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refactor: computation of inverse cholesky composition for correlated

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fits simplified. Threshold for ill conditioned correlation matrix
warning raised to 1e8.
This commit is contained in:
Fabian Joswig 2022-03-03 10:10:29 +00:00
parent b14314b424
commit b6eed60ec1
1 changed files with 5 additions and 9 deletions
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14
pyerrors/fits.py
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@ -458,15 +458,11 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
x0 = [0.1] * n_parms x0 = [0.1] * n_parms
if kwargs.get('correlated_fit') is True: if kwargs.get('correlated_fit') is True:
cov = covariance(y) corr = covariance(y, correlation=True)
covdiag = np.diag(1. / np.sqrt(np.diag(cov))) covdiag = np.diag(1 / np.asarray(dy_f))
corr = np.copy(cov)
for i in range(len(y)):
for j in range(len(y)):
corr[i][j] = cov[i][j] / np.sqrt(cov[i][i] * cov[j][j])
condn = np.linalg.cond(corr) condn = np.linalg.cond(corr)
if condn > 1e4: if condn > 1e8:
warnings.warn("Correlation matrix may be ill-conditioned! condition number: %1.2e" % (condn), RuntimeWarning) warnings.warn("Correlation matrix may be ill-conditioned, condition number: %1.2e" % (condn), RuntimeWarning)
chol = np.linalg.cholesky(corr) chol = np.linalg.cholesky(corr)
chol_inv = np.linalg.inv(chol) chol_inv = np.linalg.inv(chol)
chol_inv = np.dot(chol_inv, covdiag) chol_inv = np.dot(chol_inv, covdiag)
@ -487,7 +483,7 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
if output.method != 'Levenberg-Marquardt': if output.method != 'Levenberg-Marquardt':
if output.method == 'migrad': if output.method == 'migrad':
fit_result = iminuit.minimize(chisqfunc, x0, tol=1e-4) # Stopping crieterion 0.002 * tol * errordef fit_result = iminuit.minimize(chisqfunc, x0, tol=1e-4) # Stopping criterion 0.002 * tol * errordef
output.iterations = fit_result.nfev output.iterations = fit_result.nfev
else: else:
fit_result = scipy.optimize.minimize(chisqfunc, x0, method=kwargs.get('method'), tol=1e-12) fit_result = scipy.optimize.minimize(chisqfunc, x0, method=kwargs.get('method'), tol=1e-12)