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Zebra puzzle: Difference between revisions
Add Julia Constraint Programming Version
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4, zebra, german green coffee prince
5, dog, swedish white beer blue-master
</pre>
===Constraint Programming Version===
Using the rules from https://github.com/SWI-Prolog/bench/blob/master/zebra.pl
<lang Julia># Julia 1.4
using JuMP
using GLPK
c = Dict(s => i for (i, s) in enumerate(split("blue green ivory red yellow")))
n = Dict(s => i for (i, s) in enumerate(split("english japanese norwegian spanish ukrainian")))
p = Dict(s => i for (i, s) in enumerate(split("dog fox horse snails zebra")))
d = Dict(s => i for (i, s) in enumerate(split("coffee milk orangejuice tea water")))
s = Dict(s => i for (i, s) in enumerate(split("chesterfields kools luckystrikes parliaments winstons")))
model = Model(GLPK.Optimizer)
@variable(model, colors[1:5, 1:5], Bin)
@constraints(model, begin
[h in 1:5], sum(colors[h, :]) == 1
[c in 1:5], sum(colors[:, c]) == 1
end)
@variable(model, nations[1:5, 1:5], Bin)
@constraints(model, begin
[h in 1:5], sum(nations[h, :]) == 1
[n in 1:5], sum(nations[:, n]) == 1
end)
@variable(model, pets[1:5, 1:5], Bin)
@constraints(model, begin
[h in 1:5], sum(pets[h, :]) == 1
[p in 1:5], sum(pets[:, p]) == 1
end)
@variable(model, drinks[1:5, 1:5], Bin)
@constraints(model, begin
[h in 1:5], sum(drinks[h, :]) == 1
[d in 1:5], sum(drinks[:, d]) == 1
end)
@variable(model, smokes[1:5, 1:5], Bin)
@constraints(model, begin
[h in 1:5], sum(smokes[h, :]) == 1
[s in 1:5], sum(smokes[:, s]) == 1
end)
@constraint(model, [h=1:5], colors[h, c["red"]] == nations[h, n["english"]])
@constraint(model, [h=1:5], nations[h, n["spanish"]] == pets[h, p["dog"]])
@constraint(model, [h=1:5], colors[h, c["green"]] == drinks[h, d["coffee"]])
@constraint(model, [h=1:5], nations[h, n["ukrainian"]] == drinks[h, d["tea"]])
@constraint(model, [h=1:5], colors[h, c["ivory"]] == get(colors, (h+1, c["green"]), 0))
@constraint(model, [h=1:5], pets[h, p["snails"]] == smokes[h, s["winstons"]])
@constraint(model, [h=1:5], colors[h, c["yellow"]] == smokes[h, s["kools"]])
@constraint(model, drinks[3, d["milk"]] == 1)
@constraint(model, nations[1, n["norwegian"]] == 1)
@constraint(model, [h=1:5], (1-pets[h, p["fox"]]) + get(smokes,(h-1, s["chesterfields"]), 0) + get(smokes, (h+1, s["chesterfields"]), 0) >= 1)
@constraint(model, [h=1:5], (1-pets[h, p["horse"]]) + get(smokes,(h-1, s["kools"]), 0) + get(smokes, (h+1, s["kools"]), 0) >= 1)
@constraint(model, [h=1:5], drinks[h, d["orangejuice"]] == smokes[h, s["luckystrikes"]])
@constraint(model, [h=1:5], nations[h, n["japanese"]] == smokes[h, s["parliaments"]])
@constraint(model, [h=1:5], (1-nations[h, n["norwegian"]]) + get(colors, (h-1, c["blue"]), 0) + get(colors, (h+1, c["blue"]), 0) >= 1)
optimize!(model)
if termination_status(model) == MOI.OPTIMAL && primal_status(model) == MOI.FEASIBLE_POINT
m = map(1:5) do h
[Dict(values(c) .=> keys(c))[findfirst(value.(colors)[h, :] .≈ 1.0)],
Dict(values(n) .=> keys(n))[findfirst(value.(nations)[h, :] .≈ 1.0)],
Dict(values(p) .=> keys(p))[findfirst(value.(pets)[h, :] .≈ 1.0)],
Dict(values(d) .=> keys(d))[findfirst(value.(drinks)[h, :] .≈ 1.0)],
Dict(values(s) .=> keys(s))[findfirst(value.(smokes)[h, :] .≈ 1.0)]]
end
end
using DataFrames
DataFrame(colors=getindex.(m, 1),
nations=getindex.(m, 2),
pets=getindex.(m, 3),
drinks=getindex.(m, 4),
smokes=getindex.(m, 5))
</lang>
{{out}}
<pre>
: 5×5 DataFrame
: │ Row │ colors │ nations │ pets │ drinks │ smokes │
: ├─────┼──────────┼───────────┼──────────┼─────────────┼───────────────┤
: │ 1 │ yellow │ norwegian │ fox │ water │ kools │
: │ 2 │ blue │ ukrainian │ horse │ tea │ chesterfields │
: │ 3 │ red │ english │ snails │ milk │ winstons │
: │ 4 │ ivory │ spanish │ dog │ orangejuice │ luckystrikes │
: │ 5 │ green │ japanese │ zebra │ coffee │ parliaments │
</pre>
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