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feat: When least_squares is called with correlated_fit=True the routine

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first performs an uncorrelated fit in order to find appropriate starting
parameters.
This commit is contained in:
Fabian Joswig 2022-05-25 17:59:29 +01:00
parent 48636fedb2
commit 919ba68a52
1 changed files with 17 additions and 12 deletions
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29
pyerrors/fits.py
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@ -482,15 +482,15 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
chol_inv = np.linalg.inv(chol)
chol_inv = np.dot(chol_inv, covdiag)
def chisqfunc(p):
def chisqfunc_corr(p):
model = func(p, x)
chisq = anp.sum(anp.dot(chol_inv, (y_f - model)) ** 2)
return chisq
else:
def chisqfunc(p):
model = func(p, x)
chisq = anp.sum(((y_f - model) / dy_f) ** 2)
return chisq
def chisqfunc(p):
model = func(p, x)
chisq = anp.sum(((y_f - model) / dy_f) ** 2)
return chisq
output.method = kwargs.get('method', 'Levenberg-Marquardt')
if not silent:
@ -499,27 +499,32 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
if output.method != 'Levenberg-Marquardt':
if output.method == 'migrad':
fit_result = iminuit.minimize(chisqfunc, x0, tol=1e-4) # Stopping criterion 0.002 * tol * errordef
if kwargs.get('correlated_fit') is True:
fit_result = iminuit.minimize(chisqfunc_corr, fit_result.x, tol=1e-4) # Stopping criterion 0.002 * tol * errordef
output.iterations = fit_result.nfev
else:
fit_result = scipy.optimize.minimize(chisqfunc, x0, method=kwargs.get('method'), tol=1e-12)
if kwargs.get('correlated_fit') is True:
fit_result = scipy.optimize.minimize(chisqfunc_corr, fit_result.x, method=kwargs.get('method'), tol=1e-12)
output.iterations = fit_result.nit
chisquare = fit_result.fun
else:
if kwargs.get('correlated_fit') is True:
def chisqfunc_residuals(p):
def chisqfunc_residuals_corr(p):
model = func(p, x)
chisq = anp.dot(chol_inv, (y_f - model))
return chisq
else:
def chisqfunc_residuals(p):
model = func(p, x)
chisq = ((y_f - model) / dy_f)
return chisq
def chisqfunc_residuals(p):
model = func(p, x)
chisq = ((y_f - model) / dy_f)
return chisq
fit_result = scipy.optimize.least_squares(chisqfunc_residuals, x0, method='lm', ftol=1e-15, gtol=1e-15, xtol=1e-15)
if kwargs.get('correlated_fit') is True:
fit_result = scipy.optimize.least_squares(chisqfunc_residuals_corr, fit_result.x, method='lm', ftol=1e-15, gtol=1e-15, xtol=1e-15)
chisquare = np.sum(fit_result.fun ** 2)