Clean up day 25 and add explanation.

main
felixm 2024-01-25 21:50:21 -05:00
parent 0d1eff4f00
commit 40ac031eb4
2 changed files with 27 additions and 56 deletions

1
.gitignore vendored
View File

@ -1,2 +1,3 @@
Pipfile
__pycache__
*.txt

82
d25.py
View File

@ -1,58 +1,23 @@
from lib import *
def plot(graph):
import networkx as nx
import matplotlib
import matplotlib.pyplot as plt
G = nx.Graph()
for node, connected_nodes in graph.items():
for connected_node in connected_nodes:
G.add_edge(node, connected_node)
# pos = nx.spring_layout(G, k=2.0, iterations=20) # Adjust k as needed
pos = nx.shell_layout(G)
nx.draw(G, with_labels=True, node_color='lightblue', edge_color='gray', node_size=2000, font_size=15, font_weight='bold')
matplotlib.use('qtagg')
plt.show()
def solve_non_hands_free(input: Input):
graph = {}
for line in input.lines():
source, targets = line.split(":")
targets = targets.strip()
targets = targets.split(" ")
for target in targets:
if not source in graph:
graph[source] = [target]
else:
graph[source].append(target)
if not target in graph:
graph[target] = [source]
else:
graph[target].append(source)
# plot(graph) # I used this to find the nodes that have to be removed.
to_remove = (("plt", "mgb"), ("jxm", "qns"), ("dbt", "tjd"))
for a, b in to_remove:
graph[a].remove(b)
graph[b].remove(a)
to_visit = ["plt"]
seen = set(to_visit)
while to_visit:
node = to_visit.pop()
for nb in graph[node]:
if not nb in seen:
seen.add(nb)
to_visit.append(nb)
return len(seen) * (len(graph) - len(seen))
from random import choice
from collections import deque
# def plot(graph):
# import networkx as nx
# import matplotlib
# import matplotlib.pyplot as plt
# G = nx.Graph()
# for node, connected_nodes in graph.items():
# for connected_node in connected_nodes:
# G.add_edge(node, connected_node)
# # pos = nx.spring_layout(G, k=2.0, iterations=20) # Adjust k as needed
# pos = nx.shell_layout(G)
# nx.draw(G, with_labels=True, node_color='lightblue', edge_color='gray', node_size=2000, font_size=15, font_weight='bold')
# matplotlib.use('qtagg')
# plt.show()
def solve(input: Input):
graph = {}
edges = {}
@ -73,7 +38,7 @@ def solve(input: Input):
edge = tuple(sorted([src, dst]))
edges[edge] = 0
for i in range (1000):
for _ in range(100):
first_node = choice(list(graph.keys()))
seen = set([first_node])
visit = deque([first_node])
@ -85,10 +50,17 @@ def solve(input: Input):
visit.append(nb)
edge = tuple(sorted([node, nb]))
edges[edge] += 1
# Orignally, I used `plot(graph)` to visually find the nodes that have to
# be removed. I then came up with this heuristic approach. The idea is that
# we have to cross one of the three nodes when we do a breadth first
# search. By repeatedly doing that we can identify the "bridges" as the
# three edges that are used the most often.
most_visited = sorted(edges.items(), key=lambda t: t[1], reverse=True)[:3]
# to_remove = (("plt", "mgb"), ("jxm", "qns"), ("dbt", "tjd")) # found visually
# for node, count in most_visited:
# print(node, count)
# print(node, count) # should print the same as `to_remove`
for (a, b), _ in most_visited:
graph[a].remove(b)
@ -108,10 +80,8 @@ def solve(input: Input):
def main():
DAY_INPUT = "i25.txt"
print("Solution 1:", solve_non_hands_free(Input(DAY_INPUT)))
print("Solution 1:", solve(Input(DAY_INPUT)), "(hands-free)")
if __name__ == "__main__":
main()