[Numpy-discussion] vectorization of vectorization
Thu Aug 19 16:32:42 CDT 2010
This seems simple, so perhaps I am missing something, but what about this:
special.iv(orders[:, newaxis], arange(1e6)[newaxis, : ], powers[:,
newaxis]).prod(axis = 2)
This will probably use up a bit slow/memoryintensive, so you probably want
to use numexpr to speed it up a bit.
On Thu, Aug 19, 2010 at 2:22 PM, sm lkd <email@example.com> wrote:
> Here's my problem: for each value t of an array (from 0 to 1e6) a smaller
> array is computed (size between 2-6). To compute the smaller array, I have a
> function (which can be easily vectorized if necessary) which takes t and an
> array of powers of t. The return is an array of modified Bessel function
> values, i.e.:
> def funct(order, t, power):
> return special.iv(order, t)**power
> Note that order and power are arrays after this vectorization:
> vec_func = sp.vectorization(func)
> Right this is how it's used:
> for i in range(1000000):
> y[i] = vec_func(orders, t, powers).prod()
> Incredibly slow.
> Of course, it is desirable to vectorize it it terms of t. I have tried
> different methods but still cannot make it work. Any suggestions or ointers?
> Thank you.
> NumPy-Discussion mailing list
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