Abstract class, defines interface for feature selection criterion implementations based on normal model. More...

#include <criterion_normal.hpp>

Inheritance diagram for FST::Criterion_Normal< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >:

Public Types
typedef std::shared_ptr< DATAACCESSOR >	PDataAccessor
	shared pointer to the data accessor the model is learned from
typedef std::shared_ptr< SUBSET >	PSubset
	shared pointer to the feature subset being evaluated
Public Types inherited from FST::Criterion_Filter< RETURNTYPE, SUBSET >
typedef std::shared_ptr< SUBSET >	PSubset
	shared pointer to the feature subset being evaluated
Public Types inherited from FST::Criterion< RETURNTYPE, SUBSET >
typedef std::shared_ptr< SUBSET >	PSubset
	shared pointer to the feature subset being evaluated

Public Member Functions
virtual bool	initialize (PDataAccessor da)
	template method: learn model, compute _meandif, then allocate_buffers() + update_derived_model().
virtual bool	evaluate (RETURNTYPE &result, const PSubset sub) override
	template method: narrow model and _meandif to sub, then evaluate_narrowed()
virtual bool	is_monotonic () const override
DIMTYPE	get_n () const
	the full (raw, unnarrowed) feature count the model was learned for
DIMTYPE	get_d () const
	the currently active (narrowed) feature count, see evaluate()
virtual std::ostream &	print (std::ostream &os) const override
	writes a human-readable identification of the object; base default writes nothing
Public Member Functions inherited from FST::Criterion< RETURNTYPE, SUBSET >
Criterion *	clone () const override=0
Criterion *	sharing_clone () const override
Criterion *	stateless_clone () const override
Public Member Functions inherited from FST::Clonable
bool	is_clone () const
bool	is_sharing () const
	true if this instance is a sharing_clone() result (referencing, not owning, the source's structures)
bool	is_sharing_clone () const
	true if this instance is BOTH a clone and sharing – i.e. specifically a sharing_clone() result, not a clone()/stateless_clone() one

Protected Member Functions
	Criterion_Normal (const Criterion_Normal &cn)
	Copy-constructor; deep-copies _model and _meandif (subclasses copy their own buffers separately).
virtual const char *	criterion_name () const =0
	class name, used in output and error messages
virtual void	allocate_buffers ()=0
	(re)allocate criterion-specific work buffers for get_n() features (use the alloc_* helpers)
virtual void	update_derived_model ()=0
virtual bool	evaluate_narrowed (RETURNTYPE &result, const PSubset sub)=0
	narrow remaining buffers to sub, redim work buffers to get_d(), compute the criterion value. On entry _model and _meandif have ALREADY been narrowed to `sub` by evaluate(), so the implementation sees only the d selected features; get_d() is that d, and _meandif->at(i) addresses the narrowed positions. `sub` is non-empty (evaluate() rejects d==0 before the call).
void	alloc_vector (std::unique_ptr< Indexed_Vector< DATATYPE, DIMTYPE, SUBSET > > &v)
	lazy (re)allocation of a work vector for get_n() features, for use from allocate_buffers()
template<typename MATRIXVALUETYPE>
void	alloc_matrix (std::unique_ptr< Indexed_Matrix< MATRIXVALUETYPE, DIMTYPE, SUBSET > > &m, const bool LUstorage=false)
	lazy (re)allocation of a work matrix for get_n() features; LUstorage as required by Indexed_Matrix::LUdecompose() temp storage
Protected Member Functions inherited from FST::Clonable
void	set_cloned (const bool cloned=true)
	marks/unmarks this instance as a clone; call from clone()/stateless_clone() overrides (Clonable_Impl does this automatically)
void	set_sharing (const bool sharing=true)
	marks/unmarks this instance as sharing (see is_sharing())
void	set_sharing_cloned (const bool cloned=true, const bool sharing=true)
	sets both flags at once; call from sharing_clone() overrides (Clonable_Impl does this automatically)

Protected Attributes
std::unique_ptr< Model_Normal< DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR > >	_model
	the learned per-class normal (mean/covariance) model, narrowed in evaluate()
std::unique_ptr< Indexed_Vector< DATATYPE, DIMTYPE, SUBSET > >	_meandif
	mean[0]-mean[1], shared by all two-class normal criteria

Detailed Description

template<class RETURNTYPE, typename DATATYPE, typename REALTYPE, typename IDXTYPE, typename DIMTYPE, class SUBSET, class DATAACCESSOR>
class FST::Criterion_Normal< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >

Abstract class, defines interface for feature selection criterion implementations based on normal model.

Note: This class also provides the template-method skeleton shared by the concrete two-class normal criteria (Bhattacharyya, Divergence, GMahalanobis): initialize() learns the model, allocates buffers and computes the shared mean-difference vector before delegating criterion-specific pre-computation to hooks; evaluate() narrows the model and delegates the math to evaluate_narrowed(). Subclasses keep only their buffers, their pre-computation and their math.; All these criteria are defined for TWO-CLASS data only - initialize() throws fst_error when the data accessor reports a different number of classes.

