|
|
|
@ -41,8 +41,8 @@ The following methods are currently supported for scipy.optimize: |
|
|
|
|
|
|
|
|
|
See the documentation of `scipy.optimize` for more details. |
|
|
|
|
The `fmin` algorithm is the slowest although it is fairly foor proof to |
|
|
|
|
converge it (it may take many iterations). |
|
|
|
|
The leastsq` algorithm is the best but it requires parameter guess that is |
|
|
|
|
converge it (it may take very many iterations). |
|
|
|
|
The `leastsq` algorithm is the best, but it requires parameter guess that is |
|
|
|
|
reasonable. |
|
|
|
|
I don't have much success with `anneal`--it seems to behave erratically in |
|
|
|
|
my limited experience. YMMV. |
|
|
|
@ -492,7 +492,7 @@ class fit_func_base(object): |
|
|
|
|
fit_default_opts["lmfit:leastsq"] = dict(xtol=1e-8, epsfcn=1e-6) |
|
|
|
|
debug = 0 |
|
|
|
|
dbg_params = 1 |
|
|
|
|
fit_method = 'fmin' |
|
|
|
|
fit_method = 'leastsq' # changed 20150529 from fmin. Leastsq is much faster. |
|
|
|
|
fit_opts = fit_default_opts |
|
|
|
|
#fit_opts = dict(xtol=1e-5, maxfun=100000, maxiter=10000, disp=0) |
|
|
|
|
def fit(self, x, y, dy=None, fit_opts=None, Funct_hook=None, Guess=None): |
|
|
|
|