libeblearn
Public Member Functions
ebl::vote_answer< T, Tds1, Tds2, Tstate > Class Template Reference

#include <ebl_answer.h>

Inheritance diagram for ebl::vote_answer< T, Tds1, Tds2, Tstate >:
ebl::class_answer< T, Tds1, Tds2, Tstate > ebl::answer_module< T, Tds1, Tds2, Tstate > ebl::module_1_1< T, Tstate > ebl::module

List of all members.

Public Member Functions

 vote_answer (uint nclasses, double target_factor=1.0, bool binary_target=false, t_confidence conf=confidence_max, bool apply_tanh=false, const char *name="vote_answer")
virtual void fprop (Tstate &in, Tstate &out)

Detailed Description

template<typename T, typename Tds1 = T, typename Tds2 = T, class Tstate = bbstate_idx<T>>
class ebl::vote_answer< T, Tds1, Tds2, Tstate >

This module produces answers based on voting of multiple answers. It assumes multiple network outputs have been concatenated in its input.

Constructor & Destructor Documentation

template<typename T , typename Tds1 , typename Tds2 , class Tstate >
ebl::vote_answer< T, Tds1, Tds2, Tstate >::vote_answer ( uint  nclasses,
double  target_factor = 1.0,
bool  binary_target = false,
t_confidence  conf = confidence_max,
bool  apply_tanh = false,
const char *  name = "vote_answer< T, Tds1, Tds2, Tstate >" 
)

Initialize target vectors given the number of classes.

Parameters:
nclassesThe number of classes for classification.
target_factorA factor applied to targets.
binary_targetIf true, target is a scalar with -1 or 1.
confThe type of confidence.
apply_tanhIf true, a tanh is applied to inputs.

Member Function Documentation

template<typename T , typename Tds1 , typename Tds2 , class Tstate >
void ebl::vote_answer< T, Tds1, Tds2, Tstate >::fprop ( Tstate &  in,
Tstate &  out 
) [virtual]

Produce a vector of answers given input 'in'. 'out' contains answers in this order: class id and confidence.

Reimplemented from ebl::class_answer< T, Tds1, Tds2, Tstate >.

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