diff options
Diffstat (limited to 'numpy/lib/polynomial.py')
| -rw-r--r-- | numpy/lib/polynomial.py | 21 |
1 files changed, 11 insertions, 10 deletions
diff --git a/numpy/lib/polynomial.py b/numpy/lib/polynomial.py index 420ec245b..0fd9bbd79 100644 --- a/numpy/lib/polynomial.py +++ b/numpy/lib/polynomial.py @@ -426,7 +426,7 @@ def polyder(p, m=1): >>> np.polyder(p, 3) poly1d([6]) >>> np.polyder(p, 4) - poly1d([0.]) + poly1d([0]) """ m = int(m) @@ -494,11 +494,12 @@ 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/sqrt(N-dof), 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. + 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 ------- @@ -753,11 +754,11 @@ def polyval(p, x): >>> np.polyval([3,0,1], 5) # 3 * 5**2 + 0 * 5**1 + 1 76 >>> np.polyval([3,0,1], np.poly1d(5)) - poly1d([76.]) + poly1d([76]) >>> np.polyval(np.poly1d([3,0,1]), 5) 76 >>> np.polyval(np.poly1d([3,0,1]), np.poly1d(5)) - poly1d([76.]) + poly1d([76]) """ p = NX.asarray(p) @@ -1016,7 +1017,7 @@ def polydiv(u, v): (array([1.5 , 1.75]), array([0.25])) """ - truepoly = (isinstance(u, poly1d) or isinstance(u, poly1d)) + truepoly = (isinstance(u, poly1d) or isinstance(v, poly1d)) u = atleast_1d(u) + 0.0 v = atleast_1d(v) + 0.0 # w has the common type @@ -1235,7 +1236,7 @@ class poly1d: raise ValueError("Polynomial must be 1d only.") c_or_r = trim_zeros(c_or_r, trim='f') if len(c_or_r) == 0: - c_or_r = NX.array([0.]) + c_or_r = NX.array([0], dtype=c_or_r.dtype) self._coeffs = c_or_r if variable is None: variable = 'x' |