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 __call__(
self,
x,
xmin,
xmax,
maxnfev,
tolerance,
population_size,
xprob,
scale_factor,
seed=81547,
multicore=False,
verbose=None,
):
debug = False
use_local_opt = True
x, self.xmin, self.xmax = self.check_args(x, xmin, xmax)
self.fcn = func_bounds(self.fcn, xmin, xmax)
polytope, fstat = self.init_population(x, xmin, xmax, population_size, seed)
strategy_function = self.best1exp
npar = len(x)
npar_plus_1 = npar + 1
self.trial_solution = numpy.zeros(npar_plus_1, dtype=numpy.float_)
fct_vals = numpy.zeros(npar, dtype=numpy.float_)
model_par = x[:]
while self.nfev[0] < maxnfev:
for candidate in xrange(population_size):
self.trial_solution[:] = polytope[candidate][:]
strategy_function(candidate, polytope, model_par, xprob, scale_factor)
self.trial_solution[-1] = self.fcn(self.trial_solution[:-1])
if self.trial_solution[-1] < polytope[candidate, -1]:
polytope[candidate][:] = self.trial_solution[:]
if self.trial_solution[npar] < fstat:
if use_local_opt:
if debug:
print "before nm: f%s=%f" % (self.trial_solution[:-1], self.trial_solution[-1])
result = sherpa_neldermead(
self.fcn,
self.trial_solution[:-1],
xmin,
xmax,
ftol=tolerance,
maxfev=maxnfev - self.nfev[0],
initsimplex=0,
finalsimplex=[0, 1],
step=None,
)
model_par[:] = result[1]
fstat = result[2]
if debug:
print "after nm: f%s=%f" % (result[1], result[2])
print
else:
model_par[:] = self.trial_solution[:-1]
fstat = self.trial_solution[-1]
if self.check_convergence(polytope, tolerance):
return 0, model_par, fstat, self.nfev[0]
if self.nfev[0] >= maxnfev:
3, model_par, fstat, self.nfev[0]
return 3, model_par, fstat, self.nfev[0]
def __call__(self,
x,
xmin,
xmax,
maxnfev,
tolerance,
population_size,
xprob,
scale_factor,
seed=81547,
multicore=False,
verbose=None):
debug = False
use_local_opt = True
x, self.xmin, self.xmax = self.check_args(x, xmin, xmax)
self.fcn = func_bounds(self.fcn, xmin, xmax)
polytope, fstat = self.init_population(x, xmin, xmax, population_size,
seed)
strategy_function = self.best1exp
npar = len(x)
npar_plus_1 = npar + 1
self.trial_solution = numpy.zeros(npar_plus_1, dtype=numpy.float_)
fct_vals = numpy.zeros(npar, dtype=numpy.float_)
model_par = x[:]
while self.nfev[0] < maxnfev:
for candidate in xrange(population_size):
self.trial_solution[:] = polytope[candidate][:]
strategy_function(candidate, polytope, model_par, xprob,
scale_factor)
self.trial_solution[-1] = self.fcn(self.trial_solution[:-1])
if self.trial_solution[-1] < polytope[candidate, -1]:
polytope[candidate][:] = self.trial_solution[:]
if self.trial_solution[npar] < fstat:
if use_local_opt:
if debug:
print 'before nm: f%s=%f' % \
( self.trial_solution[ :-1 ], \
self.trial_solution[ -1 ] )
result = sherpa_neldermead(
self.fcn,
self.trial_solution[:-1],
xmin,
xmax,
ftol=tolerance,
maxfev=maxnfev - self.nfev[0],
initsimplex=0,
finalsimplex=[0, 1],
step=None)
model_par[:] = result[1]
fstat = result[2]
if debug:
print 'after nm: f%s=%f' % \
( result[ 1 ], result[ 2 ])
print
else:
model_par[:] = self.trial_solution[:-1]
fstat = self.trial_solution[-1]
if self.check_convergence(polytope, tolerance):
return 0, model_par, fstat, self.nfev[0]
if self.nfev[0] >= maxnfev:
3, model_par, fstat, self.nfev[0]
return 3, model_par, fstat, self.nfev[0]
