def fmin(self, **kws):
"""
Use Nelder-Mead (simplex) minimization
Parameters
----------
kws : dict
Minimizer options to pass to the scipy.optimize.fmin minimizer.
"""
self.prepare_fit()
fmin_kws = dict(full_output=True,
disp=False,
retall=True,
ftol=1.e-4,
xtol=1.e-4,
maxfun=5000 * (self.nvarys + 1))
fmin_kws.update(kws)
ret = scipy_fmin(self.penalty, self.vars, **fmin_kws)
xout, fout, niter, funccalls, warnflag, allvecs = ret
self.nfev = funccalls
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 fmin(self, **kws):
"""
Use Nelder-Mead (simplex) minimization
Parameters
----------
kws : dict
Minimizer options to pass to the scipy.optimize.fmin minimizer.
"""
self.prepare_fit()
fmin_kws = dict(full_output=True, disp=False, retall=True,
ftol=1.e-4, xtol=1.e-4,
maxfun=5000 * (self.nvarys + 1))
fmin_kws.update(kws)
ret = scipy_fmin(self.penalty, self.vars, **fmin_kws)
xout, fout, niter, funccalls, warnflag, allvecs = ret
self.nfev = funccalls
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 fmin(self, **kws):
"""
use nelder-mead (simplex) minimization
"""
self.prepare_fit()
fmin_kws = dict(full_output=True, disp=False, retall=True,
ftol=1.e-4, xtol=1.e-4,
maxfun = 5000 * (self.nvarys + 1))
fmin_kws.update(kws)
ret = scipy_fmin(self.penalty, self.vars, **fmin_kws)
xout, fout, iter, funccalls, warnflag, allvecs = ret
self.nfev = funccalls
self.chisqr = (self.penalty(xout)**2).sum()
def fmin(self, **kws):
"""
use nelder-mead (simplex) minimization
"""
self.prepare_fit()
fmin_kws = dict(full_output=True, disp=False, retall=True,
ftol=1.e-4, xtol=1.e-4,
maxfun = 5000 * (self.nvarys + 1))
fmin_kws.update(kws)
ret = scipy_fmin(self.penalty, self.vars, **fmin_kws)
xout, fout, iter, funccalls, warnflag, allvecs = ret
self.nfev = funccalls
self.chisqr = (self.penalty(xout)**2).sum()
def fmin(self, **kws):
"""
use nelder-mead (simplex) minimization
"""
self.prepare_fit()
fmin_kws = dict(full_output=True, disp=False, retall=True,
ftol=1.e-4, xtol=1.e-4,
maxfun = 5000 * (self.nvarys + 1))
fmin_kws.update(self.kws)
fmin_kws.update(kws)
# ugly hack to get number of data ndata - it causes 1 extra
# func evaluation. Given the structure of the class it has to
# happen *before* fitting, otherwise wipes best fit pars
def ret_ndata(params):
"custom function just to get the number of data points, because fmin doesn't do that"
r = self.__residual(params)
return len(r)
self.ndata = ret_ndata(self.vars) # is this the simplest way?
def penalty(params):
"local penalty function -- eval sum-squares residual"
r = self.__residual(params)
if isinstance(r, ndarray):
r = (r*r).sum()
return r
ret = scipy_fmin(penalty, self.vars, **fmin_kws)
xout, fout, iter, funccalls, warnflag, allvecs = ret
# RS: part hacked
sum_sqr = fout
self.chisqr = sum_sqr
self.residual = -99 # FIXME: residula not implemented yet
# - it is bit tricky to get to it
self.nfree = (self.ndata - self.nvarys)
self.redchi = sum_sqr / self.nfree
self.nfev = funccalls
# self.set_fitmethod('simplex')
self.fitmethod='simplex'
def fmin(self, **kws):
"""
use nelder-mead (simplex) minimization
"""
self.prepare_fit()
fmin_kws = dict(full_output=True, disp=False, retall=True,
ftol=1.e-4, xtol=1.e-4,
maxfun = 5000 * (self.nvarys + 1))
fmin_kws.update(kws)
def penalty(params):
"local penalty function -- eval sum-squares residual"
r = self.__residual(params)
if isinstance(r, ndarray):
r = (r*r).sum()
return r
ret = scipy_fmin(penalty, self.vars, **fmin_kws)
xout, fout, iter, funccalls, warnflag, allvecs = ret
self.nfev = funccalls
