ebl::scalerclass_answer< T, Tds1, Tds2, Tstate > Class Template Reference

#include <ebl_answer.h>

Inheritance diagram for ebl::scalerclass_answer< T, Tds1, Tds2, Tstate >:

List of all members.

Public Member Functions
	scalerclass_answer (uint nclasses, double target_factor=1.0, bool binary_target=false, t_confidence conf=confidence_max, bool apply_tanh=false, uint jsize=3, uint joffset=0, float mgauss=1.5, bool predict_conf=false, bool predict_bconf=false, idx< T > *biases=NULL, idx< T > *coeffs=NULL, const char *name="scalerclass_answer")
virtual void	fprop (Tstate &in, Tstate &out)
virtual void	fprop (labeled_datasource< T, Tds1, Tds2 > &ds, Tstate &out)
	Produce target matrix into 'out' for training, given a datasource 'ds'.
virtual void	update_log (classifier_meter &log, intg age, idx< T > &energy, idx< T > &answer, idx< T > &label, idx< T > &target, idx< T > &rawout)
	Update the 'log' according to this type of answer module.
virtual std::string	describe ()
	Returns a string describing this module and its parameters.
Protected Attributes
bbstate_idx< T >	jitter
	A temp buffer to extract jitter info to.
Tstate	out_class
	Temporary class buffer.
Tstate	tmp1
	Temporary buffer.
Tstate	tmp2
	Temporary buffer.
uint	jsize
	Number of jitter elements used.
uint	joffset
	Offset in jitter elements.
float	scale_mgauss
	Mean of scale confidence gaussian.
bool	predict_conf
	Predict confidence.
bool	predict_bconf
	Predicted confidence is binary or not.
uint	pconf_offset
	Offset of predicted confidence feature.
std::vector< std::string >	log_fields
	Names of variables to log.
idx< T > *	biases
	Normalization biases: scale,h,w,conf.
idx< T > *	coeffs
	Normalization coeffs: scale,h,w,conf.

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Detailed Description

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

This module gathers information from a labeled_datasource 'ds' and outputs a state of type 'Tstate'. The output state is a 1-of-n target vector given the discret label of the sample concatenated with an additional scale element.

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Constructor & Destructor Documentation

template<typename T , typename Tds1 , typename Tds2 , class Tstate >

ebl::scalerclass_answer< T, Tds1, Tds2, Tstate >::scalerclass_answer	(	uint	nclasses,
		double	target_factor = 1.0,
		bool	binary_target = false,
		t_confidence	conf = confidence_max,
		bool	apply_tanh = false,
		uint	jsize = 3,
		uint	joffset = 0,
		float	mgauss = 1.5,
		bool	predict_conf = false,
		bool	predict_bconf = false,
		idx< T > *	biases = NULL,
		idx< T > *	coeffs = NULL,
		const char *	name = "scalerclass_answer< T, Tds1, Tds2, Tstate >"
	)

Initialize target vectors given the number of classes.

Parameters:

nclasses	The number of classes for classification.
target_factor	A factor applied to targets.
binary_target	If true, target is a scalar with -1 or 1.
conf	The type of confidence.
apply_tanh	If true, a tanh is applied to inputs (targets only, not scale input).
jsize	The number of jitter elements to use.
joffset	The offset to start using jitters.
predict_conf	Add an extra feature for confidence prediction.
predict_bconf	If true, predicted confidence is binary (0,1).

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Member Function Documentation

template<typename T , typename Tds1 , typename Tds2 , class Tstate >

void ebl::scalerclass_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 >.

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

• /home/rex/ebltrunk/core/libeblearn/include/ebl_answer.h
• /home/rex/ebltrunk/core/libeblearn/include/ebl_answer.hpp