On Thursday, 4 December 2014 07:27:21 UTC+1, Nathann Cohen wrote:
>
> Also, I do not understand why you have so many expressions like:
>
> (val*bm.transpose())[0,0]
>
val*bm.transpose() is actually a number but the way Sage handles it is
awkward:
sage: M = Matrix(RR,[[1],[1]])
sage: Matrix(RR,[[-1,0]])*M
[-1.00000000000000]
sage: abs(Matrix(RR,[[-1,0]])*M)
-1.00000000000000 # some norm of the matrix??
soo I either have to do abs(abs(...)) or access the (only) element of the
matrix.
>
> If you have performance problems, do not compute a whole matrix if you
> are only interested by its [0,0] coordinate O_o
>
> You call j.transpose() repeatedly. Store jt=j.transpose() and use it.
>
> Store and use bm.transpose() instead of bm. You tanspose bm when you
> use it in the first loop, and you transpose bm() again in
> vec2int[j].transpose().
>
> Also, given that the profiling does not say much of where 99% of the
> computations is going, try to run your code without the .write() calls
> (and their internal str() calls). Make it run without storing anything
> and see how it goes. Perhaps you should store the results directly as
> Sage objects using "save" and not as strings... No idea, you need to
> test things ^^;
>
Thanks for the suggestions I'll check them out.
>
> Nathann
>
> On 4 December 2014 at 03:44, Nils Bruin <nbr...@sfu.ca <javascript:>>
> wrote:
> > On Wednesday, December 3, 2014 1:33:46 PM UTC-8, Jernej wrote:
> >>
> >>
> >> for i in xrange(1, cur):
> >> for j in xrange(i+1, cur):
> >> iv = (cache[i]*vec2int[j].transpose())[0,0]
> >
> >
> > It looks like you should rewrite this loop so that j is the out
> variable, so
> > that you can pull
> > vec2int[j].transpose()
> > to the out loop. No need to cache it.
> >
> > In any case, your profiling data indicates:
> >
> > 1 85.572 85.572 113.024 113.024
> > <string>:9(constructGraph_fast)
> > 3733128 10.413 0.000 10.413 0.000
> > matrix_space.py:145(__classcall__)
> > 1604418 9.434 0.000 19.921 0.000 {method 'transpose' of
> > 'sage.matrix.matrix_dense.Matrix_dense' objects}
> > 1604418 3.186 0.000 4.174 0.000 {method '__copy__' of
> > 'sage.matrix.matrix_generic_dense.Matrix_generic_dense' objects}
> >
> > so most time is spent in "constructGraph_fast", which I guess is your
> > program itself. The transpose is a bit noticeable but hardly constitutes
> the
> > majority of the time. The construction for the matrix spaces is incurred
> > twice: both for the transpose and for computing the product. By pulling
> the
> > transpose to the outside loop that should roughly be cut in half (and
> you'd
> > only have the sqrt of the number of transposes).
> >
> > It looks like most time is getting lost in basic python interpretation
> and
> > shuffling around elements. If you make a big matrix out of cache[i]
> vectors,
> > you can eliminate the loop over i. The only thing left would be
> inspecting
> > the elements, for which you can then write a simple cython routine
> (which
> > should be lightning fast).
>
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