> Thanks. The example was intended as an illustration of a more complex
> code in which this was an issue. However, maybe i could do it without
> the @generated. I will revisit it and see if I really did need it...
Well, it is always hard to tell what's best. If you need certainty,
then you need to benchmark. Note that in below example, Julia will
compile a specialized version of the function for each NTuple anyway.
This may be good or bad, depending on how much time compilation vs
running takes.
A case for a generated function would be if, say, the number of nested
loops would change depending on some parameter, say the dimensionality
of an array.
> Thanks, Alan
>
>
> was just an illustration of a the larger piece of code where I am making using
> On 29 Sep 2015, at 13:48, Mauro <mauro...@runbox.com> wrote:
>
>> I don't think that you need nor that you should use generated functions.
>> But maybe I'm wrong, what are you trying to achieve? This should work
>> as you want:
>>
>> function testfun2!{N}(X,Y::NTuple{N,Float64})
>> for i in eachindex(X), j in 1:N # much better to have the loop this way
>> X[i][j] = Y[j]
>> end
>> return X
>> end
>>
>> # Setup for function call
>> InnerArrayPts = 3
>> OuterArrayPts = 10
>> Xinput = [Array{Float64}(InnerArrayPts) for r in 1:OuterArrayPts]
>> Yinput = rand(InnerArrayPts)
>>
>> testfun2!(Xinput,tuple(Yinput...))
>>
>> On Tue, 2015-09-29 at 13:20, Alan Crawford <a.r.crawf...@gmail.com> wrote:
>>> I would like to preallocate memory of an array of arrays and pass it to a
>>> function to be filled in. I have created an example below that illustrates
>>> my question(s).
>>>
>>> Based on my (probably incorrect) understanding that it would be desirable
>>> to fix the type in my function, I would like to be able to pass my array of
>>> arrays, X, in a type stable way. However, I can't seem to pass
>>> Array{Array{Float64,N},1}. If, however, i do not attempt to impose the type
>>> on the function, it works.
>>>
>>> Is there a way to pass Array{Array{Float64,N},1 to my function? Do I even
>>> need to fix the type in the function to get good performance?
>>>
>>> # version of Julia: 0.4.0-rc3
>>>
>>> @generated function
>>> testfun1!{N}(X::Array{Array{Float64,1},1},Y::NTuple{N,Float64})
>>> quote
>>> for j in 1:$N, i in eachindex(X)
>>> X[i][j] = Y[j]
>>> end
>>> return X
>>> end
>>> end
>>>
>>> @generated function testfun2!{N}(X,Y::NTuple{N,Float64})
>>> quote
>>> for j in 1:$N, i in eachindex(X)
>>> X[i][j] = Y[j]
>>> end
>>> return X
>>> end
>>> end
>>>
>>> # Setup for function call
>>> InnerArrayPts = 3
>>> OuterArrayPts = 10
>>> Xinput = [Array{Float64}(InnerArrayPts) for r in 1:OuterArrayPts]
>>> Yinput = rand(InnerArrayPts)
>>>
>>> # Method Error Problem
>>> testfun1!(Xinput,tuple(Yinput...))
>>>
>>> # This works
>>> testfun2!(Xinput,tuple(Yinput...))
>>>
>>> I also tried with the following version of testfun1!() and again got a
>>> method error.
>>>
>>> @generated function
>>> testfun1!{N}(X::Array{Array{Float64,N},1},Y::NTuple{N,Float64})
>>> quote
>>> for j in 1:$N, i in eachindex(X)
>>> X[i][j] = Y[j]
>>> end
>>> return X
>>> end
>>> end
>>>
>>>
>>> I am sure i am misunderstanding something quite fundamental and/or missing
>>> something straightforward...
>>>
>>> Thanks,
>>> Alan
