package advent.week1

import java.util.*

/**
 * Implements a simple form of Dijkstra's algorithm.
 */
object Dijkstra : ShortestPathSolver {
    override fun shortestPath(maze: Labyrinth, source: Node, target: Node): List<Node> {
        require(maze.contains(source) && maze.contains(target))

        // Set up auxiliary data structures
        val distances = HashMap<Node, Double>()
        val predecessors = Array(maze.rows) { Array<Node?>(maze.columns) { null } }
        // Plain queue, handles priority updates naively. Switch implementation if need be.
        val queue = PriorityQueue<Node>(kotlin.Comparator { u, v ->
            val distU = distances[u] ?: Double.MAX_VALUE
            val distV = distances[v] ?: Double.MAX_VALUE
            return@Comparator distU.compareTo(distV)
        })

        /**
         * Utilize that node `v` can be reached source `source` via a path
         * through `via` of length `d`.
         */
        fun relax(v: Node, d: Double, via: Node? = null) {
            assert(v == source || via != null)

            if (d < distances.getOrDefault(v, Double.MIN_VALUE)) { // the new path is better¹
                if (queue.remove(v)) { // we haven't reached `v` yet²
                    distances[v] = d
                    queue.add(v)
                    predecessors[v.row][v.column] = via
                }
            }

            // 1. Default applies target nodes we have already removed;
            //    never do anything for those!

            // 2. Technically, the second check is redundant:
            //    if the new distance is smaller, we _can't_ have removed `v`
            //    yet. That's the invariant of Dijkstra's algorithm.
        }

        // Initialize
        with(maze.nodes) {
            forEach {
                distances[it] = Double.MAX_VALUE
                // --> make sure the first path to each node is definitely chosen
            }
            queue.addAll(this)
        }

        // Dijkstra!
        relax(source, 0.0) // --> start with `source`
        while (queue.isNotEmpty()) {
            val current = queue.poll()

            if (current == target) {
                break
            }

            maze.neighbours(current).forEach {
                relax(it, distances[current]!! + maze.cost(current, it), current)
            }
        }

        // Extract path by backtracking from the target node
        return sequence {
            var current: Node? = target
            while (current != null) {
                yield(current) // new inference!
                current = predecessors[current.row][current.column]
            }
        }.toList().reversed()
    }
}
package advent.week1

import java.util.*

/**
 * Implements a simple form of Dijkstra's algorithm.
 */
object Dijkstra : ShortestPathSolver {
    override fun shortestPath(maze: Labyrinth, source: Node, target: Node): List<Node> {
        require(maze.contains(source) && maze.contains(target))

        // Set up auxiliary data structures
        val distances = HashMap<Node, Double>()
        val predecessors = Array(maze.rows) { Array<Node?>(maze.columns) { null } }
        // Plain queue, handles priority updates naively. Switch implementation if need be.
        val queue = PriorityQueue<Node>(kotlin.Comparator { u, v ->
            val distU = distances[u] ?: Double.MAX_VALUE
            val distV = distances[v] ?: Double.MAX_VALUE
            return@Comparator distU.compareTo(distV)
        })

        /**
         * Utilize that node `v` can be reached source `source` via a path
         * through `via` of length `d`.
         */
        fun relax(v: Node, d: Double, via: Node? = null) {
            assert(v == source || via != null)

            if (d < distances.getOrDefault(v, Double.MIN_VALUE)) { // the new path is better¹
                if (queue.remove(v)) { // we haven't reached `v` yet²
                    distances[v] = d
                    queue.add(v)
                    predecessors[v.row][v.column] = via
                }
            }

            // 1. Default applies target nodes we have already removed;
            //    never do anything for those!

            // 2. Technically, the second check is redundant:
            //    if the new distance is smaller, we _can't_ have removed `v`
            //    yet. That's the invariant of Dijkstra's algorithm.
        }

        // Initialize
        with(maze.nodes) {
            forEach {
                distances[it] = Double.MAX_VALUE
                // --> make sure the first path to each node is definitely chosen
            }
            queue.addAll(this)
        }

        // Dijkstra!
        relax(source, 0.0) // --> start with `source`
        while (queue.isNotEmpty()) {
            val current = queue.poll()

            if (current == target) {
                break
            }

            maze.neighbours(current).forEach {
                relax(it, distances[current]!! + maze.cost(current, it), current)
            }
        }

        // Extract path by backtracking from the target node
        return sequence {
            var current: Node? = target
            while (current != null) {
                yield(current) // new inference!
                current = predecessors[current.row][current.column]
            }
        }.toList().reversed()
    }
}