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CFisherLDA Class Reference

Detailed Description

Preprocessor FisherLDA attempts to model the difference between the classes of data by performing linear discriminant analysis on input feature vectors/matrices. When the init method in FisherLDA is called with proper feature matrix X(say N number of vectors and D feature dimensions) supplied via apply_to_feature_matrix or apply_to_feature_vector methods, this creates a transformation whose outputs are the reduced T-Dimensional & class-specific distribution (where T<= number of unique classes-1). The transformation matrix is essentially a DxT matrix, the columns of which correspond to the specified number of eigenvectors which maximizes the ratio of between class matrix to within class matrix.

This class provides 3 method options to compute the transformation matrix :

CLASSIC_FLDA : This method selects W in such a way that the ratio of the between-class scatter and the within class scatter is maximized. The between class matrix is : \(\sum_b = \sum_{i=1}^C{\bf{(\mu_i-\mu)(\mu_i-\mu)^T}}\) The within class matrix is : \(\sum_w = \sum_{i=1}^C{\sum_{x_k\in}^c{\bf{(\mu_i-\mu)(\mu_i-\mu)^T}}}\) This should be choosen when N>D

CANVAR_FLDA : This method performs Canonical Variates which generalises Fisher's method to projection of more than one dimension. This is equipped to handle the cases where the within class matrix are non-invertible. Can be used for both cases(D>N or D<N). See the implementation in Bayesian Reasoning and Machine Learning by David Barber , Section 16.3

AUTO_FLDA : Automagically, the appropriate method is selected based on whether D>N (chooses CANVAR_FLDA) or D<N(chooses ::CLASSIC_FLDA)

Definition at line 91 of file FisherLDA.h.

Inheritance diagram for CFisherLDA:
[legend]

Public Member Functions

 CFisherLDA (EFLDAMethod method=AUTO_FLDA, float64_t thresh=0.01)
 
virtual ~CFisherLDA ()
 
virtual bool fit (CFeatures *features, CLabels *labels, int32_t num_dimensions=0)
 
virtual void cleanup ()
 
virtual SGMatrix< float64_tapply_to_feature_matrix (CFeatures *features)
 
virtual SGVector< float64_tapply_to_feature_vector (SGVector< float64_t > vector)
 
SGMatrix< float64_tget_transformation_matrix ()
 
SGVector< float64_tget_eigenvalues ()
 
SGVector< float64_tget_mean ()
 
virtual const char * get_name () const
 
virtual EPreprocessorType get_type () const
 
virtual bool init (CFeatures *data)
 
void set_target_dim (int32_t dim)
 
int32_t get_target_dim () const
 
void set_distance (CDistance *distance)
 
CDistanceget_distance () const
 
void set_kernel (CKernel *kernel)
 
CKernelget_kernel () const
 
virtual CFeaturesapply (CFeatures *features)
 
virtual EFeatureClass get_feature_class ()
 return that we are dense features (just fixed size matrices) More...
 
virtual EFeatureType get_feature_type ()
 return feature type More...
 
virtual CSGObjectshallow_copy () const
 
virtual CSGObjectdeep_copy () const
 
virtual bool is_generic (EPrimitiveType *generic) const
 
template<class T >
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
void unset_generic ()
 
virtual void print_serializable (const char *prefix="")
 
virtual bool save_serializable (CSerializableFile *file, const char *prefix="")
 
virtual bool load_serializable (CSerializableFile *file, const char *prefix="")
 
void set_global_io (SGIO *io)
 
SGIOget_global_io ()
 
void set_global_parallel (Parallel *parallel)
 
Parallelget_global_parallel ()
 
void set_global_version (Version *version)
 
Versionget_global_version ()
 
SGStringList< char > get_modelsel_names ()
 
void print_modsel_params ()
 
char * get_modsel_param_descr (const char *param_name)
 
index_t get_modsel_param_index (const char *param_name)
 
void build_gradient_parameter_dictionary (CMap< TParameter *, CSGObject * > *dict)
 
virtual void update_parameter_hash ()
 
virtual bool parameter_hash_changed ()
 
virtual bool equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false)
 
virtual CSGObjectclone ()
 

