Without embarrassment, I show this code to you. Part 1 takes a few minutes to complete. I will respond with an optimized version later.
import java.nio.file.Files import java.nio.file.Paths import java.util.* object Day06 { const val FILENAME = "src/main/resources/day06.txt" fun parseOrbit(s: String): Pair<String, String> { return """(.*)\)(.*)""".toRegex().matchEntire(s)?.let { it.groupValues[1] to it.groupValues[2] } ?: throw Error("Bad orbit: $s") } fun part1(): Int { return Files.readAllLines(Paths.get(FILENAME)) .map(::parseOrbit).fold(mutableMapOf<String, MutableSet<String>>()) { m, (a, b) -> val nset = (m[a] ?: mutableSetOf()) nset.add(b) m[a] = nset m }.countChildren() } fun part2(): Int { val paths = Files.readAllLines(Paths.get(FILENAME)) .map(::parseOrbit).fold(mutableMapOf<String, Set<String>>()) { m, (a, b) -> m[a] = (m[a] ?: emptySet()) + b m } val youParents = paths.filterValues { "YOU" in it }.keys.map { it to 0 } return paths.findDistanceToSan(queue = LinkedList(youParents), seen = setOf("YOU")) } private tailrec fun MutableMap<String, Set<String>>.findDistanceToSan( queue: Deque<Pair<String, Int>>, seen: Set<String> ): Int { val (node, dist) = queue.pop() return when (node) { "SAN" -> dist - 1 in seen -> findDistanceToSan(queue, seen) else -> { queue.addAll(filterValues { node in it }.keys.map { it to (dist + 1) }) queue.addAll(filterKeys { node == it }.flatMap { (k, v) -> v.map { it to (dist + 1) } }) findDistanceToSan(LinkedList(queue.filterNot { (n, d) -> n in seen }), seen + node) } } } } private fun MutableMap<String, MutableSet<String>>.countChildren() = toList().indices.fold(toMap()) { m, _ -> m.mapValues { (k, v) -> v.addAll(v.flatMap { this[it] ?: mutableSetOf() }) v } }.toList().sumBy { (k, v) -> v.size } fun main() { println(Day06.part1()) println(Day06.part2()) }
Part 2 was my old friend "Djikstra's Algorithm". It wasn't enough data to warrant breaking out A*Search, but if previous years are any indication we will probably need it at some point.
Ahhh... much better. This runs in msecs.
import java.nio.file.Files import java.nio.file.Paths import java.util.* object Day06 { private const val FILENAME = "src/main/resources/day06.txt" private fun parseOrbit(s: String): Pair<String, String> { return """(.*)\)(.*)""".toRegex().matchEntire(s)?.let { it.groupValues[1] to it.groupValues[2] } ?: throw Error("Bad orbit: $s") } private fun List<String>.countPaths() = map(::parseOrbit).fold(mapOf<String, Set<String>>()) { m, (a, b) -> val nset = (m[a] ?: emptySet()) + b m + (a to nset) }.let { orbits -> orbits.countChildren(orbits["COM"].orEmpty().map { it to 1 }, 0) } private tailrec fun Map<String, Set<String>>.countChildren( paths: List<Pair<String, Int>>, size: Int ): Int { val nextPaths = paths.flatMap { this[it.first].orEmpty().map { c -> c to it.second + 1 } } return when { nextPaths.isEmpty() -> size + paths.sumBy(Pair<String, Int>::second) else -> countChildren(nextPaths, size + paths.sumBy(Pair<String, Int>::second)) } } private fun List<String>.shortestDistanceToSanta(): Int { val paths = map(::parseOrbit).fold(mutableMapOf<String, Set<String>>()) { m, (a, b) -> m[a] = (m[a] ?: emptySet()) + b m } val youParents = paths.filterValues { "YOU" in it }.keys.map { it to 0 } return paths.findDistanceToSan(queue = LinkedList(youParents), seen = setOf("YOU")) } private tailrec fun MutableMap<String, Set<String>>.findDistanceToSan( queue: Deque<Pair<String, Int>>, seen: Set<String> ): Int { val (node, dist) = queue.pop() return when (node) { "SAN" -> dist - 1 in seen -> findDistanceToSan(queue, seen) else -> { queue.addAll(filterValues { node in it }.keys.map { it to (dist + 1) }) queue.addAll(filterKeys { node == it }.flatMap { (_, v) -> v.map { it to (dist + 1) } }) findDistanceToSan(LinkedList(queue.filterNot { (n, _) -> n in seen }), seen + node) } } } fun part1() = Files.readAllLines(Paths.get(FILENAME)).countPaths() fun part2(): Int = Files.readAllLines(Paths.get(FILENAME)).shortestDistanceToSanta() } fun main() { println(Day06.part1()) println(Day06.part2()) }
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Without embarrassment, I show this code to you. Part 1 takes a few minutes to complete. I will respond with an optimized version later.
Part 2 was my old friend "Djikstra's Algorithm". It wasn't enough data to warrant breaking out A*Search, but if previous years are any indication we will probably need it at some point.
Ahhh... much better. This runs in msecs.