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 [5]:

    

model = "wvecs + preva + beam"

rs_1 = [0.6224256292906178, 0.6893353941267388, 0.6766355140186916];
rp_1 = [0.2631578947368421, 0.21774193548387097, 0.2831858407079646];

rs_2 = [0.6864988558352403, 0.732198142414860, 0.7121495327102804];
rp_2 = [0.23423423423423423, 0.2605042016806723, 0.3008130081300813];

rs_3 = [0.6453089244851259, 0.7001545595054096, 0.6953271028037383];
rp_3 = [0.2807017543859649, 0.21774193548387097, 0.30973451327433627];

rs_4 = [0.6613272311212814, 0.7321981424148607, 0.6766355140186916];
rp_4 = [0.22522522522522523, 0.2689075630252101, 0.25203252032520324];

rs_5 = [0.6773455377574371, 0.7217928902627512, 0.6710280373831776];
rp_5 = [0.2894736842105263, 0.18548387096774194, 0.30973451327433627];

rs_6 = [0.665903890160183, 0.7136222910216719, 0.6822429906542056];
rp_6 = [0.26126126126126126, 0.19327731092436976, 0.2032520325203252];

rs_ens_1 = [0.6773455377574371, 0.732612055641422, 0.7046728971962617];
rp_ens_1 = [0.2982456140350877, 0.22580645161290322, 0.34513274336283184];

rs_ens_2 = [0.7070938215102975, 0.7523219814241486, 0.7177570093457943];
rp_ens_2 = [0.27927927927927926, 0.2773109243697479, 0.3008130081300813];

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: [0.659802,0.714884,0.68567]
Paragraph: [0.259009,0.223943,0.276459]
Weighted Single: (0.6903545842720603,0.022352430686252336)
Weighted Paragraph: (0.2527869494353814,0.02208039261743872)
Ensemble Weighted Single: (0.7185696184624317,0.02081982353629326)
Ensemble Weighted Paragraph: (0.28743816128386557,0.02947617836671902)

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 [ ]: