more refactoring and code cleaning

This commit is contained in:
Dario Mantegazza 2020-09-28 12:13:53 +02:00
parent c75ecc3d5b
commit bba6d0f428
8 changed files with 13 additions and 45 deletions

15
run.py
View file

@ -32,7 +32,6 @@ def run(show_plots=False, verbose=False):
for problem_path in problems:
prob_instance = ProblemInstance(problem_path)
if verbose:
print("\n\n#############################")
prob_instance.print_info()
if show_plots:
prob_instance.plot_data()
@ -41,15 +40,15 @@ def run(show_plots=False, verbose=False):
for improve in improvers_names:
solver = SolverTSP(solver_name, prob_instance)
use_solver_to_compute_solution(solver, improve, index, results, problem_path, verbose, show_plots)
for improve2 in [j for j in improvers_names if j not in [improve]]:
use_solver_to_compute_solution(solver, improve2, index, results, problem_path, verbose, show_plots)
for improve2 in [j for j in improvers_names if j not in [improve]]:
use_solver_to_compute_solution(solver, improve2, index, results, problem_path, verbose, show_plots)
for improve3 in [j for j in improvers_names if j not in [improve, improve2]]:
use_solver_to_compute_solution(solver, improve3, index, results, problem_path, verbose,
show_plots)
solver.pop()
for improve3 in [j for j in improvers_names if j not in [improve, improve2]]:
use_solver_to_compute_solution(solver, improve3, index, results, problem_path, verbose,
show_plots)
solver.pop()
solver.pop()
solver.pop()
if prob_instance.exist_opt and show_plots:
solver = SolverTSP("optimal", prob_instance)

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@ -1,4 +1,3 @@
from numpy.core._multiarray_umath import ndarray
from time import time as t
import numpy as np
import matplotlib.pyplot as plt
@ -20,12 +19,11 @@ available_improvers = {"2-opt": loop2opt,
class SolverTSP:
solution: ndarray
found_length: float
def __init__(self, algorithm_name, problem_instance):
# assert algorithm_name in available_solvers, f"the {algorithm_name} initializer is not available currently."
self.duration = np.inf
self.found_length = np.inf
self.algorithm_name = algorithm_name
self.algorithms = [algorithm_name]
self.name_method = "initialized with " + algorithm_name

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@ -1,17 +0,0 @@
import os
if 'AI' in os.getcwd():
from src.utils import *
from src.constructive_algorithms import *
from src.local_search import *
from src.iterated_local_search import *
from src.TSP_solver import *
from src.io_tsp import *
else:
from AI2019.src.utils import *
from AI2019.src.constructive_algorithms import *
from AI2019.src.local_search import *
from AI2019.src.meta_heuristics import *
from AI2019.src.TSP_solver import *
from AI2019.src.io_tsp import *

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@ -1,25 +1,15 @@
import numpy as np
from typing import List
from matplotlib import pyplot as plt
from numpy.core._multiarray_umath import ndarray
from src.utils import distance_euc
class ProblemInstance:
nPoints: int
best_sol: int
name: str
lines: List[str]
dist_matrix: ndarray
points: ndarray
def __init__(self, name_tsp):
self.exist_opt = False
self.optimal_tour = None
self.read_instance(name_tsp)
def read_instance(self, name_tsp):
self.dist_matrix = None
# read raw data
file_object = open(name_tsp)
data = file_object.read()
@ -54,6 +44,7 @@ class ProblemInstance:
self.optimal_tour[i] = int(line_i[0]) - 1
def print_info(self):
print("\n\n#############################")
print('name: ' + self.name)
print('nPoints: ' + str(self.nPoints))
print('best_sol: ' + str(self.best_sol))

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@ -1,4 +1,4 @@
class Iterated_Local_Search:
class IteratedLocalSearch:
def __call__(self):
pass

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@ -1,3 +0,0 @@

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@ -1,6 +1,6 @@
import numpy as np
from src import compute_length
from src.utils import compute_length
def sa(solution, instance, constant_temperature=0.95, iterations_for_each_temp=100):

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@ -1,6 +1,6 @@
import numpy as np
from src import compute_length
from src.utils import compute_length
def step2opt(solution, matrix_dist, distance):