Re: [Numpy-discussion] speeding up y[:, i] for y.shape = (big number, small number)

[Tim H. <tim...@ie...>]

David Cournapeau wrote:
> Hi,
>
>     I was wondering if there was any way to speed up the following code:
>
>  y   = N.zeros((n, K))
>  for i in range(K):
>      y[:, i] = gauss_den(data, mu[i, :], va[i, :])
>
> Where K is of order 1e1, n of order 1e5. Normally, gauss_den is a quite 
> expensive function, but the profiler tells me that the indexing y[:,i] 
> takes almost as much time as the gauss_den computation (which computes n 
> exp !). To see if the profiler is "right", i replaces with the (non 
> valid) following function:
>
>  y   = N.zeros((n, K))
>     for i in range(K):
>         yt = gauss_den(data, mu[i, :], va[i, :])
>     return y
>
> Where more than 99% of the code is spent inside gauss_den.
>
> I guess the problem is coming from the fact that y being C order, y[:, 
> i] needs accessing data in a non 'linear' way. Is there a way to speed 
> this up ? I did something like this:
>
>   y   = N.zeros((K, n))
>     for i in range(K):
>         y[i] = gauss_den(data, mu[i, :], va[i, :])
>     return y.T
>
> which works, but I don't like it very much.
Why not?

>  Isn't there any other way 
That depends on the details of gauss_den.

A general note: for this kind of microoptimization puzzle, it's much 
easier to help if you can post a self contained example, preferably 
something fairly simple that still illustrates the speed issue, that we 
can experiment with.


-tim