FYI I've opened an issue to generate the reference documentation of
applications with Shark and document these options:

https://bugs.orfeo-toolbox.org/view.php?id=1400

2017-05-12 10:06 GMT+02:00 Jordi Inglada <jordi.ingl...@cesbio.eu>:

> Hi Nafissa,
>
> You can build OTB with Shark. If you use the superbuild, you can
> activate the cmake configuration
>
> OTB_USE_SHARK:BOOL=ON
>
> which will build Shark as an OTB dependency and then build OTB to use
> Shark.
>
> Make sure that OTB is also compiled with
>
> OTB_USE_SHARK:BOOL=ON
>
> If you just reconfigure the superbuild after building without Shark, I
> think Shark is built, but OTB is not rebuilt to use Shark.
>
> Once everything is OK, you should find that for instance in
> otbcli_TrainVectorClassifier the -classifier options allows to choose
> between "rf" and "sharkrf". The first one is OpenCV's.
>
> Now, just some feedback. Shark is supposed to be faster in the learning
> step because the trees are learned in parallel (by the way, make sure
> that OpenMP is used for both Shark and OTB, check the CMakeCache.txt
> files in the build directories). Actually, in some cases, Shark can be
> way slower than OpenCV. There are several reasons for that. There are
> some inefficiencies in Shark when the number of features is very high (a
> solution is in progress, see
> https://github.com/Shark-ML/Shark/issues/179 for details). Also, Shark
> builds full trees, while OpenCV stops at a chosen depth (25 by default).
>
> Good luck.
>
> Jordi
>
> On Thu 11-May-2017 at 15:09:38 +0200, Nafissa Sfaksi <nsfa...@gmail.com>
> wrote:
> > Hello,
> >
> > I'am trying to improve my process chain for Sentinel 2 with OTB, and as
> you said few weeks before, there're two implementations of the Random
> Forest in OTB;
> > My question is about how can I choose between opencv and shark libs? and
> if it's possible to force the process to use shark instead of opencv?
> >
> > Best regards
> >
> > Le mardi 28 mars 2017 15:31:11 UTC+2, Manuel Grizonnet a écrit :
> >
> >  Hi,
> >
> >  note first there are 2 implementations of random forests now available
> in OTB since release 5.8. One from openCV and the other one from the shark
> machine learning library (which allows parallel training). Both
> implementations are wrappers around those libraries and you generally
> >  have to dig into libraries documentation to find out such information.
> >
> >  Regarding the criteria, I think that both opencv and shark
> implementations use the Gini criterion (for shark I'm quite sure). Would be
> glad if someone who know a bit more those implementations can confirm this.
> >
> >  Note also that I don't think that we have a way in otb to change this
> criteria with shark implementation even if there is a way to do this in
> shark base class. Perhaps something that could be added to the
> SharkMachineLearningModel in the future.
> >
> >  Regards,
> >
> >  2017-03-27 9:46 GMT+02:00 Nafissa Sfaksi <nsf...@gmail.com>:
> >
> >  Hi,
> >  With the tool TrainClassifier we can put several parameters but is it
> possible to see which criteria is used for the method?
> >  I mean the tool use Gini criterion or entropy criterion?
> >  Thanks for reply
> >  Nafissa
> >
> >  --
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> >  --
> >  Manuel Grizonnet
> >
> > --
>
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-- 
Manuel Grizonnet

-- 
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