# Dot function

**URL:** <https://discourse.julialang.org/t/dot-function/8644>\
**Category:** General Usage\
**Created:** [January 27, 2018, 8:50pm UTC](https://discourse.julialang.org/t/dot-function/8644 "2018-01-27T20:50:34Z")\
**Posts on this page:** 1\
**Showing post:** 3

<div class="post-metadata">

**Author:** ![tk3369](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/tk3369/32/2824_2.png) [@tk3369](https://discourse.julialang.org/u/tk3369)\
**Post date:** [January 28, 2018, 1:46am UTC](https://discourse.julialang.org/t/dot-function/8644/3 "2018-01-28T01:46:56Z")

</div>

Going off topic:

I’m trying to do a reality check since the last epic discussion about “is numba good enough”…

My test result for this example shows Julia being 2-3x slower. Maybe the code wasn’t translated in a good way? (data set: [UCI Machine Learning Repository: Wine Quality Data Set](https://archive.ics.uci.edu/ml/datasets/Wine+Quality))

Python/Numba:

```julia
In [64]: @numba.jit
    ...: def logistic_regression3(Y, X, w, iterations):
    ...: for i in range(iterations):
    ...: w -= np.dot(((1.0 / (1.0 + np.exp(-Y * np.dot(X, w))) - 1.0) * Y), X)
    ...: return w
    ...: 

In [65]: %timeit logistic_regression3(Y2, X2, w, 1000)
22.3 ms ± 464 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [66]: Y.shape
Out[66]: (1599,)

In [67]: X.shape
Out[67]: (1599, 2)

In [68]: w.shape
Out[68]: (2,)

```

Julia:

```julia
using CSV, DataFrames
df = CSV.read("/Users/tomkwong/Downloads/winequality-red.csv", 
        delim=";", types=Dict(6=>Float64, 7=>Float64))
X = convert(Array, df[[Symbol("fixed acidity"), Symbol("volatile acidity")]])
Y = df[:quality]
w = ones(size(X)[2])

function logistic_regression(Y, X, w, iterations)
    for i in 1:iterations
        w -= (((1.0 ./ (1.0 .+ e .^ (-Y .* (X * w))) - 1.0) .* Y)' * X)'
    end
    w
end

using BenchmarkTools
@benchmark logistic_regression($Y, $X, $w, 1000)

```

Julia results:

```julia
julia> @benchmark logistic_regression($Y, $X, $w, 1000)
BenchmarkTools.Trial: 
  memory estimate: 86.49 MiB
  allocs estimate: 9000
  --------------
  minimum time: 60.454 ms (9.32% GC)
  median time: 68.327 ms (14.48% GC)
  mean time: 68.327 ms (14.23% GC)
  maximum time: 80.246 ms (13.99% GC)
  --------------
  samples: 74
  evals/sample: 1

```

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