def myopt(myfcn,
xxx,
ftol,
maxfev,
seed,
pop,
xprob,
weight,
factor=4.0,
debug=False):
x = xxx[0]
xmin = xxx[1]
xmax = xxx[2]
maxfev_per_iter = 512 * x.size
def random_start(xmin, xmax):
xx = []
for ii in range(len(xmin)):
xx.append(random.uniform(xmin[ii], xmax[ii]))
return numpy.asarray(xx)
############################# NelderMead #############################
mymaxfev = min(maxfev_per_iter, maxfev)
if all(x == 0.0):
mystep = list(map(lambda fubar: 1.2 + fubar, x))
else:
mystep = list(map(lambda fubar: 1.2 * fubar, x))
if 1 == numcores:
result = neldermead(myfcn,
x,
xmin,
xmax,
maxfev=mymaxfev,
ftol=ftol,
finalsimplex=9,
step=mystep)
x = numpy.asarray(result[1], numpy.float_)
nfval = result[2]
nfev = result[4].get('nfev')
else:
ncores_nm = ncoresNelderMead()
nfev, nfval, x = \
ncores_nm(stat_cb0, x, xmin, xmax, ftol, mymaxfev, numcores)
if verbose or debug != False:
print('f_nm%s=%.14e in %d nfev' % (x, nfval, nfev))
############################# NelderMead #############################
############################## nmDifEvo #############################
xmin, xmax = _narrow_limits(4 * factor, [x, xmin, xmax], debug=False)
mymaxfev = min(maxfev_per_iter, maxfev - nfev)
if 1 == numcores:
result = difevo_nm(myfcn, x, xmin, xmax, ftol, mymaxfev, verbose,
seed, pop, xprob, weight)
nfev += result[4].get('nfev')
x = numpy.asarray(result[1], numpy.float_)
nfval = result[2]
else:
ncores_de = ncoresDifEvo()
mystep = None
tmp_nfev, tmp_fmin, tmp_par = \
ncores_de(stat_cb0, x, xmin, xmax, ftol, mymaxfev, mystep,
numcores, pop, seed, weight, xprob, verbose)
nfev += tmp_nfev
if tmp_fmin < nfval:
nfval = tmp_fmin
x = tmp_par
if verbose or debug != False:
print('f_de_nm%s=%.14e in %d nfev' % (x, nfval, nfev))
############################## nmDifEvo #############################
ofval = FUNC_MAX
while nfev < maxfev:
xmin, xmax = _narrow_limits(factor, [x, xmin, xmax], debug=False)
############################ nmDifEvo #############################
y = random_start(xmin, xmax)
mymaxfev = min(maxfev_per_iter, maxfev - nfev)
if 1 == numcores:
result = difevo_nm(myfcn, y, xmin, xmax, ftol, mymaxfev,
verbose, seed, pop, xprob, weight)
nfev += result[4].get('nfev')
if result[2] < nfval:
nfval = result[2]
x = numpy.asarray(result[1], numpy.float_)
if verbose or debug != False:
print('f_de_nm%s=%.14e in %d nfev' % \
(x, result[2], result[4].get('nfev')))
############################ nmDifEvo #############################
if debug != False:
print('ofval=%.14e\tnfval=%.14e\n' % (ofval, nfval))
if sao_fcmp(ofval, nfval, ftol) <= 0:
return x, nfval, nfev
ofval = nfval
factor *= 2
return x, nfval, nfev
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
#
from sherpa.utils import _ncpus
from sherpa.optmethods.ncoresnm import ncoresNelderMead
from sherpa.optmethods.ncoresde import ncoresDifEvo
import sherpa.optmethods.opt as tstopt
import pytest
NUMPAR = 10
NCORES_NM = ncoresNelderMead()
NCORES_DE = ncoresDifEvo()
def print_result(name, f, x, nfev):
print('%s(%s) = %g in %d nfev' % (name, x, f, nfev))
# Mapping from SherpaTestCase.assertEqualWithinTol to
# pytest.approx
# and noting that the tolerance is an absolute tolerance,
# not relative in SherpaTestCase.
#
def tst_opt(opt, fcn, x0, xmin, xmax, fmin, tol=1e-2):
def func(arg):
return fcn(arg)[0]
nfev, fval, par = opt(func, x0, xmin, xmax)