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feat: implemented eigenvalue smoothing method of hep-lat/9412087
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2 changed files with 27 additions and 2 deletions
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@ -461,7 +461,7 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
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x0 = [0.1] * n_parms
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if kwargs.get('correlated_fit') is True:
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corr = covariance(y, correlation=True)
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corr = covariance(y, correlation=True, **kwargs)
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covdiag = np.diag(1 / np.asarray(dy_f))
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condn = np.linalg.cond(corr)
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if condn > 1e8:
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