more refactoring and code cleaning

run.py

@@ -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()

src/TSP_solver.py

@@ -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

src/__init__.py

@@ -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 *

src/io_tsp.py

@@ -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)
+        self.dist_matrix = None
-
-    def read_instance(self, name_tsp):
         # 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))

src/iterated_local_search.py

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

src/local_search.py

@@ -1,3 +0,0 @@
-
-
-

src/simulated_annealing.py

@@ -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):

src/two_opt.py

@@ -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):