need a final check and should be done

run.py

@@ -1,3 +1,4 @@
+import glob
 import pandas as pd
 from src.io_tsp import ProblemInstance
 from src.TSP_solver import SolverTSP, available_improvers, available_solvers
@@ -22,8 +23,8 @@ def use_solver_to_compute_solution(solver, improve, index, results, name, verbos
 
 
 def run(show_plots=False, verbose=False):
-    # problems = glob.glob('./problems/*.tsp')
-    problems = ["./problems/eil76.tsp"]
+    problems = glob.glob('./problems/*.tsp')
+    # problems = ["./problems/eil76.tsp"]
     solvers_names = available_solvers.keys()
     improvers_names = available_improvers.keys()
     results = []
@@ -52,8 +53,7 @@ def run(show_plots=False, verbose=False):
 
         if prob_instance.exist_opt and show_plots:
             solver = SolverTSP("optimal", prob_instance)
-            # solver.name_method = "optimal"  # TODO ask umberto details
-
+            solver.solved = True
             solver.solution = np.concatenate([prob_instance.optimal_tour, [prob_instance.optimal_tour[0]]])
             solver.plot_solution()
 

src/io_tsp.py

@@ -15,9 +15,9 @@ class ProblemInstance:
     points: ndarray
 
     def __init__(self, name_tsp):
-        self.read_instance(name_tsp)
-        self.exist_opt = False  # TODO determine default value
+        self.exist_opt = False
         self.optimal_tour = None
+        self.read_instance(name_tsp)
 
     def read_instance(self, name_tsp):
         # read raw data
@@ -57,6 +57,7 @@ class ProblemInstance:
         print('name: ' + self.name)
         print('nPoints: ' + str(self.nPoints))
         print('best_sol: ' + str(self.best_sol))
+        print('exist optimal: ' + str(self.exist_opt))
 
     def plot_data(self):
         plt.figure(figsize=(8, 8))