def first_test():
way = 153
matrix = [
[999, 32, 26, 86, 11, 55, 29, 7, 89, 43, 21, 41, 48, 61, 93],
[85, 999, 29, 70, 12, 71, 5, 78, 32, 60, 25, 34, 76, 70, 48],
[15, 99, 999, 15, 79, 8, 10, 95, 84, 20, 41, 99, 42, 1, 54],
[88, 21, 35, 999, 5, 26, 78, 64, 16, 38, 29, 47, 71, 89, 91],
[48, 50, 79, 27, 999, 45, 24, 10, 85, 71, 98, 72, 5, 27, 25],
[21, 96, 61, 6, 63, 999, 90, 27, 63, 82, 88, 51, 60, 94, 56],
[57, 15, 11, 99, 82, 62, 999, 71, 80, 47, 31, 76, 35, 88, 0],
[69, 98, 15, 13, 38, 1, 5, 999, 32, 37, 11, 62, 43, 26, 43],
[75, 18, 58, 24, 12, 95, 72, 60, 999, 70, 58, 98, 77, 24, 33],
[48, 90, 67, 24, 2, 61, 41, 67, 46, 999, 41, 49, 20, 33, 38],
[49, 97, 37, 33, 5, 58, 67, 61, 39, 3, 999, 91, 9, 41, 0],
[95, 44, 74, 46, 60, 62, 80, 4, 77, 54, 76, 999, 13, 8, 37],
[75, 13, 37, 56, 12, 7, 66, 37, 28, 96, 38, 81, 999, 96, 45],
[87, 78, 37, 81, 46, 80, 59, 59, 68, 27, 24, 21, 10, 999, 57],
[28, 22, 23, 9, 93, 47, 69, 13, 20, 89, 70, 78, 7, 50, 999]
]
print('\nДлина пути должна быть равна ', way)
class_tsp = tsp()
class_tsp.start(matrix)
if class_tsp.way_price == way:
print('Первый тест пройден')
else:
print('Первый тест завален')
def opt_shortest_path(points):
t = tsp()
t.nodes = points
t.order = range(len(t.nodes))
t.dismatrix()
# 2-opt法找到最短路径
count = 0
while(count < 100000):
former_sum = t.sum(tsp=False)
opted_order = t.order[:]
# 随机生成两个交换的元素，起点终点不交换
left, right = random.randint(
1, len(t.order) - 2), random.randint(1, len(t.order) - 2)
# 防止左边界比右边界大
if left > right:
left, right = right, left
# 左右边界之间的序列反转
temp = opted_order[left:right + 1]
temp.reverse()
opted_order[left:right + 1] = temp
# 对新序列求和
if t.sum(opted_order, tsp=False) < former_sum:
t.order = opted_order
count = 0
else:
count += 1
return t.order
def seven_test():
way = 1
matrix = [[999, 0, 0], [1, 999, 1], [1, 0, 999]]
print('\nДлина пути должна быть равна ', way)
class_tsp = tsp()
class_tsp.start(matrix)
if class_tsp.way_price == way:
print('Седьмой тест пройден')
else:
print('Седьмой тест завален \n')
def five_test():
way = 2
matrix = [[999, 0, 1], [0, 999, 1], [1, 1, 999]]
print('\nДлина пути должна быть равна ', way)
class_tsp = tsp()
class_tsp.start(matrix)
if class_tsp.way_price == way:
print('Пятый тест пройден')
else:
print('Пятый тест завален')
def eight_test():
way = 0
matrix = [[999, 0, 0], [0, 999, 0], [0, 0, 999]]
print('\nДлина пути должна быть равна ', way)
class_tsp = tsp()
class_tsp.start(matrix)
if class_tsp.way_price == way:
print('Восьмой тест пройден')
else:
print('Восьмой тест завален \n')
def six_test():
way = 30
matrix = [[999, 5, 11, 9], [20, 999, 8, 7], [7, 14, 999, 8],
[12, 6, 15, 999]]
print('\nДлина пути должна быть равна ', way)
class_tsp = tsp()
class_tsp.start(matrix)
if class_tsp.way_price == way:
print('Шестой тест пройден')
else:
print('Шестой тест завален')
def solve(list_of_locations,
list_of_homes,
starting_car_location,
adjacency_matrix,
params=[]):
"""
Write your algorithm here.
