CKMeans Class Reference

Inheritance diagram for CKMeans:

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List of all members.

Detailed Description

class KMeans

Definition at line 25 of file KMeans.h.


Public Member Functions

 CKMeans ()
 CKMeans (INT k, CDistance *d)
virtual ~CKMeans ()
virtual EClassifierType get_classifier_type ()
virtual bool train ()
virtual bool load (FILE *srcfile)
virtual bool save (FILE *dstfile)
void set_k (INT p_k)
INT get_k ()
void set_max_iter (INT iter)
DREAL get_max_iter ()
void get_radi (DREAL *&radi, INT &num)
void get_centers (DREAL *&centers, INT &dim, INT &num)
void get_radiuses (DREAL **radii, INT *num)
void get_cluster_centers (DREAL **centers, INT *dim, INT *num)
INT get_dimensions ()
void set_distance (CDistance *d)
CDistance * get_distance ()
virtual CLabels * classify (CLabels *output=NULL)
virtual DREAL classify_example (INT num)
virtual void set_labels (CLabels *lab)
virtual CLabels * get_labels ()
virtual DREAL get_label (INT i)
void set_max_train_time (DREAL t)
DREAL get_max_train_time ()

Static Public Attributes

static CParallel parallel
static CIO io
static CVersion version

Protected Member Functions

void sqdist (double *x, CRealFeatures *y, double *z, int n1, int offs, int n2, int m)
void clustknb (bool use_old_mus, double *mus_start)

Protected Attributes

INT max_iter
 maximum number of iterations
INT k
 the k parameter in KMeans
INT dimensions
 number of dimensions
DREAL * R
 radi of the clusters (size k)
DREAL * mus
 centers of the clusters (size dimensions x k)
CDistance * distance
DREAL max_train_time
CLabels * labels

Constructor & Destructor Documentation

CKMeans::CKMeans (  ) 

default constructor

Definition at line 27 of file KMeans.cpp.

CKMeans::CKMeans ( INT  k,
CDistance *  d 
)

constructor

Parameters:
k parameter k
d distance

Definition at line 33 of file KMeans.cpp.

CKMeans::~CKMeans (  )  [virtual]

Definition at line 40 of file KMeans.cpp.

Member Function Documentation

virtual EClassifierType CKMeans::get_classifier_type (  )  [virtual]

get classifier type

Returns:
classifier type KMEANS

Reimplemented from CClassifier.

Definition at line 43 of file KMeans.h.

bool CKMeans::train (  )  [virtual]

train distance machine

Returns:
if training was successful

Reimplemented from CClassifier.

Definition at line 46 of file KMeans.cpp.

bool CKMeans::load ( FILE *  srcfile  )  [virtual]

load distance machine from file

Parameters:
srcfile file to load from
Returns:
if loading was successful

Reimplemented from CClassifier.

Definition at line 66 of file KMeans.cpp.

bool CKMeans::save ( FILE *  dstfile  )  [virtual]

save distance machine to file

Parameters:
dstfile file to save to
Returns:
if saving was successful

Reimplemented from CClassifier.

Definition at line 71 of file KMeans.cpp.

void CKMeans::set_k ( INT  p_k  ) 

set k

Parameters:
p_k new k

Definition at line 69 of file KMeans.h.

INT CKMeans::get_k (  ) 

get k

Returns:
the parameter k

Definition at line 79 of file KMeans.h.

void CKMeans::set_max_iter ( INT  iter  ) 

set maximum number of iterations

Parameters:
iter the new maximum

Definition at line 88 of file KMeans.h.

DREAL CKMeans::get_max_iter (  ) 

get maximum number of iterations

Returns:
maximum number of iterations

Definition at line 98 of file KMeans.h.

void CKMeans::get_radi ( DREAL *&  radi,
INT &  num 
)

get radi

Parameters:
radi current radi are stored in here
num number of radi is stored in here

Definition at line 108 of file KMeans.h.

void CKMeans::get_centers ( DREAL *&  centers,
INT &  dim,
INT &  num 
)

get centers

Parameters:
centers current centers are stored in here
dim dimensions are stored in here
num number of centers is stored in here

Definition at line 120 of file KMeans.h.

void CKMeans::get_radiuses ( DREAL **  radii,
INT *  num 
)

get radiuses (swig compatible)

Parameters:
radii current radiuses are stored in here
num number of radiuses is stored in here

Definition at line 132 of file KMeans.h.

void CKMeans::get_cluster_centers ( DREAL **  centers,
INT *  dim,
INT *  num 
)

get cluster centers (swig compatible)

Parameters:
centers current cluster centers are stored in here
dim dimensions are stored in here
num number of centers is stored in here

Definition at line 148 of file KMeans.h.

INT CKMeans::get_dimensions (  ) 

get dimensions

Returns:
number of dimensions

Definition at line 163 of file KMeans.h.

void CKMeans::sqdist ( double *  x,
CRealFeatures *  y,
double *  z,
int  n1,
int  offs,
int  n2,
int  m 
) [protected]

sqdist

Parameters:
x x
y y
z z
n1 n1
offs offset
n2 n2
m m

Definition at line 118 of file KMeans.cpp.

void CKMeans::clustknb ( bool  use_old_mus,
double *  mus_start 
) [protected]

clustknb

Parameters:
use_old_mus if old mus shall be used
mus_start mus start

Definition at line 162 of file KMeans.cpp.

void CDistanceMachine::set_distance ( CDistance *  d  )  [inherited]

set distance

Parameters:
d distance to set

Definition at line 34 of file DistanceMachine.h.

CDistance* CDistanceMachine::get_distance (  )  [inherited]

get distance

Returns:
distance

Definition at line 45 of file DistanceMachine.h.

CLabels * CClassifier::classify ( CLabels *  output = NULL  )  [virtual, inherited]

classify object

Parameters:
output classified labels
Returns:
classified labels

Reimplemented in CKNN, CLinearClassifier, CPluginEstimate, CSparseLinearClassifier, CMultiClassSVM, CSVM, CWDSVMOcas, and CKernelMachine.

Definition at line 22 of file Classifier.cpp.

virtual DREAL CClassifier::classify_example ( INT  num  )  [virtual, inherited]

classify one example

abstract base method

Parameters:
num which example to classify
Returns:
infinite float value

Reimplemented in CKernelPerceptron, CKNN, CLinearClassifier, CPluginEstimate, CSparseLinearClassifier, CMultiClassSVM, CSVM, and CWDSVMOcas.

Definition at line 47 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.

Member Data Documentation

INT CKMeans::max_iter [protected]

maximum number of iterations

Definition at line 192 of file KMeans.h.

INT CKMeans::k [protected]

the k parameter in KMeans

Definition at line 195 of file KMeans.h.

INT CKMeans::dimensions [protected]

number of dimensions

Definition at line 198 of file KMeans.h.

DREAL* CKMeans::R [protected]

radi of the clusters (size k)

Definition at line 201 of file KMeans.h.

DREAL* CKMeans::mus [protected]

centers of the clusters (size dimensions x k)

Definition at line 204 of file KMeans.h.

CDistance* CDistanceMachine::distance [protected, inherited]

the distance

Definition at line 49 of file DistanceMachine.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