diff --git a/src/TSP_solver.py b/src/TSP_solver.py index d027dd3..d7272cd 100644 --- a/src/TSP_solver.py +++ b/src/TSP_solver.py @@ -3,24 +3,26 @@ from matplotlib import pyplot as plt from numpy.core._multiarray_umath import ndarray import os if 'AI' in os.getcwd(): - from src.utils import * + from src import * else: - from AI2019.src.utils import * + from AI2019.src import * class Solver_TSP: solution: ndarray found_length: float - available_methods = {"random": lambda x: x, "nearest_neighbors": lambda x: x, - "best_nn": lambda x: x, "multi_fragment": lambda x: x} + available_initializers = {"random": random_initialier.random_method, + "nearest_neighbors": nearest_neighbor.nn, + "best_nn": nearest_neighbor.best_nn, + "multi_fragment": multi_fragment.mf} def __init__(self, method): - self.available_methods = {"random": self.random_method, "nearest_neighbors": self.nn, - "best_nn": self.best_nn, "multi_fragment": self.mf} + # self.available_methods = {"random": self.random_method, "nearest_neighbors": self.nn, + # "best_nn": self.best_nn, "multi_fragment": self.mf} self.method = method self.solved = False - assert method in self.available_methods, f"the {method} method is not available currently." + assert method in self.available_initializers, f"the {method} method is not available currently." def __call__(self, instance_, verbose=True, return_value=True): self.instance = instance_ @@ -37,69 +39,69 @@ class Solver_TSP: if return_value: return self.solution - def random_method(self, instance_): - n = int(instance_.nPoints) - solution = np.random.choice(np.arange(n), size=n, replace=False) - self.solution = np.concatenate([solution, [solution[0]]]) - self.solved = True - return self.solution - - def nn(self, instance_, starting_node=0): - dist_matrix = np.copy(instance_.dist_matrix) - n = int(instance_.nPoints) - node = starting_node - tour = [node] - for _ in range(n - 1): - for new_node in np.argsort(dist_matrix[node]): - if new_node not in tour: - tour.append(new_node) - node = new_node - break - tour.append(starting_node) - self.solution = np.array(tour) - self.solved = True - return self.solution - - def best_nn(self, instance_): - solutions, lens = [], [] - for start in range(self.instance.nPoints): - new_solution = self.nn(instance_, starting_node=start) - solutions.append(new_solution) - assert self.check_if_solution_is_valid(new_solution), "error on best_nn method" - lens.append(self.evaluate_solution(return_value=True)) - - self.solution = solutions[np.argmin(lens)] - self.solved = True - return self.solution - - def mf(self, instance): - mat = np.copy(instance.dist_matrix) - mat = np.triu(mat) - mat[mat == 0] = 100000 - solution = {str(i): [] for i in range(instance.nPoints)} - start_list = [i for i in range(instance.nPoints)] - inside = 0 - for el in np.argsort(mat.flatten()): - node1, node2 = el // instance.nPoints, el % instance.nPoints - possible_edge = [node1, node2] - if multi_fragment.check_if_available(node1, node2, solution): - if multi_fragment.check_if_not_close(possible_edge, solution): - # print("entrato", inside) - solution[str(node1)].append(node2) - solution[str(node2)].append(node1) - if len(solution[str(node1)]) == 2: - start_list.remove(node1) - if len(solution[str(node2)]) == 2: - start_list.remove(node2) - inside += 1 - # print(node1, node2, inside) - if inside == instance.nPoints - 1: - # print(f"ricostruire la solutione da {start_list}", - # f"vicini di questi due nodi {[solution[str(i)] for i in start_list]}") - solution = multi_fragment.create_solution(start_list, solution) - self.solution = solution - self.solved = True - return self.solution + # def random_method(self, instance_): + # n = int(instance_.nPoints) + # solution = np.random.choice(np.arange(n), size=n, replace=False) + # self.solution = np.concatenate([solution, [solution[0]]]) + # self.solved = True + # return self.solution + # + # def nn(self, instance_, starting_node=0): + # dist_matrix = np.copy(instance_.dist_matrix) + # n = int(instance_.nPoints) + # node = starting_node + # tour = [node] + # for _ in range(n - 1): + # for new_node in np.argsort(dist_matrix[node]): + # if new_node not in tour: + # tour.append(new_node) + # node = new_node + # break + # tour.append(starting_node) + # self.solution = np.array(tour) + # self.solved = True + # return self.solution + # + # def best_nn(self, instance_): + # solutions, lens = [], [] + # for start in range(self.instance.nPoints): + # new_solution = self.nn(instance_, starting_node=start) + # solutions.append(new_solution) + # assert self.check_if_solution_is_valid(new_solution), "error on best_nn method" + # lens.append(self.evaluate_solution(return_value=True)) + # + # self.solution = solutions[np.argmin(lens)] + # self.solved = True + # return self.solution + # + # def mf(self, instance): + # mat = np.copy(instance.dist_matrix) + # mat = np.triu(mat) + # mat[mat == 0] = 100000 + # solution = {str(i): [] for i in range(instance.nPoints)} + # start_list = [i for i in range(instance.nPoints)] + # inside = 0 + # for el in np.argsort(mat.flatten()): + # node1, node2 = el // instance.nPoints, el % instance.nPoints + # possible_edge = [node1, node2] + # if multi_fragment.check_if_available(node1, node2, solution): + # if multi_fragment.check_if_not_close(possible_edge, solution): + # # print("entrato", inside) + # solution[str(node1)].append(node2) + # solution[str(node2)].append(node1) + # if len(solution[str(node1)]) == 2: + # start_list.remove(node1) + # if len(solution[str(node2)]) == 2: + # start_list.remove(node2) + # inside += 1 + # # print(node1, node2, inside) + # if inside == instance.nPoints - 1: + # # print(f"ricostruire la solutione da {start_list}", + # # f"vicini di questi due nodi {[solution[str(i)] for i in start_list]}") + # solution = multi_fragment.create_solution(start_list, solution) + # self.solution = solution + # self.solved = True + # return self.solution def plot_solution(self): assert self.solved, "You can't plot the solution, you need to solve it first!"