ClusteringUtils.h File Reference

Utility functions for clustering management. More...

#include <IPSDKMath/IPSDKMathExports.h>
#include <IPSDKMath/Clustering/ClusteringTypes.h>
#include <iostream>

Go to the source code of this file.

Classes
class	ipsdk::math::clustering::KernelKMeansCentroid< KernelType >

Namespaces
	ipsdk
	Main namespace for IPSDK library.
	ipsdk::math
	Namespace agregating IPSDK mathematical routines and classes.

Functions
IPSDKMATH_API ipReal64	ipsdk::math::clustering::computeCompactness (const VectorColl &clustersCentersColl, const VectorColl &dataColl, const UInt32Vector &indexColl)
	function allowing to compute compactness associated to a clustering result More...
template<typename KernelType >
IPSDKMATH_API ipReal64	ipsdk::math::clustering::computeKernelCompactness (const std::vector< KernelKMeansCentroid< KernelType > > &clustersCentroids, const VectorColl &dataColl, const UInt32Vector &indexColl)
	function allowing to compute compactness associated to a Kernel clustering result More...
IPSDKMATH_API ipReal64	ipsdk::math::clustering::computeValidityIndex (const VectorColl &clustersCentersColl, const VectorColl &dataColl, const UInt32Vector &indexColl, const eValidityIndexType &validityIndexType)
	function allowing to compute a validity index of a clustering More...
IPSDKMATH_API void	ipsdk::math::clustering::generateKMeansPPInit (const ipUInt32 nbClusters, const VectorColl &inputDataColl, VectorColl &initClustersCenters)
	function allowing to generate an initial solution for K-Means algorithm using K-Means++ approach More...
template<typename KernelType >
IPSDKMATH_API void	ipsdk::math::clustering::generateKernelKMeansPPInit (const KernelType &kernel, const ipUInt32 nbClusters, const VectorColl &inputDataColl, VectorColl &initClustersCenters)
	function allowing to generate an initial solution for Kernel K-Means algorithm using K-Means++ approach More...
IPSDKMATH_API ipUInt32	ipsdk::math::clustering::computeAssignment (const VectorColl &clustersCentersColl, const Vector &data)
	function allowing to compute cluster assignment using an euclidian distance to clusters centers More...
IPSDKMATH_API ipUInt32	ipsdk::math::clustering::computeKMeansAttempt (const VectorColl &inputDataColl, const ipUInt32 nbMaxIteration, VectorColl &inOutClustersCenters, UInt32Vector &assignments)
	function allowing to compute a kmeans attempt from an initial solution More...
template<typename KernelType >
IPSDKMATH_API ipUInt32	ipsdk::math::clustering::computeKernelAssignment (const std::vector< KernelKMeansCentroid< KernelType > > &clustersCentroids, const Vector &data)
	function allowing to compute cluster assignment using an kernel distance to clusters centroids More...
template<typename KernelType >
IPSDKMATH_API ipUInt32	ipsdk::math::clustering::computeKernelKMeansAttempt (const KernelType &kernel, const VectorColl &inputDataColl, const ipUInt32 nbMaxIteration, const ipReal64 minChangeRatio, std::vector< KernelKMeansCentroid< KernelType > > &inOutClustersCentroids, UInt32Vector &assignments)
	function allowing to compute a kernel kmeans attempt from an initial solution More...
IPSDKMATH_API void	ipsdk::math::clustering::computeClustersCenters (const ipUInt32 nbClusters, const VectorColl &inputDataColl, const UInt32Vector &indexColl, VectorColl &clustersCentersColl)
	function allowing to compute clusters centers given data assignments using mean computation More...
IPSDKMATH_API ipUInt32	ipsdk::math::clustering::computeClusterPopulation (const ipUInt32 clusterIdx, const UInt32Vector &indexColl)
	function allowing to compute population associated to a given cluster More...
IPSDKMATH_API Vector	ipsdk::math::clustering::computeClusterCenter (const ipUInt32 clusterIdx, const VectorColl &inputDataColl, const UInt32Vector &indexColl)
	function allowing to compute center associated to a given cluster More...
IPSDKMATH_API ipReal64	ipsdk::math::clustering::computeClusterStdDev (const ipUInt32 clusterIdx, const VectorColl &inputDataColl, const UInt32Vector &indexColl, const Vector &clusterCenter)
	function allowing to compute standard deviation associated to a given cluster More...
IPSDKMATH_API ipReal64	ipsdk::math::clustering::radialBasisKernelSigmaFromDynamic (const VectorColl &inputDataColl, const ipReal64 ratio=0.2)
	function allowing to compute a sigma value used for radial basis kernel computations based on input data dynamic. This function computes sum of ranges over all inputDataColl dimensions and multiply this value by input ratio value
IPSDKMATH_API std::ostream &	ipsdk::math::clustering::operator<< (std::ostream &os, const KMeansClustering &clustering)
	operator allowing to write KMeansClustering data to a stream
template<typename KernelType >
IPSDKMATH_API std::ostream &	ipsdk::math::clustering::operator<< (std::ostream &os, const KernelKMeansClustering< KernelType > &clustering)
	operator allowing to write KernelKMeansClustering data to a stream
template<typename KernelType >
IPSDKMATH_API std::ostream &	ipsdk::math::clustering::operator<< (std::ostream &os, const SpectralClustering< KernelType > &clustering)
	operator allowing to write SpectralClustering data to a stream

Detailed Description

Utility functions for clustering management.

