Is there a Julia analogue to numpy.argmax?
In Python, there is numpy.argmax:
In [7]: a = np.random.rand(5,3)
In [8]: a
Out[8]:
array([[ 0.00108039, 0.16885304, 0.18129883],
[ 0.42661574, 0.78217538, 0.43942868],
[ 0.34321459, 0.53835544, 0.72364813],
[ 0.97914267, 0.40773394, 0.36358753],
[ 0.59639274, 0.67640815, 0.28126232]])
In [10]: np.argmax(a,axis=1)
Out[10]: array([2, 1, 2, 0, 1])
Is there a Julia analogue to Numpy's argmax? I only found a indmax, which only accept a vector, not a two dimensional array as np.argmax.
Answer
The fastest implementation will usually be findmax (which allows you to reduce over multiple dimensions at once, if you wish):
julia> a = rand(5, 3)
5×3 Array{Float64,2}:
0.867952 0.815068 0.324292
0.44118 0.977383 0.564194
0.63132 0.0351254 0.444277
0.597816 0.555836 0.32167
0.468644 0.336954 0.893425
julia> mxval, mxindx = findmax(a; dims=2)
([0.8679518267243425; 0.9773828942695064; … ; 0.5978162823947759; 0.8934254589671011], CartesianIndex{2}[CartesianIndex(1, 1); CartesianIndex(2, 2); … ; CartesianIndex(4, 1); CartesianIndex(5, 3)])
julia> mxindx
5×1 Array{CartesianIndex{2},2}:
CartesianIndex(1, 1)
CartesianIndex(2, 2)
CartesianIndex(3, 1)
CartesianIndex(4, 1)
CartesianIndex(5, 3)