def minimize_setcover_discrete():
"""
Discrete optimization example of minimizing setcover problem.
:return: no return value
"""
problem = SetCover()
dim = problem.dim # the dim is prepared by the class
objective = Objective(problem.fx, dim) # form up the objective function
budget = 100 * dim.get_size() # number of calls to the objective function
# if autoset is False, you should define train_size, positive_size, negative_size on your own
parameter = Parameter(budget=budget, autoset=False)
parameter.set_train_size(6)
parameter.set_positive_size(1)
parameter.set_negative_size(5)
ExpOpt.min(objective,
parameter,
repeat=10,
best_n=5,
plot=True,
plot_file="img/setcover_discrete_figure.png")
repeat = 15
result = []
for i in range(repeat):
# setup optimization problem
dim_size = 100 # dimensions
dim_regs = [[-1, 1]] * dim_size # dimension range
dim_tys = [True] * dim_size # dimension type : real
dim = Dimension(dim_size, dim_regs,
dim_tys) # form up the dimension object
objective = Objective(ackley, dim) # form up the objective function
budget = 20 * dim_size # number of calls to the objective function
# by setting autoset=false, the algorithm parameters will not be set by default
parameter = Parameter(algorithm="racos", budget=budget, autoset=False)
# so you are allowed to setup algorithm parameters of racos
parameter.set_train_size(21)
parameter.set_positive_size(1)
parameter.set_negative_size(20)
# perform the optimization
solution = Opt.min(objective, parameter)
# store the optimization result
print('solved solution is:')
solution.print_solution()
result.append(solution.get_value())
# plt.plot(objective.get_history_bestsofar())
# plt.savefig("figure.png")
result_analysis(result, 5)
t2 = time.clock()