euclidean_distance_single.jl 1.02 KB
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using MarkovProcesses
import LinearAlgebra: dot
import Distributions: Uniform

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MAKE_SECOND_AUTOMATON_TESTS = false

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load_model("SIR")
tml_obs = 0:0.5:200
set_time_bound!(SIR, 200.0)
y_obs = vectorize(simulate(SIR), :I, tml_obs)
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load_automaton("euclidean_distance_automaton")
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aut1 = create_euclidean_distance_automaton(SIR, tml_obs, y_obs, :I)
sync_SIR = SIR * aut1
σ = simulate(sync_SIR)
test = euclidean_distance(σ, :I, tml_obs, y_obs) == σ.state_lha_end[:d]

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if !test
    @show euclidean_distance(σ, :I, tml_obs, y_obs), σ.state_lha_end[:d]
    @show σ
end

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if MAKE_SECOND_AUTOMATON_TESTS
    load_automaton("euclidean_distance_automaton_2")
    aut2 = create_euclidean_distance_automaton_2(SIR, tml_obs, y_obs, :I)
    sync_SIR = SIR * aut2
    σ = simulate(sync_SIR)
    test2 = euclidean_distance(σ, :I, tml_obs, y_obs) == σ.state_lha_end[:d]
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    if !test2
        @show euclidean_distance(σ, :I, tml_obs, y_obs), σ.state_lha_end[:d]
        @show σ
    end
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else
    test2 = true
end
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test_all = test && test2
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return test_all
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