KMeans Class Reference

Implements the K-Means clustering algorithm with K-Means++ initialization. More...

#include <KMeans.hpp>

Public Member Functions
	KMeans (int n_clusters=8, int max_iter=300, double tol=1e-4, unsigned int random_state=0)
	Constructs a KMeans object. More...
	~KMeans ()
	Destructor for KMeans.
void	fit (const std::vector< std::vector< double >> &X)
	Fits the KMeans model to the data. More...
std::vector< int >	predict (const std::vector< std::vector< double >> &X) const
	Predicts the closest cluster each sample in X belongs to. More...
const std::vector< std::vector< double > > &	get_cluster_centers () const
	Returns the cluster centers. More...

Detailed Description

Implements the K-Means clustering algorithm with K-Means++ initialization.

Constructor & Destructor Documentation

KMeans()

KMeans::KMeans	(	int	n_clusters = 8,
		int	max_iter = 300,
		double	tol = 1e-4,
		unsigned int	random_state = 0
	)

Constructs a KMeans object.

Parameters

n_clusters	The number of clusters to form.
max_iter	The maximum number of iterations.
tol	The tolerance to declare convergence.
random_state	Seed for random number generator (optional).

Member Function Documentation

fit()

void KMeans::fit

(

const std::vector< std::vector< double >> &

X

)

Fits the KMeans model to the data.

Parameters

X	A vector of feature vectors.

get_cluster_centers()

const std::vector< std::vector< double > > & KMeans::get_cluster_centers

(

)

const

Returns the cluster centers.

Returns

A vector of cluster centers.

predict()

std::vector< int > KMeans::predict

(

const std::vector< std::vector< double >> &

X

)

const

Predicts the closest cluster each sample in X belongs to.

Parameters

X	A vector of feature vectors.

Returns

A vector of cluster labels.

---

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

• ml_library_include/ml/clustering/KMeans.hpp