My Advent of Code solutions in Python.
kevinyap.ca/2019/12/going-fast-in-advent-of-code/
advent-of-code
python
Switch pathfinding algorithm to A*
+35
-11
+35
-11
2016/day24.py
+35
-11
2016/day24.py
···
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1
import fileinput
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-
from collections import deque
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+
import heapq
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from itertools import combinations, permutations
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DIRS = [
···
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return False
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-
def bfs(grid, start, goal):
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horizon = deque([(start, 0)])
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seen = set(start)
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def heuristic(a, b):
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x1, y1 = a
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x2, y2 = b
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return abs(x1 - x2) + abs(y1 - y2)
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+
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+
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def a_star(grid, start, goal):
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"""
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Returns the length of the shortest path, minimizing f(n) = g(n) + h(n)
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as a search strategy. Using a closed set to skip over nodes that have
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already seen is valid so long as h(n) is admissible.
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"""
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-
while horizon:
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(x, y), dst = horizon.pop()
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frontier = []
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seen = set()
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cost_to = {start: 0} # this is g(n)
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heapq.heappush(frontier, (0, start))
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while frontier:
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_priority, (x, y) = heapq.heappop(frontier)
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if (x, y) == goal:
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return dst
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return cost_to[goal]
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seen.add((x, y))
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for dx, dy in DIRS:
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nx, ny = x + dx, y + dy
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if (nx, ny) not in seen and valid(grid, nx, ny):
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horizon.appendleft(((nx, ny), dst + 1))
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seen.add((nx, ny))
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if (nx, ny) in seen or not valid(grid, nx, ny):
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continue
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new_dist = cost_to[x, y] + 1
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if (nx, ny) not in cost_to or new_dist < cost_to[nx, ny]:
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cost_to[nx, ny] = new_dist
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priority = new_dist + heuristic(goal, (nx, ny))
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heapq.heappush(frontier, (priority, (nx, ny)))
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def navigate(distances, path, complete=False):
···
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distances = {}
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for a, b in combinations(range(max(locations) + 1), 2):
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distances[a, b] = distances[b, a] = bfs(grid, locations[a], locations[b])
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distances[a, b] = distances[b, a] = a_star(grid, locations[a], locations[b])
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paths = list(permutations(range(1, max(locations) + 1)))
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print "Fewest steps to visit all numbers:", min(navigate(distances, path) for path in paths)