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Fix A* implementation #125

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2 changes: 1 addition & 1 deletion .gitignore
Original file line number Diff line number Diff line change
Expand Up @@ -7,4 +7,4 @@ docs/site/
benchmark/.results/*
benchmark/.tune.jld
*.cov
Manifest.toml
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/Manifest.toml
238 changes: 238 additions & 0 deletions docs/Manifest.toml
Original file line number Diff line number Diff line change
@@ -0,0 +1,238 @@
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1 change: 1 addition & 0 deletions docs/Project.toml
Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
[deps]
Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
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56 changes: 31 additions & 25 deletions src/shortestpaths/astar.jl
Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand All @@ -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

Expand All @@ -56,38 +53,47 @@ 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}
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# 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))

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
7 changes: 7 additions & 0 deletions test/shortestpaths/astar.jl
Original file line number Diff line number Diff line change
Expand Up @@ -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