This repository has been archived on 2021-10-31. You can view files and clone it, but cannot push or open issues or pull requests.
AICup/run.py

50 lines
1.7 KiB
Python
Raw Normal View History

2020-09-28 09:56:36 +00:00
import glob
2020-12-20 18:39:46 +00:00
import pandas as pd
2020-12-21 14:46:50 +00:00
from aco.io_tsp import ProblemInstance
from aco.TSP_solver import TSPSolver
import os
2019-12-02 08:11:15 +00:00
2019-11-09 15:52:13 +00:00
def run(show_plots=False, verbose=False):
2020-12-21 14:46:50 +00:00
os.system("rm -f " + " ".join(glob.glob("sol/*") + glob.glob("c_prob/*")))
os.system("c++ -O2 -lpthread --std=c++11 -o c_prob/aco aco.cc opt.cc")
problems = glob.glob('./problems/*.tsp')
2020-12-21 14:46:50 +00:00
problems = ["./problems/fl1577.tsp"]
2019-10-31 17:58:06 +00:00
results = []
index = []
2020-09-28 07:30:21 +00:00
for problem_path in problems:
prob_instance = ProblemInstance(problem_path)
2019-11-09 15:52:13 +00:00
if verbose:
2020-09-28 07:30:21 +00:00
prob_instance.print_info()
2019-10-23 19:19:38 +00:00
if show_plots:
2020-09-28 07:30:21 +00:00
prob_instance.plot_data()
2019-10-31 15:17:47 +00:00
2020-12-21 14:46:50 +00:00
solver = TSPSolver(prob_instance)
solution = solver.compute_solution(verbose=verbose)
if verbose:
print(f"the total length for the solution found is {solver.found_length}",
f"while the optimal length is {solver.problem_instance.best_sol}",
f"the gap is {solver.gap}%",
f"the solution is found in {solver.duration} seconds", sep="\n")
index.append((problem_path, "'C++ ant colony optimization'"))
results.append([solver.found_length,
solver.problem_instance.best_sol,
solver.gap,
solver.duration])
with open("sol/" + prob_instance.name + ".sol", "w") as f:
print(solution, file=f)
2019-10-31 15:17:47 +00:00
2020-12-21 14:46:50 +00:00
if show_plots:
2019-11-04 05:43:54 +00:00
solver.plot_solution()
2020-12-21 14:46:50 +00:00
return pd.DataFrame(results, index=index,
columns=["tour length", "optimal solution",
"gap", "time to solve"])
2019-10-23 19:11:32 +00:00
2019-10-31 18:05:53 +00:00
2019-10-23 19:07:20 +00:00
if __name__ == '__main__':
2020-12-21 14:46:50 +00:00
df = run(show_plots=False, verbose=True)
2019-11-18 07:31:03 +00:00
df.to_csv("./results.csv")