100 lines
3.9 KiB
Python
100 lines
3.9 KiB
Python
from numpy.core._multiarray_umath import ndarray
|
|
import os
|
|
from time import time as t
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
if 'AI' in os.getcwd():
|
|
from src import *
|
|
else:
|
|
from AI2019.src import *
|
|
|
|
|
|
class Solver_TSP:
|
|
|
|
solution: ndarray
|
|
found_length: float
|
|
available_initializers = {"random": random_initialier.random_method,
|
|
"nearest_neighbors": nearest_neighbor.nn,
|
|
"best_nn": nearest_neighbor.best_nn,
|
|
"multi_fragment": multi_fragment.mf
|
|
}
|
|
|
|
available_improvements = {"2-opt": TwoOpt.loop2opt,
|
|
"2.5-opt": TwoDotFiveOpt.loop2dot5opt}
|
|
|
|
def __init__(self, initializer):
|
|
# self.available_methods = {"random": self.random_method, "nearest_neighbors": self.nn,
|
|
# "best_nn": self.best_nn, "multi_fragment": self.mf}
|
|
self.initializer = initializer
|
|
self.methods = [initializer]
|
|
self.name_method = "initialize with " + initializer
|
|
self.solved = False
|
|
assert initializer in self.available_initializers, f"the {initializer} initializer is not available currently."
|
|
|
|
def bind(self, local_or_meta):
|
|
assert local_or_meta in self.available_improvements, f"the {local_or_meta} method is not available currently."
|
|
self.methods.append(local_or_meta)
|
|
self.name_method = ", improve with " + local_or_meta
|
|
|
|
def __call__(self, instance_, verbose=True, return_value=True):
|
|
self.instance = instance_
|
|
self.solved = False
|
|
if verbose:
|
|
print(f"### solving with {self.methods} ####")
|
|
start = t()
|
|
self.solution = self.available_initializers[self.methods[0]](instance_)
|
|
assert self.check_if_solution_is_valid(self.solution), "Error the solution is not valid"
|
|
# print("init ok")
|
|
for i in range(1, len(self.methods)):
|
|
self.solution = self.available_improvements[self.methods[i]](self.solution, self.instance)
|
|
print(len(self.solution))
|
|
assert self.check_if_solution_is_valid(self.solution), "Error the solution is not valid"
|
|
print("improve ok")
|
|
|
|
end = t()
|
|
self.time_to_solve = np.around(end - start,3)
|
|
self.solved = True
|
|
self.evaluate_solution()
|
|
self._gap()
|
|
if verbose:
|
|
print(f"### solution found with {self.gap} % gap in {self.time_to_solve} seconds ####")
|
|
if return_value:
|
|
return self.solution
|
|
|
|
def plot_solution(self):
|
|
assert self.solved, "You can't plot the solution, you need to solve it first!"
|
|
plt.figure(figsize=(8, 8))
|
|
self._gap()
|
|
plt.title(f"{self.instance.name} solved with {self.name_method} solver, gap {self.gap}")
|
|
ordered_points = self.instance.points[self.solution]
|
|
plt.plot(ordered_points[:, 1], ordered_points[:, 2], 'b-')
|
|
plt.show()
|
|
|
|
def check_if_solution_is_valid(self, solution):
|
|
rights_values = np.sum([self.check_validation(i, solution[:-1]) for i in np.arange(self.instance.nPoints)])
|
|
if rights_values == self.instance.nPoints:
|
|
return True
|
|
else:
|
|
return False
|
|
|
|
def check_validation(self, node, solution):
|
|
if np.sum(solution == node) == 1:
|
|
return 1
|
|
else:
|
|
return 0
|
|
|
|
def evaluate_solution(self, return_value=False):
|
|
total_length = 0
|
|
starting_node = self.solution[0]
|
|
from_node = starting_node
|
|
for node in self.solution[1:]:
|
|
total_length += self.instance.dist_matrix[from_node, node]
|
|
from_node = node
|
|
|
|
self.found_length = total_length
|
|
if return_value:
|
|
return total_length
|
|
|
|
def _gap(self):
|
|
self.evaluate_solution(return_value=False)
|
|
self.gap = np.round(((self.found_length - self.instance.best_sol) / self.instance.best_sol) * 100, 2)
|