Member Function Documentation

◆ allocate_buffers()

template<class RETURNTYPE, typename DATATYPE, typename REALTYPE, typename IDXTYPE, typename DIMTYPE, class SUBSET, class DATAACCESSOR>

virtual void FST::Criterion_Normal< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >::allocate_buffers ( )

protectedpure virtual

(re)allocate criterion-specific work buffers for get_n() features (use the alloc_* helpers)

Implemented in FST::Criterion_Normal_Bhattacharyya< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, FST::Criterion_Normal_Divergence< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, and FST::Criterion_Normal_GMahalanobis< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >.

◆ criterion_name()

template<class RETURNTYPE, typename DATATYPE, typename REALTYPE, typename IDXTYPE, typename DIMTYPE, class SUBSET, class DATAACCESSOR>

virtual const char * FST::Criterion_Normal< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >::criterion_name ( ) const

protectedpure virtual

class name, used in output and error messages

Implemented in FST::Criterion_Normal_Bhattacharyya< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, FST::Criterion_Normal_Divergence< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, and FST::Criterion_Normal_GMahalanobis< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >.

◆ evaluate()

template<class RETURNTYPE, typename DATATYPE, typename REALTYPE, typename IDXTYPE, typename DIMTYPE, class SUBSET, class DATAACCESSOR>

bool FST::Criterion_Normal< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >::evaluate	(	RETURNTYPE &	result,
		const PSubset	sub )

overridevirtual

template method: narrow model and _meandif to sub, then evaluate_narrowed()

Implements FST::Criterion< RETURNTYPE, SUBSET >.

◆ evaluate_narrowed()

template<class RETURNTYPE, typename DATATYPE, typename REALTYPE, typename IDXTYPE, typename DIMTYPE, class SUBSET, class DATAACCESSOR>

virtual bool FST::Criterion_Normal< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >::evaluate_narrowed	(	RETURNTYPE &	result,
		const PSubset	sub )

protectedpure virtual

narrow remaining buffers to sub, redim work buffers to get_d(), compute the criterion value. On entry _model and _meandif have ALREADY been narrowed to sub by evaluate(), so the implementation sees only the d selected features; get_d() is that d, and _meandif->at(i) addresses the narrowed positions. sub is non-empty (evaluate() rejects d==0 before the call).

Implemented in FST::Criterion_Normal_Bhattacharyya< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, FST::Criterion_Normal_Divergence< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, and FST::Criterion_Normal_GMahalanobis< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >.

◆ initialize()

template<class RETURNTYPE, typename DATATYPE, typename REALTYPE, typename IDXTYPE, typename DIMTYPE, class SUBSET, class DATAACCESSOR>

bool FST::Criterion_Normal< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >::initialize ( PDataAccessor da )

virtual

template method: learn model, compute _meandif, then allocate_buffers() + update_derived_model().

Exceptions

fst_error if da does not describe exactly 2 classes.

Returns: true (failures are reported by throwing)

◆ is_monotonic()

template<class RETURNTYPE, typename DATATYPE, typename REALTYPE, typename IDXTYPE, typename DIMTYPE, class SUBSET, class DATAACCESSOR>

virtual bool FST::Criterion_Normal< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >::is_monotonic ( ) const

inlineoverridevirtual

all three normal-model filters (Bhattacharyya, Generalized Mahalanobis, Divergence) are monotonic w.r.t. subset cardinality, hence B&B-compatible; Divergence holds the property in theory only - see the numerical-instability known issue (prefer Bhattacharyya for B&B)

Reimplemented from FST::Criterion< RETURNTYPE, SUBSET >.

◆ print()

template<class RETURNTYPE, typename DATATYPE, typename REALTYPE, typename IDXTYPE, typename DIMTYPE, class SUBSET, class DATAACCESSOR>

virtual std::ostream & FST::Criterion_Normal< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >::print ( std::ostream & os ) const

inlineoverridevirtual

writes a human-readable identification of the object; base default writes nothing

Reimplemented from FST::Clonable.

◆ update_derived_model()

template<class RETURNTYPE, typename DATATYPE, typename REALTYPE, typename IDXTYPE, typename DIMTYPE, class SUBSET, class DATAACCESSOR>

virtual void FST::Criterion_Normal< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >::update_derived_model ( )

protectedpure virtual

precompute criterion-specific derivations of the freshly learned model (_meandif is already computed)

Implemented in FST::Criterion_Normal_Bhattacharyya< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, FST::Criterion_Normal_Divergence< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, and FST::Criterion_Normal_GMahalanobis< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >.

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

/Users/psms/dev/devtest1/_src_criteria/criterion_normal.hpp

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Member Function Documentation

◆ allocate_buffers()

◆ criterion_name()

◆ evaluate()

◆ evaluate_narrowed()

◆ initialize()

◆ is_monotonic()

◆ print()

◆ update_derived_model()