[Numpy-discussion] Apply a vector function to each row of a matrix

[David Huard]

On Thu, Oct 9, 2008 at 2:48 PM, Neal Becker <ndbecker2@gmail.com> wrote:

> David Huard wrote:
>
> > On Thu, Oct 9, 2008 at 9:40 AM, Neal Becker <ndbecker2@gmail.com> wrote:
> >
> >> David Huard wrote:
> >>
> >> > Neal,
> >> >
> >> > Look at: apply_along_axis
> >> >
> >> >
> >> I guess it'd be:
> >>
> >> b = empty_like(a)
> >> for row in a.shape[0]:
> >>  b[row,:] = apply_along_axis (func, row, a)
> >>
> >
> >> I don't suppose there is a way to do this without explicitly writing a
> >> loop.
> >
> >
> > Have you tried
> >
> > b = apply_along_axis(func, 1, a)
> >
> > It should work.
> >
> Yes, thanks.
>
> The doc for apply_along_axis is not clear.
>

> For one thing, it says:
> The output array. The shape of outarr depends on the return value of
> func1d. If it returns arrays with the same shape as the input arrays it
> receives, outarr has the same shape as arr.
>
> What happens if the 'if' clause is not true?
>

The shape along the axis is determined by the result's shape of your
function.

def func(x):
   ...:     return x[::2]
   ...:

> [3]: a = random.rand(3,4)

> [4]: a
 <[4]:
array([[ 0.95979758,  0.37350614,  0.77423741,  0.62520089],
       [ 0.69060211,  0.91480227,  0.60105525,  0.20184552],
       [ 0.31540644,  0.19919848,  0.72567385,  0.63987393]])

> [5]: apply_along_axis(func, 1, a)
 <[5]:
array([[ 0.95979758,  0.77423741],
       [ 0.69060211,  0.60105525],
       [ 0.31540644,  0.72567385]])

I've edited the docstring at
http://sd-2116.dedibox.fr/pydocweb/doc/numpy.lib.shape_base.apply_along_axis/

Feel free to improve on it.

David


>
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