Public Attributes

SGIOio
 
Parallelparallel
 
Versionversion
 
Parameterm_parameters
 
Parameterm_model_selection_parameters
 
Parameterm_gradient_parameters
 
uint32_t m_hash
 

Protected Member Functions

virtual void load_serializable_pre () throw (ShogunException)
 
virtual void load_serializable_post () throw (ShogunException)
 
virtual void save_serializable_pre () throw (ShogunException)
 
virtual void save_serializable_post () throw (ShogunException)
 

Protected Attributes

SGMatrix< float64_tm_transformation_matrix
 
int32_t m_num_dim
 
float64_t m_threshold
 
int32_t m_method
 
SGVector< float64_tm_mean_vector
 
SGVector< float64_tm_eigenvalues_vector
 
int32_t m_target_dim
 
CDistancem_distance
 
CKernelm_kernel
 
CEmbeddingConverterm_converter
 

Constructor & Destructor Documentation

CFisherLDA ( EFLDAMethod  method = AUTO_FLDA,
float64_t  thresh = 0.01 
)

standard constructor

Parameters
methodLDA based on : CLASSIC_FLDA/CANVAR_FLDA/AUTO_FLDA[default]
threshthreshold value for CANVAR_FLDA only. This is used to reject those basis whose singular values are less than the provided threshold. The default one is 0.01.

Definition at line 51 of file FisherLDA.cpp.

~CFisherLDA ( )
virtual

destructor

Definition at line 75 of file FisherLDA.cpp.

Member Function Documentation

virtual CFeatures* apply ( CFeatures features)
virtualinherited

generic interface for applying the preprocessor. used as a wrapper for apply_to_feature_matrix() method

Parameters
featuresthe dense input features
Returns
the result feature object after applying the preprocessor

Implements CPreprocessor.

SGMatrix< float64_t > apply_to_feature_matrix ( CFeatures features)
virtual

apply preprocessor to feature matrix

Parameters
featureson which the learned tranformation has to be applied. Sometimes it is also referred as projecting the given features.
Returns
processed feature matrix with reduced dimensions.

Reimplemented from CDimensionReductionPreprocessor.

Definition at line 293 of file FisherLDA.cpp.

SGVector< float64_t > apply_to_feature_vector ( SGVector< float64_t vector)
virtual

apply preprocessor to feature vector

Parameters
featureson which the learned transformation has to be applied.
Returns
processed feature vector with reduced dimensions.

Reimplemented from CDimensionReductionPreprocessor.

Definition at line 330 of file FisherLDA.cpp.

void build_gradient_parameter_dictionary ( CMap< TParameter *, CSGObject * > *  dict)
inherited

Builds a dictionary of all parameters in SGObject as well of those of SGObjects that are parameters of this object. Dictionary maps parameters to the objects that own them.

Parameters
dictdictionary of parameters to be built.

Definition at line 597 of file SGObject.cpp.

void cleanup ( )
virtual

cleanup

Reimplemented from CDimensionReductionPreprocessor.

Definition at line 286 of file FisherLDA.cpp.

CSGObject * clone ( )
virtualinherited

Creates a clone of the current object. This is done via recursively traversing all parameters, which corresponds to a deep copy. Calling equals on the cloned object always returns true although none of the memory of both objects overlaps.

Returns
an identical copy of the given object, which is disjoint in memory. NULL if the clone fails. Note that the returned object is SG_REF'ed

Definition at line 714 of file SGObject.cpp.

CSGObject * deep_copy ( ) const
virtualinherited

A deep copy. All the instance variables will also be copied.

Definition at line 198 of file SGObject.cpp.

bool equals ( CSGObject other,
float64_t  accuracy = 0.0,
bool  tolerant = false 
)
virtualinherited

Recursively compares the current SGObject to another one. Compares all registered numerical parameters, recursion upon complex (SGObject) parameters. Does not compare pointers!

May be overwritten but please do with care! Should not be necessary in most cases.

Parameters
otherobject to compare with
accuracyaccuracy to use for comparison (optional)
tolerantallows linient check on float equality (within accuracy)
Returns
true if all parameters were equal, false if not

Definition at line 618 of file SGObject.cpp.

bool fit ( CFeatures features,
CLabels labels,
int32_t  num_dimensions = 0 
)
virtual

fits fisher lda transformation using features and corresponding labels

Parameters
featuresusing which the transformation matrix will be formed
labelsof the given features which will be used here to find the transformation matrix unlike PCA where it is not needed.
dimensionsnumber of dimensions to retain

Definition at line 79 of file FisherLDA.cpp.

