def cost_of_random_solutions(people_in, tables_in, days):
"""
Computes the average cost of 100 random solutions
"""
config.build_smart = False
costs = 0
for _ in range(100):
people = deepcopy(people_in)
tables = deepcopy(tables_in)
solution = build_guess(people, tables, days)
costs += solution.cost
avg_cost = costs / 100.0
config.build_smart = True
return avg_cost
def main(input_data):
people = deepcopy(input_data.people)
tables = deepcopy(input_data.tables)
# for initializing axes of graph
baseline_cost = cost_of_random_solutions(people, tables, input_data.days)
dummy_solution = DummySolution(baseline_cost)
yield (dummy_solution, dummy_solution.dummy_T)
init_solution = build_guess(people, tables, input_data.days)
best_solution = deepcopy(init_solution)
print_init_cost(init_solution.cost)
if config.anneal:
for (solution, T) in anneal(init_solution):
print_progress(solution, T)
yield solution, T
else:
yield best_solution, None