Day 12: Hot Springs
Megathread guidelines
- Keep top level comments as only solutions, if you want to say something other than a solution put it in a new post. (replies to comments can be whatever)
- Code block support is not fully rolled out yet but likely will be in the middle of the event. Try to share solutions as both code blocks and using something such as https://topaz.github.io/paste/ , pastebin, or github (code blocks to future proof it for when 0.19 comes out and since code blocks currently function in some apps and some instances as well if they are running a 0.19 beta)
FAQ
- What is this?: Here is a post with a large amount of details: https://programming.dev/post/6637268
- Where do I participate?: https://adventofcode.com/
- Is there a leaderboard for the community?: We have a programming.dev leaderboard with the info on how to join in this post: https://programming.dev/post/6631465
π Thread is locked until thereβs at least 100 2 star entries on the global leaderboard
π Unlocked after 25 mins
Haskell
Phew! I struggled with this one. A lot of the code here is from my original approach, which cuts down the search space to plausible positions for each group. Unfortunately, that was still way too slowβ¦
It took an embarrassingly long time to try memoizing the search (which made precomputing valid points far less important). Anyway, here it is!
Solution
{-# LANGUAGE LambdaCase #-}
import Control.Monad
import Control.Monad.State
import Data.List
import Data.List.Split
import Data.Map (Map)
import qualified Data.Map as Map
import Data.Maybe
readInput :: String -> ([Maybe Bool], [Int])
readInput s =
let [a, b] = words s
in ( map (\case '#' -> Just True; '.' -> Just False; '?' -> Nothing) a,
map read $ splitOn "," b
)
arrangements :: ([Maybe Bool], [Int]) -> Int
arrangements (pat, gs) = evalState (searchMemo 0 groups) Map.empty
where
len = length pat
groups = zipWith startPoints gs $ zip minStarts maxStarts
where
minStarts = scanl (\a g -> a + g + 1) 0 $ init gs
maxStarts = map (len -) $ scanr1 (\g a -> a + g + 1) gs
startPoints g (a, b) =
let ps = do
(i, pat') <- zip [a .. b] $ tails $ drop a pat
guard $
all (\(p, x) -> maybe True (== x) p) $
zip pat' $
replicate g True ++ [False]
return i
in (g, ps)
clearableFrom i =
fmap snd $
listToMaybe $
takeWhile ((<= i) . fst) $
dropWhile ((< i) . snd) clearableRegions
where
clearableRegions =
let go i [] = []
go i pat =
let (a, a') = span (/= Just True) pat
(b, c) = span (== Just True) a'
in (i, i + length a - 1) : go (i + length a + length b) c
in go 0 pat
searchMemo :: Int -> [(Int, [Int])] -> State (Map (Int, Int) Int) Int
searchMemo i gs = do
let k = (i, length gs)
cached <- gets (Map.!? k)
case cached of
Just x -> return x
Nothing -> do
x <- search i gs
modify (Map.insert k x)
return x
search i gs | i >= len = return $ if null gs then 1 else 0
search i [] = return $
case clearableFrom i of
Just b | b == len - 1 -> 1
_ -> 0
search i ((g, ps) : gs) = do
let maxP = maybe i (1 +) $ clearableFrom i
ps' = takeWhile (<= maxP) $ dropWhile (< i) ps
sum <$> mapM (\p -> let i' = p + g + 1 in searchMemo i' gs) ps'
expand (pat, gs) =
(intercalate [Nothing] $ replicate 5 pat, concat $ replicate 5 gs)
main = do
input <- map readInput . lines <$> readFile "input12"
print $ sum $ map arrangements input
print $ sum $ map (arrangements . expand) input
Rust
Took me way too long, but Iβm happy with my solution now. I spent probably half an hour looking at my naive backtracking program churning away unsuccessfully before I thought of dynamic programming, meaning caching all intermediate results in a hashtable under their current state. The state is just the index into the spring array and the index into the range array, meaning there really canβt be too many different entries. Doing so worked very well, solving part 2 in 4ms.
Adding the caching required me to switch from a loop to a recursive function, which turned out way easier. Why did no one tell me to just go recursive from the start?
C
That was something! I quickly settled on the main approach for part 1 but it took some unit testing to get it all right. Then part 2 had me stumped for a bit. It was clear some kind of pruning was necessary, possibly with memoization.
Hashmaps are possible but annoying with C so I was happy to realise that, for my implementation, (num chars, num runs)
is a suitable key within the context of a single recursive search. That space is small enough to index with an array π
Scala3
def countDyn(a: List[Char], b: List[Int]): Long =
// Simple dynamic programming approach
// We fill a table T, where
// T[ ai, bi ] -> number of ways to place b[bi..] in a[ai..]
// T[ ai, bi ] = 0 if an-ai >= b[bi..].sum + bn-bi
// T[ ai, bi ] = 1 if bi == b.size - 1 && ai == a.size - b[bi] - 1
// T[ ai, bi ] =
// (place) T [ ai + b[bi], bi + 1] if ? or #
// (skip) T [ ai + 1, bi ] if ? or .
