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 embed beam"

rs_1 = [0.6224256292906178, 0.7094281298299846, 0.6934579439252336];
rp_1 = [0.21052631578947367, 0.25, 0.2920353982300885];

rs_2 = [0.6773455377574371, 0.718266253869969, 0.6672897196261682];
rp_2 = [0.23423423423423423, 0.25210084033613445, 0.21138211382113822];

rs_3 = [0.6681922196796338, 0.6769706336939721, 0.6897196261682244];
rp_3 = [0.30701754385964913, 0.22580645161290322, 0.26548672566371684];

rs_4 = [0.665903890160183, 0.7430340557275542, 0.6915887850467289];
rp_4 = [0.2702702702702703, 0.25210084033613445, 0.3008130081300813];

rs_5 = [0.6636155606407322, 0.7032457496136012, 0.6897196261682244];
rp_5 = [0.3157894736842105, 0.25, 0.3008849557522124];

rs_6 = [0.6750572082379863, 0.7167182662538699, 0.6878504672897197];
rp_6 = [0.24324324324324326, 0.226890756302521, 0.3008130081300813];

rs_ens_1 = [0.7048054919908466, 0.7187017001545595, 0.702803738317757];
rp_ens_1 = [0.34210526315789475, 0.2661290322580645, 0.336283185840708];

rs_ens_2 = [0.6933638443935927, 0.739938080495356, 0.7177570093457943];
rp_ens_2 = [0.2882882882882883, 0.25210084033613445, 0.3252032520325203];

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: [0.66209,0.711277,0.686604]
Paragraph: [0.263514,0.242816,0.278569]
Weighted Single: (0.6898408183535534,0.019873232569989)
Weighted Paragraph: (0.261440095606845,0.014818075771596488)
Ensemble Weighted Single: (0.7148627180074669,0.012555677591304982)
Ensemble Weighted Paragraph: (0.301090159565401,0.031088410309373014)

In [3]:

    

rs_ens10_1 = [0.6933638443935927, 0.717156105100463, 0.7158878504672898];
rp_ens10_1 = [0.3508771929824561, 0.22580645161290322, 0.36283185840707965];

rs_ens10_2 = [0.700228832951945, 0.7647058823529411, 0.7252336448598131];
rp_ens10_2 = [0.25225225225225223, 0.31932773109243695, 0.3333333333333333];

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))")


    

    




Ensemble10 Weighted Single: (0.7222810549547809,0.01775590166682548)
Ensemble10 Weighted Paragraph: (0.3072068754971542,0.031392758732145844)

In [ ]: