class NMMTestCase(TestCase):
def setUp(self):
self.D = 40
self.nmm_custom = NelderMeadMethod(D=self.D,
nFES=1000,
n=10,
C_a=2,
C_r=0.5,
benchmark=MyBenchmark())
self.nmm_griewank = NelderMeadMethod(D=self.D,
nFES=1000,
n=10,
C_a=5,
C_r=0.5,
benchmark=Griewank())
def test_custom_works_fine(self):
fun = MyBenchmark().function()
x = self.nmm_custom.run()
self.assertTrue(x)
self.assertAlmostEqual(fun(self.D, x[0]), x[1], delta=1e2)
def test_griewank_works_fine(self):
fun = Griewank().function()
x = self.nmm_griewank.run()
self.assertTrue(x)
self.assertAlmostEqual(fun(self.D, x[0]), x[1], delta=1e2)
# encoding=utf8
# This is temporary fix to import module from parent folder
# It will be removed when package is published on PyPI
import sys
sys.path.append('../')
# End of fix
import random
from NiaPy.algorithms.other import NelderMeadMethod
from NiaPy.util import StoppingTask, OptimizationType
from NiaPy.benchmarks import Sphere
# we will run Nelder Mead algorithm for 5 independent runs
for i in range(5):
task = StoppingTask(D=10,
nGEN=10000,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = NelderMeadMethod(NP=70, alpha=0.2, gamma=0.1, rho=-0.24, sigma=-0.1)
best = algo.run(task=task)
print('%s -> %s' % (best[0], best[1]))
def logging_example(D=100, nFES=50000):
task = TaskConvPrint(D=D, nFES=nFES, nGEN=10000, benchmark=MyBenchmark())
algo = NelderMeadMethod(task=task, n=15, C_a=1, C_r=0.5)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
def plot_example(D=100, nFES=50000):
task = TaskConvPlot(D=D, nFES=nFES, nGEN=10000, benchmark=MyBenchmark())
algo = NelderMeadMethod(task=task, n=15, C_a=1, C_r=0.5)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def simple_example(runs=10, D=10, nFES=50000):
for i in range(runs):
algo = NelderMeadMethod(D=D, nFES=nFES, n=15, C_a=1, C_r=0.5, benchmark=MyBenchmark())
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))