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72
code/TSP_solver.py
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72
code/TSP_solver.py
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import numpy as np
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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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class Solver_TSP:
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solution: ndarray
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found_length: float
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def __init__(self, method):
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self.available_methods = {"random": self.random_method, "nearest_neighbors": self.nn}
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self.method = method
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self.solved = False
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assert method in self.available_methods, f"the {method} method is not available currently."
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def __call__(self, instance_, verbose=True):
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self.instance = instance_
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if verbose:
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print("### solving ####")
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self.solution = self.available_methods[self.method](instance_)
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assert self.check_if_solution_is_valid(self.solution), "Error the solution is not valid"
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if verbose:
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print("### solution found ####")
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self._gap()
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return self.solution
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def random_method(self, instance_):
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n = int(instance_.nPoints)
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self.solution = np.random.choice(np.arange(n), size=n, replace=False)
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self.solved = True
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return self.solution
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def nn(self, instance_):
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pass
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def plot_solution(self):
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assert self.solved, "You can't plot the solution, you need to solve it first!"
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plt.figure(figsize=(8, 8))
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plt.title(self.instance.name)
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ordered_points = self.instance.points[self.solution]
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plt.plot(ordered_points[:, 1], ordered_points[:, 2], 'b-')
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def check_if_solution_is_valid(self, solution):
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rights_values = np.sum([self.check_validation(i, solution) for i in np.arange(self.instance.nPoints)])
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if rights_values == self.instance.nPoints:
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return True
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else:
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return False
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def check_validation(self, node, solution):
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if np.sum(solution == node) == 1:
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return 1
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else:
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return 0
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def evaluate_solution(self, return_value=True):
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total_length = 0
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starting_node = self.solution[0]
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from_node = starting_node
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for node in self.solution[1:]:
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total_length += self.instance.dist_matrix[from_node, node]
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from_node = node
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total_length += self.instance.dist_matrix[from_node, starting_node]
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self.found_length = total_length
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if return_value:
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return total_length
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def _gap(self):
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self.evaluate_solution(return_value=False)
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self.gap = np.round((self.found_length - self.instance.best_sol) / self.instance.best_sol * 100, 2)
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2
code/__init__.py
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2
code/__init__.py
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from code.TSP_solver import *
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from code.io_tsp import *
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63
code/io_tsp.py
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63
code/io_tsp.py
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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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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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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.int(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] = 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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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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plt.show()
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@staticmethod
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def distance_euc(zi, zj):
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xi, xj = zi[0], zj[0]
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yi, yj = zi[0], zj[0]
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return round(np.sqrt((xi - xj) ** 2 + (yi - yj) ** 2))
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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] = self.distance_euc(self.points[i][1:3], self.points[j][1:3])
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15
run.py
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15
run.py
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from code import *
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def run():
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names = [name_ for name_ in os.listdir("./problems") if "tsp" in name_]
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for name in names:
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filename = f"problems/{name}"
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instance = Instance(filename)
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instance.print_info()
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print(" --- ")
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instance.plot_data()
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if __name__ == '__main__':
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run()
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