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Diffstat (limited to 'numpy/polynomial/chebyshev.py')
| -rw-r--r-- | numpy/polynomial/chebyshev.py | 16 |
1 files changed, 7 insertions, 9 deletions
diff --git a/numpy/polynomial/chebyshev.py b/numpy/polynomial/chebyshev.py index 6745c9371..210000ec4 100644 --- a/numpy/polynomial/chebyshev.py +++ b/numpy/polynomial/chebyshev.py @@ -1149,9 +1149,6 @@ def chebval(x, c, tensor=True): ----- The evaluation uses Clenshaw recursion, aka synthetic division. - Examples - -------- - """ c = np.array(c, ndmin=1, copy=True) if c.dtype.char in '?bBhHiIlLqQpP': @@ -1585,10 +1582,11 @@ def chebfit(x, y, deg, rcond=None, full=False, w=None): default) just the coefficients are returned, when True diagnostic information from the singular value decomposition is also returned. w : array_like, shape (`M`,), optional - Weights. If not None, the contribution of each point - ``(x[i],y[i])`` to the fit is weighted by `w[i]`. Ideally the - weights are chosen so that the errors of the products ``w[i]*y[i]`` - all have the same variance. The default value is None. + Weights. If not None, the weight ``w[i]`` applies to the unsquared + residual ``y[i] - y_hat[i]`` at ``x[i]``. Ideally the weights are + chosen so that the errors of the products ``w[i]*y[i]`` all have the + same variance. When using inverse-variance weighting, use + ``w[i] = 1/sigma(y[i])``. The default value is None. .. versionadded:: 1.5.0 @@ -1952,8 +1950,8 @@ def chebpts1(npts): if _npts < 1: raise ValueError("npts must be >= 1") - x = np.linspace(-np.pi, 0, _npts, endpoint=False) + np.pi/(2*_npts) - return np.cos(x) + x = 0.5 * np.pi / _npts * np.arange(-_npts+1, _npts+1, 2) + return np.sin(x) def chebpts2(npts): |