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I just stumbled upon some inconsistencies between mean and mean! and I am wondering whether this is a bug – since we try to use both those methods in a setting where I would expect them to work consistently.
Here is a small example.
using StatsBase
x = [fill(1.0), fill(2.0), fill(3.0), fill(4.0)]
w = pweights(ones(length(x)) / length(x))
y = mean(x,w)which returns a
0-dimensional Array{Float64, 0}:
2.5So consistently I would have expected mean! to do the same if I pass y as the return value, i.e.
mean!(y,x,w)but it reports
ERROR: ArgumentError: dims argument must be provided
Stacktrace:
[1] _mean!(R::Array{Float64, 0}, A::Vector{Array{Float64, 0}}, w::ProbabilityWeights{Float64, Float64, Vector{Float64}}, dims::Nothing)
@ StatsBase ~/.julia/packages/StatsBase/WLz8A/src/weights.jl:657But even if I provide the dims I get for
mean!(y,x,w; dims=1)ERROR: MethodError: no method matching +(::Float64, ::Array{Float64, 0})
For element-wise addition, use broadcasting with dot syntax: scalar .+ array
Closest candidates are:
+(::Any, ::Any, ::Any, ::Any...)
@ Base operators.jl:578
+(::T, ::T) where T<:Union{Float16, Float32, Float64}
@ Base float.jl:408
+(::Union{Float16, Float32, Float64}, ::BigFloat)
@ Base mpfr.jl:423
...
Stacktrace:
[1] macro expansion
@ ~/.julia/packages/StatsBase/WLz8A/src/weights.jl:528 [inlined]
[...]Ah, I noticed that the same happens, also in the unweighted case, just that it skips the dimension error and directly reports the last.
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