merge main

Author: GiggleLiu

Artifacts.toml

@@ -4,7 +4,6 @@ authors = ["Jin-Guo Liu", "Martin Roa Villescas"]
 version = "0.1.0"
 
 [deps]
-Artifacts = "56f22d72-fd6d-98f1-02f0-08ddc0907c33"
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
@@ -16,7 +15,6 @@ TropicalGEMM = "a4ad3063-64a7-4bad-8738-34ed09bc0236"
 TropicalNumbers = "b3a74e9c-7526-4576-a4eb-79c0d4c32334"
 
 [compat]
-Artifacts = "1"
 CUDA = "4"
 DocStringExtensions = "0.8.6, 0.9"
 OMEinsum = "0.7"

Project.toml

@@ -6,7 +6,8 @@ using Artifacts
 
 const SUITE = BenchmarkGroup()
 
-problem = read_uai_problem("Promedus_14")
+model_filepath, evidence_filepath, solution_filepath = get_instance_filepaths("Promedus_14", "MAR")
+problem = read_instance(model_filepath; evidence_filepath, solution_filepath)
 
 optimizer = TreeSA(ntrials = 1, niters = 2, βs = 1:0.1:40)
 tn = TensorNetworkModel(problem; optimizer)

benchmark/bench_map.jl

@@ -8,7 +8,8 @@ using Artifacts
 
 const SUITE = BenchmarkGroup()
 
-problem = read_uai_problem("Promedus_14")
+model_filepath, evidence_filepath, solution_filepath = get_instance_filepaths("Promedus_14", "MAR")
+problem = read_instance(model_filepath; evidence_filepath, solution_filepath)
 
 optimizer = TreeSA(ntrials = 1, niters = 5, βs = 0.1:0.1:100)
 tn1 = TensorNetworkModel(problem; optimizer)

benchmark/bench_mar.jl

@@ -6,7 +6,8 @@ using Artifacts
 
 const SUITE = BenchmarkGroup()
 
-problem = read_uai_problem("Promedus_14")
+model_filepath, evidence_filepath, solution_filepath = get_instance_filepaths("Promedus_14", "MAR")
+problem = read_instance(model_filepath; evidence_filepath, solution_filepath)
 optimizer = TreeSA(ntrials = 1, niters = 2, βs = 1:0.1:40)
 
 # Does not marginalize any var

benchmark/bench_mmap.jl

@@ -1,7 +1,7 @@
 using TensorInference
 
 # Load the model that detailed in the README and `asia.uai`.
-instance = uai_problem_from_file(joinpath(@__DIR__, "asia.uai"))
+instance = read_instance(joinpath(@__DIR__, "asia.uai"))
 tnet = TensorNetworkModel(instance)
 
 # Get the probabilities (PR)
@@ -36,4 +36,4 @@ mmap = MMAPModel(instance; marginalized=[1,2,3,5,6,8])
 most_probable_config(mmap)
 # The total probability of having lung cancer is roughly half.
 log_probability(mmap, [1, 0])
-log_probability(mmap, [0, 0])
+log_probability(mmap, [0, 0])

example/asia/asia.jl

@@ -41,15 +41,6 @@ end
 $TYPEDSIGNATURES
 
 Set the evidence of an UAI instance.
-
-### Examples
-```jldoctest; setup=:(using TensorInference)
-julia> problem = read_uai_problem("Promedus_14"); problem.obsvars, problem.obsvals
-([42, 48, 27, 30, 29, 15, 124, 5, 148], [1, 1, 1, 1, 1, 1, 1, 1, 1])
-
-julia> set_evidence!(problem, 2=>0, 4=>1); problem.obsvars, problem.obsvals
-([2, 4], [0, 1])
-```
 """
 function set_evidence!(uai::UAIInstance, pairs::Pair{Int}...)
     empty!(uai.obsvars)

src/Core.jl

@@ -2,7 +2,6 @@ module TensorInference
 
 using OMEinsum, LinearAlgebra
 using DocStringExtensions, TropicalNumbers
-using Artifacts
 # The Tropical GEMM support
 using TropicalGEMM
 using StatsBase
@@ -12,7 +11,7 @@ export RescaledArray
 export contraction_complexity, TreeSA, GreedyMethod, KaHyParBipartite, SABipartite, MergeGreedy, MergeVectors
 
 # read and load uai files
-export read_uai_file, read_td_file, read_uai_evid_file, read_uai_mar_file, read_uai_problem, uai_problem_from_file
+export read_model_file, read_td_file, read_evidence_file, read_solution_file, read_instance
 export set_evidence!
 
 # marginals
@@ -30,8 +29,8 @@ export MMAPModel
 include("Core.jl")
 include("RescaledArray.jl")
 include("utils.jl")
-include("inference.jl")
-include("maxprob.jl")
+include("mar.jl")
+include("map.jl")
 include("mmap.jl")
 include("sampling.jl")