Input:
list_of_locations: A list of locations such that node i of the graph corresponds to name at index i of the list
list_of_homes: A list of homes
starting_car_location: The name of the starting location for the car
adjacency_matrix: The adjacency matrix from the input file
Output:
A list of locations representing the car path
A dictionary mapping drop-off location to a list of homes of TAs that got off at that particular location
NOTE: both outputs should be in terms of indices not the names of the locations themselves
"""
# This will be the map for index name to real location name
locationMap = {}
# This is the index of the starting location
startingIndex = -1
# This is the list of homes as indexes
homes = []
for i in range(len(list_of_locations)):
locationMap[i] = list_of_locations[i]
if list_of_locations[i] == starting_car_location:
startingIndex = i
if list_of_locations[i] in list_of_homes:
homes.append(i)
# Create clusters
distanceMatrix, pathMatrix = shortestDistance(adjacency_matrix)
min_cost = math.inf
for k in range(2, len(distanceMatrix) // 2):
clusters = cluster(list(locationMap.keys()), homes, distanceMatrix, k)
raw_pathway = tsp(startingIndex, clusters, distanceMatrix)
real_pathway = realPath(raw_pathway, pathMatrix)
dropOffs = smart_dropOff(real_pathway, homes, distanceMatrix)
c = cost(real_pathway, dropOffs, distanceMatrix)
if c < min_cost:
min_cost = c
min_pathway = real_pathway
min_dropOffs = dropOffs
print("Smart Cost : " + str(min_cost))
print("Naive Cost : " +
str(naive_cost(startingIndex, homes, distanceMatrix)))
return min_pathway, min_dropOffs
def three_test():
way = 41
matrix = [[999, 20, 18, 12, 8], [5, 999, 14, 7, 11],
[12, 18, 999, 6, 11], [11, 17, 11, 999, 12],
[5, 5, 5, 5, 999]]
print('\nДлина пути должна быть равна ', way)
class_tsp = tsp()
class_tsp.start(matrix)
if class_tsp.way_price == way:
print('Третий тест пройден')
else:
print('Третий тест завален')
def second_test():
way = 49
matrix = [[999, 7, 16, 21, 2, 17], [13, 999, 21, 15, 43, 23],
[25, 3, 999, 31, 17, 9], [13, 10, 27, 999, 33, 12],
[9, 2, 19, 14, 999, 51], [42, 17, 5, 9, 23, 999]]
print('\nДлина пути должна быть равна ', way)
class_tsp = tsp()
class_tsp.start(matrix)
if class_tsp.way_price == way:
print('Второй тест пройден')
else:
print('Второй тест завален')
def four_test():
way = 138
matrix = [[999, 41, 40, 48, 40, 42, 6], [48, 999, 41, 49, 42, 46, 4],
[22, 22, 999, 23, 24, 19, 2], [15, 17, 11, 999, 10, 14, 1],
[47, 43, 18, 42, 999, 52, 0], [34, 39, 30, 39, 32, 999, 2],
[34, 39, 30, 39, 32, 5, 999]]
print('\nДлина пути должна быть равна ', way)
class_tsp = tsp()
class_tsp.start(matrix)
if class_tsp.way_price == way:
print('Четвертый тест пройден')
else:
print('Четвертый тест завален')
import tsp t = tsp([(0, 0), (0, 1), (1, 0), (1, 1)]) t.solve() print(t.result)
# coding:UTF-8
# Drawing
import matplotlib.pyplot as plt
import convexhull as ch
from tsp import *
# 测试TSP 凸包算法
test = tsp()
test.readfile('TSPlib/oliver30.tsp')
test.greedy()
test.draw()
plt.savefig("TSPcomparison/01_greedySolvedTSP.png", dpi=256)
plt.show()
test.opt(100000)
test.draw()
plt.savefig("TSPcomparison/02_2-opted_greedySolvedTSP.png", dpi=256)
plt.show()
gs = ch.DrawConvexHull(test.nodes)
plt.show()
test.convexhull()
test.draw()
plt.savefig("TSPcomparison/03_chSolvedTSP.png", dpi=256)
plt.show()
test.opt(100000)
def test_hw(self):
print "\nStarting HW question:"
cities = loadFile('tsp.txt')
print "\n\nThe answer to the HW question is: %s\n" % tsp(cities)
def test_magic(self):
cities = loadFile('18_cities.txt')
self.assertAlmostEqual(3.501, tsp(cities), places=3)
def test_rectangle(self):
cities = loadFile('rectangle.txt')
self.assertEqual(10, tsp(cities))
def test_line(self):
cities = loadFile('line.txt')
self.assertEqual(12, tsp(cities))