3)
for j=1:size(x,2)
for i=1:size(x,1)
y[i,j,k] = x[i,j,k]
end
end
end
y
end
bittest(f::Function, a...) = (@test f.(a...) == BitArray(broadcast(f, a...)))
n1 = 21
n2 = 32
n3 = 17
rb = 1:5
for arr in (identity, as_sub)
@test broadcast(+, arr(eye(2)), arr([1, 4])) == [2 1; 4 5]
@test broadcast(+, arr(eye(2)), arr([1 4])) == [2 4; 1 5]
@test broadcast(+, arr([1 0]), arr([1, 4])) == [2 1; 5 4]
@test broadcast(+, arr([1, 0]), arr([1 4])) == [2 5; 1 4]
@test broadcast(+, arr([1, 0]), arr([1, 4])) == [2, 4]
@test broadcast(+, arr([1, 0]), 2) == [3, 2]
@test @inferred(broadcast(+, arr(eye(2)), arr([1, 4]))) == arr([2 1; 4 5])
@test arr(eye(2)) .+ arr([1 4]) == arr([2 4; 1 5])
@test arr([1 0]) .+ arr([1, 4]) == arr([2 1; 5 4])
@test arr([1, 0]) .+ arr([1 4]) == arr([2 5; 1 4])
@test arr([1, 0]) .+ arr([1, 4]) == arr([2, 4])
@test arr([1]) .+ arr([]) == arr([])
A = arr(eye(2)); @test broadcast!(+, A, A, arr([1, 4])) == arr([2 1; 4 5])
A = arr(eye(2)); @test broadcast!(+, A, A, arr([1 4])) == arr([2 4; 1 5])
A = arr([1 0]); @test_throws DimensionMismatch broadcast!(+, A, A, arr([1, 4]))
A = arr([1 0]); @test broadcast!(+, A, A, arr([1 4])) == arr([2 4])
A = arr([1 0]); @test broadcast!(+, A, A, 2) == arr([3 2])
@test arr([ 1 2]) .* arr([3, 4]) == [ 3 6; 4 8]
@test arr([24.0 12.0]) ./ arr([2.0, 3.0]) == [12 6; 8 4]
@test arr([1 2]) ./ arr([3, 4]) == [1/3 2/3; 1/4 2/4]
@test arr([1 2]) .\ arr([3, 4]) == [3 1.5; 4 2]
@test arr([3 4]) .^ arr([1, 2]) == [3 4; 9 16]
@test arr(BitArray([true false])) .* arr(BitArray([true, true])) == [true false; true false]
@test arr(BitArray([true false])) .^ arr(BitArray([false, true])) == [true true; true false]
@test arr(BitArray([true false])) .^ arr([0, 3]) == [true true; true false]
M = arr([11 12; 21 22])
@test broadcast_getindex(M, eye(Int, 2).+1,arr([1, 2])) == [21 11; 12 22]
@test_throws BoundsError broadcast_getindex(M, eye(Int, 2).+1,arr([1, -1]))
@test_throws BoundsError broadcast_getindex(M, eye(Int, 2).+1,arr([1, 2]), [2])
@test broadcast_getindex(M, eye(Int, 2).+1,arr([2, 1]), [1]) == [22 12; 11 21]
A = arr(zeros(2,2))
broadcast_setindex!(A, arr([21 11; 12 22]), eye(Int, 2).+1,arr([1, 2]))
@test A == M
broadcast_setindex!(A, 5, [1,2], [2 2])
@test A == [11 5; 21 5]
broadcast_setindex!(A, 7, [1,2], [1 2])
@test A == fill(7, 2, 2)
A = arr(zeros(3,3))
broadcast_setindex!(A, 10:12, 1:3, 1:3)
@test A == diagm(10:12)
@test_throws BoundsError broadcast_setindex!(A, 7, [1,-1], [1 2])
for f in ((==), (<) , (!=), (<=))
bittest(f, arr(eye(2)), arr([1, 4]))
bittest(f, arr(eye(2)), arr([1 4]))
bittest(f, arr([0, 1]), arr([1 4]))
bittest(f, arr([0 1]), arr([1, 4]))
bittest(f, arr([1, 0]), arr([1, 4]))
bittest(f, arr(rand(rb, n1, n2, n3)), arr(rand(rb, n1, n2, n3)))
bittest(f, arr(rand(rb, 1, n2, n3)), arr(rand(rb, n1, 1, n3)))
