In [3]:

    

using StatsBase

s = [874, 1293, 1070] ./ 3237
p = [224, 242, 236] ./ 702

s = WeightVec(s)
p = WeightVec(p)


    

    
Out[3]:





StatsBase.WeightVec{Float64,Array{Float64,1}}([0.319088,0.344729,0.336182],1.0)

In [4]:

    

model = "wvecs + preva"

rs_1 = [0.5926773455377574, 0.6290571870170015, 0.611214953271028];
rp_1 = [0.21929824561403508, 0.1532258064516129, 0.1592920353982301];

rs_2 = [0.6361556064073226, 0.6780185758513931, 0.6205607476635514];
rp_2 = [0.21621621621621623, 0.14285714285714285, 0.16260162601626016];

rs_3 = [0.597254004576659, 0.624420401854714, 0.6261682242990654];
rp_3 = [0.19298245614035087, 0.11290322580645161, 0.17699115044247787];

rs_4 = [0.6475972540045767, 0.6687306501547987, 0.6037383177570094];
rp_4 = [0.23423423423423423, 0.1512605042016806, 0.14634146341463414];

rs_5 = [0.6109839816933639, 0.6228748068006182, 0.6355140186915887];
rp_5 = [0.22807017543859648, 0.13709677419354838, 0.17699115044247787];

rs_6 = [0.6475972540045767, 0.6609907120743034, 0.6186915887850467];
rp_6 = [0.3063063063063063, 0.15126050420168066, 0.17073170731707318];

rs_ens_1 = [0.620137299771167, 0.6367851622874807, 0.6355140186915887];
rp_ens_1 = [0.21052631578947367, 0.14516129032258066, 0.17699115044247787];

rs_ens_2 = [0.6521739130434783, 0.6965944272445821, 0.6205607476635514];
rp_ens_2 = [0.24324324324324326, 0.17647058823529413, 0.17886178861788618];

rs_m = (rs_1 + rs_2 + rs_3 + rs_4 + rs_5 + rs_6) / 6
rp_m = (rp_1 + rp_2 + rp_3 + rp_4 + rp_5 + rp_6) / 6

rs_ens_m = (rs_ens_1 + rs_ens_2) / 2
rp_ens_m = (rp_ens_1 + rp_ens_2) / 2

println("Single: $(rs_m)")
println("Paragraph: $(rp_m)")
println("Weighted Single: $(mean_and_std(rs_m, s))")
println("Weighted Paragraph: $(mean_and_std(rp_m, p))")
println("Ensemble Weighted Single: $(mean_and_std(rs_ens_m, s))")
println("Ensemble Weighted Paragraph: $(mean_and_std(rp_ens_m, p))")


    

    




Single: [0.622044,0.647349,0.619315]
Paragraph: [0.232851,0.141434,0.165492]
Weighted Single: (0.6312496852817634,0.013171685496880681)
Weighted Paragraph: (0.17869189533865765,0.038380701227138776)
Ensemble Weighted Single: (0.6456687997011055,0.017427009137950523)
Ensemble Weighted Paragraph: (0.18764999248759273,0.027770623522909314)

In [3]:

    

rs_ens10_1 = [];
rp_ens10_1 = [];

rs_ens10_2 = [];
rp_ens10_2 = [];

rs_ens_m = (rs_ens10_1 + rs_ens10_2) / 2
rp_ens_m = (rp_ens10_1 + rp_ens10_2) / 2

println("Ensemble10 Weighted Single: $(mean_and_std(rs_ens_m, s))")
println("Ensemble10 Weighted Paragraph: $(mean_and_std(rp_ens_m, p))")


    

    




MethodError: no method matching mean_and_std(::Array{Any,1}, ::StatsBase.WeightVec{Float64,Array{Float64,1}})
Closest candidates are:
  mean_and_std(::AbstractArray{T<:Real,N}, ::StatsBase.WeightVec{W,Vec<:AbstractArray{T<:Real,1}}) at /home/cano/.julia/v0.5/StatsBase/src/moments.jl:124
  mean_and_std(::AbstractArray{T<:Real,N}, ::StatsBase.WeightVec{W,Vec<:AbstractArray{T<:Real,1}}, ::Int64) at /home/cano/.julia/v0.5/StatsBase/src/moments.jl:130

In [ ]: