ML::kmeans Namespace Reference

Classes
struct	KMeansParams

Functions
void	fit (const raft::handle_t &handle, const KMeansParams &params, const float *X, int n_samples, int n_features, const float *sample_weight, float *centroids, float &inertia, int &n_iter)
	Compute k-means clustering for each sample in the input. More...
void	fit (const raft::handle_t &handle, const KMeansParams &params, const double *X, int n_samples, int n_features, const double *sample_weight, double *centroids, double &inertia, int &n_iter)
void	fit (const raft::handle_t &handle, const KMeansParams &params, const float *X, int64_t n_samples, int64_t n_features, const float *sample_weight, float *centroids, float &inertia, int64_t &n_iter)
void	fit (const raft::handle_t &handle, const KMeansParams &params, const double *X, int64_t n_samples, int64_t n_features, const double *sample_weight, double *centroids, double &inertia, int64_t &n_iter)
void	predict (const raft::handle_t &handle, const KMeansParams &params, const float *centroids, const float *X, int n_samples, int n_features, const float *sample_weight, bool normalize_weights, int *labels, float &inertia)
	Predict the closest cluster each sample in X belongs to. More...
void	predict (const raft::handle_t &handle, const KMeansParams &params, const double *centroids, const double *X, int n_samples, int n_features, const double *sample_weight, bool normalize_weights, int *labels, double &inertia)
void	predict (const raft::handle_t &handle, const KMeansParams &params, const float *centroids, const float *X, int64_t n_samples, int64_t n_features, const float *sample_weight, bool normalize_weights, int64_t *labels, float &inertia)
void	predict (const raft::handle_t &handle, const KMeansParams &params, const double *centroids, const double *X, int64_t n_samples, int64_t n_features, const double *sample_weight, bool normalize_weights, int64_t *labels, double &inertia)
void	transform (const raft::handle_t &handle, const KMeansParams &params, const float *centroids, const float *X, int n_samples, int n_features, float *X_new)
	Transform X to a cluster-distance space. More...
void	transform (const raft::handle_t &handle, const KMeansParams &params, const double *centroids, const double *X, int n_samples, int n_features, double *X_new)
void	transform (const raft::handle_t &handle, const KMeansParams &params, const float *centroids, const float *X, int64_t n_samples, int64_t n_features, float *X_new)
void	transform (const raft::handle_t &handle, const KMeansParams &params, const double *centroids, const double *X, int64_t n_samples, int64_t n_features, double *X_new)

Function Documentation

fit() [1/4]

void ML::kmeans::fit	(	const raft::handle_t &	handle,
		const KMeansParams &	params,
		const double *	X,
		int	n_samples,
		int	n_features,
		const double *	sample_weight,
		double *	centroids,
		double &	inertia,
		int &	n_iter
	)

fit() [2/4]

void ML::kmeans::fit	(	const raft::handle_t &	handle,
		const KMeansParams &	params,
		const double *	X,
		int64_t	n_samples,
		int64_t	n_features,
		const double *	sample_weight,
		double *	centroids,
		double &	inertia,
		int64_t &	n_iter
	)

fit() [3/4]

void ML::kmeans::fit	(	const raft::handle_t &	handle,
		const KMeansParams &	params,
		const float *	X,
		int	n_samples,
		int	n_features,
		const float *	sample_weight,
		float *	centroids,
		float &	inertia,
		int &	n_iter
	)

Compute k-means clustering for each sample in the input.

Parameters

[in]	handle	The handle to the cuML library context that manages the CUDA resources.
[in]	params	Parameters for KMeans model.
[in]	X	Training instances to cluster. It must be noted that the data must be in row-major format and stored in device accessible location.
[in]	n_samples	Number of samples in the input X.
[in]	n_features	Number of features or the dimensions of each sample.
[in]	sample_weight	The weights for each observation in X.
[in,out]	centroids	[in] When init is InitMethod::Array, use centroids as the initial cluster centers [out] Otherwise, generated centroids from the kmeans algorithm is stored at the address pointed by 'centroids'.
[out]	inertia	Sum of squared distances of samples to their closest cluster center.
[out]	n_iter	Number of iterations run.

