74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
import numpy as np
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from matplotlib import pyplot as plt
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from numpy.core._multiarray_umath import ndarray
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class Solver_TSP:
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solution: ndarray
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found_length: float
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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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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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def __call__(self, instance_, verbose=True, return_value=True):
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self.instance = instance_
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if verbose:
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print("### solving ####")
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self.solution = self.available_methods[self.method](instance_)
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assert self.check_if_solution_is_valid(self.solution), "Error the solution is not valid"
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if verbose:
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print("### solution found ####")
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self._gap()
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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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self.solution = np.random.choice(np.arange(n), size=n, replace=False)
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self.solved = True
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return self.solution
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def nn(self, instance_):
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pass
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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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plt.title(self.instance.name)
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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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def check_if_solution_is_valid(self, solution):
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rights_values = np.sum([self.check_validation(i, solution) 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=True):
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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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total_length += self.instance.dist_matrix[from_node, starting_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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