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| Processor< Tag, LabelType, T1, C1, T2, C2 > Class Template Reference |

`#include <vigra/random_forest/rf_preprocessing.hxx>`

## Detailed Description

### template<class Tag, class LabelType, class T1, class C1, class T2, class C2>
class vigra::Processor< Tag, LabelType, T1, C1, T2, C2 >

Class used while preprocessing (currently used only during learn)

This class is internally used by the Random Forest learn function. Different split functors may need to process the data in different manners (i.e., regression labels that should not be touched and classification labels that must be converted into a integral format)

This Class only exists in specialized versions, where the Tag class is fixed.

The Tag class is determined by Splitfunctor::Preprocessor_t . Currently it can either be ClassificationTag or RegressionTag. look At the RegressionTag specialisation for the basic interface if you ever happen to care.... - or need some sort of vague new preprocessor. new preprocessor ( Soft labels or whatever)

The documentation for this class was generated from the following file:

- vigra/random_forest/rf_preprocessing.hxx

© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany | html generated using doxygen and Python vigra 1.11.0 (Fri May 19 2017)