In [1]:

    

using StatsBase

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

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


    

    
Out[1]:





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

In [2]:

    

model = "cnn wvecs + preva"

rs_1 = [0.6155606407322655, 0.6877897990726429, 0.6710280373831776];
rp_1 = [0.2894736842105263, 0.18548387096774194, 0.23893805309734514];

rs_2 = [0.6681922196796338, 0.7275541795665634, 0.708411214953271];
rp_2 = [0.24324324324324326, 0.2605042016806723, 0.3008130081300813];

rs_3 = [0.6453089244851259, 0.6970633693972179, 0.6915887850467289];
rp_3 = [0.3157894736842105, 0.21774193548387097, 0.3008849557522124];

rs_4 = [0.6453089244851259, 0.7306501547987616, 0.6728971962616822];
rp_4 = [0.1891891891891892, 0.2773109243697479, 0.25203252032520324];

rs_5 = [0.6773455377574371, 0.7171561051004637, 0.6654205607476635];
rp_5 = [0.30701754385964913, 0.1774193548387097, 0.3185840707964602];

rs_6 = [0.6567505720823799, 0.7167182662538699, 0.6822429906542056];
rp_6 = [0.24324324324324326, 0.20168067226890757, 0.17886178861788618];

rs_ens_1 = [0.6704805491990846, 0.7295208655332303, 0.6953271028037383];
rp_ens_1 = [0.30701754385964913, 0.20161290322580644, 0.35398230088495575];

rs_ens_2 = [0.700228832951945, 0.7554179566563467, 0.719626168224299];
rp_ens_2 = [0.26126126126126126, 0.2689075630252101, 0.2845528455284553];

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: [0.651411,0.712822,0.681931]
Paragraph: [0.264659,0.220023,0.265019]
Weighted Single: (0.6860299100672973,0.024816667266293946)
Weighted Paragraph: (0.24939300531709346,0.02130274809836909)
Ensemble Weighted Single: (0.715481293959086,0.023604586461041497)
Ensemble Weighted Paragraph: (0.2790987889838995,0.034828945176342954)

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 [ ]: