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

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

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

Public Member Functions
	Clusterer (DataPointer< T > samples=nullptr, size_t clusters=0)
virtual std::vector< mltk::Point< size_t > >	clusters ()
std::vector< mltk::Point< T > >	centers ()
Public Member Functions inherited from mltk::Learner< T >
	Learner (DataPointer< T > _samples)
	Learner (const Learner< T > &learner)
virtual bool	train ()=0
	Function that execute the training phase of a Learner. More...
virtual double	evaluate (const Point< T > &p, bool raw_value=false)=0
	Returns the class of a feature point based on the trained Learner. More...
virtual mltk::Point< double >	batchEvaluate (const Data< T > &data)
	evaluate a batch of points. More...
virtual std::string	getFormulationString ()=0
	getFormulationString Returns a string that represents the formulation of the learner (Primal or Dual). 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...

Protected Attributes
Callable	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< T > >	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
	Learning rate. More...
double	start_time
	Initial time. More...
double	max_time
	Maximum time of training. More...
int	steps
	Number of steps in the data. More...
int	ctot
	Number of updates of the weights. More...
double	EPS
	Max precision. More...
double	MIN_INC
	Minimun Increment. More...
int	MAX_IT
	Max number of iterations. More...
int	MAX_UP
	Max number of updates. More...
int	MAX_EPOCH
int	verbose
	Verbose level of the output. More...
Timer	timer
	Timer used to measure the time elapsed in the execution of a Learner. More...
size_t	seed
	seed for random operations. More...
double	pred_prob

Member Data Documentation

dist_function

template<typename T , typename Callable = metrics::dist::Euclidean <T>>

Callable mltk::clusterer::Clusterer< T, Callable >::dist_function

protected

Function used to compute the metrics between two points.

m_centers

template<typename T , typename Callable = metrics::dist::Euclidean <T>>

std::vector<mltk::Point<T> > mltk::clusterer::Clusterer< T, Callable >::m_centers

protected

Vector with the centers of the clusters.

m_clusters

template<typename T , typename Callable = metrics::dist::Euclidean <T>>

std::vector<mltk::Point<size_t> > mltk::clusterer::Clusterer< T, Callable >::m_clusters

protected

Clusters of points.

n_clusters

template<typename T , typename Callable = metrics::dist::Euclidean <T>>

size_t mltk::clusterer::Clusterer< T, Callable >::n_clusters {}

protected

Number of clusters for the cluster method.

---

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

• clusterer/Clusterer.hpp