mltk::clusterer::KMeans< T, Callable > Class Template Reference

Wrapper for the implementation of the K-Means clustering algorithm. More...

#include <KMeans.hpp>

Inheritance diagram for mltk::clusterer::KMeans< T, Callable >:

Collaboration diagram for mltk::clusterer::KMeans< T, Callable >:

Public Member Functions
	KMeans (const Data< T > &samples, size_t k, std::string initialization="random", size_t seed=0, int verbose=0)
bool	train () override
	Function that execute the training phase of a Learner. More...
double	assign_clusters (const std::vector< mltk::PointPointer< T >> &points)
double	clusters_variance (const Clusters &clusters)
void	compute_centers ()
double	evaluate (const Point< T > &p, bool raw_value=false) override
	Returns the class of a feature point based on the trained Learner. More...
std::vector< mltk::Point< size_t > >	clusters () override
std::string	getFormulationString () override
	getFormulationString Returns a string that represents the formulation of the learner (Primal or Dual). More...
Public Member Functions inherited from mltk::clusterer::Clusterer< double >
	Clusterer (DataPointer< double > samples=nullptr, size_t clusters=0)
std::vector< mltk::Point< double > >	centers ()
Public Member Functions inherited from mltk::Learner< T >
	Learner (DataPointer< T > _samples)
	Learner (const Learner< T > &learner)
virtual mltk::Point< double >	batchEvaluate (const Data< T > &data)
	evaluate a batch of points. More...
auto	getSamples ()
	Get the Data used by the learner. More...
double	getElapsedTime () const
	Get the elapsed time in the training phase of the Learner. More...
int	getCtot () const
	Get the total number of updates of the Learner. More...
int	getSteps () const
	getSteps Returns the number of steps through the data by the Learner. More...
int	getUpdates () const
	getUpdates Returns the number of updates needed to get to the the solution. More...
double	getMaxTime () const
	getMaxTime Returns the maximum running time in the training phase of the Learner. More...
double	getPredictionProbability () const
	Get the probability of the last prediction. More...
void	setSeed (const size_t _seed)
	Set the seed to be used by the learner. More...
virtual void	setSamples (const Data< T > &data)
	setSamples Set the samples used by the Learner. More...
virtual void	setSamples (DataPointer< T > data)
	setSamples Set the samples used by the Learner. More...
void	setTimer (Timer _timer)
	setTimer Set the timer used by the Learner. More...
void	setSteps (int _steps)
	Set the partial number of steps used in the training phase of the Learner. More...
void	setCtot (int _ctot)
	Set the partial number of updates of the Learner. More...
void	setVerbose (int _verbose)
	Set the level of verbose. More...
void	setStartTime (double stime)
	setStartTime Set the initial time of the Learner. More...
void	setMaxTime (double maxtime)
	Set the max time of execution. More...
void	setEPS (double eps)
	setEPS Set the precision of the Learner. More...
void	setMaxIterations (int max_it)
	setMaxIterations Set the max number of iterations of the Learner. More...
void	setMaxEpochs (int MAX_EPOCHS)
	Set the max number of epochs for the learner training. More...
void	setMaxUpdates (int max_up)
	setMaxIterations Set the max number of updates of the Learner. More...
void	setLearningRate (double learning_rate)
	Set the learning rate of the Learner. More...

Additional Inherited Members
Protected Attributes inherited from mltk::clusterer::Clusterer< double >
metrics::dist::Euclidean< double >	dist_function
	Function used to compute the metrics between two points. More...
size_t	n_clusters
	Number of clusters for the cluster method. More...
std::vector< mltk::Point< double > >	m_centers
	Vector with the centers of the clusters. More...
std::vector< mltk::Point< size_t > >	m_clusters
	Clusters of points. More...
Protected Attributes inherited from mltk::Learner< T >
std::shared_ptr< Data< T > >	samples
	Samples used in the model training. More...
double	rate = 0.5f
	Learning rate. More...
double	start_time = 0.0f
	Initial time. More...
double	max_time = 110
	Maximum time of training. More...
int	steps = 0
	Number of steps in the data. More...
int	ctot = 0
	Number of updates of the weights. More...
double	EPS = 0.0000001
	Max precision. More...
double	MIN_INC = 1.001
	Minimun Increment. More...
int	MAX_IT = 1E9
	Max number of iterations. More...
int	MAX_UP = 1E9
	Max number of updates. More...
int	MAX_EPOCH = 1E9
int	verbose = 1
	Verbose level of the output. More...
Timer	timer = Timer()
	Timer used to measure the time elapsed in the execution of a Learner. More...
size_t	seed = 0
	seed for random operations. More...
double	pred_prob = 1.0

Detailed Description

template<typename T = double, typename Callable = metrics::dist::Euclidean<T>>
class mltk::clusterer::KMeans< T, Callable >

Wrapper for the implementation of the K-Means clustering algorithm.

Member Function Documentation

evaluate()

template<typename T , typename Callable >

double mltk::clusterer::KMeans< T, Callable >::evaluate ( const Point< T > &  p, bool  raw_value = false  )

overridevirtual

Returns the class of a feature point based on the trained Learner.

Parameters

p	Point to be evaluated.

Returns

Prediction made by the learner.

Implements mltk::Learner< T >.

getFormulationString()

template<typename T , typename Callable >

std::string mltk::clusterer::KMeans< T, Callable >::getFormulationString ( )

overridevirtual

getFormulationString Returns a string that represents the formulation of the learner (Primal or Dual).

Returns

std::string

Implements mltk::Learner< T >.

train()

template<typename T , typename Callable >

bool mltk::clusterer::KMeans< T, Callable >::train ( )

overridevirtual

Function that execute the training phase of a Learner.

Returns

void

Implements mltk::Learner< T >.

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

• KMeans.hpp