CDistance * get_distance ( ) const
inherited

getter for distance

Returns
distance

Definition at line 88 of file DimensionReductionPreprocessor.cpp.

SGVector< float64_t > get_eigenvalues ( )
Returns
get eigenvalues of LDA

Definition at line 349 of file FisherLDA.cpp.

virtual EFeatureClass get_feature_class ( )
virtualinherited

return that we are dense features (just fixed size matrices)

Implements CPreprocessor.

Reimplemented in CRandomFourierGaussPreproc.

virtual EFeatureType get_feature_type ( )
virtualinherited

return feature type

Implements CPreprocessor.

Reimplemented in CRandomFourierGaussPreproc.

SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 235 of file SGObject.cpp.

Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 277 of file SGObject.cpp.

Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 290 of file SGObject.cpp.

CKernel * get_kernel ( ) const
inherited

getter for kernel

Returns
kernel

Definition at line 101 of file DimensionReductionPreprocessor.cpp.

SGVector< float64_t > get_mean ( )
Returns
get mean vector of the original data

Definition at line 354 of file FisherLDA.cpp.

SGStringList< char > get_modelsel_names ( )
inherited
Returns
vector of names of all parameters which are registered for model selection

Definition at line 498 of file SGObject.cpp.

char * get_modsel_param_descr ( const char *  param_name)
inherited

Returns description of a given parameter string, if it exists. SG_ERROR otherwise

Parameters
param_namename of the parameter
Returns
description of the parameter

Definition at line 522 of file SGObject.cpp.

index_t get_modsel_param_index ( const char *  param_name)
inherited

Returns index of model selection parameter with provided index

Parameters
param_namename of model selection parameter
Returns
index of model selection parameter with provided name, -1 if there is no such

Definition at line 535 of file SGObject.cpp.

virtual const char* get_name ( ) const
virtual
Returns
object name

Reimplemented from CDimensionReductionPreprocessor.

Definition at line 143 of file FisherLDA.h.

int32_t get_target_dim ( ) const
inherited

getter for target dimension

Returns
target dimension

Definition at line 76 of file DimensionReductionPreprocessor.cpp.

SGMatrix< float64_t > get_transformation_matrix ( )
Returns
get transformation matrix which contains the required number of eigenvectors

Definition at line 344 of file FisherLDA.cpp.

virtual EPreprocessorType get_type ( ) const
virtual
Returns
a type of preprocessor

Reimplemented from CDimensionReductionPreprocessor.

Definition at line 146 of file FisherLDA.h.

bool init ( CFeatures data)
virtualinherited

init set true by default, should be defined if dimension reduction preprocessor is using some initialization

Implements CPreprocessor.

Reimplemented in CPCA, and CKernelPCA.

Definition at line 58 of file DimensionReductionPreprocessor.cpp.

bool is_generic ( EPrimitiveType *  generic) const
virtualinherited

If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.

Parameters
genericset to the type of the generic if returning TRUE
Returns
TRUE if a class template.

Definition at line 296 of file SGObject.cpp.

bool load_serializable ( CSerializableFile file,
const char *  prefix = "" 
)
virtualinherited

Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!

Parameters
filewhere to load from
prefixprefix for members
Returns
TRUE if done, otherwise FALSE

Definition at line 369 of file SGObject.cpp.

void load_serializable_post ( )
throw (ShogunException
)
protectedvirtualinherited

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_POST is called.

Exceptions
ShogunExceptionwill be thrown if an error occurs.

Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.

Definition at line 426 of file SGObject.cpp.

void load_serializable_pre ( )
throw (ShogunException
)
protectedvirtualinherited

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_PRE is called.

Exceptions
ShogunExceptionwill be thrown if an error occurs.

Reimplemented in CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.

Definition at line 421 of file SGObject.cpp.

bool parameter_hash_changed ( )
virtualinherited
Returns
whether parameter combination has changed since last update

Definition at line 262 of file SGObject.cpp.

void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 474 of file SGObject.cpp.

void print_serializable ( const char *  prefix = "")
virtualinherited

prints registered parameters out

Parameters
prefixprefix for members

Definition at line 308 of file SGObject.cpp.

bool save_serializable ( CSerializableFile file,
const char *  prefix = "" 
)
virtualinherited

Save this object to file.

