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refactor: Classification of fit method in fits.least_squares simplified,

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precision of imiunit solver adjusted, prefitting for alternative methods
removed.
This commit is contained in:
Fabian Joswig 2022-02-02 10:05:48 +00:00
parent e92b1d9e9b
commit d602bea5b7
1 changed files with 9 additions and 14 deletions
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23
pyerrors/fits.py
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@ -96,7 +96,7 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
initial_guess : list
can provide an initial guess for the input parameters. Relevant for
non-linear fits with many parameters.
method : str
method : str, optional
can be used to choose an alternative method for the minimization of chisquare.
The possible methods are the ones which can be used for scipy.optimize.minimize and
migrad of iminuit. If no method is specified, Levenberg-Marquard is used.
@ -487,26 +487,21 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
chisq = anp.sum(((y_f - model) / dy_f) ** 2)
return chisq
if 'method' in kwargs and not (kwargs.get('method', 'Levenberg-Marquardt') == 'Levenberg-Marquardt'):
output.method = kwargs.get('method')
if not silent:
print('Method:', kwargs.get('method'))
if kwargs.get('method') == 'migrad':
fit_result = iminuit.minimize(chisqfunc, x0)
fit_result = iminuit.minimize(chisqfunc, fit_result.x)
output.method = kwargs.get('method', 'Levenberg-Marquardt')
if not silent:
print('Method:', output.method)
if output.method != 'Levenberg-Marquardt':
if output.method == 'migrad':
fit_result = iminuit.minimize(chisqfunc, x0, tol=1e-4) # Stopping crieterion 0.002 * tol * errordef
output.iterations = fit_result.nfev
else:
fit_result = scipy.optimize.minimize(chisqfunc, x0, method=kwargs.get('method'))
fit_result = scipy.optimize.minimize(chisqfunc, fit_result.x, method=kwargs.get('method'), tol=1e-12)
fit_result = scipy.optimize.minimize(chisqfunc, x0, method=kwargs.get('method'), tol=1e-12)
output.iterations = fit_result.nit
chisquare = fit_result.fun
else:
output.method = 'Levenberg-Marquardt'
if not silent:
print('Method: Levenberg-Marquardt')
if kwargs.get('correlated_fit') is True:
def chisqfunc_residuals(p):
model = func(p, x)