Public Member Functions
	PrecomputedPatternResponse (int n_bins)

void	allocate (int samples, int feats)
	allocate the response matrix

template<class TrainintSetType , class FeatureType >
void	Precompute (const TrainintSetType &set, const std::vector< FeatureType > &feats, int max_concurrent_jobs)

void	PrepareBins ()
	compute bin

void	InitializeWeights ()
	Initialize ugual weights.

template<class Classifier >
void	ExecuteClassifier (const Classifier &c, bool correct_tables, double _wp, double _wn, int _match) const

Public Attributes
int	n_samples
	number of samples

int	n_features
	number of feature preallocated

int *	category
	category list

double *	weights
	weights list

ResponseType *	response
	response matrix. For performance reason it each row is a feature and columns are samples

std::pair< ResponseType, ResponseType > *	response_range
	precomputed response ranges

int *	bin
	precomputed bin matrix (rows features, columns samples)

int	number_of_bins
	Quantization level.

Member Function Documentation

template<class ResponseType>

template<class Classifier >

void PrecomputedPatternResponse< ResponseType >::ExecuteClassifier	(	const Classifier &	c,
		bool *	correct_tables,
		double *	_wp,
		double *	_wn,
		int *	_match
	)		const

inline

Run an Indirect Classifier (classifier running on precomputed feature response) and report statistics

Parameters

c	a binary classifier
correct_tables	an array of bool, n_samples long
_wp,_wn,_match	pointer to a single variable, modified in exit

template<class ResponseType>

template<class TrainintSetType , class FeatureType >

void PrecomputedPatternResponse< ResponseType >::Precompute	(	const TrainintSetType &	set,
		const std::vector< FeatureType > &	feats,
		int	max_concurrent_jobs
	)

inline

Parameters

feats a vector of features to be precomputed

Note: it is possible to Precompute again features keeping old weights if the size is the same

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

Public Member Functions