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

#include <ebl_answer.h>

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

List of all members.

Public Member Functions
	class_answer (uint nclasses, double target_factor=1.0, bool binary_target=false, t_confidence conf=confidence_max, bool apply_tanh=false, const char *name="class_answer", int force_class=-1)
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 bool	correct (Tstate &answer, Tstate &label)
	Returns true if 'answer' matches with 'label'.
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
idx< T >	targets
	The targets for training.
idx< T >	target
	Temporary buffer for 1 target.
t_confidence	conf_type
	The confidence type.
T	conf_ratio
	Ratio to normalize confidence to 1.
T	conf_shift
	to be subtracted before div conf_ratio
bool	binary_target
	Target is binary or not.
bool	resize_output
	Resize output or not.
bool	apply_tanh
	If true, apply tanh to inputs.
Tstate	tmp
	Temporary buffer.
bbstate_idx< Tds2 >	last_label
	Last label set by fprop.
tanh_module< T, Tstate >	mtanh
	A tanh module.
T	target_min
T	target_max
int	force_class

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Detailed Description

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

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Constructor & Destructor Documentation

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

ebl::class_answer< T, Tds1, Tds2, Tstate >::class_answer	(	uint	nclasses,
		double	target_factor = 1.0,
		bool	binary_target = false,
		t_confidence	conf = confidence_max,
		bool	apply_tanh = false,
		const char *	name = "class_answer< T, Tds1, Tds2, Tstate >",
		int	force_class = -1
	)

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.
force_class	If >= 0, force answers to this class.

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Member Function Documentation

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

void ebl::class_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::answer_module< T, Tds1, Tds2, Tstate >.

Reimplemented in ebl::scalerclass_answer< T, Tds1, Tds2, Tstate >, and ebl::vote_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