Functions
void	single_linkage (const raft::handle_t &handle, const float X, int n_rows, int n_cols, size_t n_clusters, ML::distance::DistanceType metric, int children, int *labels, bool use_knn=false, int c=15)
	Computes single-linkage hierarchical clustering on a dense input feature matrix and outputs the labels, dendrogram, and minimum spanning tree. More...

Function Documentation

◆ single_linkage()

void ML::linkage::single_linkage	(	const raft::handle_t &	handle,
		const float *	X,
		int	n_rows,
		int	n_cols,
		size_t	n_clusters,
		ML::distance::DistanceType	metric,
		int *	children,
		int *	labels,
		bool	use_knn = `false`,
		int	c = `15`
	)

Computes single-linkage hierarchical clustering on a dense input feature matrix and outputs the labels, dendrogram, and minimum spanning tree.

Parameters

[in]	handle	raft handle to encapsulate expensive resources
[in]	X	dense feature matrix on device, C contiguous
[in]	n_rows	number of rows in X
[in]	n_cols	number of columns in X
[in]	n_clusters	the number of clusters to fit.
[in]	metric	distance metric to use. Must be supported by the dense pairwise distances API.
[out]	children	the output dendrogram, shape=(n_rows - 1, 2), C contiguous
[out]	labels	the output labels, shape=(n_rows,)
[in]	use_knn	whether to construct a knn graph instead of the full n^2 pairwise distance matrix. This can be faster for very large datasets or in cases where lower memory usage is required.
[in]	c	tunes the number of neighbors when `use_knn` is true, where `n_neighbors=log(n_rows) + c`.