class TestSimulatedAnnealing(TestCase):
def setUp(self):
profiles_df = pd.read_excel("profillängen.xlsx")
self.dataset = profiles_df.sort_values(profiles_df.columns[0],
ascending=False)
self.normal_profile_selection = np.array([1000, 2000, 3000, 5000])
self.cutting_tolerance = 10
# check for permutation depth
self.pr = cProfile.Profile()
self.pr.enable()
permutations = Permutations(profiles_df.copy(),
self.normal_profile_selection,
self.cutting_tolerance)
self.combinations_df = permutations.get_combinations_dataframe(
self.dataset)
"""finish any test"""
p = Stats(self.pr)
p.strip_dirs()
p.sort_stats('cumtime')
p.print_stats()
print
"\n--->>>"
#create random solution
#initial_solution = get_random_solution(self.dataset, self.combinations_df, self.normal_profile_selection)
def test_simulate_annealing(self):
sa_parameter = {
"cycles": 100,
"trails": 10,
"P_start": 0.7,
"P_end": 0.001
}
parameter = json.dumps(sa_parameter)
self.simulated_annealing = SimulatedAnnealing(
self.dataset, self.combinations_df, self.normal_profile_selection,
parameter)
self.simulated_annealing.start()
best_costs = self.simulated_annealing.best_results_list
best_scrap_list = []
for profile_dict in best_costs:
scrap_sum = 0
for uuid, raw_profile in profile_dict.items():
scrap_sum += raw_profile.scrap
best_scrap_list.append(scrap_sum)
plt.plot(best_scrap_list)
plt.show()
def test_random_solution_generation(self):
sa_parameter = {
"cycles": 100,
"trails": 50,
"P_start": 0.7,
"P_end": 0.001
}
parameter = json.dumps(sa_parameter)
self.simulated_annealing = SimulatedAnnealing(self.dataset,
self.combinations_df,
parameter)
dict = self.simulated_annealing.get_random_solution(
self.dataset, self.combinations_df, self.normal_profile_selection)
