Linear (Fisher/Gaussian shared-covariance) discriminant classifier. More...

#include <classifier_lda.hpp>

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

Public Types
typedef Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR >	parent
	the abstract base type
typedef Clonable_Impl< Classifier_LDA, parent >	parent_impl
	implements clone() via the copy-constructor below
typedef std::shared_ptr< DATAACCESSOR >	PDataAccessor
	shared pointer to the data accessor supplying train/test blocks
typedef std::shared_ptr< SUBSET >	PSubset
	shared pointer to the feature subset being trained/classified on
typedef DATAACCESSOR::PPattern	PPattern
	one data sample as exposed by DATAACCESSOR (a raw pointer to its feature vector)
Public Types inherited from FST::Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR >
typedef std::shared_ptr< DATAACCESSOR >	PDataAccessor
	shared pointer to the data accessor supplying train/test blocks
typedef std::shared_ptr< SUBSET > const	PSubset
	shared pointer to the (fixed, non-reassignable) feature subset being evaluated
typedef DATAACCESSOR::PPattern	PPattern
	one data sample as exposed by DATAACCESSOR (a raw pointer to its feature vector)

Public Member Functions
void	initialize (const PDataAccessor da)
	Pre-allocates model and matrix buffers and records the class/feature counts of `da`. Must be called before the first train(); throws fst_error when `da` has fewer than 2 classes.
virtual bool	classify (DIMTYPE &cls, const PPattern &pattern) override
virtual bool	classify_scores (std::vector< RETURNTYPE > &scores, const PPattern &pattern) override
virtual bool	train (const PDataAccessor da, const PSubset sub) override
	Learns per-class Gaussian parameters on the designated training part, pools the per-class covariances (weighted by the class priors) into one within-class covariance, inverts it once through the shared Indexed_Matrix LU machinery, and derives the closed-form linear discriminants a_c, b_c (see class doc).
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::Clonable_Impl< Classifier_LDA< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR > >
Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR > *	clone () const override
	dispatches to the DEEP_KIND mechanism (default CLONE_DEEP: Derived's copy-constructor)
Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR > *	sharing_clone () const override
	dispatches to the SHARING_KIND mechanism (default CLONE_INHERIT: defers to Base, which throws by family convention)
Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR > *	stateless_clone () const override
	dispatches to the STATELESS_KIND mechanism (default CLONE_INHERIT: defers to Base, which throws by family convention)
Public Member Functions inherited from FST::Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR >
virtual bool	train (const PDataAccessor da, const PSubset sub)=0
	learns from designated training part of data
virtual bool	test (RETURNTYPE &result, const PDataAccessor da)
virtual bool	test_confusion (std::vector< RETURNTYPE > &confusion, DIMTYPE &classes, const PDataAccessor da)
virtual bool	test_scores (std::vector< std::pair< RETURNTYPE, DIMTYPE > > &scores, const PDataAccessor da)
virtual bool	test_scores_matrix (std::vector< RETURNTYPE > &scores, std::vector< DIMTYPE > &labels, DIMTYPE &classes, const PDataAccessor da)
virtual bool	test_metric (RETURNTYPE &result, const PDataAccessor da, const Metric_Spec< RETURNTYPE > &spec)
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 Attributes
std::unique_ptr< Model_Normal< DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR > >	_model
	per-class means/covariances (learned by train(), see Model_Normal)
std::unique_ptr< Indexed_Matrix< REALTYPE, DIMTYPE, SUBSET > >	_LUtemp
	LU scratch (lazy allocation).
std::unique_ptr< Indexed_Matrix< REALTYPE, DIMTYPE, SUBSET > >	_pooled
	pooled within-class covariance
std::unique_ptr< Indexed_Matrix< REALTYPE, DIMTYPE, SUBSET > >	_pooledinv
	its inverse
DIMTYPE	_classes
	number of classes, as recorded by initialize()
DIMTYPE	_features
	raw (full) feature count, as recorded by initialize()
DIMTYPE	_subfeatures
	d = current subset size
std::vector< DIMTYPE >	_index
	subset position -> raw feature index (first d entries valid)
std::vector< RETURNTYPE >	_cs_sc
	classify() score scratch (member to avoid per-pattern allocation)
std::vector< REALTYPE >	_a
	classes x d discriminant directions, row-major
std::vector< REALTYPE >	_b
	classes discriminant offsets
std::vector< REALTYPE >	_m
	d-entry centering point of the discriminants: the prior-weighted mean of the class means (see train())

