diff options
Diffstat (limited to 'numpy/lib/polynomial.py')
| -rw-r--r-- | numpy/lib/polynomial.py | 14 |
1 files changed, 7 insertions, 7 deletions
diff --git a/numpy/lib/polynomial.py b/numpy/lib/polynomial.py index e9df783b4..56fcce621 100644 --- a/numpy/lib/polynomial.py +++ b/numpy/lib/polynomial.py @@ -494,11 +494,11 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False): cov : bool or str, optional If given and not `False`, return not just the estimate but also its covariance matrix. By default, the covariance are scaled by - chi2/dof, where dof = M - (deg + 1), i.e., the weights are presumed - to be unreliable except in a relative sense and everything is scaled - such that the reduced chi2 is unity. This scaling is omitted if - ``cov='unscaled'``, as is relevant for the case that the weights are - 1/sigma**2, with sigma known to be a reliable estimate of the + chi2/dof, where dof = M - (deg + 1), i.e., the weights are presumed + to be unreliable except in a relative sense and everything is scaled + such that the reduced chi2 is unity. This scaling is omitted if + ``cov='unscaled'``, as is relevant for the case that the weights are + 1/sigma**2, with sigma known to be a reliable estimate of the uncertainty. Returns @@ -1394,9 +1394,9 @@ class poly1d: def __getitem__(self, val): ind = self.order - val if val > self.order: - return 0 + return self.coeffs.dtype.type(0) if val < 0: - return 0 + return self.coeffs.dtype.type(0) return self.coeffs[ind] def __setitem__(self, key, val): |