udpate

run.py

@@ -1,11 +1,13 @@
 from src import *
-from concorde.tsp import TSPSolver
-
+import pandas as pd
+from time import time as t
 
 def run(show_plots=False):
     # names = [name_ for name_ in os.listdir("./problems") if "tsp" in name_]
-    names = ["ch130.tsp"]
-
+    names = ["eil76.tsp"]
+    methods = ["random", "nearest_neighbors", "best_nn"]
+    results = []
+    index = []
     for name in names:
         print("\n\n#############################")
         filename = f"problems/{name}"
@@ -14,31 +16,24 @@ def run(show_plots=False):
         if show_plots:
             instance.plot_data()
 
-        solver = TSPSolver.from_data(
-            instance.points[:, 1]*100,
-            instance.points[:, 2]*100,
-            norm="EUC_2D"
-        )
-        solution = solver.solve(verbose=False)
-        tour_opt = np.copy(solution.tour)
-
-
-        for method in ["random", "nearest_neighbors", "best_nn"]:
+        for method in methods:
             solver = Solver_TSP(method)
+            start = t()
             solver(instance, return_value=False)
+            end = t()
 
             print(f"the total length for the solution found is {solver.found_length}",
                   f"while the optimal length is {instance.best_sol}",
                   f"the gap is {solver.gap} %", sep="\n")
+            index.append((name, method))
+            results.append([solver.found_length, instance.best_sol, solver.gap, start - end])
+
             if show_plots:
                 solver.plot_solution()
 
-        solver.method = "optimal"
-        solver.solution = np.concatenate([tour_opt, [tour_opt[0]]])
-        solver.solved = True
-        solver.plot_solution()
-        print(solver.evaluate_solution(return_value=True))
+    index = pd.MultiIndex.from_tuples(index, names=['problem', 'method'])
 
+    return pd.DataFrame(results, index=index, columns=["tour length", "optimal solution", "gap", "time to solve"])
 
 if __name__ == '__main__':
-    run(show_plots=True)
+    print(run(show_plots=True))