def _initialize(self):
# initialize the function parameters
self.alpha, self.beta, self.gamma, self.delta = self.func_params(
self.problem)
# reset the restart history
self.restart_history = []
# initialize the point
if self.x0 is None:
if not self.problem.has_bounds():
raise Exception(
"Either provide an x0 or a problem with variable bounds!")
# initialize randomly and make sure 5% is left for creating the initial simplex
X = np.random.random(self.problem.n_var)
self.x0 = denormalize(X, self.problem.xl, self.problem.xu)
# parse the initial population from array or population object
pop = pop_from_array_or_individual(self.x0)
# if the simplex has not the correct number of points
if len(pop) == 1:
# the corresponding x values
x0 = pop[0].X
# if lower and upper bounds are given take 5% of the range and add
if self.problem.has_bounds():
self.simplex_scaling = 0.05 * (self.problem.xu -
self.problem.xl)
# no bounds are given do it based on x0 - MATLAB procedure
else:
self.simplex_scaling = 0.05 * x0
# some value needs to be added if x0 is zero
self.simplex_scaling[self.simplex_scaling == 0] = 0.00025
# initialize the simplex
X = self.initialize_simplex(x0)
# create a population object
pop = pop.merge(Population().new("X", X))
# evaluate the values that are not already evaluated
evaluate_if_not_done_yet(self.evaluator,
self.problem,
pop,
algorithm=self)
elif len(pop) != self.problem.n_var + 1:
raise Exception(
"Provided initial population has size of %s, but should have size of %s"
% (len(pop), self.problem.n_var + 1))
# sort by its corresponding function values
self.pop = pop[np.argsort(pop.get("F")[:, 0])]
self.opt = self.pop[0]
def _initialize(self):
pop = pop_from_sampling(self.problem, self.sampling, self.n_initial_samples)
evaluate_if_not_done_yet(self.evaluator, self.problem, pop, algorithm=self)
for i in np.argsort(pop.get("F")[:, 0]):
algorithm = get_algorithm("nelder-mead",
problem=self.problem,
x0=pop[i],
termination=NelderAndMeadTermination(x_tol=1e-3, f_tol=1e-3),
evaluator=self.evaluator
)
algorithm.initialize()
self.algorithms.append(algorithm)
self.pop = pop