CSparseLinearClassifier Class Reference

Inheritance diagram for CSparseLinearClassifier:

Inheritance graph
[legend]

List of all members.


Detailed Description

class SparseLinearClassifier

Definition at line 19 of file SparseLinearClassifier.h.


Public Member Functions

 CSparseLinearClassifier ()
virtual ~CSparseLinearClassifier ()
virtual CLabelsclassify (CLabels *output=NULL)
virtual DREAL classify_example (INT vec_idx)
 get output for example "vec_idx"
void get_w (DREAL **dst_w, INT *dst_dims)
void set_w (DREAL *src_w, INT src_w_dim)
void set_bias (DREAL b)
DREAL get_bias ()
void set_features (CSparseFeatures< DREAL > *feat)
CSparseFeatures< DREAL > * get_features ()
virtual bool train ()
virtual bool load (FILE *srcfile)
virtual bool save (FILE *dstfile)
virtual void set_labels (CLabels *lab)
virtual CLabelsget_labels ()
virtual DREAL get_label (INT i)
void set_max_train_time (DREAL t)
DREAL get_max_train_time ()
virtual EClassifierType get_classifier_type ()

Static Public Attributes

static CParallel parallel
static CIO io
static CVersion version

Protected Attributes

INT w_dim
DREALw
DREAL bias
CSparseFeatures< DREAL > * features
DREAL max_train_time
CLabelslabels

Constructor & Destructor Documentation

CSparseLinearClassifier::CSparseLinearClassifier (  ) 

default constructor

Definition at line 13 of file SparseLinearClassifier.cpp.

CSparseLinearClassifier::~CSparseLinearClassifier (  )  [virtual]

Definition at line 18 of file SparseLinearClassifier.cpp.


Member Function Documentation

CLabels * CSparseLinearClassifier::classify ( CLabels output = NULL  )  [virtual]

classify all examples

Parameters:
output resulting labels
Returns:
resulting labels

Reimplemented from CClassifier.

Definition at line 24 of file SparseLinearClassifier.cpp.

virtual DREAL CSparseLinearClassifier::classify_example ( INT  vec_idx  )  [virtual]

get output for example "vec_idx"

Reimplemented from CClassifier.

Definition at line 34 of file SparseLinearClassifier.h.

void CSparseLinearClassifier::get_w ( DREAL **  dst_w,
INT dst_dims 
)

get w

Parameters:
dst_w store w in this argument
dst_dims dimension of w

Definition at line 44 of file SparseLinearClassifier.h.

void CSparseLinearClassifier::set_w ( DREAL src_w,
INT  src_w_dim 
)

set w

Parameters:
src_w new w
src_w_dim dimension of new w

Definition at line 59 of file SparseLinearClassifier.h.

void CSparseLinearClassifier::set_bias ( DREAL  b  ) 

set bias

Parameters:
b new bias

Definition at line 69 of file SparseLinearClassifier.h.

DREAL CSparseLinearClassifier::get_bias (  ) 

get bias

Returns:
bias

Definition at line 78 of file SparseLinearClassifier.h.

void CSparseLinearClassifier::set_features ( CSparseFeatures< DREAL > *  feat  ) 

set features

Parameters:
feat features to set

Definition at line 87 of file SparseLinearClassifier.h.

CSparseFeatures<DREAL>* CSparseLinearClassifier::get_features (  ) 

get features

Returns:
features

Definition at line 98 of file SparseLinearClassifier.h.

virtual bool CClassifier::train (  )  [virtual, inherited]

train classifier

Returns:
whether training was successful

Reimplemented in CKernelPerceptron, CKNN, CPerceptron, CPluginEstimate, CGMNPSVM, CGNPPSVM, CGPBTSVM, CLibSVM, CLibSVMMultiClass, CLibSVMOneClass, CMPDSVM, CSubGradientSVM, CSVMLin, CSVMOcas, CSVMSGD, CWDSVMOcas, CHierarchical, CKMeans, and CLibSVR.

Definition at line 31 of file Classifier.h.

virtual bool CClassifier::load ( FILE *  srcfile  )  [virtual, inherited]

load Classifier from file

abstract base method

Parameters:
srcfile file to load from
Returns:
failure

Reimplemented in CKernelPerceptron, CKNN, CLinearClassifier, CSVM, CHierarchical, and CKMeans.

Definition at line 56 of file Classifier.h.

virtual bool CClassifier::save ( FILE *  dstfile  )  [virtual, inherited]

save Classifier to file

abstract base method

Parameters:
dstfile file to save to
Returns:
failure

Reimplemented in CKernelPerceptron, CKNN, CLinearClassifier, CSVM, CHierarchical, and CKMeans.

Definition at line 65 of file Classifier.h.

virtual void CClassifier::set_labels ( CLabels lab  )  [virtual, inherited]

set labels

Parameters:
lab labels

Definition at line 71 of file Classifier.h.

virtual CLabels* CClassifier::get_labels (  )  [virtual, inherited]

get labels

Returns:
labels

Definition at line 82 of file Classifier.h.

virtual DREAL CClassifier::get_label ( INT  i  )  [virtual, inherited]

get one specific label

Parameters:
i index of label to get
Returns:
value of label at index i

Definition at line 89 of file Classifier.h.

void CClassifier::set_max_train_time ( DREAL  t  )  [inherited]

set maximum training time

Parameters:
t maximimum training time

Definition at line 95 of file Classifier.h.

DREAL CClassifier::get_max_train_time (  )  [inherited]

get maximum training time

Returns:
maximum training time

Definition at line 101 of file Classifier.h.

virtual EClassifierType CClassifier::get_classifier_type (  )  [virtual, inherited]

get classifier type

Returns:
classifier type NONE

Reimplemented in CKernelPerceptron, CKNN, CPerceptron, CGMNPSVM, CGNPPSVM, CGPBTSVM, CLibSVM, CLibSVMMultiClass, CLibSVMOneClass, CMPDSVM, CSubGradientSVM, CSVMLin, CSVMOcas, CSVMSGD, CWDSVMOcas, CHierarchical, CKMeans, and CLibSVR.

Definition at line 107 of file Classifier.h.


Member Data Documentation

dimension of w

Definition at line 102 of file SparseLinearClassifier.h.

w

Definition at line 104 of file SparseLinearClassifier.h.

bias

Definition at line 106 of file SparseLinearClassifier.h.

features

Definition at line 108 of file SparseLinearClassifier.h.

DREAL CClassifier::max_train_time [protected, inherited]

maximum training time

Definition at line 111 of file Classifier.h.

CLabels* CClassifier::labels [protected, inherited]

labels

Definition at line 114 of file Classifier.h.

CParallel CSGObject::parallel [static, inherited]

Definition at line 105 of file SGObject.h.

CIO CSGObject::io [static, inherited]

Definition at line 106 of file SGObject.h.

CVersion CSGObject::version [static, inherited]

Definition at line 107 of file SGObject.h.


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

SHOGUN Machine Learning Toolbox - Documentation