Namespaces
	crn::kmedoids
	k medoids

Classes
struct	crn::IterativeClustering< T, type >
	A utility class to create clusters iteratively. More...

class	crn::kMeans< T, type, type >
	k-means clustering algorithm More...

class	crn::IterativekNN< DataType, DistFunc >
	Iterative kNN. More...

class	crn::LazyDistanceMatrix< DATA, DISTANCE >
	Lazy computation of a distance matrix. More...

class	crn::SpectralClustering
	Spectral clustering. More...

Functions
template<typename ITER , typename std::enable_if< std::is_copy_assignable< typename std::iterator_traits< ITER >::value_type >::value &&std::is_assignable< typename std::add_lvalue_reference< typename std::decay< typename std::iterator_traits< ITER >::value_type >::type >::type, int >::value &&traits::HasLT< typename std::iterator_traits< ITER >::value_type >::value &&IsVectorOverR< typename std::iterator_traits< ITER >::value_type >::value, int >::type = 0>
std::pair< typename std::iterator_traits< ITER > ::value_type, typename std::iterator_traits< ITER > ::value_type >	crn::TwoMeans (ITER beg, ITER en, double stop_crit=0.00001)
	2-means clustering algorithm for scalar data More...

template<typename ITER >
std::pair< typename std::iterator_traits< ITER > ::value_type, typename std::iterator_traits< ITER > ::value_type >	crn::TwoMeans (ITER beg, ITER en, typename std::iterator_traits< ITER >::value_type p1, typename std::iterator_traits< ITER >::value_type p2, double stop_crit=0.00001) noexcept(std::is_nothrow_constructible< typename std::iterator_traits< ITER >::value_type >::value &&std::is_nothrow_copy_assignable< typename std::iterator_traits< ITER >::value_type >::value)
	2-means clustering algorithm for scalar data More...

template<typename DATA , typename DISTANCE >
LazyDistanceMatrix< DATA, DISTANCE >	crn::MakeLazyDistanceMatrix (const std::vector< DATA > &d, DISTANCE &&dist)
	Helper to avoid typing long type names. More...

std::vector< double >	crn::ComputeLOF (const SquareMatrixDouble &distmat, size_t k)
	Compute the Local Outlier Factor for each element from the distance matrix. More...

std::vector< double >	crn::ComputeLOF (const std::vector< std::vector< double >> &distmat, size_t k)
	Compute the Local Outlier Factor for each element from the distance matrix. More...

std::vector< double >	crn::ComputeLoOP (const SquareMatrixDouble &distmat, size_t k, double lambda)
	compute the local outlier probability for each element from the distance matrix More...

std::vector< double >	crn::ComputeLoOP (const std::vector< std::vector< double >> &distmat, size_t k, double lambda)
	compute the local outlier probability for each element from the distance matrix More...

enum	crn::AProClusters { crn::AProClusters::MEDIUM, crn::AProClusters::LOW }
	Strategies to limit the number of classes in affinity propagation. More...

std::pair< std::vector< size_t > , std::vector< size_t > >	crn::AffinityPropagation (const SquareMatrixDouble &distance_matrix, AProClusters nclusters, double damping=0.5, size_t stable_iters_stop=10, size_t max_iter=100)
	Computes clusters and their prototypes. More...

std::pair< std::vector< size_t > , std::vector< size_t > >	crn::AffinityPropagation (const SquareMatrixDouble &distance_matrix, double preference, double damping=0.5, size_t stable_iters_stop=10, size_t max_iter=100)
	Computes clusters and their prototypes. More...

std::pair< std::vector< size_t > , std::vector< size_t > >	crn::AffinityPropagation (const SquareMatrixDouble &distance_matrix, const std::vector< double > &preference, double damping=0.5, size_t stable_iters_stop=10, size_t max_iter=100)
	Computes clusters and their prototypes. More...

Detailed Description

Enumeration Type Documentation

enum crn::AProClusters

strong

Strategies to limit the number of classes in affinity propagation.

Enumerator
MEDIUM
LOW

Definition at line 36 of file CRNAffinityPropagation.h.