Author

E.Noirfalise

Date

2018/07/26

Copyright

2018 Reactiv'IP, all right reserved.

Function Documentation

computeCompactness()

IPSDKMATH_API ipReal64 ipsdk::math::clustering::computeCompactness	(	const VectorColl &	clustersCentersColl,
		const VectorColl &	dataColl,
		const UInt32Vector &	indexColl
	)

function allowing to compute compactness associated to a clustering result

Warning

there is no check on input collections size and content

computeKernelCompactness()

template<typename KernelType >

IPSDKMATH_API ipReal64 ipsdk::math::clustering::computeKernelCompactness	(	const std::vector< KernelKMeansCentroid< KernelType > > &	clustersCentroids,
		const VectorColl &	dataColl,
		const UInt32Vector &	indexColl
	)

function allowing to compute compactness associated to a Kernel clustering result

Warning

there is no check on input collections size and content

computeValidityIndex()

IPSDKMATH_API ipReal64 ipsdk::math::clustering::computeValidityIndex	(	const VectorColl &	clustersCentersColl,
		const VectorColl &	dataColl,
		const UInt32Vector &	indexColl,
		const eValidityIndexType &	validityIndexType
	)

function allowing to compute a validity index of a clustering

See also

Cluster Validity Measurement Techniques, Ferenc Kovács, Csaba Legány, Attila Babos, AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases Pages 388-393

Warning

there is no check on input collections size and content

generateKMeansPPInit()

IPSDKMATH_API void ipsdk::math::clustering::generateKMeansPPInit	(	const ipUInt32	nbClusters,
		const VectorColl &	inputDataColl,
		VectorColl &	initClustersCenters
	)

function allowing to generate an initial solution for K-Means algorithm using K-Means++ approach

Warning

there is no check on data size consistency

Exceptions

ipsdk::math::IPSDKMathException

if nbClusters > inputDataColl.size()

generateKernelKMeansPPInit()

template<typename KernelType >

IPSDKMATH_API void ipsdk::math::clustering::generateKernelKMeansPPInit	(	const KernelType &	kernel,
		const ipUInt32	nbClusters,
		const VectorColl &	inputDataColl,
		VectorColl &	initClustersCenters
	)

function allowing to generate an initial solution for Kernel K-Means algorithm using K-Means++ approach

Warning

there is no check on data size consistency

Exceptions

ipsdk::math::IPSDKMathException

if nbClusters > inputDataColl.size()

computeAssignment()

IPSDKMATH_API ipUInt32 ipsdk::math::clustering::computeAssignment	(	const VectorColl &	clustersCentersColl,
		const Vector &	data
	)

function allowing to compute cluster assignment using an euclidian distance to clusters centers

Warning

there is no check on input collections size and content

computeKMeansAttempt()

IPSDKMATH_API ipUInt32 ipsdk::math::clustering::computeKMeansAttempt	(	const VectorColl &	inputDataColl,
		const ipUInt32	nbMaxIteration,
		VectorColl &	inOutClustersCenters,
		UInt32Vector &	assignments
	)

function allowing to compute a kmeans attempt from an initial solution

Warning

there is no check on input collections size and content

Returns

number of proceeded iterations (nbMaxIteration stands for failure)

computeKernelAssignment()

template<typename KernelType >

IPSDKMATH_API ipUInt32 ipsdk::math::clustering::computeKernelAssignment	(	const std::vector< KernelKMeansCentroid< KernelType > > &	clustersCentroids,
		const Vector &	data
	)

function allowing to compute cluster assignment using an kernel distance to clusters centroids

Warning

there is no check on input collections size and content

computeKernelKMeansAttempt()

template<typename KernelType >

IPSDKMATH_API ipUInt32 ipsdk::math::clustering::computeKernelKMeansAttempt	(	const KernelType &	kernel,
		const VectorColl &	inputDataColl,
		const ipUInt32	nbMaxIteration,
		const ipReal64	minChangeRatio,
		std::vector< KernelKMeansCentroid< KernelType > > &	inOutClustersCentroids,
		UInt32Vector &	assignments
	)

function allowing to compute a kernel kmeans attempt from an initial solution

Warning

there is no check on input collections size and content

Returns

number of proceeded iterations (nbMaxIteration stands for failure)

computeClustersCenters()

IPSDKMATH_API void ipsdk::math::clustering::computeClustersCenters	(	const ipUInt32	nbClusters,
		const VectorColl &	inputDataColl,
		const UInt32Vector &	indexColl,
		VectorColl &	clustersCentersColl
	)

function allowing to compute clusters centers given data assignments using mean computation

Warning

there is no check on input collections size and content

computeClusterPopulation()

IPSDKMATH_API ipUInt32 ipsdk::math::clustering::computeClusterPopulation	(	const ipUInt32	clusterIdx,
		const UInt32Vector &	indexColl
	)

function allowing to compute population associated to a given cluster

Warning

there is no check on input collections size and content

computeClusterCenter()

IPSDKMATH_API Vector ipsdk::math::clustering::computeClusterCenter	(	const ipUInt32	clusterIdx,
		const VectorColl &	inputDataColl,
		const UInt32Vector &	indexColl
	)

function allowing to compute center associated to a given cluster

Warning

there is no check on input collections size and content

computeClusterStdDev()

IPSDKMATH_API ipReal64 ipsdk::math::clustering::computeClusterStdDev	(	const ipUInt32	clusterIdx,
		const VectorColl &	inputDataColl,
		const UInt32Vector &	indexColl,
		const Vector &	clusterCenter
	)

function allowing to compute standard deviation associated to a given cluster

Warning

there is no check on input collections size and content