158 lines
3.8 KiB
Julia
158 lines
3.8 KiB
Julia
# Bogumił Kamiński, 2021
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# Codes for appendix B
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# the solutions for exercises from a given chapter assume that
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# there are packages loaded, variables and functions defined in the user's
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# Julia session in a state that reflects the point of computations
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# at the position of the chapter where a given exercise is formulated
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# Code for exercise 3.1
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using Statistics
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using BenchmarkTools
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aq = [10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
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8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
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13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
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9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
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11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47
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14.0 9.96 14.0 8.1 14.0 8.84 8.0 7.04
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6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25
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4.0 4.26 4.0 3.1 4.0 5.39 19.0 12.50
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12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56
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7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
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5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89];
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@benchmark [cor($aq[:, i], $aq[:, i+1]) for i in 1:2:7]
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@benchmark [cor(view($aq, :, i), view($aq, :, i+1)) for i in 1:2:7]
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# Code for exercise 3.2
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function dice_distribution(dice1, dice2)
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distribution = Dict{Int, Int}()
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for i in dice1
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for j in dice2
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s = i + j
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if haskey(distribution, s)
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distribution[s] += 1
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else
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distribution[s] = 1
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end
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end
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end
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return distribution
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end
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function test_dice()
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all_dice = [[1, x2, x3, x4, x5, x6]
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for x2 in 2:11
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for x3 in x2:11
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for x4 in x3:11
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for x5 in x4:11
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for x6 in x5:11]
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two_standard = dice_distribution(1:6, 1:6)
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for d1 in all_dice, d2 in all_dice
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test = dice_distribution(d1, d2)
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if test == two_standard
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println(d1, " ", d2)
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end
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end
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end
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test_dice()
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# Code for exercise 3.3
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plot(scatter(data.set1.x, data.set1.y; legend=false),
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scatter(data.set2.x, data.set2.y; legend=false),
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scatter(data.set3.x, data.set3.y; legend=false),
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scatter(data.set4.x, data.set4.y; legend=false))
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# Code for exercise 3.4
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parse.(Int, ["1", "2", "3"])
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# Code for exercise 4.1
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years_table = freqtable(years)
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plot(names(years_table, 1), years_table; legend=false,
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xlabel="year", ylabel="# of movies")
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# Code for exercise 4.2
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s3 = Symbol.(s1)
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@benchmark sort($s3)
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@benchmark unique($s1)
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@benchmark unique($s2)
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@benchmark unique($s3)
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# Code for exercise 5.1
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v = ["1", "2", missing, "4"]
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[ismissing(x) ? missing : parse(Int, x) for x in v]
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map(v) do x
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if ismissing(x)
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return missing
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else
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return parse(Int, x)
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end
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end
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using Missings
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passmissing(parse).(Int, v)
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# Code for exercise 5.2
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using Dates
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Date(2021, 1, 1):Month(1):Date(2021, 12, 1)
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collect(Date(2021, 1, 1):Month(1):Date(2021, 12, 1))
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# Code for exercise 6.1
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using BenchmarkTools
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@benchmark $puzzles."Rating"
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# Code for exercise 6.2
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using StatsBase
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summarystats(puzzles[puzzles.Popularity .== 100, "NbPlays"])
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summarystats(puzzles[puzzles.Popularity .== -100, "NbPlays"])
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# Code for exercise 6.3
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sum(length, values(rating_mapping))
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nrow(good)
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# Code for exercise 7.1
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using BenchmarkTools
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x = rand(10^6);
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@btime DataFrame(x=$x);
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@btime DataFrame(x=$x; copycols=false);
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# Code for exercise 7.2
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df1 = DataFrame(a=1,b=2)
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df2 = DataFrame(b=3, a=4)
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vcat(df1, df2)
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vcat(df1, df2, cols=:orderequal)
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# Code for exercise 7.3
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function walk_unique_2ahead()
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walk = DataFrame(x=0, y=0)
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for _ in 1:10
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current = walk[end, :]
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push!(walk, sim_step(current))
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end
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return all(walk[i, :] != walk[i+2, :] for i in 1:9)
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end
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Random.seed!(2);
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proptable([walk_unique_2ahead() for _ in 1:10^5])
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# Code for exercise 7.4
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@time wide = DataFrame(ones(1, 10_000), :auto);
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@time Tables.columntable(wide);
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