GEP::ClassificationMOIndividual Class Reference

#include <MOIndividual.h>

Inheritance diagram for GEP::ClassificationMOIndividual:

Collaboration diagram for GEP::ClassificationMOIndividual:

Public Member Functions
	ClassificationMOIndividual ()
	ClassificationMOIndividual (const ClassificationMOIndividual &)
	ClassificationMOIndividual (const ClassificationMOIndividual &, bool tf)
virtual	~ClassificationMOIndividual ()
bool	classify ()
void	evaluate (const u_short &user_param=eval_type(0), const size_t pop_age=0)
std::vector< double >	calculateFitness (const size_t pop_age=0)
void	reset ()
std::string	toString () const
Static Public Member Functions
static u_short	solutionSize ()
Public Attributes
u_int	TP
u_int	FN
u_int	FP
u_int	TN
bool	zero_output
Static Public Attributes
static double	roundingThreshold = 0.5

Constructor & Destructor Documentation

GEP::ClassificationMOIndividual::ClassificationMOIndividual ( )

Default constructor

GEP::ClassificationMOIndividual::ClassificationMOIndividual ( const ClassificationMOIndividual & copy )

Copy constructor

GEP::ClassificationMOIndividual::ClassificationMOIndividual	(	const ClassificationMOIndividual &	,
		bool	tf
	)

Half-copy constructor

virtual GEP::ClassificationMOIndividual::~ClassificationMOIndividual ( ) [inline, virtual]

Default destructor

Member Function Documentation

std::vector< double > GEP::ClassificationMOIndividual::calculateFitness ( const size_t pop_age = 0 ) [virtual]

Uses specificity and sensitivity measure:

 mo_fitness.first = ((TP/(TP+FN)), mo_fitness.second = (TN/(TN+FP))).

Maximum fitness = 1 and 1.

Reimplemented from GEP::Individual< std::vector< double >, double, u_short >.

bool GEP::ClassificationMOIndividual::classify ( )

Classification uses rounding threshold to obtain predicted result (0 or 1) (see evaluate()).

void GEP::ClassificationMOIndividual::evaluate	(	const u_short &	user_param = `eval_type(0)`,
		const size_t	pop_age = `0`
	)			`[virtual]`

Evaluation uses classify() output which is then compared with actual output passed in user_param (actual).

   if (_chromosome.getValue() < roundingThreshold) predicted = 0; else predicted 1;
   if(actual == 0) { //Positive
   if(predicted == 1) FP++;
   else TP++;
   }
   else { //Negative
   if(predicted == 1) TN++;
   else FN++;
   }