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feat: rounding errors in inversion of cholesky decomposed correlation
matrix reduced.
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1 changed files with 1 additions and 2 deletions
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@ -477,8 +477,7 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
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if condn > 1 / np.sqrt(np.finfo(float).eps):
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warnings.warn("Correlation matrix may be ill-conditioned, condition number: {%1.2e}" % (condn), RuntimeWarning)
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chol = np.linalg.cholesky(corr)
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chol_inv = np.linalg.inv(chol)
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chol_inv = np.dot(chol_inv, covdiag)
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chol_inv = scipy.linalg.solve(chol, covdiag)
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def chisqfunc_corr(p):
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model = func(p, x)
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