3 ms·

So does Matlab/Octave and R, so Julia's choice is unsurprising to many people in the target demographic.

jordigh 8y ago
And Matlab and Octave in turn inherited it from Fortran, which inherited it from classical numerical analysis. One-based indexing has a good, long history.
- creativeembassy 8y ago
  To use a silly example... can I finally realize my dream of two-based indexing?
  - ChrisRackauckas 8y ago
    Yes. And 1.5-based indexing, though I would recommend using rational numbers instead of floating point numbers for this one.
    - JulianMorrison 8y ago
      And imaginary is right out.
      - stabbles 8y ago
        Maybe you could use complex numbers to index matrices
        3JPLW 8y ago
        Sure can: # Magic julia> Base.to_indices(A, inds, I::Tuple{Complex, Vararg{Any}}) = (real(I[1]), imag(I[1]), to_indices(A, Base._maybetail(Base._maybetail(inds)), Base.tail(I))...) julia> A 4×5 reshape(::UnitRange{Int64}, 4, 5) with eltype Int64: 1 5 9 13 17 2 6 10 14 18 3 7 11 15 19 4 8 12 16 20 julia> A[3 + 2im] 7 ```
        ChrisRackauckas 8y ago
        And quaternions for image stacks in ML? Troll, but not too troll...
        elcritch 8y ago
        That’s a right kind of wrong... and could work with FEA tensor too... ;)
        jordigh 8y ago
        Story time! When Octave was getting a lot of traffic from the first Coursera machine learning class, when it was just Andrew Ng doing a course and before the Coursera org existed, we were getting a lot of novice users who would do something like some_matrix(i,j) = 5 and get cryptic errors about how matrices cannot be indexed by complex numbers. This is because by default, in Matlab and Octave `i` and `j` are functions that evaluate to the imaginary unit. You have to overwrite the function names with `i=2, j=3` or whatever beforehand, which novices often forget. This was happening often enough that I pushed some patches to warn, "did you forget to assign i or j?" if someone tried to index matrices with complex numbers. Point is, people in Matlab and Octave often unintentionally try to index by complex numbers.

And Matlab and Octave in turn inherited it from Fortran, which inherited it from classical numerical analysis. One-based indexing has a good, long history.

creativeembassy 8y ago
To use a silly example... can I finally realize my dream of two-based indexing?
- ChrisRackauckas 8y ago
  Yes. And 1.5-based indexing, though I would recommend using rational numbers instead of floating point numbers for this one.
  - JulianMorrison 8y ago
    And imaginary is right out.
    - stabbles 8y ago
      Maybe you could use complex numbers to index matrices
      - 3JPLW 8y ago
        Sure can: # Magic julia> Base.to_indices(A, inds, I::Tuple{Complex, Vararg{Any}}) = (real(I[1]), imag(I[1]), to_indices(A, Base._maybetail(Base._maybetail(inds)), Base.tail(I))...) julia> A 4×5 reshape(::UnitRange{Int64}, 4, 5) with eltype Int64: 1 5 9 13 17 2 6 10 14 18 3 7 11 15 19 4 8 12 16 20 julia> A[3 + 2im] 7 ```
      - ChrisRackauckas 8y ago
        And quaternions for image stacks in ML? Troll, but not too troll...
        elcritch 8y ago
        That’s a right kind of wrong... and could work with FEA tensor too... ;)
      - jordigh 8y ago
        Story time! When Octave was getting a lot of traffic from the first Coursera machine learning class, when it was just Andrew Ng doing a course and before the Coursera org existed, we were getting a lot of novice users who would do something like some_matrix(i,j) = 5 and get cryptic errors about how matrices cannot be indexed by complex numbers. This is because by default, in Matlab and Octave `i` and `j` are functions that evaluate to the imaginary unit. You have to overwrite the function names with `i=2, j=3` or whatever beforehand, which novices often forget. This was happening often enough that I pushed some patches to warn, "did you forget to assign i or j?" if someone tried to index matrices with complex numbers. Point is, people in Matlab and Octave often unintentionally try to index by complex numbers.