On Wed, Jan 26, 2011 at 7:22 AM, eat <e.antero.ta...@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? > > 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) Josef > > Regards, > eat > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
