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feat: correlated_fit now throws an exception when the correlation matrix

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is ill conditioned with respect to the machine precision.

Criterion for warning for ill-conditioned covariance matrix changed to cond > sqrt(eps)

Test added.
This commit is contained in:
Fabian Joswig 2022-03-09 12:24:52 +00:00
parent a069f84264
commit e6813a6c8e
2 changed files with 10 additions and 2 deletions
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6
pyerrors/fits.py
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@ -464,8 +464,10 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
corr = covariance(y, correlation=True)
covdiag = np.diag(1 / np.asarray(dy_f))
condn = np.linalg.cond(corr)
if condn > 1e8:
warnings.warn("Correlation matrix may be ill-conditioned, condition number: %1.2e" % (condn), RuntimeWarning)
if condn > 0.1 / np.finfo(float).eps:
raise Exception(f"Cannot invert correlation matrix as its condition number exceeds machine precision ({condn:1.2e})")
if condn > 1 / np.sqrt(np.finfo(float).eps):
warnings.warn("Correlation matrix may be ill-conditioned, condition number: {%1.2e}" % (condn), RuntimeWarning)
chol = np.linalg.cholesky(corr)
chol_inv = np.linalg.inv(chol)
chol_inv = np.dot(chol_inv, covdiag)

6
tests/fits_test.py
View file

@ -375,6 +375,12 @@ def test_fit_no_autograd():
pe.total_least_squares(oy, oy, func)
def test_singular_correlated_fit():
obs1 = pe.pseudo_Obs(1.0, 0.1, 'test')
with pytest.raises(Exception):
pe.fits.fit_lin([0, 1], [obs1, obs1], correlated_fit=True)
def test_ks_test():
def f(a, x):
y = a[0] + a[1] * x