Private Member Functions
	Classifier_LDA (const Classifier_LDA &cld)
	Deep-copies the learned model and the pooled-covariance matrices (LU scratch, pooled covariance, its inverse); the discriminant vectors _a/_b and _index copy by value.

Private Attributes
friend	parent_impl

Additional Inherited Members
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)

Detailed Description

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

Linear (Fisher/Gaussian shared-covariance) discriminant classifier.

The pooled within-class covariance (per-class Model_Normal covariances weighted by the class priors) yields the closed-form linear discriminants delta_c(x) = a_c^T x + b_c with a_c = S_pooled^{-1} mu_c and b_c = -1/2 mu_c^T a_c + log P(c). Deterministic and RNG-free; the single matrix inverse goes through the same Indexed_Matrix LU machinery as the normal-model filters and the normal Bayes classifier — including the optional Eigen-backed path (FST4_USE_EIGEN), which stays in parity.

Complements gmahalanobis (a two-class filter) and Classifier_Normal_Bayes (per-class covariances, QDA-like): one shared covariance = linear boundaries, K classes supported, roughly K times cheaper per training than the Bayes classifier at equal subset size (one inverse, not K). classify_scores() exposes the discriminants (log-posteriors up to a shared shift; computed in centered form relative to the prior-weighted mean of the class means, so a large common feature offset costs no digits) for the ranking metrics (AUC); they are uncalibrated - monotone in the posterior, but neither probabilities nor comparable across trainings. Like the other normal-model components, it fails on singular pooled covariance (e.g. constant features or subset size exceeding the sample count).

Member Function Documentation

◆ classify()

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

bool FST::Classifier_LDA< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >::classify	(	DIMTYPE &	cls,
		const PPattern &	pattern )

overridevirtual

Classifies one pattern; on success cls receives the predicted class index (0-based, in [0,classes)). Returns false when no decision can be made at all - the model has not been trained, or the implementation hit an unrecoverable condition; false is not a "reject" option and aborts the enclosing test loop.

Implements FST::Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR >.

◆ classify_scores()

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

bool FST::Classifier_LDA< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >::classify_scores	(	std::vector< RETURNTYPE > &	scores,
		const PPattern &	pattern )

overridevirtual

per-class real scores for one pattern (any monotone transform of the class posteriors); enables ranking-based metrics (AUC). Default: not supported.

Reimplemented from FST::Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR >.

◆ print()

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

virtual std::ostream & FST::Classifier_LDA< 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.

◆ train()

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

bool FST::Classifier_LDA< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >::train	(	const PDataAccessor	da,
		const PSubset	sub )

overridevirtual

Learns per-class Gaussian parameters on the designated training part, pools the per-class covariances (weighted by the class priors) into one within-class covariance, inverts it once through the shared Indexed_Matrix LU machinery, and derives the closed-form linear discriminants a_c, b_c (see class doc).

Precondition: initialize() has been called for a data accessor with the same class and feature counts as da.

Returns: false if the pooled covariance is singular (any resulting discriminant coefficient is non-finite); true otherwise.

Member Data Documentation

◆ parent_impl

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

friend FST::Classifier_LDA< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >::parent_impl

private

grants Clonable_Impl access to the copy-constructor

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

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

Public Types

Public Member Functions

Protected Attributes

Private Member Functions

Private Attributes

Additional Inherited Members

Detailed Description

Member Function Documentation

◆ classify()

◆ classify_scores()

◆ print()

◆ train()

Member Data Documentation

◆ parent_impl