In [1]:

    

using PairwiseListMatrices
using Benchmarks
using Base.Test
using Gadfly


    

    




WARNING: UUID.jl is deprecated, please use Base.Random.uuid1(), Base.Random.uuid4(), and Base.Random.UUID instead.

In [2]:

    

const SAMPLES = collect(5:50:2000)
const TIME = zeros(Float64, length(SAMPLES)*2)
const NAMES = vcat([ ["pairwiselistmatrix", "full"] for i in 1:length(SAMPLES) ]...)
const XS = vcat([ [x, x] for x in SAMPLES ]...);


    

In [3]:

    

k = 0
for sample in SAMPLES
    list = PairwiseListMatrix(rand(div(sample*(sample-1),2)))
    mat = full(list)
    @test_approx_eq mean(list) mean(mat)
    k += 1
    TIME[k] = @elapsed mean(list)
    k += 1
    TIME[k] = @elapsed mean(mat)
end

plot(x=XS, y=TIME, color=NAMES, Geom.point, Geom.smooth)


    

    
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In [4]:

    

k = 0
for sample in SAMPLES
    list = PairwiseListMatrix(rand(div(sample*(sample-1),2)))
    mat = full(list)
    @test_approx_eq mean(list, 1) mean(mat, 1)
    k += 1
    TIME[k] = @elapsed mean(list, 1)
    k += 1
    TIME[k] = @elapsed mean(mat, 1)
end

plot(x=XS, y=TIME, color=NAMES, Geom.point, Geom.smooth)


    

    
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In [5]:

    

k = 0
for sample in SAMPLES
    list = PairwiseListMatrix(rand(div(sample*(sample-1),2)))
    mat = full(list)
    @test_approx_eq sum(list) sum(mat)
    k += 1
    TIME[k] = @elapsed sum(list)
    k += 1
    TIME[k] = @elapsed sum(mat)
end

plot(x=XS, y=TIME, color=NAMES, Geom.point, Geom.smooth)


    

    
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In [6]:

    

k = 0
for sample in SAMPLES
    list = PairwiseListMatrix(rand(div(sample*(sample-1),2)))
    mat = full(list)
    @test_approx_eq sum(list, 1) sum(mat, 1)
    k += 1
    TIME[k] = @elapsed sum(list, 1)
    k += 1
    TIME[k] = @elapsed sum(mat, 1)
end

plot(x=XS, y=TIME, color=NAMES, Geom.point, Geom.smooth)


    

    
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In [7]:

    

import PairwiseListMatrices: mean_nodiag

mean_nodiag(m::Matrix, region) = (squeeze(sum(m, region), region) .- diag(m)) ./ (size(m, region)-1)

function mean_nodiag{T}(m::Matrix{T})
    nrow, ncol = size(m)
    total = zero(T)
    for i in 1:(ncol-1)
      for j in (i+1):ncol
        total += m[i, j]
      end
    end
    total / div(ncol*(ncol-1), 2)
end


    

    
Out[7]:





mean_nodiag (generic function with 4 methods)

In [8]:

    

k = 0
for sample in SAMPLES
    list = PairwiseListMatrix(rand(div(sample*(sample-1),2)))
    mat = full(list)
    @test_approx_eq mean_nodiag(list) mean_nodiag(mat)
    k += 1
    TIME[k] = @elapsed mean_nodiag(list)
    k += 1
    TIME[k] = @elapsed mean_nodiag(mat)
end

plot(x=XS, y=TIME, color=NAMES, Geom.point, Geom.smooth)


    

    
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In [9]:

    

k = 0
for sample in SAMPLES
    list = PairwiseListMatrix(rand(div(sample*(sample-1),2)))
    mat = full(list)
    @test_approx_eq mean_nodiag(list, 1) mean_nodiag(mat, 1)
    k += 1
    TIME[k] = @elapsed mean_nodiag(list, 1)
    k += 1
    TIME[k] = @elapsed mean_nodiag(mat, 1)
end

plot(x=XS, y=TIME, color=NAMES, Geom.point, Geom.smooth)


    

    
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In [10]:

    

k = 0
for sample in SAMPLES
    list = PairwiseListMatrix{Float64,Any,false}[ PairwiseListMatrix(rand(div(150*(150-1),2))) for i in 1:sample ]
    mat = Matrix{Float64}[ full(l) for l in list ]  
    @test_approx_eq sum(list) sum(mat)
    k += 1
    TIME[k] = @elapsed sum(list)
    k += 1
    TIME[k] = @elapsed sum(mat)
end

plot(x=XS, y=TIME, color=NAMES, Geom.point, Geom.smooth)


    

    
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In [11]:

    

k = 0
for sample in SAMPLES
    list = PairwiseListMatrix{Float64,Any,false}[ PairwiseListMatrix(rand(div(150*(150-1),2))) for i in 1:sample ]
    mat = Matrix{Float64}[ full(l) for l in list ]  
    @test_approx_eq mean(list) mean(mat)
    k += 1
    TIME[k] = @elapsed mean(list)
    k += 1
    TIME[k] = @elapsed mean(mat)
end

plot(x=XS, y=TIME, color=NAMES, Geom.point, Geom.smooth)


    

    
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