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feat: a warning is now issued when an estimated covariance matrix is not

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positive semi-definite. Docstrings extended.
This commit is contained in:
Fabian Joswig 2022-02-24 16:12:37 +00:00
parent b960ce0e3d
commit 24a0df6a2a
2 changed files with 17 additions and 6 deletions
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15
pyerrors/fits.py
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@ -652,7 +652,13 @@ def residual_plot(x, y, func, fit_res):
def covariance_matrix(y): def covariance_matrix(y):
"""Returns the covariance matrix of y.""" """Returns the covariance matrix of y.
Parameters
----------
y : list or numpy.ndarray
List or one dimensional array of Obs
"""
length = len(y) length = len(y)
cov = np.zeros((length, length)) cov = np.zeros((length, length))
for i, item in enumerate(y): for i, item in enumerate(y):
@ -661,7 +667,12 @@ def covariance_matrix(y):
cov[i, j] = item.dvalue ** 2 cov[i, j] = item.dvalue ** 2
else: else:
cov[i, j] = covariance(item, jtem) cov[i, j] = covariance(item, jtem)
return cov + cov.T - np.diag(np.diag(cov)) cov = cov + cov.T - np.diag(np.diag(cov))
eigenvalues = np.linalg.eigh(cov)[0]
if not np.all(eigenvalues >= 0):
warnings.warn("Covariance matrix is not positive semi-definite", RuntimeWarning)
print("Eigenvalues of the covariance matrix:", eigenvalues)
return cov
def error_band(x, func, beta): def error_band(x, func, beta):

8
pyerrors/obs.py
View file

@ -1341,10 +1341,10 @@ def covariance(obs1, obs2, correlation=False, **kwargs):
If abs(covariance(obs1, obs2)) > obs1.dvalue * obs2.dvalue, the covariance If abs(covariance(obs1, obs2)) > obs1.dvalue * obs2.dvalue, the covariance
is constrained to the maximum value. is constrained to the maximum value.
Keyword arguments Parameters
----------------- ----------
correlation -- if true the correlation instead of the covariance is correlation : bool
returned (default False) if true the correlation instead of the covariance is returned (default False)
""" """
def expand_deltas(deltas, idx, shape, new_idx): def expand_deltas(deltas, idx, shape, new_idx):