73 lines
1.6 KiB
Julia
73 lines
1.6 KiB
Julia
using Distributions
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using Random
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using Plots
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mutable struct BanditArm
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m::Number #the win rate
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m_estimate::Number #How to estimate the win rate
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N::Number #Number of samples
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BanditArm(m) = new(m,0,0)
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end
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function pull(ban::BanditArm)
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return rand(Normal(0,1)) + ban.m
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end
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function update(ban::BanditArm, x::Number) #x is a sample number
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ban.N += 1
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ban.m_estimate = ((1 - 1/ban.N) * ban.m_estimate) + (1 / (ban.N * x))
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end
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function run_experiment(m1::Number,m2::Number,m3::Number,ϵ::Number,N::Number)
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bandits = [BanditArm(m1),BanditArm(m2),BanditArm(m3)]
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means = [m1,m2,m3]
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true_best = argmax(means)
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count_suboptimal = 0
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data = Array{Number}(undef,N)
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for i in 1:N
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p = rand()
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if p < ϵ
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j = rand(1:size(bandits)[1])
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else
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j = argmax([b.m_estimate for b in bandits])
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end
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x = pull(bandits[j])
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update(bandits[j],x)
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if j != true_best
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count_suboptimal += 1
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end
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data[i] = x
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end
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gr();
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cumulative_average = cumsum(data) ./ Array(1:N)
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plot(cumulative_average,xaxis=:log)
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plot!(ones(N) .* m1,xaxis=:log)
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plot!(ones(N) .* m2,xaxis=:log)
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display(plot!(ones(N) .* m3,xaxis=:log))
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for b in bandits
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println(b.m_estimate)
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end
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println("Perccent suboptimal for ϵ = $ϵ: ", count_suboptimal / N)
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return cumulative_average
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end
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m1 = 1.5
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m2 = 2.5
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m3 = 3.5
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c_1 = run_experiment(m1,m2,m3,0.1,100000);
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c_05 = run_experiment(m1,m2,m3,0.05,100000);
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c_01 = run_experiment(m1,m2,m3,0.01,100000);
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plot(c_1,show=false)
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plot!(c_05,show=false)
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plot!(c_01)
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