//
def t(ai: Int, bi: Int, tbl: Map[(Int, Int), Long]): Long =
if ai >= a.size then
if bi >= b.size then 1L else 0L
else
val place = Option.when(
bi < b.size && // need to have piece left
ai + b(bi) <= a.size && // piece needs to fit
a.slice(ai, ai + b(bi)).forall(_ != '.') && // must be able to put piece there
(ai + b(bi) == a.size || a(ai + b(bi)) != '#') // piece needs to actually end
)((ai + b(bi) + 1, bi + 1)).flatMap(tbl.get).getOrElse(0L)
val skip = Option.when(a(ai) != '#')((ai + 1, bi)).flatMap(tbl.get).getOrElse(0L)
place + skip
@tailrec def go(ai: Int, tbl: Map[(Int, Int), Long]): Long =
if ai == 0 then t(ai, 0, tbl) else go(ai - 1, tbl ++ b.indices.inclusive.map(bi => (ai, bi) -> t(ai, bi, tbl)).toMap)
go(a.indices.inclusive.last + 1, Map())
def countLinePossibilities(repeat: Int)(a: String): Long =
a match
case s"$pattern $counts" =>
val p2 = List.fill(repeat)(pattern).mkString("?")
val c2 = List.fill(repeat)(counts).mkString(",")
countDyn(p2.toList, c2.split(",").map(_.toInt).toList)
case _ => 0L
def task1(a: List[String]): Long = a.map(countLinePossibilities(1)).sum
def task2(a: List[String]): Long = a.map(countLinePossibilities(5)).sum
(Edit: fixed mangling of &<)
Iβm struggling to fully understand your solution. Could you tell me, why do you return 1
when at the end of a
and b
? And why do you start from size + 1
?
T counts the number of ways to place the blocks with lengths specified in b in the remaining a.size - ai slots. If there are no more slots left, there are two cases: Either there are also no more blocks left, then everything is fine, and the current situation is 1 way to place the blocks in the slots. Otherwise, there are still blocks left, and no more space to place them in. This means the current sitution is incorrect, so we contribute 0 ways to place the blocks. This is what the if bi >= b.size then 1L else 0L
does.
The start at size + 1
is necessary, as we need to compute every table entry before it may get looked up. When placing the last block, we may check the entry (ai + b(bi) + 1, bi + 1)
, where ai + b(bi)
may already equal a.size
(in the case where the block ends exactly at the end of a
). The + 1
in the entry is necessary, as we need to skip a slot after every block: If we looked at (ai + b(bi), bi + 1)
, we could start at a.size
, but then, for e.g. b = [2, 3]
, we would consider ...#####.
a valid placement.
Let me know if there are still things unclear :)
Thanks for the detailed explanation. It helped a lot, especially what the tbl
actually holds.
Iβve read your code again and I get how it works, but it still feels kinda strange that we are considering values outside of range of a
and b
, and that we are marking them as correct. Like in first row of the example ???.### 1,1,3
, there is no spring at 8
and no group at 3
but we are marking (8,3)
and (7,3)
as correct. In my mind, first position that should be marked as correct is 4,2
, because thatβs where group of 3 can fit.
Python
Let me know if you have any questions or feedback!
import dataclasses
import functools
from .solver import Solver
class MatchState:
pass
@dataclasses.dataclass
class NotMatching(MatchState):
pass
@dataclasses.dataclass
class Matching(MatchState):
current_length: int
desired_length: int
@functools.cache
def _match_one_template(template: str, groups: tuple[int, ...]) -> int:
if not groups:
if '#' in template:
return 0
else:
return 1
state: MatchState = NotMatching()
remaining_groups: list[int] = list(groups)
options_in_other_branches: int = 0
for i in range(len(template)):
match (state, template[i]):
case (NotMatching(), '.'):
pass
case (NotMatching(), '?'):
options_in_other_branches += _match_one_template(template[i+1:], tuple(remaining_groups))
if not remaining_groups:
return options_in_other_branches
group, *remaining_groups = remaining_groups
state = Matching(1, group)
case (NotMatching(), '#'):
if not remaining_groups:
return options_in_other_branches
group, *remaining_groups = remaining_groups
state = Matching(1, group)
case (Matching(current_length, desired_length), '.') if current_length == desired_length:
state = NotMatching()
case (Matching(current_length, desired_length), '.') if current_length < desired_length:
return options_in_other_branches
case (Matching(current_length, desired_length), '?') if current_length == desired_length:
state = NotMatching()
case (Matching(current_length, desired_length), '?') if current_length < desired_length:
state = Matching(current_length + 1, desired_length)
case (Matching(current_length, desired_length), '#') if current_length < desired_length:
state = Matching(current_length + 1, desired_length)
case (Matching(current_length, desired_length), '#') if current_length == desired_length:
return options_in_other_branches
case _:
raise RuntimeError(f'unexpected {state=} with {template=} position {i} and {remaining_groups=}')
match state, remaining_groups:
case NotMatching(), []:
return options_in_other_branches + 1
case Matching(current, desired), [] if current == desired:
return options_in_other_branches + 1
case (NotMatching(), _) | (Matching(_, _), _):
return options_in_other_branches
raise RuntimeError(f'unexpected {state=} with {template=} at end of template and {remaining_groups=}')
def _unfold(template: str, groups: tuple[int, ...]) -> tuple[str, tuple[int, ...]]:
return '?'.join([template] * 5), groups * 5
class Day12(Solver):
def __init__(self):
super().__init__(12)
self.input: list[tuple[str, tuple[int]]] = []
def presolve(self, input: str):
lines = input.rstrip().split('\n')
for line in lines:
template, groups = line.split(' ')
self.input.append((template, tuple(int(group) for group in groups.split(','))))
def solve_first_star(self) -> int:
return sum(_match_one_template(template, groups) for template, groups in self.input)
def solve_second_star(self) -> int:
return sum(_match_one_template(*_unfold(template, groups)) for template, groups in self.input)