def nelder_mead(problem, x):
solver = NelderMead(X=x)
solver.initialize(problem)
solver._initialize()
solver.n_gen = 1
solver.next()
return solver
def test_sphere_with_constraints(self):
problem = SphereWithConstraints()
for algorithm in [GA(), NelderMead(), PatternSearch(), CuckooSearch()]:
f, f_opt = test(problem, algorithm)
self.assertAlmostEqual(f, f_opt, places=3)
print(problem.__class__.__name__, algorithm.__class__.__name__,
"Yes")
def test_against_scipy(self):
problem = get_problem("rosenbrock")
problem.xl = None
problem.xu = None
x0 = np.array([0.5, 0.5])
hist_scipy = []
def fun(x):
hist_scipy.append(x)
return problem.evaluate(x)
scipy_minimize(fun, x0, method='Nelder-Mead')
hist_scipy = np.array(hist_scipy)
hist = []
def callback(x):
if x.shape == 2:
hist.extend(x)
else:
hist.append(x)
problem.callback = callback
minimize(
problem,
NelderMead(X=x0,
max_restarts=0,
termination=get_termination("n_eval", len(hist_scipy))))
hist = np.row_stack(hist)[:len(hist_scipy)]
self.assertTrue(np.all(hist - hist_scipy < 1e-7))
def test_sphere(self):
problem = Sphere()
for algorithm in [NelderMead(), PatternSearch(), PSO(), GA()]:
f, f_opt = test(problem, algorithm)
self.assertAlmostEqual(f, f_opt, places=5)
print(problem.__class__.__name__, algorithm.__class__.__name__,
"Yes")
def test_sphere_no_bounds(self):
problem = SphereNoBounds()
x0 = np.random.random(problem.n_var)
for algorithm in [NelderMead(x0=x0), PatternSearch(x0=x0)]:
f, f_opt = test(problem, algorithm)
self.assertAlmostEqual(f, f_opt, places=5)
print(problem.__class__.__name__, algorithm.__class__.__name__,
"Yes")
from pymoo.algorithms.so_nelder_mead import NelderMead
from pymoo.factory import get_problem
from pymoo.optimize import minimize
problem = get_problem("sphere")
algorithm = NelderMead(n_max_restarts=10)
res = minimize(problem, algorithm, seed=1, verbose=False)
print(res.X)
print(res.F)
from pymoo.optimize import minimize
from pymoo.vendor.vendor_scipy import CG, BFGS, Powell
class MySphere(Problem):
def __init__(self, n_var=3):
super().__init__(n_var=n_var,
n_obj=1,
n_constr=0,
xl=None,
xu=None,
type_var=anp.double)
def _evaluate(self, x, out, *args, **kwargs):
out["F"] = anp.sum(anp.square(x + 10), axis=1)
problem = MySphere()
x0 = np.random.random(problem.n_var)
algorithms = [NelderMead(x0=x0), CG(x0=x0), BFGS(x0=x0), Powell(x0=x0)]
for algorithm in algorithms:
res = minimize(problem, algorithm, seed=1, verbose=False)
print(
f"{algorithm.__class__.__name__}: Best solution found: X = {res.X} | F = {res.F} | CV = {res.F}"
)
def test_with_bounds_with_restart(self):
problem = get_problem("rastrigin")
method = NelderMead(X=np.array([1, 1]), max_restarts=2)
minimize(problem, method, verbose=False)
def test_with_bounds_no_initial_point(self):
problem = get_problem("rastrigin")
method = NelderMead(max_restarts=0)
minimize(problem, method, verbose=False)
def test_no_bounds(self):
problem = get_problem("rastrigin")
problem.xl = None
problem.xu = None
method = NelderMead(X=np.array([1, 1]), max_restarts=0)
minimize(problem, method, verbose=False)
from pymoo.algorithms.so_nelder_mead import NelderMead
from pymoo.factory import get_problem
from pymoo.optimize import minimize
problem = get_problem("sphere")
algorithm = NelderMead()
res = minimize(problem, algorithm, seed=1, verbose=False)
print("Best solution found: \nX = %s\nF = %s" % (res.X, res.F))
prefix = "runs"
# name of the experiment
name = "nelder-mead-0.3.2"
# number of runs to execute
n_runs = 10
# path were the files for this experiment are saved
path = os.path.join(prefix, name)
for _problem in setup:
problem = get_problem(_problem)
method = NelderMead(n_max_local_restarts=2)
for run in range(1, n_runs + 1):
fname = "%s_%s.run" % (_problem, run)
_in = os.path.join(path, fname)
_out = "results/%s/%s/%s_%s.out" % (name, _problem.replace("_", "/"), _problem, run)
data = {
'args': [problem, method],
'kwargs': {
'seed': run,
},
'in': _in,
'out': _out,
}