[mlpack-svn] [MLPACK] #270: LSH test doesn't work across systems

[MLPACK Trac]

#270: LSH test doesn't work across systems
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  Reporter:  rcurtin                                |        Owner:              
      Type:  defect                                 |       Status:  new         
  Priority:  minor                                  |    Milestone:  mlpack 1.0.5
 Component:  mlpack                                 |   Resolution:              
  Keywords:  lsh, test, probabilistic test, random  |     Blocking:              
Blocked By:                                         |  
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Description changed by rcurtin:

Old description:

> Currently the LSH test operates by setting the random seed specifically
> to 0 (for that test) and then running it and ensuring that the results
> are the same as they are expected to be for that random seed.
> Unfortunately, the results may vary across platforms and versions of
> Boost (because the random number generator code can change).
>
> In r14346 I've commented out the BOOST_REQUIRE statements in the test
> because it is known to not work on different configurations; the test now
> basically just tests whether or not LSH compiles and whether or not it
> runs without exceptions.
>
> Ideally we should construct some sort of probabilistic test; if we can
> find something that LSH guarantees probabilistically (for instance, rank-
> approximate nearest neighbors guarantees the approximate rank of the
> neighbors with a known, calculable probability), then we can write a test
> for that easily.

New description:

 Currently the LSH test operates by setting the random seed specifically to
 0 (for that test) and then running it and ensuring that the results are
 the same as they are expected to be for that random seed.  Unfortunately,
 the results may vary across platforms and versions of Boost (because the
 random number generator code may change in different versions).

 In r14346 I've commented out the BOOST_REQUIRE statements in the test
 because it is known to not work on different configurations; the test now
 basically just tests whether or not LSH compiles and whether or not it
 runs without exceptions.

 Ideally we should construct some sort of probabilistic test; if we can
 find something that LSH guarantees probabilistically (for instance, rank-
 approximate nearest neighbors guarantees the approximate rank of the
 neighbors with a known, calculable probability), then we can write a test
 for that easily.

--

-- 
Ticket URL: <http://trac.research.cc.gatech.edu/fastlab/ticket/270#comment:1>
MLPACK <www.fast-lab.org>
MLPACK is an intuitive, fast, and scalable C++ machine learning library developed by the FASTLAB at Georgia Tech under Dr. Alex Gray.