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| author | Matthias Bussonnier <bussonniermatthias@gmail.com> | 2022-07-08 15:36:07 +0200 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2022-07-08 16:36:07 +0300 |
| commit | bb9e79fa27037da4db161ff7d1907a29f7cc5714 (patch) | |
| tree | 47822a30ae66ecadca7ff639843eae20fc611401 /numpy/polynomial | |
| parent | 7cd7fc643ac3cc49dea43583ab4a8ecf8164b9cc (diff) | |
| download | numpy-bb9e79fa27037da4db161ff7d1907a29f7cc5714.tar.gz | |
DOC: Double backticks in lagfit. (#21948)
Some of the values in the documentation of lagfit are in single-backticks,
though single backticks usually means that this is a reference to
something else that sphinx tries to resolve. Here I update values that
reference nothing to use double backticks (verbatim), or emphasis.
Diffstat (limited to 'numpy/polynomial')
| -rw-r--r-- | numpy/polynomial/laguerre.py | 14 |
1 files changed, 7 insertions, 7 deletions
diff --git a/numpy/polynomial/laguerre.py b/numpy/polynomial/laguerre.py index 5d058828d..2eacceced 100644 --- a/numpy/polynomial/laguerre.py +++ b/numpy/polynomial/laguerre.py @@ -1282,7 +1282,7 @@ def lagfit(x, y, deg, rcond=None, full=False, w=None): .. math:: p(x) = c_0 + c_1 * L_1(x) + ... + c_n * L_n(x), - where `n` is `deg`. + where ``n`` is `deg`. Parameters ---------- @@ -1317,8 +1317,8 @@ def lagfit(x, y, deg, rcond=None, full=False, w=None): ------- coef : ndarray, shape (M,) or (M, K) Laguerre coefficients ordered from low to high. If `y` was 2-D, - the coefficients for the data in column k of `y` are in column - `k`. + the coefficients for the data in column *k* of `y` are in column + *k*. [residuals, rank, singular_values, rcond] : list These values are only returned if ``full == True`` @@ -1355,7 +1355,7 @@ def lagfit(x, y, deg, rcond=None, full=False, w=None): Notes ----- - The solution is the coefficients of the Laguerre series `p` that + The solution is the coefficients of the Laguerre series ``p`` that minimizes the sum of the weighted squared errors .. math:: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2, @@ -1365,10 +1365,10 @@ def lagfit(x, y, deg, rcond=None, full=False, w=None): .. math:: V(x) * c = w * y, - where `V` is the weighted pseudo Vandermonde matrix of `x`, `c` are the + where ``V`` is the weighted pseudo Vandermonde matrix of `x`, ``c`` are the coefficients to be solved for, `w` are the weights, and `y` are the observed values. This equation is then solved using the singular value - decomposition of `V`. + decomposition of ``V``. If some of the singular values of `V` are so small that they are neglected, then a `RankWarning` will be issued. This means that the @@ -1378,7 +1378,7 @@ def lagfit(x, y, deg, rcond=None, full=False, w=None): spurious and have large contributions from roundoff error. Fits using Laguerre series are probably most useful when the data can - be approximated by ``sqrt(w(x)) * p(x)``, where `w(x)` is the Laguerre + be approximated by ``sqrt(w(x)) * p(x)``, where ``w(x)`` is the Laguerre weight. In that case the weight ``sqrt(w(x[i]))`` should be used together with data values ``y[i]/sqrt(w(x[i]))``. The weight function is available as `lagweight`. |