From 7ac6af59009cd44b08c10a2da221545d6de5c06c Mon Sep 17 00:00:00 2001 From: UmbertoJr Date: Wed, 23 Oct 2019 21:07:20 +0200 Subject: [PATCH] update --- code/TSP_solver.py | 72 ++++++++++++++++++++++++++++++++++++++++++++++ code/__init__.py | 2 ++ code/io_tsp.py | 63 ++++++++++++++++++++++++++++++++++++++++ run.py | 15 ++++++++++ 4 files changed, 152 insertions(+) create mode 100644 code/TSP_solver.py create mode 100644 code/__init__.py create mode 100644 code/io_tsp.py create mode 100644 run.py diff --git a/code/TSP_solver.py b/code/TSP_solver.py new file mode 100644 index 0000000..9ffc0a5 --- /dev/null +++ b/code/TSP_solver.py @@ -0,0 +1,72 @@ +import numpy as np +from matplotlib import pyplot as plt +from numpy.core._multiarray_umath import ndarray + + +class Solver_TSP: + + solution: ndarray + found_length: float + + def __init__(self, method): + self.available_methods = {"random": self.random_method, "nearest_neighbors": self.nn} + self.method = method + self.solved = False + assert method in self.available_methods, f"the {method} method is not available currently." + + def __call__(self, instance_, verbose=True): + self.instance = instance_ + if verbose: + print("### solving ####") + self.solution = self.available_methods[self.method](instance_) + assert self.check_if_solution_is_valid(self.solution), "Error the solution is not valid" + if verbose: + print("### solution found ####") + self._gap() + return self.solution + + def random_method(self, instance_): + n = int(instance_.nPoints) + self.solution = np.random.choice(np.arange(n), size=n, replace=False) + self.solved = True + return self.solution + + def nn(self, instance_): + pass + + def plot_solution(self): + assert self.solved, "You can't plot the solution, you need to solve it first!" + plt.figure(figsize=(8, 8)) + plt.title(self.instance.name) + ordered_points = self.instance.points[self.solution] + plt.plot(ordered_points[:, 1], ordered_points[:, 2], 'b-') + + def check_if_solution_is_valid(self, solution): + rights_values = np.sum([self.check_validation(i, solution) 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=True): + 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 + + total_length += self.instance.dist_matrix[from_node, starting_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) diff --git a/code/__init__.py b/code/__init__.py new file mode 100644 index 0000000..08a6137 --- /dev/null +++ b/code/__init__.py @@ -0,0 +1,2 @@ +from code.TSP_solver import * +from code.io_tsp import * \ No newline at end of file diff --git a/code/io_tsp.py b/code/io_tsp.py new file mode 100644 index 0000000..866875d --- /dev/null +++ b/code/io_tsp.py @@ -0,0 +1,63 @@ +import numpy as np +from typing import List +from matplotlib import pyplot as plt +from numpy.core._multiarray_umath import ndarray + + +class Instance: + nPoints: int + best_sol: int + name: str + lines: List[str] + dist_matrix: ndarray + points: ndarray + + def __init__(self, name_tsp): + self.read_instance(name_tsp) + + def read_instance(self, name_tsp): + # read raw data + file_object = open(name_tsp) + data = file_object.read() + file_object.close() + self.lines = data.splitlines() + + # store data set information + self.name = self.lines[0].split(' ')[2] + self.nPoints = np.int(self.lines[3].split(' ')[2]) + self.best_sol = np.int(self.lines[5].split(' ')[2]) + + # read all data points and store them + self.points = np.zeros((self.nPoints, 3)) + for i in range(self.nPoints): + line_i = self.lines[7 + i].split(' ') + self.points[i, 0] = line_i[0] + self.points[i, 1] = line_i[1] + self.points[i, 2] = line_i[2] + + self.create_dist_matrix() + + def print_info(self): + print('name: ' + self.name) + print('nPoints: ' + str(self.nPoints)) + print('best_sol: ' + str(self.best_sol)) + + def plot_data(self): + plt.figure(figsize=(8, 8)) + plt.title(self.name) + plt.scatter(self.points[:, 1], self.points[:, 2]) + plt.show() + + @staticmethod + def distance_euc(zi, zj): + xi, xj = zi[0], zj[0] + yi, yj = zi[0], zj[0] + return round(np.sqrt((xi - xj) ** 2 + (yi - yj) ** 2)) + + def create_dist_matrix(self): + self.dist_matrix = np.zeros((self.nPoints, self.nPoints)) + + for i in range(self.nPoints): + for j in range(i, self.nPoints): + self.dist_matrix[i, j] = self.distance_euc(self.points[i][1:3], self.points[j][1:3]) + diff --git a/run.py b/run.py new file mode 100644 index 0000000..cccdd72 --- /dev/null +++ b/run.py @@ -0,0 +1,15 @@ +from code import * + + +def run(): + names = [name_ for name_ in os.listdir("./problems") if "tsp" in name_] + for name in names: + filename = f"problems/{name}" + instance = Instance(filename) + instance.print_info() + print(" --- ") + instance.plot_data() + + +if __name__ == '__main__': + run()