On 21/03/07, Andrew Corrigan <[EMAIL PROTECTED]> wrote:
> Thanks for pointing that out. Technically that works, but it doesn't really
> express this operation as concisely and as naturally as I'd like to be able
> to.
>
> What I really want is to be able to write:
>
> >>> a = array([lambda x: x**
Good point! I think I will, Thanks a lot.
___
Numpy-discussion mailing list
Numpy-discussion@scipy.org
http://projects.scipy.org/mailman/listinfo/numpy-discussion
On Mar 21, 2007, at 6:58 AM, Anne Archibald wrote:
> Vectorizing apply is what you're looking for, by the sound of it:
> In [13]: a = array([lambda x: x**2, lambda x: x**3])
>
> In [14]: b = arange(5)
>
> In [15]: va = vectorize(lambda f, x: f(x))
>
> In [16]: va(a[:,newaxis],b[newaxis,:])
> Out[
On Wednesday 21 March 2007 09:52, Andrew Corrigan wrote:
> Anne Archibald gmail.com> writes:
> > Vectorizing apply is what you're looking for, by the sound of it:
> > In [13]: a = array([lambda x: x**2, lambda x: x**3])
> >
> > In [14]: b = arange(5)
> >
> > In [15]: va = vectorize(lambda f, x: f(
Anne Archibald gmail.com> writes:
> Vectorizing apply is what you're looking for, by the sound of it:
> In [13]: a = array([lambda x: x**2, lambda x: x**3])
>
> In [14]: b = arange(5)
>
> In [15]: va = vectorize(lambda f, x: f(x))
>
> In [16]: va(a[:,newaxis],b[newaxis,:])
> Out[16]:
> array([
On 21/03/07, Andrew Corrigan <[EMAIL PROTECTED]> wrote:
> This is a feature I've been wanting for a long time, so I'm really glad that
> Shane brought this up.
>
> While I was hoping for a gain in speed, that isn't the only reason that I
> would
> like to see this added. In fact, the most compel
Robert Kern gmail.com> writes:
>
> Shane Holloway wrote:
> > To the vector-processing masters of numpy!
> >
> > I'm wanting to optimize calling a list (or array) of callable
> > objects. Consider the following:
> >
> > vCallables = numpy.array([ > classes, builtin functions>])
> > vParam1
Shane Holloway wrote:
> To the vector-processing masters of numpy!
>
> I'm wanting to optimize calling a list (or array) of callable
> objects. Consider the following:
>
> vCallables = numpy.array([ classes, builtin functions>])
> vParam1 = numpy.array([])
> vParam2 = numpy.array([])
> vParam3
To the vector-processing masters of numpy!
I'm wanting to optimize calling a list (or array) of callable
objects. Consider the following:
vCallables = numpy.array([])
vParam1 = numpy.array([])
vParam2 = numpy.array([])
vParam3 = numpy.array([])
vResults = numpy.array([None for e in vCallables]
