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fix: index of num diff jacobian in least squares fit corrected.

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Fabian Joswig 2022-10-05 17:54:25 +01:00
parent ee4149e498
commit 99e130d33c
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1 changed files with 10 additions and 1 deletions
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11
pyerrors/fits.py
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@ -416,7 +416,10 @@ def _prior_fit(x, y, func, priors, silent=False, **kwargs):
if not m.fmin.is_valid:
raise Exception('The minimization procedure did not converge.')
hess_inv = np.linalg.pinv(jacobian(jacobian(chisqfunc))(params))
hess = jacobian(jacobian(chisqfunc))(params)
if kwargs.get('num_grad') is True:
hess = hess[0]
hess_inv = np.linalg.pinv(hess)
def chisqfunc_compact(d):
model = func(d[:n_parms], x)
@ -424,6 +427,8 @@ def _prior_fit(x, y, func, priors, silent=False, **kwargs):
return chisq
jac_jac = jacobian(jacobian(chisqfunc_compact))(np.concatenate((params, y_f, p_f)))
if kwargs.get('num_grad') is True:
jac_jac = jac_jac[0]
deriv = -hess_inv @ jac_jac[:n_parms, n_parms:]
@ -591,6 +596,8 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
hess = jacobian(jacobian(chisqfunc))(fitp)
except TypeError:
raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
if kwargs.get('num_grad') is True:
hess = hess[0]
if kwargs.get('correlated_fit') is True:
def chisqfunc_compact(d):
@ -605,6 +612,8 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
return chisq
jac_jac = jacobian(jacobian(chisqfunc_compact))(np.concatenate((fitp, y_f)))
if kwargs.get('num_grad') is True:
jac_jac = jac_jac[0]
# Compute hess^{-1} @ jac_jac[:n_parms, n_parms:] using LAPACK dgesv
try: