def difevo(fcn,
x0,
xmin,
xmax,
maxfev=None,
ftol=EPSILON,
xprob=0.9,
weighting_factor=0.8,
population_size=None,
multicore=True,
verbose=0):
x0, xmin, xmax = _check_args(x0, xmin, xmax)
npar = len(x0)
if maxfev is None:
maxfev = 4096 * npar * 16
if population_size is None:
population_size = 16 * npar
starttime = time.time()
nm_result = sherpa_neldermead(fcn,
x0,
xmin,
xmax,
ftol=ftol,
maxfev=maxfev,
initsimplex=0,
finalsimplex=9,
step=None,
verbose=0)
print nm_result
print '%f secs' % (time.time() - starttime)
if nm_result[0]:
dif_evo = DifEvo(fcn)
maxfev_per_iter = min(maxfev - nm_result[4].get('nfev'), 512 * npar)
factor = 4.0
myx = nm_result[1]
myxmin, myxmax = _narrow_limit(4 * factor, [myx, xmin, xmax],
debug=True)
starttime = time.time()
de_result = dif_evo(myx, myxmin, myxmax, maxfev_per_iter, ftol,
population_size, xprob, weighting_factor)
print de_result
print '%f secs' % (time.time() - starttime)
de_result = list(de_result)
de_result[-1] += nm_result[4].get('nfev')
return get_result(de_result, maxfev)
else:
return nm_result
def difevo(
fcn,
x0,
xmin,
xmax,
maxfev=None,
ftol=EPSILON,
xprob=0.9,
weighting_factor=0.8,
population_size=None,
multicore=True,
verbose=0,
):
x0, xmin, xmax = _check_args(x0, xmin, xmax)
npar = len(x0)
if maxfev is None:
maxfev = 4096 * npar * 16
if population_size is None:
population_size = 16 * npar
starttime = time.time()
nm_result = sherpa_neldermead(
fcn, x0, xmin, xmax, ftol=ftol, maxfev=maxfev, initsimplex=0, finalsimplex=9, step=None, verbose=0
)
print nm_result
print "%f secs" % (time.time() - starttime)
if nm_result[0]:
dif_evo = DifEvo(fcn)
maxfev_per_iter = min(maxfev - nm_result[4].get("nfev"), 512 * npar)
factor = 4.0
myx = nm_result[1]
myxmin, myxmax = _narrow_limit(4 * factor, [myx, xmin, xmax], debug=True)
starttime = time.time()
de_result = dif_evo(myx, myxmin, myxmax, maxfev_per_iter, ftol, population_size, xprob, weighting_factor)
print de_result
print "%f secs" % (time.time() - starttime)
de_result = list(de_result)
de_result[-1] += nm_result[4].get("nfev")
return get_result(de_result, maxfev)
else:
return nm_result
def optneldermead(
afcn,
x0,
xmin,
xmax,
ftol=EPSILON,
maxfev=None,
initsimplex=0,
finalsimplex=None,
step=None,
multicore=False,
verbose=None,
):
x, xmin, xmax = _check_args(x0, xmin, xmax)
nfev, fcn = func_counter(afcn)
if step is None or (numpy.iterable(step) and len(step) != len(x)):
step = 1.2 * numpy.ones(x.shape, numpy.float_, numpy.isfortran(x))
elif numpy.isscalar(step):
step = step * numpy.ones(x.shape, numpy.float_, numpy.isfortran(x))
if maxfev is None:
maxfev = 1024 * len(x)
if finalsimplex is None:
finalsimplex = [0, 2]
if False == is_iterable(finalsimplex):
finalsimplex = [finalsimplex]
# For internal use only:
debug = True
def myloop(xxx, mymaxfev, mystep):
nms = classicNelderMead(fcn)
mynfev = 0
for myfinalsimplex in finalsimplex:
starttime = time.time()
result = nms(
xxx, xmin, xmax, mymaxfev - mynfev, ftol, mystep, initsimplex, myfinalsimplex, multicore, verbose
)
mynfev += result[3]
if 0 != result[0]:
return result[0], result[1], result[2], mynfev
if debug:
print "neldermead::myloop: result = ", result, " took ", time.time() - starttime, " secs"
if multicore:
return result[0], result[1], result[2], mynfev + nfev[0]
else:
return result[0], result[1], result[2], mynfev
def myneldermead(xxx, mymaxfev, mystep, fold=numpy.float_(numpy.finfo(numpy.float_).max)):
result = myloop(xxx, mymaxfev, mystep)
mynfev = result[3]
fnew = result[2]
if (
fnew < fold * 0.995
and 0 == Knuth_close(fnew, fold, ftol)
and 0 == Knuth_close(fnew, fold, ftol)
and 0 == result[0]
and mynfev < mymaxfev
):
if debug:
print "neldermead: fnew = ", fnew, "\tfold = ", fold, "\tx = ", result[1]
result = myneldermead(result[1], mymaxfev - mynfev, step, fold=fnew)
mynfev += result[3]
return result[0], result[1], result[2], mynfev
# result = myneldermead( x, maxfev, step )
result = myloop(x, maxfev, step)
return get_result(result, maxfev)
def optneldermead(afcn,
x0,
xmin,
xmax,
ftol=EPSILON,
maxfev=None,
initsimplex=0,
finalsimplex=None,
step=None,
multicore=False,
verbose=None):
x, xmin, xmax = _check_args(x0, xmin, xmax)
nfev, fcn = func_counter(afcn)
if step is None or (numpy.iterable(step) and len(step) != len(x)):
step = 1.2 * numpy.ones(x.shape, numpy.float_, numpy.isfortran(x))
elif numpy.isscalar(step):
step = step * numpy.ones(x.shape, numpy.float_, numpy.isfortran(x))
if maxfev is None:
maxfev = 1024 * len(x)
if finalsimplex is None:
finalsimplex = [0, 2]
if False == is_iterable(finalsimplex):
finalsimplex = [finalsimplex]
# For internal use only:
debug = True
def myloop(xxx, mymaxfev, mystep):
nms = classicNelderMead(fcn)
mynfev = 0
for myfinalsimplex in finalsimplex:
starttime = time.time()
result = nms(xxx, xmin, xmax, mymaxfev - mynfev, ftol, mystep,
initsimplex, myfinalsimplex, multicore, verbose)
mynfev += result[3]
if 0 != result[0]:
return result[0], result[1], result[2], mynfev
if debug:
print 'neldermead::myloop: result = ', result, ' took ', time.time(
) - starttime, ' secs'
if multicore:
return result[0], result[1], result[2], mynfev + nfev[0]
else:
return result[0], result[1], result[2], mynfev
def myneldermead(xxx,
mymaxfev,
mystep,
fold=numpy.float_(numpy.finfo(numpy.float_).max)):
result = myloop(xxx, mymaxfev, mystep)
mynfev = result[3]
fnew = result[2]
if fnew < fold * 0.995 and 0 == Knuth_close( fnew, fold, ftol ) and \
0 == Knuth_close( fnew, fold, ftol ) and \
0 == result[0] and mynfev < mymaxfev:
if debug:
print 'neldermead: fnew = ', fnew, '\tfold = ', fold, \
'\tx = ', result[ 1 ]
result = myneldermead(result[1],
mymaxfev - mynfev,
step,
fold=fnew)
mynfev += result[3]
return result[0], result[1], result[2], mynfev
#result = myneldermead( x, maxfev, step )
result = myloop(x, maxfev, step)
return get_result(result, maxfev)