Parameters
filewhere to save the object; will be closed during returning if PREFIX is an empty string.
prefixprefix for members
Returns
TRUE if done, otherwise FALSE

Definition at line 314 of file SGObject.cpp.

void save_serializable_post ( )
throw (ShogunException
)
protectedvirtualinherited

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_POST is called.

Exceptions
ShogunExceptionwill be thrown if an error occurs.

Reimplemented in CKernel.

Definition at line 436 of file SGObject.cpp.

void save_serializable_pre ( )
throw (ShogunException
)
protectedvirtualinherited

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_PRE is called.

Exceptions
ShogunExceptionwill be thrown if an error occurs.

Reimplemented in CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.

Definition at line 431 of file SGObject.cpp.

void set_distance ( CDistance distance)
inherited

setter for distance

Parameters
distancedistance to set

Definition at line 81 of file DimensionReductionPreprocessor.cpp.

void set_generic ( )
inherited

Definition at line 41 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 46 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 51 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 56 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 61 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 66 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 71 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 76 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 81 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 86 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 91 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 96 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 101 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 106 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 111 of file SGObject.cpp.

void set_generic ( )
inherited

set generic type to T

void set_global_io ( SGIO io)
inherited

set the io object

Parameters
ioio object to use

Definition at line 228 of file SGObject.cpp.

void set_global_parallel ( Parallel parallel)
inherited

set the parallel object

Parameters
parallelparallel object to use

Definition at line 241 of file SGObject.cpp.

void set_global_version ( Version version)
inherited

set the version object

Parameters
versionversion object to use

Definition at line 283 of file SGObject.cpp.

void set_kernel ( CKernel kernel)
inherited

setter for kernel

Parameters
kernelkernel to set

Definition at line 94 of file DimensionReductionPreprocessor.cpp.

void set_target_dim ( int32_t  dim)
inherited

setter for target dimension

Parameters
dimtarget dimension

Definition at line 70 of file DimensionReductionPreprocessor.cpp.

CSGObject * shallow_copy ( ) const
virtualinherited

A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.

Reimplemented in CGaussianKernel.

Definition at line 192 of file SGObject.cpp.

void unset_generic ( )
inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 303 of file SGObject.cpp.

void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 248 of file SGObject.cpp.

Member Data Documentation

SGIO* io
inherited

io

Definition at line 369 of file SGObject.h.

CEmbeddingConverter* m_converter
protectedinherited

embedding converter to be used

Definition at line 127 of file DimensionReductionPreprocessor.h.

CDistance* m_distance
protectedinherited

distance to be used

Definition at line 121 of file DimensionReductionPreprocessor.h.

SGVector<float64_t> m_eigenvalues_vector
protected

eigenvalues vector

Definition at line 166 of file FisherLDA.h.

Parameter* m_gradient_parameters
inherited

parameters wrt which we can compute gradients

Definition at line 384 of file SGObject.h.

uint32_t m_hash
inherited

Hash of parameter values

Definition at line 387 of file SGObject.h.

CKernel* m_kernel
protectedinherited

kernel to be used

Definition at line 124 of file DimensionReductionPreprocessor.h.

SGVector<float64_t> m_mean_vector
protected

mean vector

Definition at line 164 of file FisherLDA.h.

int32_t m_method
protected

m_method

Definition at line 162 of file FisherLDA.h.

Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 381 of file SGObject.h.

int32_t m_num_dim
protected

num dim

Definition at line 158 of file FisherLDA.h.

Parameter* m_parameters
inherited

parameters

Definition at line 378 of file SGObject.h.

int32_t m_target_dim
protectedinherited

target dim of dimensionality reduction preprocessor

Definition at line 118 of file DimensionReductionPreprocessor.h.

float64_t m_threshold
protected

m_threshold

Definition at line 160 of file FisherLDA.h.

SGMatrix<float64_t> m_transformation_matrix
protected

transformation matrix

Definition at line 156 of file FisherLDA.h.

Parallel* parallel
inherited

parallel

Definition at line 372 of file SGObject.h.

Version* version
inherited

version

Definition at line 375 of file SGObject.h.


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

SHOGUN Machine Learning Toolbox - Documentation