76 lines
2.5 KiB
Python
76 lines
2.5 KiB
Python
import numpy as np
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from typing import List
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from matplotlib import pyplot as plt
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from numpy.core._multiarray_umath import ndarray
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from src.utils import distance_euc
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class Instance:
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nPoints: int
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best_sol: int
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name: str
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lines: List[str]
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dist_matrix: ndarray
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points: ndarray
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def __init__(self, name_tsp):
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self.read_instance(name_tsp)
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self.exist_opt = None # TODO determine default value
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self.optimal_tour = None
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def read_instance(self, name_tsp):
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# read raw data
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file_object = open(name_tsp)
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data = file_object.read()
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file_object.close()
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self.lines = data.splitlines()
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# store data set information
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self.name = self.lines[0].split(' ')[2]
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self.nPoints = np.int(self.lines[3].split(' ')[2])
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self.best_sol = np.float(self.lines[5].split(' ')[2])
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# read all data points and store them
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self.points = np.zeros((self.nPoints, 3))
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for i in range(self.nPoints):
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line_i = self.lines[7 + i].split(' ')
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self.points[i, 0] = int(line_i[0])
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self.points[i, 1] = line_i[1]
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self.points[i, 2] = line_i[2]
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self.create_dist_matrix()
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self.exist_opt = False
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if [name for name in ["eil76", "kroA100"] if name in name_tsp]:
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self.exist_opt = True
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file_object = open(name_tsp.replace(".tsp", ".opt.tour"))
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data = file_object.read()
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file_object.close()
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lines = data.splitlines()
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# read all data points and store them
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self.optimal_tour = np.zeros(self.nPoints, dtype=np.int)
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for i in range(self.nPoints):
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line_i = lines[5 + i].split(' ')
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self.optimal_tour[i] = int(line_i[0]) - 1
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def print_info(self):
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print('name: ' + self.name)
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print('nPoints: ' + str(self.nPoints))
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print('best_sol: ' + str(self.best_sol))
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def plot_data(self):
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plt.figure(figsize=(8, 8))
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plt.title(self.name)
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plt.scatter(self.points[:, 1], self.points[:, 2])
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for i, txt in enumerate(np.arange(self.nPoints)): # tour_found[:-1]
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plt.annotate(txt, (self.points[i, 1], self.points[i, 2]))
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plt.show()
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def create_dist_matrix(self):
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self.dist_matrix = np.zeros((self.nPoints, self.nPoints))
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for i in range(self.nPoints):
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for j in range(i, self.nPoints):
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self.dist_matrix[i, j] = distance_euc(self.points[i][1:3], self.points[j][1:3])
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self.dist_matrix += self.dist_matrix.T
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