Please try https://github.com/JuliaStats/Distances.jl/pull/44
On Monday, June 13, 2016 at 8:14:01 PM UTC+2, Mauro wrote:
>
> > function myjaccard2(a::Array{Float64,1}, b::Array{Float64,1})
> > num = 0.
> > den = 0.
> > for I in 1:length(a)
> > @inbounds ai = a[I]
> > @inbounds bi = b[I]
> > num = num + min(ai,bi)
> > den = den + max(ai,bi)
> > end
> > 1. - num/den
> > end
> >
> >
> >
> > function testDistances2(v1::Array{Float64,1}, v2::Array{Float64,1})
> > for i in 1:50000
> > myjaccard2(v1,v2)
> > end
> > end
>
> I recommend using the values returned for something, otherwise the
> compiler sometimes eliminates the loop (but not here):
>
> julia> function testDistances2(v1::Array{Float64,1}, v2::Array{Float64,1})
> out = 0.0
> for i in 1:50000
> out += myjaccard2(v1,v2)
> end
> out
> end
>
> > @time testDistances2(v1,v2)
> > machine 3.217329 seconds (200.01 M allocations: 2.981 GB, 19.91% gc
> time)
>
> I cannot reproduce this, when I run it I get no allocations:
>
> julia> v2 = rand(10^4);
>
> # warm-up
> julia> @time testDistances2(v1,v2)
> 3.604478 seconds (8.15 k allocations: 401.797 KB, 0.42% gc time)
> 24999.00112162811
>
> julia> @time testDistances2(v1,v2)
> 3.647563 seconds (5 allocations: 176 bytes)
> 24999.00112162811
>
> What version of Julia are you running. Me 0.4.5.
>
> > function myjaccard5(a::Array{Float64,1}, b::Array{Float64,1})
> > num = 0.
> > den = 0.
> > for I in 1:length(a)
> > @inbounds ai = a[I]
> > @inbounds bi = b[I]
> > abs_m = abs(ai-bi)
> > abs_p = abs(ai+bi)
> > num += abs_p - abs_m
> > den += abs_p + abs_m
> > end
> > 1. - num/den
> > end
> >
> >
> > function testDistances5(a::Array{Float64,1}, b::Array{Float64,1})
> > for i in 1:5000
> > myjaccard5(a,b)
> > end
> > end
> >
> > end
> >
> >
> > julia> @time testDistances5(v1,v2)
> > 0.166979 seconds (4 allocations: 160 bytes)
> >
> >
> >
> > We see that using abs is faster.
> >
> > I do not do a pull request beccause
> >
> > I would expect a good implementation to be 2 or 3 times slower than
> > Euclidean, and I have not
> > that yet.
> >
> > Le lundi 13 juin 2016 13:43:00 UTC+2, Kristoffer Carlsson a écrit :
> >>
> >> It seems weird to me that you guys want to call Jaccard distance with
> >> float arrays. AFAIK Jaccard distance measures the distance between two
> >> distinct samples from a pair of sets so basically between two
> Vector{Bool},
> >> see:
> >>
> http://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.jaccard.html
>
> >>
> >> "Computes the Jaccard-Needham dissimilarity between two boolean 1-D
> >> arrays."
> >>
> >> Is there some more general formulation of it that extends to vectors in
> a
> >> continuous vector space?
> >>
> >> And, to note, Jaccard is type stable for inputs of Vector{Bool} in
> >> Distances.jl.
> >>
> >> On Monday, June 13, 2016 at 3:53:14 AM UTC+2, jean-pierre both wrote:
> >>>
> >>>
> >>>
> >>> 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
> >>>
> >>
>
