ipsdk::math::clustering::KernelKMeansCentroid< KernelType > Class Template Reference

Public Member Functions
ipUInt64	getNbSamples () const
	retrieve number of added samples associated to centroid
ipUInt32	getDictionarySize () const
	retrieve dictionary size associated to centroid
void	addSample (const Vector &sample)
	agregate sample to centroid More...
IPSDK_FORCEINLINE ipReal64	comparable_distance (const Vector &sample) const
	compute comparable distance from a sample to cluster centroid More...
IPSDK_FORCEINLINE ipReal64	distance (const Vector &sample) const
	compute distance from a sample to cluster centroid More...
ipReal64	getClusterTerm () const
	retrieve cluster term associated to centroid
const VectorColl &	getDictionary () const
	retrieve dictionary associated to centroid
const Real64Vector &	getWeights () const
	retrieve weight factors associated to centroid
void	clear ()
	method allowing to clear centroid data
Constructors and destructor
	KernelKMeansCentroid (const KernelType &kernel)
	~KernelKMeansCentroid ()

Protected Member Functions
void	updateClusterTerm () const
void	updateMinStrength ()

Protected Attributes
KernelType	_kernel
	kernel associated to cluster dictionary
ipReal64	_accuracyThreshold
	accuracy threshold value for approximate linear dependence condition
ipUInt32	_maxDictionarySize
	maximum dictionary size for processing
VectorColl	_dictionary
Real64Vector	_weights
	weight factors for each elements of dictionary
ipUInt64	_nbSamples
	number of added samples
std::pair< ipReal64, bool >	_clusterTermInfo
Matrix	_matK
	K Matrix used to store approximate linear dependance system condition.
Matrix	_invMatK
	Inverse of K Matrix.
std::pair< ipReal64, ipUInt32 >	_minStrengthInfo
Matrix	_wkMat
	working array
Vector	_wkVec
	working array
Vector	_wkVecA
	working array
Vector	_wkVecMixedTerms
	working array

Algorithm parameters
const KernelType &	getKernel () const
	retrieve kernel associated to centroid
void	setAccuracyThreshold (const ipReal64 accuracyThreshold)
	set accuracy threshold for approximate linear dependence condition More...
ipReal64	getAccuracyThreshold () const
	retrieve accuracy threshold for approximate linear dependence condition
void	setMaxDictionarySize (const ipUInt32 maxDictionarySize)
	set maximum dictionary size for processing More...
ipUInt32	getMaxDictionarySize () const
	retrieve maximum dictionary size for processing
static ipReal64	getDefaultAccuracyThreshold ()
	retrieve default accuracy threshold value
static ipUInt32	getDefaultMaxDictionarySize ()
	retrieve default maximum dictionary size for processing

Member Function Documentation

setAccuracyThreshold()

template<typename KernelType >

void ipsdk::math::clustering::KernelKMeansCentroid< KernelType >::setAccuracyThreshold

(

const ipReal64

accuracyThreshold

)

set accuracy threshold for approximate linear dependence condition

Exceptions

ipsdk::math::IPSDKMathException

if accuracyThreshold < 0

setMaxDictionarySize()

template<typename KernelType >

void ipsdk::math::clustering::KernelKMeansCentroid< KernelType >::setMaxDictionarySize

(

const ipUInt32

maxDictionarySize

)

set maximum dictionary size for processing

Exceptions

ipsdk::math::IPSDKMathException

if maxDictionarySize == 0

addSample()

template<typename KernelType >

void ipsdk::math::clustering::KernelKMeansCentroid< KernelType >::addSample

(

const Vector &

sample

)

agregate sample to centroid

Warning

there is no check on vectors size consistency

comparable_distance()

template<typename KernelType >

IPSDK_FORCEINLINE ipReal64 ipsdk::math::clustering::KernelKMeansCentroid< KernelType >::comparable_distance ( const Vector &  sample ) const

inline

compute comparable distance from a sample to cluster centroid

Warning

there is no check on vectors size consistency

distance()

template<typename KernelType >

IPSDK_FORCEINLINE ipReal64 ipsdk::math::clustering::KernelKMeansCentroid< KernelType >::distance ( const Vector &  sample ) const

inline

compute distance from a sample to cluster centroid

Warning

there is no check on vectors size consistency

updateClusterTerm()

template<typename KernelType >

void ipsdk::math::clustering::KernelKMeansCentroid< KernelType >::updateClusterTerm ( ) const

protected

method allowing to update cluster term used in scalar product decomposition

updateMinStrength()

template<typename KernelType >

void ipsdk::math::clustering::KernelKMeansCentroid< KernelType >::updateMinStrength ( )

protected

method allowing to update sample point into dictionary with minimum "strength"

Member Data Documentation

_dictionary

template<typename KernelType >

VectorColl ipsdk::math::clustering::KernelKMeansCentroid< KernelType >::_dictionary

protected

dictionary associated to centroid (collection of sample points)

_clusterTermInfo

template<typename KernelType >

std::pair<ipReal64, bool> ipsdk::math::clustering::KernelKMeansCentroid< KernelType >::_clusterTermInfo

mutableprotected

cluster term used in scalar product decomposition and flag indicating whether term is up to date

_minStrengthInfo

template<typename KernelType >

std::pair<ipReal64, ipUInt32> ipsdk::math::clustering::KernelKMeansCentroid< KernelType >::_minStrengthInfo

protected

sample point into dictionary with minimum "strength" (used only in case where dictionary size reach maximum allowed)

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

• /media/reactivipdev/Nightly/IPSDK_Patch/IPSDK_4_1/IPSDK_Nightly/include/public/IPSDKMath/Clustering/ClusteringUtils.h
• /media/reactivipdev/Nightly/IPSDK_Patch/IPSDK_4_1/IPSDK_Nightly/include/public/IPSDKMath/Clustering/KernelKMeansCentroid.h