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DOC: make curve_fit nomenclature same as leastsq #3187

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merged 1 commit into from

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Carl Sandrock Ralf Gommers
Carl Sandrock

The curve_fit help used N for the number of data points and M for the number of parameters, but lasts which has these reversed. Fixes issue #3172

Carl Sandrock alchemyst DOC: make curve_fit nomenclature same as leastsq
The curve_fit help used N for the number of data points and M for the number of parameters, but lasts which has these reversed. Fixes issue #3172
848dea0
Ralf Gommers rgommers merged commit 40896a5 into from
Ralf Gommers
Owner

Thanks @alchemyst, merged.

Travis failure unrelated, some mpmath tests that randomly failed.

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Commits on Jan 5, 2014
  1. Carl Sandrock

    DOC: make curve_fit nomenclature same as leastsq

    alchemyst authored
    The curve_fit help used N for the number of data points and M for the number of parameters, but lasts which has these reversed. Fixes issue #3172
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Showing with 4 additions and 4 deletions.
  1. +4 −4 scipy/optimize/minpack.py
8 scipy/optimize/minpack.py
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@@ -461,17 +461,17 @@ def curve_fit(f, xdata, ydata, p0=None, sigma=None, absolute_sigma=False, **kw):
The model function, f(x, ...). It must take the independent
variable as the first argument and the parameters to fit as
separate remaining arguments.
- xdata : An N-length sequence or an (k,N)-shaped array
+ xdata : An M-length sequence or an (k,M)-shaped array
for functions with k predictors.
The independent variable where the data is measured.
- ydata : N-length sequence
+ ydata : M-length sequence
The dependent data --- nominally f(xdata, ...)
- p0 : None, scalar, or M-length sequence
+ p0 : None, scalar, or N-length sequence
Initial guess for the parameters. If None, then the initial
values will all be 1 (if the number of parameters for the function
can be determined using introspection, otherwise a ValueError
is raised).
- sigma : None or N-length sequence, optional
+ sigma : None or M-length sequence, optional
If not None, these values are used as weights in the
least-squares problem.
absolute_sigma : bool, optional
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