52 lines
1.4 KiB
Python
52 lines
1.4 KiB
Python
|
# -*- encoding: utf-8 -*-
|
||
|
|
|
||
|
|
import sys
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import parse
|
||
|
|
from docplex.mp.model import Model
|
||
|
|
|
||
|
|
lines = sys.stdin.read().splitlines()
|
||
|
|
|
||
|
|
sensor_to_beacon: dict[tuple[int, int], tuple[int, int]] = {}
|
||
|
|
|
||
|
|
for line in lines:
|
||
|
|
r = parse.parse(
|
||
|
|
"Sensor at x={sx}, y={sy}: closest beacon is at x={bx}, y={by}", line
|
||
|
|
)
|
||
|
|
sensor_to_beacon[int(r["sx"]), int(r["sy"])] = (int(r["bx"]), int(r["by"]))
|
||
|
|
|
||
|
|
xy_max = 4_000_000 if max(sensor_to_beacon) > (1_000, 0) else 20
|
||
|
|
row = 2_000_000 if max(sensor_to_beacon) > (1_000, 0) else 10
|
||
|
|
|
||
|
|
no_beacons_row_l: list[np.ndarray] = []
|
||
|
|
|
||
|
|
for (sx, sy), (bx, by) in sensor_to_beacon.items():
|
||
|
|
d = abs(sx - bx) + abs(sy - by) # closest
|
||
|
|
|
||
|
|
no_beacons_row_l.append(sx - np.arange(0, d - abs(sy - row) + 1))
|
||
|
|
no_beacons_row_l.append(sx + np.arange(0, d - abs(sy - row) + 1))
|
||
|
|
|
||
|
|
beacons_at_row = set(bx for (bx, by) in sensor_to_beacon.values() if by == row)
|
||
|
|
no_beacons_row = set(np.concatenate(no_beacons_row_l)).difference(beacons_at_row)
|
||
|
|
|
||
|
|
print(f"answer 1 is {len(no_beacons_row)}")
|
||
|
|
|
||
|
|
# === part 2 ===
|
||
|
|
|
||
|
|
m = Model()
|
||
|
|
|
||
|
|
x, y = m.continuous_var_list(2, ub=xy_max, name=["x", "y"])
|
||
|
|
|
||
|
|
for (sx, sy), (bx, by) in sensor_to_beacon.items():
|
||
|
|
d = abs(sx - bx) + abs(sy - by)
|
||
|
|
m.add_constraint(m.abs(x - sx) + m.abs(y - sy) >= d + 1, ctname=f"ct_{sx}_{sy}")
|
||
|
|
|
||
|
|
m.set_objective("min", x + y)
|
||
|
|
|
||
|
|
s = m.solve()
|
||
|
|
|
||
|
|
# 10621647166538
|
||
|
|
answer_2 = 4_000_000 * int(s.get_value(x)) + int(s.get_value(y))
|
||
|
|
print(f"answer 2 is {answer_2}")
|