You could Use `AbstractVecOrMat` and `idx=1` as default value, but you
could also use `sub` or `slice` to avoid copying the matrix columns since
*julia> **A = randn(2,2)*
*2x2 Array{Float64,2}:*
* -0.124018 1.3846 *
* -0.259116 -1.19279*
*julia> **isa(sub(A, :,1), AbstractVector{Float64})*
*true*
2015-02-16 8:11 GMT-05:00 Kristoffer Carlsson <[email protected]>:
> Actually the best thing would be if I could call a function with the
>
> dist(point_1::AbstractVector{T}, point_2::AbstractVector{T})
>
> signature because then I could just use all the stuff in the Distances.jl
> package
>
>
>
> On Monday, February 16, 2015 at 2:05:17 PM UTC+1, Kristoffer Carlsson
> wrote:
>>
>> I am writing some code where I sometimes want to calculate the distance
>> between two vectors and sometimes the distance between a column in a matrix
>> and another vector.
>>
>> This is in a very hot spot of my program so any unnecessary copying is
>> unacceptable.
>>
>> What I am currently doing is basically having two versions of the
>> distance function, one for vector-vector and one for column-vector:
>>
>> # Reduced euclidian distances
>>
>> # Vector-Vector
>> @inline function euclidean_distance_red{T <: FloatingPoint}(point_1::
>> AbstractVector{T},
>> point_2::
>> AbstractVector{T})
>> dist = 0.0
>> for i = 1:length(point_1)
>> @inbounds dist += abs2(point_1[i] - point_2[i])
>> end
>> return dist
>> end
>>
>> # Column-Vector
>> @inline function euclidean_distance_red{T <: FloatingPoint}(data::
>> AbstractMatrix{T},
>> point::
>> AbstractVector{T},
>> idx::Int)
>> dist = 0.0
>> for i = 1:length(point)
>> @inbounds dist += abs2(data[i, idx] - point[i])
>> end
>> return dist
>> end
>>
>>
>> This feels unnecessary since the transformation from the second to the
>> first function is obvious. Is there anyway I can collapse these two
>> functions into one that will come with zero performance loss?
>>
>> Best regards,
>> Kristoffer Carlsson
>>
>
