I use a simple function for this:
function newdim(A::AbstractArray, d::Integer)
@assert 0 < d <= ndims(A)+1
dim = size(A)
reshape(A, dim[1:d-1]..., 1, dim[d:end]...)
end
But having syntax for a newaxis would be great. See also:
https://github.com/JuliaLang/julia/issues/5405
https://github.com/JuliaLang/julia/issues/4774 (search for newaxis)
Bob
On Mon, Sep 12, 2016 at 5:00 PM, Tim Holy <tim.h...@gmail.com> wrote:
> julia> a = rand(3)
> 3-element Array{Float64,1}:
> 0.47428
> 0.505429
> 0.198919
>
> julia> reshape(a, (3,1))
> 3×1 Array{Float64,2}:
> 0.47428
> 0.505429
> 0.198919
>
> julia> reshape(a, (1,3))
> 1×3 Array{Float64,2}:
> 0.47428 0.505429 0.198919
>
> Is that what you want? (Note that for both of them, the result is
> 2-dimensional.)
>
>
>
> --Tim
>
>
>
> On Monday, September 12, 2016 6:47:04 PM CDT Neal Becker wrote:
>
> > I haven't studied it, but I guess that newaxis increases the
> dimensionality,
>
> > while specifying 0 for the stride. Can reshape do that?
>
> >
>
> > Tim Holy wrote:
>
> > > I'm not certain I understand what `np.newaxis` does, but doesn't
> `reshape`
>
> > > do the same thing? (newaxis does look like a convenient way to specify
>
> > > shape, though.)
>
> > >
>
> > > Best,
>
> > > --Tim
>
> > >
>
> > > On Monday, September 12, 2016 3:28:56 PM CDT Neal Becker wrote:
>
> > >> Some time ago I asked this question
>
> > >> http://stackoverflow.com/questions/25486506/julia-
> broadcasting-equivalent
>
> > >> -of -numpy-newaxis
>
> > >>
>
> > >> As a more interesting example, here is some real python code I use:
>
> > >> dist = mag_sqr (demod_out[:,np.newaxis] - const.map[np.newaxis,:])
>
> > >>
>
> > >> where demod_out, const.map are each vectors, mag_sqr performs
>
> > >> element-wise euclidean distance, and the result is a 2D array whose
> 1st
>
> > >> axis matches the 1st axis of demod_out, and the 2nd axis matches the
> 2nd
>
> > >> axis of const.map.
>
> > >>
>
> > >>
>
> > >> From the answers I've seen, julia doesn't really have an equivalent
>
> > >> functionality. The idea here is, without allocating a new array,
>
> > >> manipulate the strides to cause broadcasting.
>
> > >>
>
> > >> AFAICT, the best for Julia would be just forget the vectorized code,
> and
>
> > >> explicitly write out loops to perform the computation. OK, I guess,
> but
>
> > >> maybe not as readable.
>
> > >>
>
> > >> Is there any news on this front?
>
>
>
>
>
