ml::KMeans< T > Class Template Reference

#include <kmeans.hpp>

Public Member Functions
	KMeans (size_t k, size_t max_iters=300, T tol=1e-4, int random_state=-1)
	Constructor with number of clusters.
void	fit (const Matrix< T > &data)
	Fit the K-Means model to the data.
std::vector< int >	predict (const Matrix< T > &data) const
	Predict cluster labels for the given data.
std::vector< int >	fit_predict (const Matrix< T > &data)
	Fit the model and predict cluster labels.
Matrix< T >	cluster_centers () const
	Get the cluster centroids.
T	inertia () const
	Get the within-cluster sum of squares (inertia)
size_t	n_iter () const
	Get the number of iterations performed.

Detailed Description

template<typename T>
class ml::KMeans< T >

Examples

/home/runner/work/deep-learning-algo-impls/deep-learning-algo-impls/include/ml/ml.hpp.

Definition at line 41 of file kmeans.hpp.

Constructor & Destructor Documentation

KMeans()

template<typename T >

ml::KMeans< T >::KMeans	(	size_t	k,
		size_t	max_iters = 300,
		T	tol = 1e-4,
		int	random_state = -1
	)

Constructor with number of clusters.

Parameters

k	Number of clusters
max_iters	Maximum number of iterations
tol	Tolerance for convergence
random_state	Random seed for reproducibility

Definition at line 11 of file kmeans.cpp.

Member Function Documentation

cluster_centers()

template<typename T >

Matrix< T > ml::KMeans< T >::cluster_centers

(

)

const

Get the cluster centroids.

Returns

Matrix containing cluster centroids

Examples

/home/runner/work/deep-learning-algo-impls/deep-learning-algo-impls/include/ml/kmeans.hpp.

Definition at line 88 of file kmeans.cpp.

fit()

template<typename T >

void ml::KMeans< T >::fit

(

const Matrix< T > &

data

)

Fit the K-Means model to the data.

Parameters

data	Input data matrix where rows are samples and columns are features

Examples

/home/runner/work/deep-learning-algo-impls/deep-learning-algo-impls/include/ml/kmeans.hpp.

Definition at line 20 of file kmeans.cpp.

fit_predict()

template<typename T >

std::vector< int > ml::KMeans< T >::fit_predict

(

const Matrix< T > &

data

)

Fit the model and predict cluster labels.

Parameters

data	Input data matrix

Returns

Vector of cluster labels (0 to k-1)

Examples

/home/runner/work/deep-learning-algo-impls/deep-learning-algo-impls/include/ml/kmeans.hpp.

Definition at line 82 of file kmeans.cpp.

inertia()

template<typename T >

T ml::KMeans< T >::inertia

(

)

const

Get the within-cluster sum of squares (inertia)

Returns

The inertia value

Examples

/home/runner/work/deep-learning-algo-impls/deep-learning-algo-impls/include/ml/kmeans.hpp.

Definition at line 96 of file kmeans.cpp.

n_iter()

template<typename T >

size_t ml::KMeans< T >::n_iter

(

)

const

Get the number of iterations performed.

Returns

Number of iterations

Examples

/home/runner/work/deep-learning-algo-impls/deep-learning-algo-impls/include/ml/kmeans.hpp.

Definition at line 104 of file kmeans.cpp.

predict()

template<typename T >

std::vector< int > ml::KMeans< T >::predict

(

const Matrix< T > &

data

)

const

Predict cluster labels for the given data.

Parameters

data	Input data matrix

Returns

Vector of cluster labels (0 to k-1)

Examples

/home/runner/work/deep-learning-algo-impls/deep-learning-algo-impls/include/ml/kmeans.hpp.

Definition at line 74 of file kmeans.cpp.

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

• include/ml/kmeans.hpp
• src/ml/kmeans.cpp