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["Distributed", "Mmap", "Random", "Serialization"] +uuid = "1a1011a3-84de-559e-8e89-a11a2f7dc383" + +[[deps.SimpleTraits]] +deps = ["InteractiveUtils", "MacroTools"] +git-tree-sha1 = "5d7e3f4e11935503d3ecaf7186eac40602e7d231" +uuid = "699a6c99-e7fa-54fc-8d76-47d257e15c1d" +version = "0.9.4" + +[[deps.Sockets]] +uuid = "6462fe0b-24de-5631-8697-dd941f90decc" + +[[deps.SparseArrays]] +deps = ["LinearAlgebra", "Random"] +uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" + +[[deps.StaticArrays]] +deps = ["LinearAlgebra", "Random", "Statistics"] +git-tree-sha1 = "cd56bf18ed715e8b09f06ef8c6b781e6cdc49911" +uuid = "90137ffa-7385-5640-81b9-e52037218182" +version = "1.4.4" + +[[deps.Statistics]] +deps = ["LinearAlgebra", "SparseArrays"] +uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" + +[[deps.TOML]] +deps = ["Dates"] +uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76" + +[[deps.Tar]] +deps = ["ArgTools", "SHA"] +uuid = "a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e" + +[[deps.Test]] +deps = ["InteractiveUtils", "Logging", "Random", "Serialization"] +uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40" + +[[deps.UUIDs]] +deps = ["Random", "SHA"] +uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4" + +[[deps.Unicode]] +uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5" + +[[deps.Zlib_jll]] +deps = ["Libdl"] +uuid = "83775a58-1f1d-513f-b197-d71354ab007a" + +[[deps.libblastrampoline_jll]] +deps = ["Artifacts", "Libdl", "OpenBLAS_jll"] +uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" + +[[deps.nghttp2_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" + +[[deps.p7zip_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" diff --git a/docs/Project.toml b/docs/Project.toml index 48e2de9da..4c9a194b2 100644 --- a/docs/Project.toml +++ b/docs/Project.toml @@ -1,5 +1,6 @@ [deps] Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4" +Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6" [compat] Documenter = "~0.26.2" diff --git a/src/shortestpaths/astar.jl b/src/shortestpaths/astar.jl index e4adfe0dc..b81148f41 100644 --- a/src/shortestpaths/astar.jl +++ b/src/shortestpaths/astar.jl @@ -6,12 +6,11 @@ function reconstruct_path!(total_path, # a vector to be filled with the shortest path came_from, # a vector holding the parent of each node in the A* exploration end_idx, # the end vertex - g) # the graph - - E = edgetype(g) + g, # the graph + edgetype_to_return::Type{E}=edgetype(g)) where {E<:AbstractEdge} curr_idx = end_idx while came_from[curr_idx] != curr_idx - pushfirst!(total_path, E(came_from[curr_idx], curr_idx)) + pushfirst!(total_path, edgetype_to_return(came_from[curr_idx], curr_idx)) curr_idx = came_from[curr_idx] end end @@ -21,19 +20,17 @@ function a_star_impl!(g, # the graph open_set, # an initialized heap containing the active vertices closed_set, # an (initialized) color-map to indicate status of vertices g_score, # a vector holding g scores for each node - f_score, # a vector holding f scores for each node came_from, # a vector holding the parent of each node in the A* exploration distmx, - heuristic) - - E = edgetype(g) - total_path = Vector{E}() + heuristic, + edgetype_to_return::Type{E}) where {E<:AbstractEdge} + total_path = Vector{edgetype_to_return}() @inbounds while !isempty(open_set) current = dequeue!(open_set) if current == goal - reconstruct_path!(total_path, came_from, current, g) + reconstruct_path!(total_path, came_from, current, g, edgetype_to_return) return total_path end @@ -56,26 +53,28 @@ function a_star_impl!(g, # the graph end """ - a_star(g, s, t[, distmx][, heuristic]) + a_star(g, s, t[, distmx][, heuristic][, edgetype_to_return]) + +Compute a shortest path using the [A* search algorithm](http://en.wikipedia.org/wiki/A%2A_search_algorithm). -Return a vector of edges comprising the shortest path between vertices `s` and `t` -using the [A* search algorithm](http://en.wikipedia.org/wiki/A%2A_search_algorithm). -An optional heuristic function and edge distance matrix may be supplied. If missing, -the distance matrix is set to [`Graphs.DefaultDistance`](@ref) and the heuristic is set to -`n -> 0`. +# Arguments +- `g::AbstractGraph`: the graph +- `s::Integer`: the source vertex +- `t::Integer`: the target vertex +- `distmx::AbstractMatrix`: an optional (possibly sparse) `n × n` matrix of edge weights. It is set to `weights(g)` by default (which itself falls back on [`Graphs.DefaultDistance`](@ref)). +- `heuristic::Function`: an optional function mapping each vertex to a lower estimate of the remaining distance from `v` to `t`. It is set to `v -> 0` by default (which corresponds to Dijkstra's algorithm) +- `edgetype_to_return::Type{E}`: the eltype `E<:AbstractEdge` of the vector of edges returned. It is set to `edgetype(g)` by default. Note that the two-argument constructor `E(u, v)` must be defined, even for weighted edges: if it isn't, consider using `E = Graphs.SimpleEdge`. """ function a_star(g::AbstractGraph{U}, # the g s::Integer, # the start vertex t::Integer, # the end vertex distmx::AbstractMatrix{T}=weights(g), - heuristic::Function=n -> zero(T)) where {T, U} - - E = Edge{eltype(g)} - + heuristic::Function=n -> zero(T), + edgetype_to_return::Type{E}=edgetype(g)) where {T, U, E<:AbstractEdge} # if we do checkbounds here, we can use @inbounds in a_star_impl! checkbounds(distmx, Base.OneTo(nv(g)), Base.OneTo(nv(g))) - open_set = PriorityQueue{Integer, T}() + open_set = PriorityQueue{U, T}() enqueue!(open_set, s, 0) closed_set = zeros(Bool, nv(g)) @@ -83,11 +82,18 @@ function a_star(g::AbstractGraph{U}, # the g g_score = fill(Inf, nv(g)) g_score[s] = 0 - f_score = fill(Inf, nv(g)) - f_score[s] = heuristic(s) - came_from = fill(-one(s), nv(g)) came_from[s] = s - a_star_impl!(g, t, open_set, closed_set, g_score, f_score, came_from, distmx, heuristic) + a_star_impl!( + g, + t, + open_set, + closed_set, + g_score, + came_from, + distmx, + heuristic, + edgetype_to_return + ) end diff --git a/test/shortestpaths/astar.jl b/test/shortestpaths/astar.jl index 7143a5bff..510f45f7e 100644 --- a/test/shortestpaths/astar.jl +++ b/test/shortestpaths/astar.jl @@ -15,4 +15,11 @@ g = complete_graph(4) w = float([1 1 1 4; 1 1 1 1; 1 1 1 1; 4 1 1 1]) @test length(a_star(g, 1, 4, w)) == 2 + + # test for #120 + struct MyFavoriteEdgeType <: AbstractEdge{Int} + s::Int + d::Int + end + @test eltype(a_star(g, 1, 4, w, n -> 0, MyFavoriteEdgeType)) == MyFavoriteEdgeType end