constructive algorithms
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UmbertoJr
parent
dad6b9bca5
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2 changed files with 138 additions and 132 deletions
138
src/constructive_algorithms.py
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138
src/constructive_algorithms.py
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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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else:
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from AI2019.src.utils import *
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class random_initialier:
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@staticmethod
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def random_method(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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return np.concatenate([solution, [solution[0]]])
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class nearest_neighbor:
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@staticmethod
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def nn(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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return np.array(tour)
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@staticmethod
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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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class multi_fragment:
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@staticmethod
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def check_if_available(n1, n2, sol):
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if len(sol[str(n1)]) < 2 and len(sol[str(n2)]) < 2:
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return True
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else:
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return False
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@staticmethod
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def check_if_not_close(edge_to_append, sol):
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n1, n2 = edge_to_append
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from_city = n2
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if len(sol[str(from_city)]) == 0:
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return True
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partial_tour = [from_city]
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end = False
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iterazione = 0
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while not end:
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if len(sol[str(from_city)]) == 1:
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if from_city == n1:
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return_value = False
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end = True
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elif iterazione > 1:
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# print(f"iterazione {iterazione}, elementi dentro partial {len(partial_tour)}",
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# f"from city {from_city}")
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return_value = True
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end = True
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else:
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from_city = sol[str(from_city)][0]
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partial_tour.append(from_city)
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iterazione += 1
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else:
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# print(from_city, partial_tour, sol[str(from_city)])
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for node_connected in sol[str(from_city)]:
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# print(node_connected)
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if node_connected not in partial_tour:
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from_city = node_connected
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partial_tour.append(node_connected)
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# print(node_connected, sol[str(from_city)])
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iterazione += 1
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return return_value
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@staticmethod
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def create_solution(start_sol, sol):
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assert len(start_sol) == 2, "too many cities with just one link"
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end = False
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n1, n2 = start_sol
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from_city = n2
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sol_list = [n1, n2]
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iterazione = 0
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while not end:
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for node_connected in sol[str(from_city)]:
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iterazione += 1
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if node_connected not in sol_list:
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from_city = node_connected
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sol_list.append(node_connected)
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# print(f"prossimo {node_connected}",
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# f"possibili {sol[str(from_city)]}",
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# f"ultim tour {sol_list[-5:]}")
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if iterazione > 300:
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end = True
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sol_list.append(n1)
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return sol_list
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@staticmethod
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def mf(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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return solution
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132
src/utils.py
132
src/utils.py
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@ -9,135 +9,3 @@ def compute_lenght(solution, dist_matrix):
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from_node = node
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from_node = node
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return total_length
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return total_length
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class random_initialier:
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@staticmethod
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def random_method(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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return np.concatenate([solution, [solution[0]]])
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class nearest_neighbor:
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@staticmethod
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def nn(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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return np.array(tour)
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@staticmethod
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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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class multi_fragment:
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@staticmethod
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def check_if_available(n1, n2, sol):
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if len(sol[str(n1)]) < 2 and len(sol[str(n2)]) < 2:
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return True
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else:
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return False
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@staticmethod
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def check_if_not_close(edge_to_append, sol):
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n1, n2 = edge_to_append
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from_city = n2
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if len(sol[str(from_city)]) == 0:
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return True
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partial_tour = [from_city]
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end = False
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iterazione = 0
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while not end:
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if len(sol[str(from_city)]) == 1:
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if from_city == n1:
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return_value = False
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end = True
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elif iterazione > 1:
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# print(f"iterazione {iterazione}, elementi dentro partial {len(partial_tour)}",
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# f"from city {from_city}")
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return_value = True
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end = True
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else:
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from_city = sol[str(from_city)][0]
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partial_tour.append(from_city)
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iterazione += 1
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else:
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# print(from_city, partial_tour, sol[str(from_city)])
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for node_connected in sol[str(from_city)]:
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# print(node_connected)
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if node_connected not in partial_tour:
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from_city = node_connected
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partial_tour.append(node_connected)
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# print(node_connected, sol[str(from_city)])
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iterazione += 1
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return return_value
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@staticmethod
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def create_solution(start_sol, sol):
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assert len(start_sol) == 2, "too many cities with just one link"
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end = False
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n1, n2 = start_sol
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from_city = n2
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sol_list = [n1, n2]
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iterazione = 0
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while not end:
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for node_connected in sol[str(from_city)]:
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iterazione += 1
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if node_connected not in sol_list:
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from_city = node_connected
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sol_list.append(node_connected)
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# print(f"prossimo {node_connected}",
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# f"possibili {sol[str(from_city)]}",
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# f"ultim tour {sol_list[-5:]}")
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if iterazione > 300:
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end = True
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sol_list.append(n1)
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return sol_list
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@staticmethod
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def mf(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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return solution
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