It also seems unnecessary to restrict it to Float64 and Array:
function myjaccard2{T}(A::AbstractArray{T,1}, B::AbstractArray{T, 1})
num = zero(T)
den = zero(T)
for (a,b) in zip(A,B)
num += min(a,b)
den += max(a,b)
end
return 1.0 - num/den
end
Maybe also use T1, T2 and then promote to a common type ;)
Best,
Simon
Am Montag, 13. Juni 2016 03:53:14 UTC+2 schrieb jean-pierre both:
>
>
>
> I encountered in my application with Distances.Jaccard compared with
> Distances.Euclidean
> It was very slow.
>
> For example with 2 vecteurs Float64 of size 11520
>
> I get the following
> julia> D=Euclidean()
> Distances.Euclidean()
> julia> @time for i in 1:500
> evaluate(D,v1,v2)
> end
> 0.002553 seconds (500 allocations: 7.813 KB)
>
> and with Jaccard
>
> julia> D=Jaccard()
> Distances.Jaccard()
> @time for i in 1:500
> evaluate(D,v1,v2)
> end
> 1.995046 seconds (40.32 M allocations: 703.156 MB, 9.68% gc time)
>
> With a simple loop for computing jaccard :
>
>
> function myjaccard2(a::Array{Float64,1}, b::Array{Float64,1})
> num = 0
> den = 0
> for i in 1:length(a)
> num = num + min(a[i],b[i])
> den = den + max(a[i],b[i])
> end
> 1. - num/den
> end
> myjaccard2 (generic function with 1 method)
>
> julia> @time for i in 1:500
> myjaccard2(v1,v2)
> end
> 0.451582 seconds (23.04 M allocations: 351.592 MB, 20.04% gc time)
>
> I do not see the problem in jaccard distance implementation in the
> Distances packages
>