bittest(f, arr(rand(rb, 1, n2, 1)), arr(rand(rb, n1, 1, n3)))
bittest(f, arr(bitrand(n1, n2, n3)), arr(bitrand(n1, n2, n3)))
end
end
r1 = 1:1
r2 = 1:5
ratio = [1,1/2,1/3,1/4,1/5]
@test r1.*r2 == [1:5;]
@test r1./r2 == ratio
m = [1:2;]'
@test m.*r2 == [1:5 2:2:10]
@test m./r2 ≈ [ratio 2ratio]
@test m./[r2;] ≈ [ratio 2ratio]
@test @inferred(broadcast(+,[0,1.2],reshape([0,-2],1,1,2))) == reshape([0 -2; 1.2 -0.8],2,1,2)
rt = Base.return_types(broadcast, Tuple{typeof(+), Array{Float64, 3}, Array{Int, 1}})
@test length(rt) == 1 && rt[1] == Array{Float64, 3}
rt = Base.return_types(broadcast!, Tuple{Function, Array{Float64, 3}, Array{Float64, 3}, Array{Int, 1}})
@test length(rt) == 1 && rt[1] == Array{Float64, 3}
# f.(args...) syntax (#15032)
let x = [1,3.2,4.7], y = [3.5, pi, 1e-4], α = 0.2342
@test sin.(x) == broadcast(sin, x)
@test sin.(α) == broadcast(sin, α)
@test sin.(3.2) == broadcast(sin, 3.2) == sin(3.2)
@test factorial.(3) == broadcast(factorial, 3)
@test atan2.(x, y) == broadcast(atan2, x, y)
@test atan2.(x, y') == broadcast(atan2, x, y')
@test atan2.(x, α) == broadcast(atan2, x, α)
@test atan2.(α, y') == broadcast(atan2, α, y')
end
# issue 14725
let a = Number[2, 2.0, 4//2, 2+0im] / 2
@test eltype(a) == Number
end
let a = Real[2, 2.0, 4//2] / 2
@test eltype(a) == Real
end
let a = Real[2, 2.0, 4//2] / 2.0
@test eltype(a) == Float64
end
# issue 16164
let a = broadcast(Float32, [3, 4, 5])
@test eltype(a) == Float32
end
# broadcasting scalars:
@test sin.(1) === broadcast(sin, 1) === sin(1)
@test (()->1234).() === broadcast(()->1234) === 1234
# issue #4883
@test isa(broadcast(tuple, [1 2 3], ["a", "b", "c"]), Matrix{Tuple{Int,String}})
@test isa(broadcast((x,y)->(x==1?1.0:x,y), [1 2 3], ["a", "b", "c"]), Matrix{Tuple{Real,String}})
let a = length.(["foo", "bar"])
@test isa(a, Vector{Int})
@test a == [3, 3]
end
let a = sin.([1, 2])
@test isa(a, Vector{Float64})
@test a ≈ [0.8414709848078965, 0.9092974268256817]
end
# PR #17300: loop fusion
@test (x->x+1).((x->x+2).((x->x+3).(1:10))) == collect(7:16)
let A = [sqrt(i)+j for i = 1:3, j=1:4]
@test atan2.(log.(A), sum(A,1)) == broadcast(atan2, broadcast(log, A), sum(A, 1))
end
let x = sin.(1:10)
@test atan2.((x->x+1).(x), (x->x+2).(x)) == broadcast(atan2, x+1, x+2) == broadcast(atan2, x.+1, x.+2)
@test sin.(atan2.([x+1,x+2]...)) == sin.(atan2.(x+1,x+2)) == @. sin(atan2(x+1,x+2))
@test sin.(atan2.(x, 3.7)) == broadcast(x -> sin(atan2(x,3.7)), x)
@test atan2.(x, 3.7) == broadcast(x -> atan2(x,3.7), x) == broadcast(atan2, x, 3.7)
end
# Use side effects to check for loop fusion.
let g = Int[]
f17300(x) = begin; push!(g, x); x+2; end
f17300.(f17300.(f17300.(1:3)))
@test g == [1,3,5, 2,4,6, 3,5,7]
empty!(g)
@. f17300(f17300(f17300(1:3)))
@test g == [1,3,5, 2,4,6, 3,5,7]
end
# fusion with splatted args:
let x = sin.(1:10), a = [x]
@test cos.(x) == cos.(a...)
