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Fix A* implementation (JuliaGraphs#125)
* Fix A* with additional argument edgetype_to_return * Concrete key type in A* priority queue * Add back closed_set and type restriction heuristic::Function
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Diff for: ‎docs/Project.toml

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@@ -1,5 +1,6 @@
11
[deps]
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Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
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Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
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[compat]
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Documenter = "~0.26.2"

Diff for: ‎src/shortestpaths/astar.jl

+31-25
Original file line numberDiff line numberDiff line change
@@ -6,12 +6,11 @@
66
function reconstruct_path!(total_path, # a vector to be filled with the shortest path
77
came_from, # a vector holding the parent of each node in the A* exploration
88
end_idx, # the end vertex
9-
g) # the graph
10-
11-
E = edgetype(g)
9+
g, # the graph
10+
edgetype_to_return::Type{E}=edgetype(g)) where {E<:AbstractEdge}
1211
curr_idx = end_idx
1312
while came_from[curr_idx] != curr_idx
14-
pushfirst!(total_path, E(came_from[curr_idx], curr_idx))
13+
pushfirst!(total_path, edgetype_to_return(came_from[curr_idx], curr_idx))
1514
curr_idx = came_from[curr_idx]
1615
end
1716
end
@@ -21,19 +20,17 @@ function a_star_impl!(g, # the graph
2120
open_set, # an initialized heap containing the active vertices
2221
closed_set, # an (initialized) color-map to indicate status of vertices
2322
g_score, # a vector holding g scores for each node
24-
f_score, # a vector holding f scores for each node
2523
came_from, # a vector holding the parent of each node in the A* exploration
2624
distmx,
27-
heuristic)
28-
29-
E = edgetype(g)
30-
total_path = Vector{E}()
25+
heuristic,
26+
edgetype_to_return::Type{E}) where {E<:AbstractEdge}
27+
total_path = Vector{edgetype_to_return}()
3128

3229
@inbounds while !isempty(open_set)
3330
current = dequeue!(open_set)
3431

3532
if current == goal
36-
reconstruct_path!(total_path, came_from, current, g)
33+
reconstruct_path!(total_path, came_from, current, g, edgetype_to_return)
3734
return total_path
3835
end
3936

@@ -56,38 +53,47 @@ function a_star_impl!(g, # the graph
5653
end
5754

5855
"""
59-
a_star(g, s, t[, distmx][, heuristic])
56+
a_star(g, s, t[, distmx][, heuristic][, edgetype_to_return])
57+
58+
Compute a shortest path using the [A* search algorithm](http://en.wikipedia.org/wiki/A%2A_search_algorithm).
6059
61-
Return a vector of edges comprising the shortest path between vertices `s` and `t`
62-
using the [A* search algorithm](http://en.wikipedia.org/wiki/A%2A_search_algorithm).
63-
An optional heuristic function and edge distance matrix may be supplied. If missing,
64-
the distance matrix is set to [`Graphs.DefaultDistance`](@ref) and the heuristic is set to
65-
`n -> 0`.
60+
# Arguments
61+
- `g::AbstractGraph`: the graph
62+
- `s::Integer`: the source vertex
63+
- `t::Integer`: the target vertex
64+
- `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)).
65+
- `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)
66+
- `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`.
6667
"""
6768
function a_star(g::AbstractGraph{U}, # the g
6869
s::Integer, # the start vertex
6970
t::Integer, # the end vertex
7071
distmx::AbstractMatrix{T}=weights(g),
71-
heuristic::Function=n -> zero(T)) where {T, U}
72-
73-
E = Edge{eltype(g)}
74-
72+
heuristic::Function=n -> zero(T),
73+
edgetype_to_return::Type{E}=edgetype(g)) where {T, U, E<:AbstractEdge}
7574
# if we do checkbounds here, we can use @inbounds in a_star_impl!
7675
checkbounds(distmx, Base.OneTo(nv(g)), Base.OneTo(nv(g)))
7776

78-
open_set = PriorityQueue{Integer, T}()
77+
open_set = PriorityQueue{U, T}()
7978
enqueue!(open_set, s, 0)
8079

8180
closed_set = zeros(Bool, nv(g))
8281

8382
g_score = fill(Inf, nv(g))
8483
g_score[s] = 0
8584

86-
f_score = fill(Inf, nv(g))
87-
f_score[s] = heuristic(s)
88-
8985
came_from = fill(-one(s), nv(g))
9086
came_from[s] = s
9187

92-
a_star_impl!(g, t, open_set, closed_set, g_score, f_score, came_from, distmx, heuristic)
88+
a_star_impl!(
89+
g,
90+
t,
91+
open_set,
92+
closed_set,
93+
g_score,
94+
came_from,
95+
distmx,
96+
heuristic,
97+
edgetype_to_return
98+
)
9399
end

Diff for: ‎test/shortestpaths/astar.jl

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Original file line numberDiff line numberDiff line change
@@ -15,4 +15,11 @@
1515
g = complete_graph(4)
1616
w = float([1 1 1 4; 1 1 1 1; 1 1 1 1; 4 1 1 1])
1717
@test length(a_star(g, 1, 4, w)) == 2
18+
19+
# test for #120
20+
struct MyFavoriteEdgeType <: AbstractEdge{Int}
21+
s::Int
22+
d::Int
23+
end
24+
@test eltype(a_star(g, 1, 4, w, n -> 0, MyFavoriteEdgeType)) == MyFavoriteEdgeType
1825
end

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