# RogerBW's Blog

 The Weekly Challenge 213: Shortest Fun 23 April 2023 I’ve been doing the Weekly Challenges. The latest involved an unusual sort and a pathfinding problem. (Note that this is open until 23 April 2023.) Task 1: Fun Sort You are given a list of positive integers. Write a script to sort the all even integers first then all odds in ascending order. Normally for something like this I'd use a `filter`, `grep` or similar structure, but in this case I need to build two separate lists. Raku shows off its rich core library: ``````sub funsort(@l0) { my %h = classify { \$_ %% 2 ?? 'even' !! 'odd' }, @l0.sort(); my @a; for ("even", "odd") -> \$mode { if (%h{\$mode}) { @a.append(%h{\$mode}.List); } } return @a; } `````` while in the other languages it was easier to be explicit, as in Python: ``````def funsort(l0): l = l0 l.sort() a = [] b = [] for k in l: if k % 2 == 0: a.append(k) else: b.append(k) a.extend(b) return a `````` Task 2: Shortest Route You are given a list of bidirectional routes defining a network of nodes, as well as source and destination node numbers. Write a script to find the route from source to destination that passes through fewest nodes. I've done Dijkstra before, but this time I did a simple exhaustive search without repetition. Using a breadth-first search guarantees that the first path found is a shortest path (because all length N paths are tested before any length N+1 paths), so we exit at that point. Sets (which I think of as contentless hashes) are great. But Lua, Perl and PostScript don't have them, so I ended up using a hash and ignoring the value part. Python, Rust and Ruby have set difference operators and I was able to use them. Raku does too, but I didn't manage to get that one to work. In Rust and Python I carried a set of unused nodes along with the path-to-date, trading off memory to get a little more speed. In the other languages this was trickier, and I just worked it up on the fly. Here's the Rust. ``````fn shortestroute(r0: Vec>, origin: u32, destination: u32) -> Vec { `````` This goes in two stages. First, break down the input into a list of possible exits from each node. (Specifically, a map from each node to a set of exits.) `````` let mut r: HashMap> = HashMap::new(); for rt in r0 { for rp in rt.windows(2) { r.entry(rp[0]) .and_modify(|s| { (*s).insert(rp[1]); }) .or_insert(HashSet::from([rp[1]])); r.entry(rp[1]) .and_modify(|s| { (*s).insert(rp[0]); }) .or_insert(HashSet::from([rp[0]])); } } `````` Then, starting at the origin, build paths that go to to each exit, and do this repeatedly until we reach the destination. This is a fairly standard breadth-first search framework as I've used in many previous PWC entries. `````` let mut out = Vec::new(); let mut stack = VecDeque::new(); stack.push_back((vec![origin], HashSet::from([origin]))); while stack.len() > 0 { let s = stack.pop_front().unwrap(); let l = s.0.last().unwrap(); if *l == destination { out = s.0; break; } else { for pd in r.get(&l).unwrap().difference(&s.1) { let mut q = s.clone(); q.0.push(*pd); q.1.remove(pd); stack.push_back(q); } } } out } `````` Full code on github. 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