Re: [eigen] Documentation : it's a sprint!!

On Wed, Jul 7, 2010 at 10:30 AM, Carlos Becker <carlosbecker@xxxxxxxxx> wrote:

Ok, finally back to the reductions/visitors tutorial. I was thinking of including a nice example in the end, using broadcasting and partial reductions to find the nearest neighbour of a vector between all the columns in a given matrix, with something like:

VectorXf v;
MatrixXf m;

int index;
(m.colwise() - v).array().square().colwise().sum().minCoeff(&index);

I think it would give a good insight of Eigen and, since this is tutorial number 7, the user should already have a good background on Eigen. I could explain what is happening at each '.' in the previous _expression_ to make it clear and show how powerful Eigen is.

However, I know that this might be a bit advanced for a starters' tutorial, so I will wait for your thoughts on this.

Cheers,
Carlos

On Thu, Jul 1, 2010 at 5:12 PM, Carlos Becker <carlosbecker@xxxxxxxxx> wrote:
Thanks. It was to know what to put in each subsection since I made one for visitors, one for reductions and one for broadcasting. So now it is clear. Thanks

On Thu, Jul 1, 2010 at 3:10 PM, Benoit Jacob <jacob.benoit.1@xxxxxxxxx> wrote:

2010/7/1 Carlos Becker <carlosbecker@xxxxxxxxx>:

> I thought that with matrix.colwise().sum(), colwise() is treated as a
> visitor, and with += it is a broadcasting operation. This is because I read
> this on your tutorial proposal:
> 7. Reductions, visitors, and broadcasting
> - for all kinds of matrices/arrays
> - sum() etc...
> - mention partial reductions with .colwise()...
> - mention broadcasting e.g. m.colwise() += vector;
> I thought that partial reductions were done with visitors and broadcasting
> was something similar but with 'write access'. Or maybe both are visitors, I
> guess I am missing many concepts here.

computing a sum is a reduction (or partial reduction) not a visitor.

a visitor is when you want to find, as output, a location inside of a matrix.

For example, this is a reduction:

result = matrix.maxCoeff();

this is a visitor:

Index i, j;
matrix.maxCoeff(&i, &j);

Anyway, don't bother learning a new area of Eigen just to write docs
about it :-)

Benoit

>
>
> On Thu, Jul 1, 2010 at 2:46 PM, Benoit Jacob <jacob.benoit.1@xxxxxxxxx>
> wrote:
>>
>> Broadcasting means e.g.
>>
>> matrix.colwise() += vector;
>>
>> Visitors are e.g.
>>
>> Index i, j;
>> matrix.maxCoeff(&i, &j);
>>
>> These are two really different things, no?
>> Benoit
>>
>> 2010/7/1 Carlos Becker <carlosbecker@xxxxxxxxx>:
>> > Just a quick question: what would be the difference between visitors and
>> > broadcasting? Seems to me that broadcasting is able to 'visit' column or
>> > row-wise, also modifying the data inside the matrix/array object, am I
>> > right? I haven't used broadcasting with eigen before.
>> >
>> >
>> >
>> > On Wed, Jun 30, 2010 at 1:30 PM, Carlos Becker <carlosbecker@xxxxxxxxx>
>> > wrote:
>> >>
>> >> oh ok sorry, got confused since I thought that someone was alreay
>> >> writing
>> >> the sparse tut
>> >>
>> >>
>> >> On Wed, Jun 30, 2010 at 1:29 PM, Gael Guennebaud
>> >> <gael.guennebaud@xxxxxxxxx> wrote:
>> >>>
>> >>> nevermind, this C07_TutorialSparse.dox file is an old one...
>> >>>
>> >>> gael
>> >>>
>> >>> On Wed, Jun 30, 2010 at 2:26 PM, Carlos Becker
>> >>> <carlosbecker@xxxxxxxxx>
>> >>> wrote:
>> >>> > I am starting with the reductions/visitors/broadcasting tutorial and
>> >>> > just
>> >>> > noticed that the sparse tutorial is named as C07_TutorialSparse.dox.
>> >>> > According to the order in http://eigen.tuxfamily.org/dox-devel/ it
>> >>> > should be
>> >>> > C08 and C07 is to be the one I am doing. This is a silly question
>> >>> > but
>> >>> > just
>> >>> > wanted to make sure we are all following the same conventions.
>> >>> > Carlos
>> >>> >
>> >>> > On Sun, Jun 27, 2010 at 1:59 PM, Gael Guennebaud
>> >>> > <gael.guennebaud@xxxxxxxxx>
>> >>> > wrote:
>> >>> >>
>> >>> >> On Sun, Jun 27, 2010 at 12:41 PM, Carlos Becker
>> >>> >> <carlosbecker@xxxxxxxxx>
>> >>> >> wrote:
>> >>> >> > Mmm I am trying to think of a straightforward explanation for
>> >>> >> > this.
>> >>> >> > What
>> >>> >> > do
>> >>> >> > you think about calling them fixed-size and dynamic-size blocks,
>> >>> >> > where
>> >>> >> > the
>> >>> >> > former differs from the later because its size is known at
>> >>> >> > compile-time.
>> >>> >>
>> >>> >> a slightly more precise variant: "the latter are optimized versions
>> >>> >> of
>> >>> >> the former when the size is known at compile-time." I just added
>> >>> >> the
>> >>> >> word "optimized"
>> >>> >>
>> >>> >> you might also have a look at the reference tables, section "Sub
>> >>> >> matrices" to see how they are presented.. The old version of this
>> >>> >> section is available online there:
>> >>> >>
>> >>> >>
>> >>> >>
>> >>> >>
>> >>> >> http://eigen.tuxfamily.org/dox-devel/TutorialCore.html#TutorialCoreMatrixBlocks
>> >>> >>
>> >>> >> gael
>> >>> >>
>> >>> >> >
>> >>> >> >
>> >>> >> > On Sun, Jun 27, 2010 at 11:02 AM, Carlos Becker
>> >>> >> > <carlosbecker@xxxxxxxxx>
>> >>> >> > wrote:
>> >>> >> >>
>> >>> >> >> Yes, I got that and actually it was my mistake since I supposed
>> >>> >> >> that it
>> >>> >> >> was only for fixed-size matrices, so now I am changing it.
>> >>> >> >> Thanks,
>> >>> >> >>
>> >>> >> >>
>> >>> >> >> 2010/6/27 Björn Piltz <bjornpiltz@xxxxxxxxxxxxxx>
>> >>> >> >>>
>> >>> >> >>> "The following tables show a summary of Eigen's block
>> >>> >> >>> operations
>> >>> >> >>> and
>> >>> >> >>> how
>> >>> >> >>> they are applied to fixed- and dynamic-sized Eigen objects."
>> >>> >> >>> This quote and the following table gives the impression that
>> >>> >> >>> the
>> >>> >> >>> fixed
>> >>> >> >>> size functions are only available for fixed size matrices. But
>> >>> >> >>> using
>> >>> >> >>> fixed
>> >>> >> >>> size vs dynamic size functions acually only determin the return
>> >>> >> >>> type.
>> >>> >> >>> Björn
>> >>> >> >
>> >>> >> >
>> >>> >>
>> >>> >>
>> >>> >
>> >>> >
>> >>>
>> >>>
>> >>
>> >
>> >
>>
>>
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