Function Documentation

std::pair< std::vector< size_t >, std::vector< size_t > > crn::AffinityPropagation	(	const SquareMatrixDouble &	distance_matrix,
		AProClusters	nclusters,
		double	damping = `0.5`,
		size_t	stable_iters_stop = `10`,
		size_t	max_iter = `100`
	)

Computes clusters and their prototypes.

Computes clusters and their prototypes

Parameters

[in]	distance_matrix	the distance matrix of the elements to cluster
[in]	nclusters	the strategy to limit the number of clusters
[in]	damping	the damping rate. 0 = no damping, default = 0.5. The higher the value the slower the algorithm will converge. Low values may result in oscillations.
[in]	stable_iters_stop	number of consecutive identical clusterings to stop the algorithm (default = 10).
[in]	max_iter	maximum number of iterations (default = 100).

Returns: a pair containing in first the indexes of the cluster prototypes and in second the cluster number for each element.

Definition at line 128 of file CRNAffinityPropagation.cpp.

std::pair< std::vector< size_t >, std::vector< size_t > > crn::AffinityPropagation	(	const SquareMatrixDouble &	distance_matrix,
		double	preference,
		double	damping = `0.5`,
		size_t	stable_iters_stop = `10`,
		size_t	max_iter = `100`
	)

Computes clusters and their prototypes.

Computes clusters and their prototypes

Parameters

[in]	distance_matrix	the distance matrix of the elements to cluster
[in]	preference	the "preference" value that drives the number of cluster (e.g: mean, median or max distance). The higher the value, the smaller the number of clusters.
[in]	damping	the damping rate. 0 = no damping, default = 0.5. The higher the value the slower the algorithm will converge. Low values may result in oscillations.
[in]	stable_iters_stop	number of consecutive identical clusterings to stop the algorithm (default = 10).
[in]	max_iter	maximum number of iterations (default = 100).

Returns: a pair containing in first the indexes of the cluster prototypes and in second the cluster number for each element.

Definition at line 160 of file CRNAffinityPropagation.cpp.

std::pair< std::vector< size_t >, std::vector< size_t > > crn::AffinityPropagation	(	const SquareMatrixDouble &	distance_matrix,
		const std::vector< double > &	preference,
		double	damping = `0.5`,
		size_t	stable_iters_stop = `10`,
		size_t	max_iter = `100`
	)

Computes clusters and their prototypes.

Computes clusters and their prototypes

Parameters

[in]	distance_matrix	the distance matrix of the elements to cluster
[in]	preference	the "preference" values that tells which elements are more likely to be cluster prototypes and that drives the number of cluster. The higher the values, the smaller the number of clusters.
[in]	damping	the damping rate. 0 = no damping, default = 0.5. The higher the value the slower the algorithm will converge. Low values may result in oscillations.
[in]	stable_iters_stop	number of consecutive identical clusterings to stop the algorithm (default = 10).
[in]	max_iter	maximum number of iterations (default = 100).

Returns: a pair containing in first the indexes of the cluster prototypes and in second the cluster number for each element.

Definition at line 178 of file CRNAffinityPropagation.cpp.

std::vector< double > crn::ComputeLOF	(	const SquareMatrixDouble &	distmat,
		size_t	k
	)

Compute the Local Outlier Factor for each element from the distance matrix.

Compute the Local Outlier Factor for each element from the distance matrix

Parameters

[in]	distamat	a square matrix
[in]	k	the size of the neighborhood

Exceptions

ExceptionDomain	k<=1
ExceptionDimension	distmat is not square
ExceptionLogic	k > distmat.size()

Returns: the list of LOFs

Definition at line 89 of file CRNOutliers.cpp.

std::vector< double > crn::ComputeLOF	(	const std::vector< std::vector< double >> &	distmat,
		size_t	k
	)

Compute the Local Outlier Factor for each element from the distance matrix.

