随机生成点的坐标并依据点集生成距离矩阵,通过点的坐标实现可视化
c代码看我的这篇文章tsp动态规划递归解法c++
python
from typing import List, Tuple
import matplotlib.pyplot as plt
from random import randint
N: int = 4
MAX: int = 0x7f7f7f7f
distances: List[List[int]] = [[0 for _ in range(N)] for _ in range(N)]
path: List[List[int]] = [[0 for _ in range(1 << (N - 1))] for _ in range(N)]
dp: List[List[int]] = [[0 for _ in range(1 << (N - 1))] for _ in range(N)]
points: List[Tuple[int, int ]]
def creatDistances():
global distances, dp
# for i in range(N):
# for j in range(N):
# if i == j:
# distances[i][j] = MAX
# else:
# temp = randint(1, 10)
# while temp == 0:
# temp = randint(1, 10)
# distances[i][j] = temp
# x=[[MAX, 3, 6, 7],
# [5, MAX, 2, 3],
# [6, 4, MAX, 2],
# [3, 7, 5, MAX]]
# for i in range(N):
# for j in range(N):
# distances[i][j] = x[i][j]
creatpoints()
for i in range(N):
dp[i][0] = distances[i][0]
def printDistances():
global distances
print("代价矩阵为:")
for i in range(N):
for j in range(N):
if distances[i][j] == MAX:
s = "INF"
print(f"{s:<17}", end=" ")
else:
print(f"{distances[i][j]:<17}", end=" ")
print()
for i, point in enumerate(points):
plt.text(*point, f'{i }', fontsize=12, ha='center', va='bottom')
plt.scatter(*zip(*points))
def removeCity(j: int, k: int) -> int:
return j - (1 << (k - 1))
def printPath(i: int, j: int) -> None:
if j != 0:
print(f"{i} -> ", end="")
next_city = path[i][j]
plt.plot((points[i][0],points[next_city][0]), (points[i][1],points[next_city][1]))
printPath(next_city, removeCity(j, next_city))
else:
print(f"{i} -> {0}")
plt.plot((points[i][0],0), (points[i][1],0))
def creatpoints() ->None:
ldasc: int = 1
hdasc: int = 10
dapr: int = N - 1
global points
points = [(0,0)]+[(randint(ldasc, hdasc), randint(ldasc, hdasc)) for i in range(dapr)]
for i in range(N):
for j in range(i, N):
if i == j:
distances[i][j] = MAX
else:
distances[i][j] = distances[j][i] = ((points[i][0]-points[j][0])**2+(points[i][1]-points[j][1])**2)**.5
def drewpoints() ->None:
global points
def TSP(v: int, s: int) -> int:
global distances, dp, path
if dp[v][s] != 0:
return dp[v][s]
min = MAX
for k in range(1, N):
if ((s >> (k - 1)) & 1) == 1:
t = TSP(k, removeCity(s, k))
if (t + distances[v][k]) < min:
min = t + distances[v][k]
path[v][s] = k
dp[v][s] = min
return min
if __name__ == "__main__":
creatDistances()
printDistances()
print(f"最短距离为:{TSP(0, (1 << (N - 1)) - 1)}")
print("最短路径为:")
printPath(0, (1 << (N - 1)) - 1)
print(points)
plt.show()