I (think I) would be in favor of changing [1; 2; 3] to mean a 1-column
matrix. Arguments against?
Kevin
On Tue, Feb 24, 2015 at 11:45 AM, Bill Hart <goodwillh...@googlemail.com>
wrote:
> Julia distinguishes between them at the level of types, yes. I was talking
> about the syntax for creating them. There they become conflated.
>
> [1; 2; 3] is precisely the same thing as [1, 2, 3]
>
> Sorry for not being clearer about that.
>
> It's clear this is done intentionally, not accidentally. It's just
> extremely confusing and I'll bet it will bite somewhere down the track.
> It's especially bad for metaprogramming for example.
>
> Bill.
>
> On 24 February 2015 at 20:39, David P. Sanders <dpsand...@gmail.com>
> wrote:
>
>>
>>
>> El martes, 24 de febrero de 2015, 8:18:28 (UTC-6), Bill Hart escribió:
>>>
>>> The specific use case where I hit this as a problem is in wrapping
>>> external libraries like flint and pari, which unlike julia do not conflate
>>> 1xn matrices with flat arrays.
>>>
>>> However, I have just realised that Julia only conflates column vectors
>>> with flat arrays, not row vectors.
>>>
>>
>> That is not my interpretation. Julia carefully distinguishes between the
>> different options:
>>
>> (i) a 1D object (which could be called a "vector"), i.e. with 1 index
>> (what I understand you mean by "flat array"):
>>
>> julia> v = [3, 4, 5, 6]
>> 4-element Array{Int64,1}:
>> 3
>> 4
>> 5
>> 6
>>
>> This is (unfortunately?) displayed in a column, but is just a
>> one-dimensional list of things.
>>
>> (ii) A matrix object of size 1 x n (which could be called a "row
>> vector", but is actually a matrix):
>>
>> julia> w = [3 4 5 6]
>> 1x4 Array{Int64,2}:
>> 3 4 5 6
>>
>> (iii) A matrix of size n x 1 (which could be called a "column vector",
>> but is actually a matrix):
>> julia> z = reshape([3, 4, 5, 6], (4,1))
>> 4x1 Array{Int64,2}:
>> 3
>> 4
>> 5
>> 6
>>
>> (The output looks superficially the same as that for v, but the object is
>> of a different type -- Array{Int64, 2} vs. Array{Int64, 1}. I.e. there are
>> now *two* indices.)
>>
>> I do not find the nomenclature "row vector" and "column vector" useful in
>> this (or almost any other!) context.
>>
>> David.
>>
>>
>>
>>>
>>> Therefore I can overload my constructor for the external flint and pari
>>> types to interpret Julia flat arrays as column vectors. Fortunately, I only
>>> need to do that in one place. I can't imagine overloading every single
>>> function for every possible combination of flat array vs matrix for each
>>> parameter to the function.
>>>
>>> The only reason I did things that way is because I initially thought
>>> Julia arrays had cartesian product semantics (pointwise operations) rather
>>> than matrix semantics.
>>>
>>> So actually, I guess I can live with the way things are right now.
>>>
>>> On 24 February 2015 at 14:29, Jiahao Chen <jia...@mit.edu> wrote:
>>>
>>>> Clearly Julia wants to deliberately conflate row and column vectors,
>>>>> which is fine if you aren't working with an algorithm that depends on the
>>>>> individual dimensions of your matrices, and you aren't interfacing to an
>>>>> external library.
>>>>>
>>>>
>>>> Could you elaborate on what you mean here? As part of Issue #4474 I've
>>>> been trying to collect specific examples.
>>>>
>>>
>>>
>
