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fix: pinv replaced by inv for inversion of hessian in fit module, exceptions and warnings added.

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This commit is contained in:
Fabian Joswig 2022-05-25 14:32:49 +01:00
parent f62581ba69
commit 9ad0146d96
1 changed files with 18 additions and 2 deletions
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20
pyerrors/fits.py
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@ -263,10 +263,18 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
output.chisquare_by_expected_chisquare)
fitp = out.beta
hess = jacobian(jacobian(odr_chisquare))(np.concatenate((fitp, out.xplus.ravel())))
condn = np.linalg.cond(hess)
if condn > 1e8:
warnings.warn("Hessian matrix might be ill-conditioned ({0:1.2e}), error propagation might be unreliable.".format(condn), RuntimeWarning)
try:
hess_inv = np.linalg.pinv(jacobian(jacobian(odr_chisquare))(np.concatenate((fitp, out.xplus.ravel()))))
hess_inv = np.linalg.inv(hess)
except TypeError:
raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
except np.linalg.LinAlgError:
raise Exception("Cannot invert hessian matrix.")
except Exception:
raise Exception("Unkown error in connection with Hessian inverse.")
def odr_chisquare_compact_x(d):
model = func(d[:n_parms], d[n_parms:n_parms + m].reshape(x_shape))
@ -541,10 +549,18 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
output.chisquare_by_expected_chisquare)
fitp = fit_result.x
hess = jacobian(jacobian(chisqfunc))(fitp)
condn = np.linalg.cond(hess)
if condn > 1e8:
warnings.warn("Hessian matrix might be ill-conditioned ({0:1.2e}), error propagation might be unreliable.".format(condn), RuntimeWarning)
try:
hess_inv = np.linalg.pinv(jacobian(jacobian(chisqfunc))(fitp))
hess_inv = np.linalg.inv(hess)
except TypeError:
raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
except np.linalg.LinAlgError:
raise Exception("Cannot invert hessian matrix.")
except Exception:
raise Exception("Unkown error in connection with Hessian inverse.")
if kwargs.get('correlated_fit') is True:
def chisqfunc_compact(d):