@test atan2.(x,x) == atan2.(a..., a...) == atan2.([x, x]...)
@test atan2.(x, cos.(x)) == atan2.(a..., cos.(x)) == broadcast(atan2, x, cos.(a...)) == broadcast(atan2, a..., cos.(a...))
@test ((args...)->cos(args[1])).(x) == cos.(x) == ((y,args...)->cos(y)).(x)
end
@test atan2.(3,4) == atan2(3,4) == (() -> atan2(3,4)).()
# fusion with keyword args:
let x = [1:4;]
f17300kw(x; y=0) = x + y
@test f17300kw.(x) == x
@test f17300kw.(x, y=1) == f17300kw.(x; y=1) == f17300kw.(x; [(:y,1)]...) == x .+ 1
@test f17300kw.(sin.(x), y=1) == f17300kw.(sin.(x); y=1) == sin.(x) .+ 1
@test sin.(f17300kw.(x, y=1)) == sin.(f17300kw.(x; y=1)) == sin.(x .+ 1)
end
# splice escaping of @.
let x = [4, -9, 1, -16]
@test [2, 3, 4, 5] == @.(1 + sqrt($sort(abs(x))))
end
# interaction of @. with let
@test [1,4,9] == @. let x = [1,2,3]; x^2; end
# interaction of @. with for loops
let x = [1,2,3], y = x
@. for i = 1:3
y = y^2 # should convert to y .= y.^2
end
@test x == [1,256,6561]
end
# interaction of @. with function definitions
let x = [1,2,3]
@. f(x) = x^2
@test f(x) == [1,4,9]
end
# PR #17510: Fused in-place assignment
let x = [1:4;], y = x
y .= 2:5
@test y === x == [2:5;]
y .= factorial.(x)
@test y === x == [2,6,24,120]
y .= 7
@test y === x == [7,7,7,7]
y .= factorial.(3)
@test y === x == [6,6,6,6]
f17510() = 9
y .= f17510.()
@test y === x == [9,9,9,9]
y .-= 1
@test y === x == [8,8,8,8]
@. y -= 1:4 # @. should convert to .-=
@test y === x == [7,6,5,4]
x[1:2] .= 1
@test y === x == [1,1,5,4]
@. x[1:2] .+= [2,3] # use .+= to make sure @. works with dotted assignment
@test y === x == [3,4,5,4]
@. x[:] .= 0 # use .= to make sure @. works with dotted assignment
@test y === x == [0,0,0,0]
@. x[2:end] = 1:3 # @. should convert to .=
@test y === x == [0,1,2,3]
end
let a = [[4, 5], [6, 7]]
a[1] .= 3
@test a == [[3, 3], [6, 7]]
end
let d = Dict(:foo => [1,3,7], (3,4) => [5,9])
d[:foo] .+= 2
@test d[:foo] == [3,5,9]
d[3,4] .-= 1
@test d[3,4] == [4,8]
end
let identity = error, x = [1,2,3]
x .= 1 # make sure it goes to broadcast!(Base.identity, ...), not identity
@test x == [1,1,1]
end
# make sure scalars are inlined, which causes f.(x,scalar) to lower to a "thunk"
import Base.Meta: isexpr
@test isexpr(expand(:(f.(x,y))), :call)
@test isexpr(expand(:(f.(x,1))), :thunk)
@test isexpr(expand(:(f.(x,1.0))), :thunk)
@test isexpr(expand(:(f.(x,$π))), :thunk)
@test isexpr(expand(:(f.(x,"hello"))), :thunk)
@test isexpr(expand(:(f.(x,$("hello")))), :thunk)
# PR #17623: Fused binary operators
@test [true] .* [true] == [true]
@test [1,2,3] .|> (x->x+1) == [2,3,4]
let g = Int[], ⊕ = (a,b) -> let c=a+2b; push!(g, c); c; end
@test [1,2,3] .⊕ [10,11,12] .⊕ [100,200,300] == [221,424,627]
@test g == [21,221,24,424,27,627] # test for loop fusion
end
# Fused unary operators
@test .√[3,4,5] == sqrt.([3,4,5])
@test .![true, true, false] == [false, false, true]
@test .-[1,2,3] == -[1,2,3] == .+[-1,-2,-3] == [-1,-2,-3]
# PR 16988
@test Base.promote_op(+, Bool) === Int
@test isa(broadcast(+, [true]), Array{Int,1})
# issue #17304
let foo = [[1,2,3],[4,5,6],[7,8,9]]
@test max.(foo...) == broadcast(max, foo...) == [7,8,9]
end
# Issue 17314
@test broadcast(x->log(log(log(x))), [1000]) == [log(log(log(1000)))]
let f17314 = x -> x < 0 ? false : x
@test eltype(broadcast(f17314, 1:3)) === Int
@test eltype(broadcast(f17314, -1:1)) === Integer
@test eltype(broadcast(f17314, Int[])) === Union{Bool,Int}
end
let io = IOBuffer()
broadcast(x->print(io,x), 1:5) # broadcast with side effects
@test take!(io) == [0x31,0x32,0x33,0x34,0x35]
end
# Issue 18176
let f18176(a, b, c) = a + b + c
@test f18176.(1.0:2, 3, 4) == f18176.(3.0, 1.0:2, 4.0) == broadcast(f18176, 3, 4, 1.0:2)
end
# Issue #17984
let A17984 = []
@test isa(abs.(A17984), Array{Any,1})
end
# Issue #16966
@test parse.(Int, "1") == 1
@test parse.(Int, ["1", "2"]) == [1, 2]
@test trunc.((Int,), [1.2, 3.4]) == [1, 3]
@test abs.((1, -2)) == (1, 2)
@test broadcast(+, 1.0, (0, -2.0)) == (1.0,-1.0)
@test broadcast(+, 1.0, (0, -2.0), [1]) == [2.0, 0.0]
@test broadcast(*, ["Hello"], ", ", ["World"], "!") == ["Hello, World!"]
let s = "foo"
@test s .* ["bar", "baz"] == ["foobar", "foobaz"] == "foo" .* ["bar", "baz"]
end
# Ensure that even strange constructors that break `T(x)::T` work with broadcast
struct StrangeType18623 end
StrangeType18623(x) = x
StrangeType18623(x,y) = (x,y)
@test @inferred(broadcast(StrangeType18623, 1:3)) == [1,2,3]
@test @inferred(broadcast(StrangeType18623, 1:3, 4:6)) == [(1,4),(2,5),(3,6)]
@test typeof(Int.(Number[1, 2, 3])) === typeof((x->Int(x)).(Number[1, 2, 3]))
@test @inferred(broadcast(CartesianIndex, 1:2)) == [CartesianIndex(1), CartesianIndex(2)]
@test @inferred(broadcast(CartesianIndex, 1:2, 3:4)) == [CartesianIndex(1,3), CartesianIndex(2,4)]
# Issue 18622
@test @inferred(broadcast(muladd, [1.0], [2.0], [3.0])) == [5.0]
@test @inferred(broadcast(tuple, 1:3, 4:6, 7:9)) == [(1,4,7), (2,5,8), (3,6,9)]
# 19419
@test @inferred(broadcast(round, Int, [1])) == [1]
# https://discourse.julialang.org/t/towards-broadcast-over-combinations-of-sparse-matrices-and-scalars/910
let
f(A, n) = broadcast(x -> +(x, n), A)
@test @inferred(f([1.0], 1)) == [2.0]
g() = (a = 1; Base.Broadcast._broadcast_eltype(x -> x + a, 1.0))
@test @inferred(g()) === Float64
end
# Ref as 0-dimensional array for broadcast
@test (-).(C_NULL, C_NULL)::UInt == 0
@test (+).(1, Ref(2)) == fill(3)
@test (+).(Ref(1), Ref(2)) == fill(3)
@test (+).([[0,2], [1,3]], [1,-1]) == [[1,3], [0,2]]
@test (+).([[0,2], [1,3]], Ref{Vector{Int}}([1,-1])) == [[1,1], [2,2]]
