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Add a function to generate tensor network model by optimized EinCode #90

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Jan 30, 2024
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Add a function to generate tensor network model by optimized EinCode #90

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15 changes: 15 additions & 0 deletions src/Core.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -175,6 +175,21 @@ function TensorNetworkModel(
TensorNetworkModel(collect(LT, vars), code, tensors, evidence, mars)
end

"""
$(TYPEDSIGNATURES)
"""
function TensorNetworkModel(
model::UAIModel{T}, code;
evidence = Dict{Int,Int}(),
mars = [[i] for i=1:model.nvars],
vars = [1:model.nvars...]
)::TensorNetworkModel where{T}
@debug "constructing tensor network model from code"
tensors = Array{T}[[ones(T, [model.cards[i] for i in mar]...) for mar in mars]..., [t.vals for t in model.factors]...]

return TensorNetworkModel(vars, code, tensors, evidence, mars)
end

"""
$(TYPEDSIGNATURES)

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12 changes: 12 additions & 0 deletions test/map.jl
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Expand Up @@ -2,6 +2,18 @@ using Test
using OMEinsum
using TensorInference

@testset "load from code" begin
model = problem_from_artifact("uai2014", "MAR", "Promedus", 14)

tn1 = TensorNetworkModel(read_model(model);
evidence=read_evidence(model),
optimizer = TreeSA(ntrials = 3, niters = 2, βs = 1:0.1:80))

tn2 = TensorNetworkModel(read_model(model), tn1.code, evidence=read_evidence(model))

@test tn1.code == tn2.code
end

@testset "gradient-based tensor network solvers" begin
model = problem_from_artifact("uai2014", "MAR", "Promedus", 14)

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