def inspect_ga_performance():
from solver.meta_heur import AngularGeneticMinSumSolver, AngularGeneticLocalMinSumSolver
from instance_generation import create_random_celest_graph
dh = DataHolder(DataFrame())
graphs = []
ga_lms = AngularGeneticLocalMinSumSolver(cb=dh)
ga_ms = AngularGeneticMinSumSolver(cb=dh)
for n in range(15, 25):
for m in range(5):
graph = create_random_celest_graph(n)
graphs.append(graph)
dh.graph = graph
ga_lms.solve(graph)
ga_ms.solve(graph)
df_graphs = DataFrame([{
"graph_name": g.name ,
"vert_amount": g.vert_amount,
"edge_amount": g.edge_amount
} for g in graphs])
print(dh.dataFrame)
dh.dataFrame.to_pickle("GA_comparison.pk")
df_graphs.to_pickle("GA_comparison_graphs.pk")
def main():
config = _load_config()
session = get_session(config.url_path)
graphs = []
if config.seed is None:
seed = int(np.random.default_rng(None).integers(np.array([2**63]))[0])
config.seed = seed
else:
seed = int(config.seed)
gen = np.random.default_rng(seed)
counter = 0
assert len(config.vertex_shape) % 2 == 0
shapes = np.array(config.vertex_shape).reshape(round(len(config.vertex_shape)/2), 2)
l_shapes = len(shapes)
for n in range(config.min_n, config.max_n):
size = config.cel_min
while size <= config.cel_max:
for i in range(config.repetitions):
graphs.append(create_random_celest_graph(n,vertex_shape=shapes[counter % l_shapes], celest_bounds=(size,size+config.cel_range), seed=gen))
counter += 1
size += config.cel_step
task = Task(name=config.name, task_type="CelestialGraphInstances", status=Task.STATUS_OPTIONS.FINISHED)
# Convert the namespace config into database config
task_config_holder = ConfigHolder.fromNamespace(config, task=task, ignored_attributes=["url_path", "name", "config"])
task.jobs = [TaskJobs(graph=graph, task=task) for graph in graphs]
session.add(task)
session.commit()
print(counter, "instances were created. Corresponding task is", task.id)
def create_graph():
d1 = get_distance(9, 3)
d2 = get_distance(9, 2)
d = d1 + np.random.default_rng().random() * (d2 - d1)
body = CelestialBody((0, 0), d, None)
g = create_circle_n_k(9, 4)
graph = CelestialGraph([body], g.vertices, g.edges)
visualize_graph_2d(graph)
for i in range(10):
rand_g = create_random_celest_graph(9, celest_bounds=(0.4, 0.6))
visualize_graph_2d(rand_g)
def create_graph():
d1 = get_distance(9,3)
d2 = get_distance(9,2)
d = d1 + np.random.default_rng().random() * (d2-d1)
body = CelestialBody((0,0), d, None)
g = create_circle_n_k(9,4)
graph = CelestialGraph([body], g.vertices, g.edges)
visualize_graph_2d(graph)
for i in range(10):
for j in range(3):
rand_c = create_random_celest_graph(9+i,celest_bounds=(0.4,0.6))
rand_g = create_random_instance(9+i, edge_chance=random.uniform(0.3, 1))
visualize_graph_2d(rand_g, savePath=f"instance_vis/rand_e{9+i}_{j}.pdf")
visualize_graph_2d(rand_c, savePath=f"instance_vis/cel_e{9+i}_{j}.pdf")
def main():
solution_example_intro()
#color_solver_check()
#correct_msc_instances()
#visualize_solutions_overview()
#visualize_geo_solutions_overview()
#visualize_solutions_overview_second_batch()
#bnb_sols()
#vis_full_circle_solutions()
return
graphs = [
create_random_celest_graph(i,
celest_bounds=(j * 0.1 + 0.1,
j * 0.1 + 0.1),
seed=None) for i in range(5, 8)
for j in range(5)
]
def main():
#solution_example_intro()
#color_solver_check()
#correct_msc_instances()
#visualize_solutions_overview()
#visualize_geo_solutions_overview()
#visualize_solutions_overview_second_batch()
#bnb_sols()
#vis_full_circle_solutions()
create_graph()
#test_makespan_bipartite()
#general_instances_test()
#inspect_ga_performance()
#_use_test()
return
graphs = [create_random_celest_graph(i, celest_bounds=(j*0.1+0.1, j*0.1+0.1), seed=None) for i in range(5, 8) for j in range(5)]