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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: if not m.fmin.is_valid:
raise Exception('The minimization procedure did not converge.') 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): def chisqfunc_compact(d):
model = func(d[:n_parms], x) model = func(d[:n_parms], x)
@ -424,6 +427,8 @@ def _prior_fit(x, y, func, priors, silent=False, **kwargs):
return chisq return chisq
jac_jac = jacobian(jacobian(chisqfunc_compact))(np.concatenate((params, y_f, p_f))) 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:] 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) hess = jacobian(jacobian(chisqfunc))(fitp)
except TypeError: except TypeError:
raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None 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: if kwargs.get('correlated_fit') is True:
def chisqfunc_compact(d): def chisqfunc_compact(d):
@ -605,6 +612,8 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
return chisq return chisq
jac_jac = jacobian(jacobian(chisqfunc_compact))(np.concatenate((fitp, y_f))) 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 # Compute hess^{-1} @ jac_jac[:n_parms, n_parms:] using LAPACK dgesv
try: try: