day(15): move Dijkstra to separate class
Signed-off-by: Matej Focko <mfocko@redhat.com>
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1 changed files with 83 additions and 96 deletions
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@ -3,117 +3,104 @@ package year2021.day15
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import readInput
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import java.util.*
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fun validAdjacentIndices(input: List<List<Int>>, y0: Int, x0: Int): Iterable<Pair<Int, Int>> =
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(y0 - 1..y0 + 1)
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.flatMap { y -> (x0 - 1..x0 + 1).map { x -> Pair(y, x) } }
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.filter { (y, x) ->
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(y != y0 || x != x0) && (y == y0 || x == x0) && (y >= 0 && y < input.size) && (x >= 0 && x < input[y].size)
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}
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fun validAdjacentIndicesExtended(input: List<List<Int>>, y0: Int, x0: Int): Iterable<Pair<Int, Int>> =
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(y0 - 1..y0 + 1)
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.flatMap { y -> (x0 - 1..x0 + 1).map { x -> Pair(y, x) } }
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.filter { (y, x) ->
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(y != y0 || x != x0) && (y == y0 || x == x0) && (y >= 0 && y < 5 * input.size) && (x >= 0 && x < 5 * input[0].size)
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}
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data class Coordinate(val y: Int, val x: Int)
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fun part1(input: List<List<Int>>): Int {
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val final = Coordinate(input.size - 1, input.last().size - 1)
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val queue = PriorityQueue<Pair<Int, Coordinate>>() { a, b ->
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a.first - b.first
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}
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val distances = mutableMapOf<Coordinate, Int>()
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val processed = mutableSetOf<Coordinate>()
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queue.add(Pair(0, Coordinate(0, 0)))
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distances[Coordinate(0, 0)] = 0
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for (y in input.indices) {
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for (x in input[y].indices) {
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queue.add(Pair(Int.MAX_VALUE, Coordinate(y, x)))
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}
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}
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while (queue.isNotEmpty()) {
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val (distance, coordinate) = queue.remove()!!
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if (processed.contains(coordinate)) {
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continue
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}
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processed.add(coordinate)
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if (coordinate == final) {
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return distance
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}
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for ((y, x) in validAdjacentIndices(input, coordinate.y, coordinate.x)) {
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val neighbour = Coordinate(y, x)
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val alternative = distance + input[y][x]
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if (alternative < (distances[neighbour] ?: Int.MAX_VALUE)) {
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distances[neighbour] = alternative
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queue.add(Pair(alternative, neighbour))
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}
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}
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}
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return Int.MAX_VALUE
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data class Coordinate(val y: Int, val x: Int) {
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fun adjacent(): Iterable<Coordinate> =
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listOf(
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Coordinate(y - 1, x),
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Coordinate(y + 1, x),
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Coordinate(y, x + 1),
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Coordinate(y, x - 1)
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)
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}
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fun getDistance(input: List<List<Int>>, y: Int, x: Int): Int {
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val riskLevel = input[y % input.size][x % input[0].size]
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val added = y / input.size + x / input[0].size
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open class Dijkstra(
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private val input: List<List<Int>>
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) {
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private val queue: PriorityQueue<Pair<Int, Coordinate>> =
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PriorityQueue<Pair<Int, Coordinate>> { a, b ->
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a.first - b.first
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}
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private val distances = mutableMapOf<Coordinate, Int>()
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private val processed = mutableSetOf<Coordinate>()
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if (riskLevel + added > 9) {
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return riskLevel + added - 9
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fun getDistance(coordinate: Coordinate): Int =
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distances[coordinate] ?: Int.MAX_VALUE
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open fun getCost(coordinate: Coordinate): Int =
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input[coordinate.y][coordinate.x]
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private fun relax(distance: Int, neighbour: Coordinate): Boolean {
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val alternative = distance + getCost(neighbour)
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if (alternative >= getDistance(neighbour)) {
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return false
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}
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distances[neighbour] = alternative
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queue.add(Pair(alternative, neighbour))
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return true
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}
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return riskLevel + added
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open fun neighbours(coordinate: Coordinate): Iterable<Coordinate> =
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coordinate.adjacent().filter { (y, x) ->
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(y >= 0 && y < input.size) && (x >= 0 && x < input[y].size)
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}
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fun run(start: Coordinate, end: Coordinate): Dijkstra {
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queue.add(Pair(0, start))
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distances[start] = 0
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while (queue.isNotEmpty()) {
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val (distance, coordinate) = queue.remove()!!
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if (processed.contains(coordinate)) {
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continue
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}
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processed.add(coordinate)
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if (coordinate == end) {
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return this
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}
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neighbours(coordinate).forEach { relax(distance, it) }
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}
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return this
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}
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}
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fun part2(input: List<List<Int>>): Int {
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val final = Coordinate(5 * input.size - 1, 5 * input.last().size - 1)
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val queue = PriorityQueue<Pair<Int, Coordinate>>() { a, b ->
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a.first - b.first
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}
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val distances = mutableMapOf<Coordinate, Int>()
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val processed = mutableSetOf<Coordinate>()
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queue.add(Pair(0, Coordinate(0, 0)))
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distances[Coordinate(0, 0)] = 0
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for (y in input.indices) {
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for (x in input[y].indices) {
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for (i in 0 until 5) {
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queue.add(Pair(Int.MAX_VALUE, Coordinate(i * y, i * x)))
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}
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}
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fun part1(input: List<List<Int>>): Int =
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Coordinate(input.size - 1, input.last().size - 1).let { end ->
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Dijkstra(input)
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.run(
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Coordinate(0, 0),
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end
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).getDistance(end)
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}
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while (queue.isNotEmpty()) {
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val (distance, coordinate) = queue.remove()!!
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if (processed.contains(coordinate)) {
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continue
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}
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processed.add(coordinate)
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if (coordinate == final) {
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return distance
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class DijkstraOnExtended(private val input: List<List<Int>>) : Dijkstra(input) {
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override fun neighbours(coordinate: Coordinate): Iterable<Coordinate> =
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coordinate.adjacent().filter { (y, x) ->
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(y >= 0 && y < 5 * input.size) && (x >= 0 && x < 5 * input[0].size)
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}
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for ((y, x) in validAdjacentIndicesExtended(input, coordinate.y, coordinate.x)) {
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val neighbour = Coordinate(y, x)
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val alternative = distance + getDistance(input, y, x)
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override fun getCost(coordinate: Coordinate): Int {
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val (y, x) = coordinate
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if (alternative < (distances[neighbour] ?: Int.MAX_VALUE)) {
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distances[neighbour] = alternative
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queue.add(Pair(alternative, neighbour))
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}
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}
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val added = y / input.size + x / input[0].size
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val riskLevel = input[y % input.size][x % input[0].size] + added
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return if (riskLevel > 9) riskLevel - 9 else riskLevel
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}
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return Int.MAX_VALUE
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}
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fun part2(input: List<List<Int>>): Int =
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Coordinate(5 * input.size - 1, 5 * input.last().size - 1).let { end ->
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DijkstraOnExtended(input)
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.run(
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Coordinate(0, 0),
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end
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).getDistance(end)
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}
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fun main() {
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val sample = readInput(15, "sample").map { row -> row.map { it.digitToInt() } }
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val input = readInput(15, "input").map { row -> row.map { it.digitToInt() } }
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