solver
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UmbertoJr
parent
c2aaa9e5f7
commit
f5b513f75c
1 changed files with 72 additions and 70 deletions
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@ -3,24 +3,26 @@ from matplotlib import pyplot as plt
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from numpy.core._multiarray_umath import ndarray
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import os
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if 'AI' in os.getcwd():
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from src.utils import *
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from src import *
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else:
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from AI2019.src.utils import *
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from AI2019.src import *
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class Solver_TSP:
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solution: ndarray
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found_length: float
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available_methods = {"random": lambda x: x, "nearest_neighbors": lambda x: x,
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"best_nn": lambda x: x, "multi_fragment": lambda x: x}
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available_initializers = {"random": random_initialier.random_method,
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"nearest_neighbors": nearest_neighbor.nn,
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"best_nn": nearest_neighbor.best_nn,
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"multi_fragment": multi_fragment.mf}
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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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"best_nn": self.best_nn, "multi_fragment": self.mf}
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# self.available_methods = {"random": self.random_method, "nearest_neighbors": self.nn,
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# "best_nn": self.best_nn, "multi_fragment": self.mf}
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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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assert method in self.available_initializers, f"the {method} method is not available currently."
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def __call__(self, instance_, verbose=True, return_value=True):
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self.instance = instance_
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@ -37,69 +39,69 @@ class Solver_TSP:
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if return_value:
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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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solution = np.random.choice(np.arange(n), size=n, replace=False)
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self.solution = np.concatenate([solution, [solution[0]]])
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self.solved = True
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return self.solution
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def nn(self, instance_, starting_node=0):
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dist_matrix = np.copy(instance_.dist_matrix)
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n = int(instance_.nPoints)
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node = starting_node
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tour = [node]
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for _ in range(n - 1):
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for new_node in np.argsort(dist_matrix[node]):
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if new_node not in tour:
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tour.append(new_node)
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node = new_node
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break
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tour.append(starting_node)
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self.solution = np.array(tour)
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self.solved = True
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return self.solution
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def best_nn(self, instance_):
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solutions, lens = [], []
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for start in range(self.instance.nPoints):
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new_solution = self.nn(instance_, starting_node=start)
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solutions.append(new_solution)
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assert self.check_if_solution_is_valid(new_solution), "error on best_nn method"
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lens.append(self.evaluate_solution(return_value=True))
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self.solution = solutions[np.argmin(lens)]
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self.solved = True
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return self.solution
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def mf(self, instance):
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mat = np.copy(instance.dist_matrix)
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mat = np.triu(mat)
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mat[mat == 0] = 100000
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solution = {str(i): [] for i in range(instance.nPoints)}
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start_list = [i for i in range(instance.nPoints)]
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inside = 0
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for el in np.argsort(mat.flatten()):
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node1, node2 = el // instance.nPoints, el % instance.nPoints
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possible_edge = [node1, node2]
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if multi_fragment.check_if_available(node1, node2, solution):
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if multi_fragment.check_if_not_close(possible_edge, solution):
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# print("entrato", inside)
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solution[str(node1)].append(node2)
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solution[str(node2)].append(node1)
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if len(solution[str(node1)]) == 2:
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start_list.remove(node1)
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if len(solution[str(node2)]) == 2:
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start_list.remove(node2)
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inside += 1
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# print(node1, node2, inside)
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if inside == instance.nPoints - 1:
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# print(f"ricostruire la solutione da {start_list}",
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# f"vicini di questi due nodi {[solution[str(i)] for i in start_list]}")
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solution = multi_fragment.create_solution(start_list, solution)
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self.solution = solution
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self.solved = True
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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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# solution = np.random.choice(np.arange(n), size=n, replace=False)
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# self.solution = np.concatenate([solution, [solution[0]]])
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# self.solved = True
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# return self.solution
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#
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# def nn(self, instance_, starting_node=0):
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# dist_matrix = np.copy(instance_.dist_matrix)
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# n = int(instance_.nPoints)
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# node = starting_node
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# tour = [node]
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# for _ in range(n - 1):
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# for new_node in np.argsort(dist_matrix[node]):
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# if new_node not in tour:
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# tour.append(new_node)
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# node = new_node
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# break
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# tour.append(starting_node)
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# self.solution = np.array(tour)
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# self.solved = True
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# return self.solution
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#
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# def best_nn(self, instance_):
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# solutions, lens = [], []
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# for start in range(self.instance.nPoints):
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# new_solution = self.nn(instance_, starting_node=start)
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# solutions.append(new_solution)
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# assert self.check_if_solution_is_valid(new_solution), "error on best_nn method"
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# lens.append(self.evaluate_solution(return_value=True))
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#
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# self.solution = solutions[np.argmin(lens)]
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# self.solved = True
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# return self.solution
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#
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# def mf(self, instance):
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# mat = np.copy(instance.dist_matrix)
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# mat = np.triu(mat)
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# mat[mat == 0] = 100000
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# solution = {str(i): [] for i in range(instance.nPoints)}
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# start_list = [i for i in range(instance.nPoints)]
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# inside = 0
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# for el in np.argsort(mat.flatten()):
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# node1, node2 = el // instance.nPoints, el % instance.nPoints
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# possible_edge = [node1, node2]
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# if multi_fragment.check_if_available(node1, node2, solution):
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# if multi_fragment.check_if_not_close(possible_edge, solution):
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# # print("entrato", inside)
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# solution[str(node1)].append(node2)
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# solution[str(node2)].append(node1)
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# if len(solution[str(node1)]) == 2:
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# start_list.remove(node1)
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# if len(solution[str(node2)]) == 2:
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# start_list.remove(node2)
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# inside += 1
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# # print(node1, node2, inside)
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# if inside == instance.nPoints - 1:
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# # print(f"ricostruire la solutione da {start_list}",
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# # f"vicini di questi due nodi {[solution[str(i)] for i in start_list]}")
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# solution = multi_fragment.create_solution(start_list, solution)
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# self.solution = solution
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# self.solved = True
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# return self.solution
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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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