Minutes 040607: Difference between revisions

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* Agree to
* Agree to
** Sample derive from DataObject
** Sample derive from DataObject
** SampleAlgorithmBase derive from DataObject
** SampleAlgorithmBase derive from ProcessObject
* Generators  
* Generators  
** Only the ones that do not need inputs
** Only the ones that do not need inputs
Line 17: Line 17:
* Deprecation Strategy
* Deprecation Strategy
** Keep the old classes, delegating to the new classes?
** Keep the old classes, delegating to the new classes?
*** Better for maintenance.
** Keep the old classes around, with a CMake configuration option?
** Keep the old classes around, with a CMake configuration option?
*** Brings the complication of requiring duplicate dashboard to ensure that both versions of the code still work.
** ListSampleBase
*** Candidate for deprecation
*** Search algorithm
** Distances
*** Have them as plug in objects or as Functor
** KdTree
*** Maybe not to derive from DataObject ?
*** Maybe should derive from Sample ?
*** Identify Use cases before deciding.
*** Allow other distance (non-Euclidean) to be used.
** Classifiers
*** Do not quite fit in a pipeline style...
*** We need to study Use Cases.
*** Some in Statistics / Some in Algorithms (for Images).


== Action Items ==
== Action Items ==

Latest revision as of 19:02, 9 April 2007

Attendees

  • Jim Miller
  • Nick Tuicston
  • Luis Ibanez

Discussion

  • Statistics
    • Jim suggests to add a cross-sample
  • Agree to
    • Sample derive from DataObject
    • SampleAlgorithmBase derive from ProcessObject
  • Generators
    • Only the ones that do not need inputs
    • That comes down to the random number generators
  • Deprecation Strategy
    • Keep the old classes, delegating to the new classes?
      • Better for maintenance.
    • Keep the old classes around, with a CMake configuration option?
      • Brings the complication of requiring duplicate dashboard to ensure that both versions of the code still work.
    • ListSampleBase
      • Candidate for deprecation
      • Search algorithm
    • Distances
      • Have them as plug in objects or as Functor
    • KdTree
      • Maybe not to derive from DataObject ?
      • Maybe should derive from Sample ?
      • Identify Use cases before deciding.
      • Allow other distance (non-Euclidean) to be used.
    • Classifiers
      • Do not quite fit in a pipeline style...
      • We need to study Use Cases.
      • Some in Statistics / Some in Algorithms (for Images).

Action Items