Hi,

On Wed, Jan 26, 2011 at 2:35 PM, <josef.pktd@gmail.com> wrote:
On Wed, Jan 26, 2011 at 7:22 AM, eat <e.antero.tammi@gmail.com> wrote:
> Hi,
>
> I just noticed a document/ implementation conflict with tril and triu.
> According tril documentation it should return of same shape and data-type as
> called. But this is not the case at least with dtype bool.
>
> The input shape is referred as (M, N) in tril and triu, but as (N, M) in
> tri.
> Inconsistent?
Any comments about the names for rows and cols. I prefer (M, N).
>
> Also I'm not very happy with the performance, at least dtype bool can be
> accelerated as follows.
>
> In []: M= ones((2000, 3000), dtype= bool)
> In []: timeit triu(M)
> 10 loops, best of 3: 173 ms per loop
> In []: timeit triu_(M)
> 10 loops, best of 3: 107 ms per loop
>
> In []: M= asarray(M, dtype= int)
> In []: timeit triu(M)
> 10 loops, best of 3: 160 ms per loop
> In []: timeit triu_(M)
> 10 loops, best of 3: 163 ms per loop
>
> In []: M= asarray(M, dtype= float)
> In []: timeit triu(M)
> 10 loops, best of 3: 195 ms per loop
> In []: timeit triu_(M)
> 10 loops, best of 3: 157 ms per loop
>
> I have attached a crude 'fix' incase someone is interested.

You could open a ticket for this.

just one comment:
I don't think this is readable, especially if we only look at the
source of the function with np.source

out= mul(ge(so(ar(m.shape[0]), ar(m.shape[1])), -k), m)

from np.source(np.tri) with numpy 1.5.1
m = greater_equal(subtract.outer(arange(N), arange(M)),-k)
I agree, thats why I called it crude. Before opening a ticket I'll try to figure out if there exists somewhere in numpy .astype functionality, but not copying if allready proper dtype.
 
Also I'm afraid that I can't produce sufficient testing.

Regards, eat

Josef

>
> Regards,
> eat
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