[mlpack-svn] [MLPACK] #314: HMM<GMM < > > does not scale to more than 7 dimensions for the observations for unsupervised training

[MLPACK Trac]

#314: HMM<GMM < > >  does not scale to more than 7 dimensions for the
observations for unsupervised training
--------------------------+-------------------------------------------------
  Reporter:  fleischhauf  |        Owner:  rcurtin 
      Type:  defect       |       Status:  accepted
  Priority:  major        |    Milestone:          
 Component:  mlpack       |   Resolution:          
  Keywords:               |     Blocking:          
Blocked By:               |  
--------------------------+-------------------------------------------------
Changes (by rcurtin):

  * milestone:  mlpack 1.0.9 =>


Comment:

 Hi Nik,

 I've spent the past couple days trying to reproduce this issue, but I
 can't.  I found a system with the same configuration (Ubuntu 13.10,
 x86_64, same gcc version, same Boost version, same Armadillo version;
 different processor though).  I ran with valgrind in both debug and non-
 debug mode, but valgrind reported no invalid accesses or memory issues.
 In addition, I couldn't get it to segfault.

 Then, I slightly modified the main executable to set the random seed to
 `std::time(NULL)`, to see if it was an odd problem caused by the
 particular way the random data was created.  I tried running this for the
 past couple of days (probably hundreds of runs) but was unable to ever
 produce a random seed that could cause a segfault.  If the issue was an
 Armadillo issue, it should have issued an error because the program was
 compiled in debug mode, but I don't see any Armadillo error either.

 Without any ability to reproduce the issue, I'm leaning towards this being
 a hardware issue; maybe a bit of bad RAM or something like that.  I'm
 sorry I can't give a better answer than that, but unless you have a way to
 reproduce the issue on multiple machines I can't actually debug it any
 further.

 Alternately, you could run the program in gdb, and procure a backtrace
 when the segfault occurs.  That is what I was going to do if I could
 reproduce the issue.

 Sorry for the long delay in response to this; it took me a while to find
 time to dig up a system with the same specs.

-- 
Ticket URL: <http://trac.research.cc.gatech.edu/fastlab/ticket/314#comment:10>
MLPACK <www.fast-lab.org>
MLPACK is an intuitive, fast, and scalable C++ machine learning library developed at Georgia Tech.