60 lines
1.6 KiB
Julia
60 lines
1.6 KiB
Julia
using MetagraphOptimization
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using Random
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RNG = Random.MersenneTwister(321)
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function test_known_graph(name::String, n)
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@testset "Test $name Graph ($n)" begin
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graph = parse_dag(joinpath(@__DIR__, "..", "input", "$name.txt"), ABCModel())
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props = get_properties(graph)
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test_random_walk(RNG, graph, n)
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end
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end
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function test_random_walk(RNG, g::DAG, n::Int64)
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@testset "Test Random Walk ($n)" begin
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# the purpose here is to do "random" operations and reverse them again and validate that the graph stays the same and doesn't diverge
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reset_graph!(g)
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@test is_valid(g)
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properties = get_properties(g)
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for i in 1:n
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# choose push or pop
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if rand(RNG, Bool)
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# push
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opt = get_operations(g)
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# choose one of split/reduce
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option = rand(RNG, 1:2)
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if option == 1 && !isempty(opt.nodeReductions)
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push_operation!(g, rand(RNG, collect(opt.nodeReductions)))
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elseif option == 2 && !isempty(opt.nodeSplits)
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push_operation!(g, rand(RNG, collect(opt.nodeSplits)))
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else
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i = i - 1
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end
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else
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# pop
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if (can_pop(g))
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pop_operation!(g)
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else
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i = i - 1
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end
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end
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end
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reset_graph!(g)
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@test is_valid(g)
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@test properties == get_properties(g)
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end
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end
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test_known_graph("AB->AB", 10000)
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test_known_graph("AB->ABBB", 10000)
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test_known_graph("AB->ABBBBB", 1000)
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