Compute the Local Outlier Factor for each element from the distance matrix

Parameters

[in]	distamat	a square matrix
[in]	k	the size of the neighborhood

Exceptions

ExceptionDomain	k<=1
ExceptionDimension	distmat is not square
ExceptionLogic	k > distmat.size()

Returns: the list of LOFs

Definition at line 106 of file CRNOutliers.cpp.

std::vector< double > crn::ComputeLoOP	(	const SquareMatrixDouble &	distmat,
		size_t	k,
		double	lambda
	)

compute the local outlier probability for each element from the distance matrix

Compute the Local Outlier Probability for each element from the distance matrix

Parameters

[in]	distamat	a square matrix
[in]	k	the size of the neighborhood
[in]	lambda	the precision of the density estimation (lambda=1 -> 68%, 2 -> 95%, 3 -> 99.7%)

Exceptions

ExceptionDomain	k<=1 or lambda<=0
ExceptionDimension	distmat is not square
ExceptionLogic	k > distmat.size()

Returns: the list of LoOPs

Definition at line 182 of file CRNOutliers.cpp.

std::vector< double > crn::ComputeLoOP	(	const std::vector< std::vector< double >> &	distmat,
		size_t	k,
		double	lambda
	)

compute the local outlier probability for each element from the distance matrix

Compute the Local Outlier Probability for each element from the distance matrix

Parameters

[in]	distamat	a square matrix
[in]	k	the size of the neighborhood
[in]	lambda	the precision of the density estimation (lambda=1 -> 68%, 2 -> 95%, 3 -> 99.7%)

Exceptions

ExceptionDomain	k<=1 or lambda<=0
ExceptionDimension	distmat is not square
ExceptionLogic	k > distmat.size()

Returns: the list of LoOPs

Definition at line 200 of file CRNOutliers.cpp.

template<typename DATA , typename DISTANCE >

LazyDistanceMatrix<DATA, DISTANCE> crn::MakeLazyDistanceMatrix	(	const std::vector< DATA > &	d,
		DISTANCE &&	dist
	)

inline

Helper to avoid typing long type names.

Parameters

[in]	d	the population
[in]	dist	the distance function

Returns: a distance matrix with lazy computation of distances

Definition at line 107 of file CRNLazyDistanceMatrix.h.

template<typename ITER , typename std::enable_if< std::is_copy_assignable< typename std::iterator_traits< ITER >::value_type >::value &&std::is_assignable< typename std::add_lvalue_reference< typename std::decay< typename std::iterator_traits< ITER >::value_type >::type >::type, int >::value &&traits::HasLT< typename std::iterator_traits< ITER >::value_type >::value &&IsVectorOverR< typename std::iterator_traits< ITER >::value_type >::value, int >::type = 0>

std::pair<typename std::iterator_traits<ITER>::value_type, typename std::iterator_traits<ITER>::value_type> crn::TwoMeans	(	ITER	beg,
		ITER	en,
		double	stop_crit = `0.00001`
	)

2-means clustering algorithm for scalar data

2-means clustering utility for scalar data.

Exceptions

ExceptionDomain the range is empty

Parameters

[in]	beg	iterator on the first element
[in]	en	iterator after the last element
[in]	stop_crit	the maximal difference between two iterations to stop the algorithm

Author: Yann LEYDIER

Date: Jul 2013

Version: 0.1

Definition at line 60 of file CRN2Means.h.

template<typename ITER >

std::pair<typename std::iterator_traits<ITER>::value_type, typename std::iterator_traits<ITER>::value_type> crn::TwoMeans	(	ITER	beg,
		ITER	en,
		typename std::iterator_traits< ITER >::value_type	p1,
		typename std::iterator_traits< ITER >::value_type	p2,
		double	stop_crit = `0.00001`
	)

noexcept

2-means clustering algorithm for scalar data

2-means clustering utility for scalar data.

Exceptions

ExceptionDomain the range is empty

Parameters

[in]	beg	iterator on the first element
[in]	en	iterator after the last element
[in]	p1	first prototype
[in]	p2	first prototype
[in]	stop_crit	the maximal difference between two iterations to stop the algorithm

Author: Yann LEYDIER

Date: Jul 2013

Version: 0.1

Definition at line 123 of file CRN2Means.h.

Namespaces

Classes

Functions

Detailed Description

Enumeration Type Documentation

Function Documentation