blob: 565953d9428aafb9393e87349c2562766faeadba [file] [log] [blame]
use std::collections::{
HashMap,
BinaryHeap,
};
use std::collections::hash_map::Entry::{
Occupied,
Vacant,
};
use std::hash::Hash;
use scored::MinScored;
use super::visit::{
EdgeRef,
GraphBase,
IntoEdges,
VisitMap,
Visitable,
};
use algo::Measure;
/// [Generic] A* shortest path algorithm.
///
/// Computes the shortest path from `start` to `finish`, including the total path cost.
///
/// `finish` is implicitly given via the `is_goal` callback, which should return `true` if the
/// given node is the finish node.
///
/// The function `edge_cost` should return the cost for a particular edge. Edge costs must be
/// non-negative.
///
/// The function `estimate_cost` should return the estimated cost to the finish for a particular
/// node. For the algorithm to find the actual shortest path, it should be admissible, meaning that
/// it should never overestimate the actual cost to get to the nearest goal node. Estimate costs
/// must also be non-negative.
///
/// The graph should be `Visitable` and implement `IntoEdges`.
///
/// ```
/// use petgraph::Graph;
/// use petgraph::algo::astar;
///
/// let mut g = Graph::new();
/// let a = g.add_node((0., 0.));
/// let b = g.add_node((2., 0.));
/// let c = g.add_node((1., 1.));
/// let d = g.add_node((0., 2.));
/// let e = g.add_node((3., 3.));
/// let f = g.add_node((4., 2.));
/// g.extend_with_edges(&[
/// (a, b, 2),
/// (a, d, 4),
/// (b, c, 1),
/// (b, f, 7),
/// (c, e, 5),
/// (e, f, 1),
/// (d, e, 1),
/// ]);
///
/// let path = astar(&g, a, |finish| finish == f, |e| *e.weight(), |_| 0);
/// assert_eq!(path, Some((6, vec![a, d, e, f])));
/// ```
///
/// Returns the total cost + the path of subsequent `NodeId` from start to finish, if one was
/// found.
pub fn astar<G, F, H, K, IsGoal>(graph: G, start: G::NodeId, mut is_goal: IsGoal,
mut edge_cost: F, mut estimate_cost: H)
-> Option<(K, Vec<G::NodeId>)>
where G: IntoEdges + Visitable,
IsGoal: FnMut(G::NodeId) -> bool,
G::NodeId: Eq + Hash,
F: FnMut(G::EdgeRef) -> K,
H: FnMut(G::NodeId) -> K,
K: Measure + Copy,
{
let mut visited = graph.visit_map();
let mut visit_next = BinaryHeap::new();
let mut scores = HashMap::new();
let mut path_tracker = PathTracker::<G>::new();
let zero_score = K::default();
scores.insert(start, zero_score);
visit_next.push(MinScored(estimate_cost(start), start));
while let Some(MinScored(_, node)) = visit_next.pop() {
if is_goal(node) {
let path = path_tracker.reconstruct_path_to(node);
let cost = scores[&node];
return Some((cost, path));
}
// Don't visit the same node several times, as the first time it was visited it was using
// the shortest available path.
if !visited.visit(node) {
continue
}
// This lookup can be unwrapped without fear of panic since the node was necessarily scored
// before adding him to `visit_next`.
let node_score = scores[&node];
for edge in graph.edges(node) {
let next = edge.target();
if visited.is_visited(&next) {
continue
}
let mut next_score = node_score + edge_cost(edge);
match scores.entry(next) {
Occupied(ent) => {
let old_score = *ent.get();
if next_score < old_score {
*ent.into_mut() = next_score;
path_tracker.set_predecessor(next, node);
} else {
next_score = old_score;
}
},
Vacant(ent) => {
ent.insert(next_score);
path_tracker.set_predecessor(next, node);
}
}
let next_estimate_score = next_score + estimate_cost(next);
visit_next.push(MinScored(next_estimate_score, next));
}
}
None
}
struct PathTracker<G>
where G: GraphBase,
G::NodeId: Eq + Hash,
{
came_from: HashMap<G::NodeId, G::NodeId>,
}
impl<G> PathTracker<G>
where G: GraphBase,
G::NodeId: Eq + Hash,
{
fn new() -> PathTracker<G> {
PathTracker {
came_from: HashMap::new(),
}
}
fn set_predecessor(&mut self, node: G::NodeId, previous: G::NodeId) {
self.came_from.insert(node, previous);
}
fn reconstruct_path_to(&self, last: G::NodeId) -> Vec<G::NodeId> {
let mut path = vec![last];
let mut current = last;
loop {
if let Some(&previous) = self.came_from.get(&current) {
path.push(previous);
current = previous;
} else {
break
}
}
path.reverse();
path
}
}