It'd be nice to allow up to one missing dimension in the dimension tuple,
and calculate it automatically. Something like this works nicely, where
the missing dimension is specified by ():
function Base.reshape{N}(a::AbstractArray, dims::NTuple{N,Union(Int,())})
missing_mask = [isa(x,Tuple) for x in dims]
sum(missing_mask) == 1 || throw(DimensionMismatch("new dimensions
$(dims) may only have up to one omitted dimension"))
sz = length(a) / sum(dims[!missing_mask])
sz == int(sz) || throw(DimensionMismatch("array size $(length(a)) must
be divisible by the product of the new dimensions $(dims[!missing_mask])"))
reshape(a,map(x->isa(x,Tuple) ? int(sz) : x, dims))
end
Then: reshape(linspace(0, 10), ((), 1)) would do the trick.
On Tuesday, April 29, 2014 2:07:41 PM UTC-4, Tom Nickson wrote:
>
> Is there an built-in way to convert a vector to a column matrix without
> transposing twice?
> I can make a matrix into a vector with vec or squeeze, but I can't see a
> simple way to convert from a 1d array to a 2d without initialising it,
> calling reshape and having to measure the size.
>
> I've put this definition in my .juliarc:
>
> colmat{T}(x::Array{T, 1}) = reshape(x, (length(x), 1))
>
> and do colmat(linspace(0, 10)), for example.
>
> Is there a better way?
>