fit() [4/4]

void ML::kmeans::fit	(	const raft::handle_t &	handle,
		const KMeansParams &	params,
		const float *	X,
		int64_t	n_samples,
		int64_t	n_features,
		const float *	sample_weight,
		float *	centroids,
		float &	inertia,
		int64_t &	n_iter
	)

predict() [1/4]

void ML::kmeans::predict	(	const raft::handle_t &	handle,
		const KMeansParams &	params,
		const double *	centroids,
		const double *	X,
		int	n_samples,
		int	n_features,
		const double *	sample_weight,
		bool	normalize_weights,
		int *	labels,
		double &	inertia
	)

predict() [2/4]

void ML::kmeans::predict	(	const raft::handle_t &	handle,
		const KMeansParams &	params,
		const double *	centroids,
		const double *	X,
		int64_t	n_samples,
		int64_t	n_features,
		const double *	sample_weight,
		bool	normalize_weights,
		int64_t *	labels,
		double &	inertia
	)

predict() [3/4]

void ML::kmeans::predict	(	const raft::handle_t &	handle,
		const KMeansParams &	params,
		const float *	centroids,
		const float *	X,
		int	n_samples,
		int	n_features,
		const float *	sample_weight,
		bool	normalize_weights,
		int *	labels,
		float &	inertia
	)

Predict the closest cluster each sample in X belongs to.

Parameters

[in]	handle	The handle to the cuML library context that manages the CUDA resources.
[in]	params	Parameters for KMeans model.
[in]	centroids	Cluster centroids. It must be noted that the data must be in row-major format and stored in device accessible location.
[in]	X	New data to predict.
[in]	n_samples	Number of samples in the input X.
[in]	n_features	Number of features or the dimensions of each sample in 'X' (value should be same as the dimension for each cluster centers in 'centroids').
[in]	sample_weight	The weights for each observation in X.
[in]	normalize_weights	True if the weights should be normalized
[out]	labels	Index of the cluster each sample in X belongs to.
[out]	inertia	Sum of squared distances of samples to their closest cluster center.

predict() [4/4]

void ML::kmeans::predict	(	const raft::handle_t &	handle,
		const KMeansParams &	params,
		const float *	centroids,
		const float *	X,
		int64_t	n_samples,
		int64_t	n_features,
		const float *	sample_weight,
		bool	normalize_weights,
		int64_t *	labels,
		float &	inertia
	)

transform() [1/4]

void ML::kmeans::transform	(	const raft::handle_t &	handle,
		const KMeansParams &	params,
		const double *	centroids,
		const double *	X,
		int	n_samples,
		int	n_features,
		double *	X_new
	)

transform() [2/4]

void ML::kmeans::transform	(	const raft::handle_t &	handle,
		const KMeansParams &	params,
		const double *	centroids,
		const double *	X,
		int64_t	n_samples,
		int64_t	n_features,
		double *	X_new
	)

transform() [3/4]

void ML::kmeans::transform	(	const raft::handle_t &	handle,
		const KMeansParams &	params,
		const float *	centroids,
		const float *	X,
		int	n_samples,
		int	n_features,
		float *	X_new
	)

Transform X to a cluster-distance space.

Parameters

[in]	handle	The handle to the cuML library context that manages the CUDA resources.
[in]	params	Parameters for KMeans model.
[in]	centroids	Cluster centroids. It must be noted that the data must be in row-major format and stored in device accessible location.
[in]	X	Training instances to cluster. It must be noted that the data must be in row-major format and stored in device accessible location.
[in]	n_samples	Number of samples in the input X.
[in]	n_features	Number of features or the dimensions of each sample in 'X' (it should be same as the dimension for each cluster centers in 'centroids').
[out]	X_new	X transformed in the new space..

transform() [4/4]

void ML::kmeans::transform	(	const raft::handle_t &	handle,
		const KMeansParams &	params,
		const float *	centroids,
		const float *	X,
		int64_t	n_samples,
		int64_t	n_features,
		float *	X_new
	)