CFeatures Class Reference

Inheritance diagram for CFeatures:

Inheritance graph
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List of all members.

Public Member Functions

 CFeatures (int32_t size)
 CFeatures (const CFeatures &orig)
 CFeatures (char *fname)
virtual CFeaturesduplicate () const =0
virtual ~CFeatures ()
virtual EFeatureType get_feature_type ()=0
virtual EFeatureClass get_feature_class ()=0
virtual int32_t add_preproc (CPreProc *p)
 set preprocessor
virtual CPreProcdel_preproc (int32_t num)
 del current preprocessor
CPreProcget_preproc (int32_t num)
 get current preprocessor
void set_preprocessed (int32_t num)
bool is_preprocessed (int32_t num)
int32_t get_num_preprocessed ()
 get whether specified preprocessor (or all if num=1) was/were already applied
int32_t get_num_preproc ()
void clean_preprocs ()
 clears all preprocs
int32_t get_cache_size ()
virtual int32_t get_num_vectors ()=0
virtual bool reshape (int32_t num_features, int32_t num_vectors)
virtual int32_t get_size ()=0
void list_feature_obj ()
virtual bool load (char *fname)
virtual bool save (char *fname)
bool check_feature_compatibility (CFeatures *f)


Detailed Description

The class Features is the base class of all feature objects. It can be understood as a dense real valued feature matrix (with e.g. columns as single feature vectors), a set of strings, graphs or any other arbitrary collection of objects. As a result this class is kept very general and implements only very weak interfaces to

In addition it provides helpers to check e.g. for compability of feature objects.

Currently there are 3 general feature classes, which are CSimpleFeatures (dense matrices), CSparseFeatures (sparse matrices), CStringFeatures (a set of strings) from which all the specific features like CRealFeatures (dense real valued feature matrices) are derived.

Definition at line 71 of file Features.h.


Constructor & Destructor Documentation

CFeatures::CFeatures ( int32_t  size  ) 

constructor

Parameters:
size cache size

Definition at line 18 of file Features.cpp.

CFeatures::CFeatures ( const CFeatures orig  ) 

copy constructor

Definition at line 25 of file Features.cpp.

CFeatures::CFeatures ( char *  fname  ) 

constructor

Parameters:
fname filename to load features from

Definition at line 33 of file Features.cpp.

CFeatures::~CFeatures (  )  [virtual]

Definition at line 41 of file Features.cpp.


Member Function Documentation

int32_t CFeatures::add_preproc ( CPreProc p  )  [virtual]

set preprocessor

add preprocessor

Parameters:
p preprocessor to set
Returns:
something inty

Definition at line 48 of file Features.cpp.

bool CFeatures::check_feature_compatibility ( CFeatures f  ) 

check feature compatibility

Parameters:
f features to check for compatibility
Returns:
if features are compatible

Definition at line 223 of file Features.cpp.

void CFeatures::clean_preprocs (  ) 

clears all preprocs

clears all preprocs

Definition at line 101 of file Features.cpp.

CPreProc * CFeatures::del_preproc ( int32_t  num  )  [virtual]

del current preprocessor

delete preprocessor from list caller has to clean up returned preproc

Parameters:
num index of preprocessor in list

Definition at line 107 of file Features.cpp.

virtual CFeatures* CFeatures::duplicate (  )  const [pure virtual]

int32_t CFeatures::get_cache_size (  ) 

get cache size

Returns:
cache size

Definition at line 166 of file Features.h.

virtual EFeatureClass CFeatures::get_feature_class (  )  [pure virtual]

virtual EFeatureType CFeatures::get_feature_type (  )  [pure virtual]

int32_t CFeatures::get_num_preproc (  ) 

get number of preprocessors

Returns:
number of preprocessors

Definition at line 157 of file Features.h.

int32_t CFeatures::get_num_preprocessed (  ) 

get whether specified preprocessor (or all if num=1) was/were already applied

get the number of applied preprocs

Returns:
number of applied preprocessors

Definition at line 87 of file Features.cpp.

virtual int32_t CFeatures::get_num_vectors (  )  [pure virtual]

CPreProc * CFeatures::get_preproc ( int32_t  num  ) 

get current preprocessor

get specified preprocessor

Parameters:
num index of preprocessor in list

Definition at line 78 of file Features.cpp.

virtual int32_t CFeatures::get_size (  )  [pure virtual]

bool CFeatures::is_preprocessed ( int32_t  num  ) 

get whether specified preprocessor was already applied

Parameters:
num index of preprocessor in list

Definition at line 145 of file Features.h.

void CFeatures::list_feature_obj (  ) 

list feature object

Definition at line 152 of file Features.cpp.

bool CFeatures::load ( char *  fname  )  [virtual]

load features from file

Parameters:
fname filename to load from
Returns:
if loading was successful

Reimplemented in CByteFeatures, CCharFeatures, CIntFeatures, CRealFeatures, CShortFeatures, CShortRealFeatures, CStringFeatures< ST >, CWordFeatures, CStringFeatures< uint8_t >, and CStringFeatures< uint16_t >.

Definition at line 213 of file Features.cpp.

virtual bool CFeatures::reshape ( int32_t  num_features,
int32_t  num_vectors 
) [virtual]

in case there is a feature matrix allow for reshaping

NOT IMPLEMENTED!

Parameters:
num_features new number of features
num_vectors new number of vectors
Returns:
if reshaping was succesful

Reimplemented in CSimpleFeatures< ST >, CSimpleFeatures< uint8_t >, CSimpleFeatures< uint16_t >, CSimpleFeatures< float64_t >, CSimpleFeatures< int32_t >, CSimpleFeatures< char >, CSimpleFeatures< float32_t >, and CSimpleFeatures< int16_t >.

Definition at line 184 of file Features.h.

bool CFeatures::save ( char *  fname  )  [virtual]

save features to file

Parameters:
fname filename to save to
Returns:
if saving was successful

Reimplemented in CByteFeatures, CCharFeatures, CIntFeatures, CRealFeatures, CShortFeatures, CShortRealFeatures, CStringFeatures< ST >, CWordFeatures, CStringFeatures< uint8_t >, and CStringFeatures< uint16_t >.

Definition at line 218 of file Features.cpp.

void CFeatures::set_preprocessed ( int32_t  num  ) 

set applied flag for preprocessor

Parameters:
num index of preprocessor in list

Definition at line 139 of file Features.h.


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

SHOGUN Machine Learning Toolbox - Documentation