def anneal(self, schedule='cauchy', **kws):
"""
use simulated annealing
"""
sched = 'fast'
if schedule in ('cauchy', 'boltzmann'):
sched = schedule
self.prepare_fit()
sakws = dict(full_output=1, schedule=sched,
maxiter = 2000 * (self.nvarys + 1))
sakws.update(self.kws)
sakws.update(kws)
def penalty(params):
"local penalty function -- anneal wants sum-squares residual"
r = self.__residual(params)
if isinstance(r, ndarray):
r = (r*r).sum()
return r
saout = scipy_anneal(penalty, self.vars, **sakws)
self.sa_out = saout
return
def anneal(self, schedule='cauchy', **kws):
"""
use simulated annealing
"""
sched = 'fast'
if schedule in ('cauchy', 'boltzmann'):
sched = schedule
self.prepare_fit()
sakws = dict(full_output=1, schedule=sched,
maxiter = 2000 * (self.nvarys + 1))
sakws.update(self.kws)
sakws.update(kws)
print("WARNING: scipy anneal appears unusable!")
saout = scipy_anneal(self.penalty, self.vars, **sakws)
self.sa_out = saout
return
def anneal(self, schedule='cauchy', **kws):
"""
use simulated annealing
"""
sched = 'fast'
if schedule in ('cauchy', 'boltzmann'):
sched = schedule
self.prepare_fit()
sakws = dict(full_output=1,
schedule=sched,
maxiter=2000 * (self.nvarys + 1))
sakws.update(self.kws)
sakws.update(kws)
print("WARNING: scipy anneal appears unusable!")
saout = scipy_anneal(self.penalty, self.vars, **sakws)
self.sa_out = saout
return
def anneal(self, schedule='cauchy', **kws):
"""
use simulated annealing
"""
sched = 'fast'
if schedule in ('cauchy', 'boltzmann'):
sched = schedule
self.prepare_fit()
sakws = dict(full_output=1,
schedule=sched,
maxiter=2000 * (self.nvarys + 1))
sakws.update(self.kws)
sakws.update(kws)
def penalty(params):
"local penalty function -- anneal wants sum-squares residual"
r = self.__residual(params)
return (r * r).sum()
saout = scipy_anneal(penalty, self.vars, **sakws)
self.sa_out = saout
return