# Check that broadcast!(f, A) populates A via independent calls to f (#12277, #19722),
# and similarly for broadcast!(f, A, numbers...) (#19799).
@test let z = 1; A = broadcast!(() -> z += 1, zeros(2)); A[1] != A[2]; end
@test let z = 1; A = broadcast!(x -> z += x, zeros(2), 1); A[1] != A[2]; end
# broadcasting for custom AbstractArray
struct Array19745{T,N} <: AbstractArray{T,N}
data::Array{T,N}
end
Base.getindex(A::Array19745, i::Integer...) = A.data[i...]
Base.size(A::Array19745) = size(A.data)
Base.Broadcast._containertype{T<:Array19745}(::Type{T}) = Array19745
Base.Broadcast.promote_containertype(::Type{Array19745}, ::Type{Array19745}) = Array19745
Base.Broadcast.promote_containertype(::Type{Array19745}, ::Type{Array}) = Array19745
Base.Broadcast.promote_containertype(::Type{Array19745}, ct) = Array19745
Base.Broadcast.promote_containertype(::Type{Array}, ::Type{Array19745}) = Array19745
Base.Broadcast.promote_containertype(ct, ::Type{Array19745}) = Array19745
Base.Broadcast.broadcast_indices(::Type{Array19745}, A) = indices(A)
Base.Broadcast.broadcast_indices(::Type{Array19745}, A::Ref) = ()
getfield19745(x::Array19745) = x.data
getfield19745(x) = x
Base.Broadcast.broadcast_c(f, ::Type{Array19745}, A, Bs...) =
Array19745(Base.Broadcast.broadcast_c(f, Array, getfield19745(A), map(getfield19745, Bs)...))
@testset "broadcasting for custom AbstractArray" begin
a = randn(10)
aa = Array19745(a)
@test a .+ 1 == @inferred(aa .+ 1)
@test a .* a' == @inferred(aa .* aa')
@test isa(aa .+ 1, Array19745)
@test isa(aa .* aa', Array19745)
end
# broadcast should only "peel off" one container layer
@test get.([Nullable(1), Nullable(2)]) == [1, 2]
let io = IOBuffer()
broadcast(x -> print(io, x), [Nullable(1.0)])
@test String(take!(io)) == "Nullable{Float64}(1.0)"
end
# Test that broadcast's promotion mechanism handles closures accepting more than one argument.
# (See issue #19641 and referenced issues and pull requests.)
let f() = (a = 1; Base.Broadcast._broadcast_eltype((x, y) -> x + y + a, 1.0, 1.0))
@test @inferred(f()) == Float64
end
@testset "broadcast resulting in BitArray" begin
let f(x) = x ? true : "false"
ba = f.([true])
@test ba isa BitArray
@test ba == [true]
a = f.([false])
@test a isa Array{String}
@test a == ["false"]
@test f.([true, false]) == [true, "false"]
end
end
# Test that broadcast treats type arguments as scalars, i.e. containertype yields Any,
# even for subtypes of abstract array. (https://github.com/JuliaStats/DataArrays.jl/issues/229)
@testset "treat type arguments as scalars, DataArrays issue 229" begin
@test Base.Broadcast.containertype(AbstractArray) == Any
@test broadcast(==, [1], AbstractArray) == BitArray([false])
@test broadcast(==, 1, AbstractArray) == false
end
# Test that broadcasting identity where the input and output Array shapes do not match
# yields the correct result, not merely a partial copy. See pull request #19895 for discussion.
let N = 5
@test iszero(ones(N, N) .= zeros(N, N))
@test iszero(ones(N, N) .= zeros(N, 1))
@test iszero(ones(N, N) .= zeros(1, N))
@test iszero(ones(N, N) .= zeros(1, 1))
end
@testset "test broadcast for matrix of matrices" begin
A = fill(zeros(2,2), 4, 4)
A[1:3,1:3] .= [ones(2,2)]
@test all(A[1:3,1:3] .== [ones(2,2)])
end