70 lines
2.6 KiB
Python
70 lines
2.6 KiB
Python
import numpy as np
|
|
from matplotlib import pyplot as plt
|
|
from aco.utils import distance_euc
|
|
|
|
class ProblemInstance:
|
|
def __init__(self, name_tsp):
|
|
self.exist_opt = False
|
|
self.optimal_tour = None
|
|
self.dist_matrix = None
|
|
|
|
# read raw data
|
|
self.file_name = name_tsp
|
|
file_object = open(self.file_name)
|
|
data = file_object.read()
|
|
file_object.close()
|
|
self.lines = data.splitlines()
|
|
|
|
# store data set information
|
|
self.name = self.lines[0].split(' ')[2]
|
|
self.nPoints = np.int(self.lines[3].split(' ')[2])
|
|
self.best_sol = np.float(self.lines[5].split(' ')[2])
|
|
|
|
# read all data points and store them
|
|
self.points = np.zeros((self.nPoints, 3))
|
|
for i in range(self.nPoints):
|
|
line_i = self.lines[7 + i].split(' ')
|
|
self.points[i, 0] = int(line_i[0])
|
|
self.points[i, 1] = line_i[1]
|
|
self.points[i, 2] = line_i[2]
|
|
|
|
self.create_dist_matrix()
|
|
if self.file_name in ["./problems/eil76.tsp",
|
|
"./problems/kroA100.tsp",
|
|
"../problems/eil76.tsp",
|
|
"../problems/kroA100.tsp"]:
|
|
self.exist_opt = True
|
|
file_object = open(self.file_name.replace(".tsp", ".opt.tour"))
|
|
data = file_object.read()
|
|
file_object.close()
|
|
lines = data.splitlines()
|
|
|
|
# read all data points and store them
|
|
self.optimal_tour = np.zeros(self.nPoints, dtype=np.int)
|
|
for i in range(self.nPoints):
|
|
line_i = lines[5 + i].split(' ')
|
|
self.optimal_tour[i] = int(line_i[0]) - 1
|
|
|
|
def print_info(self):
|
|
print("\n\n#############################")
|
|
print('name: ' + self.name)
|
|
print('nPoints: ' + str(self.nPoints))
|
|
print('best_sol: ' + str(self.best_sol))
|
|
print('exist optimal: ' + str(self.exist_opt))
|
|
|
|
def plot_data(self, show_numbers=False):
|
|
plt.figure(figsize=(8, 8))
|
|
plt.title(self.name)
|
|
plt.scatter(self.points[:, 1], self.points[:, 2])
|
|
if show_numbers:
|
|
for i, txt in enumerate(np.arange(self.nPoints)): # tour_found[:-1]
|
|
plt.annotate(txt, (self.points[i, 1], self.points[i, 2]))
|
|
plt.show()
|
|
|
|
def create_dist_matrix(self):
|
|
self.dist_matrix = np.zeros((self.nPoints, self.nPoints))
|
|
|
|
for i in range(self.nPoints):
|
|
for j in range(i, self.nPoints):
|
|
self.dist_matrix[i, j] = distance_euc(self.points[i][1:3], self.points[j][1:3])
|
|
self.dist_matrix += self.dist_matrix.T
|