101 lines
3.9 KiB
Python
101 lines
3.9 KiB
Python
from numpy.core._multiarray_umath import ndarray
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import os
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from time import time as t
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import numpy as np
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import matplotlib.pyplot as plt
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if 'AI' in os.getcwd():
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from src import *
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else:
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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_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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}
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available_improvements = {"2-opt": TwoOpt.loop2opt,
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"2.5-opt": TwoDotFiveOpt.loop2dot5opt}
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# ,
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# "simulated_annealing": Simulated_Annealing,
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# "iterated_local_search": Iterated_Local_Search}
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def __init__(self, initializer):
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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.initializer = initializer
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self.methods = [initializer]
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self.name_method = "initialize with " + initializer
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self.solved = False
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assert initializer in self.available_initializers, f"the {initializer} initializer is not available currently."
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def bind(self, local_or_meta):
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assert local_or_meta in self.available_improvements, f"the {local_or_meta} method is not available currently."
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self.methods.append(local_or_meta)
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self.name_method = ", improve with " + local_or_meta
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def __call__(self, instance_, verbose=True, return_value=True):
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self.instance = instance_
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self.solved = False
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if verbose:
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print(f"### solving with {self.methods} ####")
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start = t()
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self.solution = self.available_initializers[self.methods[0]](instance_)
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assert self.check_if_solution_is_valid(self.solution), "Error the solution is not valid"
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for i in range(1, len(self.methods)):
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self.solution = self.available_improvements[self.methods[i]](self.solution, self.instance)
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assert self.check_if_solution_is_valid(self.solution), "Error the solution is not valid"
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end = t()
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self.time_to_solve = np.around(end - start,3)
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self.solved = True
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self.evaluate_solution()
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self._gap()
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if verbose:
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print(f"### solution found with {self.gap} % gap in {self.time_to_solve} seconds ####")
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if return_value:
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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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plt.figure(figsize=(8, 8))
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self._gap()
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plt.title(f"{self.instance.name} solved with {self.name_method} solver, gap {self.gap}")
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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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plt.show()
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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[:-1]) 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=False):
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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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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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