From 94500b84d4355e322efafc4f21c9d29ffc676826 Mon Sep 17 00:00:00 2001
From: Jonas Hietala <tradet.h@gmail.com>
Date: Mon, 21 Jul 2014 15:20:57 +0200
Subject: [PATCH] Main example for priority queue using dijkstra's algorithm.

---
 src/libcollections/priority_queue.rs | 138 ++++++++++++++++++++++++++-
 1 file changed, 137 insertions(+), 1 deletion(-)

diff --git a/src/libcollections/priority_queue.rs b/src/libcollections/priority_queue.rs
index 256621d08e54a..9451f2521c89a 100644
--- a/src/libcollections/priority_queue.rs
+++ b/src/libcollections/priority_queue.rs
@@ -8,7 +8,143 @@
 // option. This file may not be copied, modified, or distributed
 // except according to those terms.
 
-//! A priority queue implemented with a binary heap
+//! A priority queue implemented with a binary heap.
+//!
+//! # Example
+//!
+//! This is a larger example which implements [Dijkstra's algorithm][dijkstra]
+//! to solve the [shortest path problem][sssp] on a [directed graph][dir_graph].
+//! It showcases how to use the `PriorityQueue` with custom types.
+//!
+//! [dijkstra]: http://en.wikipedia.org/wiki/Dijkstra%27s_algorithm
+//! [sssp]: http://en.wikipedia.org/wiki/Shortest_path_problem
+//! [dir_graph]: http://en.wikipedia.org/wiki/Directed_graph
+//!
+//! ```
+//! use std::collections::PriorityQueue;
+//! use std::uint;
+//!
+//! #[deriving(Eq, PartialEq)]
+//! struct State {
+//!     cost: uint,
+//!     position: uint
+//! }
+//!
+//! // The priority queue depends on `Ord`.
+//! // Explicitly implement the trait so the queue becomes a min-heap
+//! // instead of a max-heap.
+//! impl Ord for State {
+//!     fn cmp(&self, other: &State) -> Ordering {
+//!         // Notice that the we flip the ordering here
+//!         other.cost.cmp(&self.cost)
+//!     }
+//! }
+//!
+//! // `PartialOrd` needs to be implemented as well.
+//! impl PartialOrd for State {
+//!     fn partial_cmp(&self, other: &State) -> Option<Ordering> {
+//!         Some(self.cmp(other))
+//!     }
+//! }
+//!
+//! // Each node is represented as an `uint`, for a shorter implementation.
+//! struct Edge {
+//!     node: uint,
+//!     cost: uint
+//! }
+//!
+//! // Dijkstra's shortest path algorithm.
+//!
+//! // Start at `start` and use `dist` to track the current shortest distance
+//! // to each node. This implementation isn't memory efficient as it may leave duplicate
+//! // nodes in the queue. It also uses `uint::MAX` as a sentinel value,
+//! // for a simpler implementation.
+//! fn shortest_path(adj_list: &Vec<Vec<Edge>>, start: uint, goal: uint) -> uint {
+//!     // dist[node] = current shortest distance from `start` to `node`
+//!     let mut dist = Vec::from_elem(adj_list.len(), uint::MAX);
+//!
+//!     let mut pq = PriorityQueue::new();
+//!
+//!     // We're at `start`, with a zero cost
+//!     *dist.get_mut(start) = 0u;
+//!     pq.push(State { cost: 0u, position: start });
+//!
+//!     // Examine the frontier with lower cost nodes first (min-heap)
+//!     loop {
+//!         let State { cost, position } = match pq.pop() {
+//!             None => break, // empty
+//!             Some(s) => s
+//!         };
+//!
+//!         // Alternatively we could have continued to find all shortest paths
+//!         if position == goal { return cost }
+//!
+//!         // Important as we may have already found a better way
+//!         if cost > dist[position] { continue }
+//!
+//!         // For each node we can reach, see if we can find a way with
+//!         // a lower cost going through this node
+//!         for edge in adj_list[position].iter() {
+//!             let next = State { cost: cost + edge.cost, position: edge.node };
+//!
+//!             // If so, add it to the frontier and continue
+//!             if next.cost < dist[next.position] {
+//!                 pq.push(next);
+//!                 // Relaxation, we have now found a better way
+//!                 *dist.get_mut(next.position) = next.cost;
+//!             }
+//!         }
+//!     }
+//!
+//!     // Goal not reachable
+//!     uint::MAX
+//! }
+//!
+//! fn main() {
+//!     // This is the directed graph we're going to use.
+//!     // The node numbers correspond to the different states,
+//!     // and the edge weights symbolises the cost of moving
+//!     // from one node to another.
+//!     // Note that the edges are one-way.
+//!     //
+//!     //                  7
+//!     //          +-----------------+
+//!     //          |                 |
+//!     //          v   1        2    |
+//!     //          0 -----> 1 -----> 3 ---> 4
+//!     //          |        ^        ^      ^
+//!     //          |        | 1      |      |
+//!     //          |        |        | 3    | 1
+//!     //          +------> 2 -------+      |
+//!     //           10      |               |
+//!     //                   +---------------+
+//!     //
+//!     // The graph is represented as an adjecency list where each index,
+//!     // corresponding to a node value, has a list of outgoing edges.
+//!     // Chosen for it's efficiency.
+//!     let graph = vec![
+//!         // Node 0
+//!         vec![Edge { node: 2, cost: 10 },
+//!              Edge { node: 1, cost: 1 }],
+//!         // Node 1
+//!         vec![Edge { node: 3, cost: 2 }],
+//!         // Node 2
+//!         vec![Edge { node: 1, cost: 1 },
+//!              Edge { node: 3, cost: 3 },
+//!              Edge { node: 4, cost: 1 }],
+//!         // Node 3
+//!         vec![Edge { node: 0, cost: 7 },
+//!              Edge { node: 4, cost: 2 }],
+//!         // Node 4
+//!         vec![]];
+//!
+//!     assert_eq!(shortest_path(&graph, 0, 1), 1);
+//!     assert_eq!(shortest_path(&graph, 0, 3), 3);
+//!     assert_eq!(shortest_path(&graph, 3, 0), 7);
+//!     assert_eq!(shortest_path(&graph, 0, 4), 5);
+//!     assert_eq!(shortest_path(&graph, 4, 0), uint::MAX);
+//! }
+//! ```
 
 #![allow(missing_doc)]