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docs: warning about ill conditioned hessian more detailed.

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This commit is contained in:
Fabian Joswig 2022-05-25 15:20:31 +01:00
parent 967ddb0ecd
commit 48636fedb2
1 changed files with 4 additions and 2 deletions
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6
pyerrors/fits.py
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@ -269,7 +269,8 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
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)
warnings.warn("Hessian matrix might be ill-conditioned ({0:1.2e}), error propagation might be unreliable.\n \
Maybe try rescaling the problem such that all parameters are of O(1).".format(condn), RuntimeWarning)
try:
hess_inv = np.linalg.inv(hess)
except np.linalg.LinAlgError:
@ -556,7 +557,8 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
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)
warnings.warn("Hessian matrix might be ill-conditioned ({0:1.2e}), error propagation might be unreliable.\n \
Maybe try rescaling the problem such that all parameters are of O(1).".format(condn), RuntimeWarning)
try:
hess_inv = np.linalg.inv(hess)
except np.linalg.LinAlgError: