#include <FeatureSelector.h>
Inheritance diagram for prapi::SequentialSelector:
This class is capable of performing (feature) subset selection with the SFS, SFFS, SBS, and SBFS methods. In fact, due to the abstraction of performance evaluation, this class serves just as an algorithm that can be used in selecting a suboptimal subset from just about anything. A common example is finding a suboptimal subset of model samples from a large number of candidates.
Public Methods  
SequentialSelector (GoodnessMeasure &measure, bool forward=true, bool floating=true)  
Create a new (feature) selector.  
void  setParams (bool forward, bool floating) 
Set search parameters.  
bool  isFloating () const 
See if the search is of "floating" type.  
bool  isForward () const 
See if the search goes forward.  
util::List< int >  optimize (int totalCount, int desiredCount=1) 
Find a suboptimal subset of items. 

Create a new (feature) selector. The different types of selection schemes can be achieved with the following parameter combinations: Method  forward floating + SBS  false false SBFS  false true SFS  true false SFFS  true true


Find a suboptimal subset of items. The implementation of this method is methoddependent. By convention, each implementation should fire a SelectionEvent every time an optimization round is finished.
Implements prapi::SubSetSelector. 