[mlpack-svn] [MLPACK] #273: Support for instantiated kernels and metrics is somewhat poor

MLPACK Trac trac at coffeetalk-1.cc.gatech.edu
Thu Apr 25 16:52:04 EDT 2013

#273: Support for instantiated kernels and metrics is somewhat poor
 Reporter:  rcurtin  |        Owner:                                                    
     Type:  defect   |       Status:  new                                               
 Priority:  major    |    Milestone:  mlpack 1.1.0                                      
Component:  mlpack   |     Keywords:  metric, kernel, instantiated, mahalanobis distance
 Blocking:           |   Blocked By:                                                    
 In many situations it is useful to have a kernel or metric that saves
 state.  One instance is the Mahalanobis distance, which must store the
 covariance matrix.  Another instance is the polynomial kernel and
 hyperbolic tangent kernel, which each have runtime parameters that cannot
 be template parameters (doubles not ints).

 This means that all methods that depend on some sort of kernel or metric
 must be able to seamlessly work with instantiated kernels and metrics.
 Right now, all things work well with kernels and metrics that do not store
 any state (that is, they can be called statically).

 This is something of a master ticket aimed at making this support correct
 and consistent across mlpack.  There will probably be some reverse
 compatibility issues so the non-reverse-compatible changes should be saved
 until mlpack 1.1.0.

Ticket URL: <http://trac.research.cc.gatech.edu/fastlab/ticket/273>
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.

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