# Apply reduction along specific axes

**URL:** <https://discourse.julialang.org/t/apply-reduction-along-specific-axes/3301>\
**Category:** New to Julia\
**Created:** [April 20, 2017, 5:16pm UTC](https://discourse.julialang.org/t/apply-reduction-along-specific-axes/3301 "2017-04-20T17:16:01Z")\
**Posts on this page:** 1\
**Showing post:** 16

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**Author:** ![ndbecker](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/ndbecker/32/10829_2.png) [@ndbecker](https://discourse.julialang.org/u/ndbecker)\
**Post date:** [May 3, 2017, 6:19pm UTC](https://discourse.julialang.org/t/apply-reduction-along-specific-axes/3301/16 "2017-05-03T18:19:52Z")

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wow! amazing! Now I have  
import StatsFuns  
using BenchmarkTools  
function logsumexp1(u, axes)  
m = maximum(u, axes)  
return m .+ log.(sum(exp.(u .- m), axes))  
end  
u = rand(10,100000);  
v = rand(100000,10);  
@btime logsumexp1($u, 2);  
@btime logsumexp1($u, 1);  
@btime logsumexp1($v, 2);  
@btime logsumexp1($v, 1);  
15.428 ms (15 allocations: 7.63 MiB)  
22.213 ms (8 allocations: 9.92 MiB)  
17.118 ms (18 allocations: 9.92 MiB)  
15.849 ms (5 allocations: 7.63 MiB)

For comparison with scipy, I get:  
%timeit logsumexp(u,0)  
22.2 ms ± 98.8 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [103]: %timeit logsumexp(u,1)  
17.7 ms ± 68.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [107]: %timeit logsumexp(v,0)  
20.7 ms ± 209 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [108]: %timeit logsumexp(v,1)  
24.9 ms ± 151 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

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