def lbfgsb(self, **kws):
"""
use l-bfgs-b minimization
"""
self.prepare_fit()
lb_kws = dict(factr=1000.0, approx_grad=True, m=20,
maxfun=2000 * (self.nvarys + 1),
# bounds = zip(self.vmin, self.vmax),
)
lb_kws.update(self.kws)
lb_kws.update(kws)
xout, fout, info = scipy_lbfgsb(self.penalty, self.vars, **lb_kws)
self.nfev = info['funcalls']
self.message = info['task']
self.chisqr = self.residual = self.__residual(xout)
self.ndata = 1
self.nfree = 1
if isinstance(self.residual, ndarray):
self.chisqr = (self.chisqr**2).sum()
self.ndata = len(self.residual)
self.nfree = self.ndata - self.nvarys
self.redchi = self.chisqr/self.nfree
self.unprepare_fit()
return
def lbfgsb(self, **kws):
"""
Use l-bfgs-b minimization
Parameters
----------
kws : dict
Minimizer options to pass to the
scipy.optimize.lbfgsb.fmin_l_bfgs_b function.
"""
self.prepare_fit()
lb_kws = dict(
factr=1000.0,
approx_grad=True,
m=20,
maxfun=2000 * (self.nvarys + 1),
)
lb_kws.update(self.kws)
lb_kws.update(kws)
xout, fout, info = scipy_lbfgsb(self.penalty, self.vars, **lb_kws)
self.nfev = info['funcalls']
self.message = info['task']
self.chisqr = self.residual = self.__residual(xout)
self.ndata = 1
self.nfree = 1
if isinstance(self.residual, ndarray):
self.chisqr = (self.chisqr**2).sum()
self.ndata = len(self.residual)
self.nfree = self.ndata - self.nvarys
self.redchi = self.chisqr / self.nfree
self.unprepare_fit()
return
def lbfgsb(self, **kws):
"""
Use l-bfgs-b minimization
Parameters
----------
kws : dict
Minimizer options to pass to the
scipy.optimize.lbfgsb.fmin_l_bfgs_b function.
"""
self.prepare_fit()
lb_kws = dict(factr=1000.0, approx_grad=True, m=20,
maxfun=2000 * (self.nvarys + 1),
)
lb_kws.update(self.kws)
lb_kws.update(kws)
xout, fout, info = scipy_lbfgsb(self.penalty, self.vars, **lb_kws)
self.nfev = info['funcalls']
self.message = info['task']
self.chisqr = self.residual = self.__residual(xout)
self.ndata = 1
self.nfree = 1
if isinstance(self.residual, ndarray):
self.chisqr = (self.chisqr**2).sum()
self.ndata = len(self.residual)
self.nfree = self.ndata - self.nvarys
self.redchi = self.chisqr/self.nfree
self.unprepare_fit()
return
def lbfgsb(self, **kws):
"""
use l-bfgs-b minimization
"""
self.prepare_fit()
lb_kws = dict(
factr=1000.0,
approx_grad=True,
m=20,
maxfun=2000 * (self.nvarys + 1),
# bounds = zip(self.vmin, self.vmax),
)
lb_kws.update(self.kws)
lb_kws.update(kws)
xout, fout, info = scipy_lbfgsb(self.penalty, self.vars, **lb_kws)
self.nfev = info['funcalls']
self.message = info['task']
self.chisqr = self.residual = self.__residual(xout)
self.ndata = 1
self.nfree = 1
if isinstance(self.residual, ndarray):
self.chisqr = (self.chisqr**2).sum()
self.ndata = len(self.residual)
self.nfree = self.ndata - self.nvarys
self.redchi = self.chisqr / self.nfree
self.unprepare_fit()
return
def lbfgsb(self, **kws):
"""
use l-bfgs-b minimization
"""
self.prepare_fit()
lb_kws = dict(factr=1000.0, approx_grad=True, m=20,
maxfun = 2000 * (self.nvarys + 1),
# bounds = zip(self.vmin, self.vmax),
)
lb_kws.update(self.kws)
lb_kws.update(kws)
xout, fout, info = scipy_lbfgsb(self.penalty, self.vars, **lb_kws)
self.nfev = info['funcalls']
self.message = info['task']
self.chisqr = (self.penalty(xout)**2).sum()
def lbfgsb(self, **kws):
"""
use l-bfgs-b minimization
"""
self.prepare_fit()
lb_kws = dict(factr=1000.0, approx_grad=True, m=20,
maxfun = 2000 * (self.nvarys + 1),
# bounds = zip(self.vmin, self.vmax),
)
lb_kws.update(self.kws)
lb_kws.update(kws)
xout, fout, info = scipy_lbfgsb(self.penalty, self.vars, **lb_kws)
self.nfev = info['funcalls']
self.message = info['task']
self.chisqr = (self.penalty(xout)**2).sum()
