Description Details Methods Author(s) See Also

A virtual class for indexing structures of different nearest-neighbor search algorithms.

The BiocNeighborIndex class is a virtual base class on which other index objects are built. There are 4 concrete subclasses:

`KmknnIndex`

: exact nearest-neighbor search with the KMKNN algorithm.`VptreeIndex`

: exact nearest-neighbor search with a VP tree.`AnnoyIndex`

: approximate nearest-neighbor search with the Annoy algorithm.`HnswIndex`

: approximate nearest-neighbor search with the HNSW algorithm.

These objects hold indexing structures for a given data set - see the associated documentation pages for more details. It also retains information about the input data as well as the sample names.

The main user-accessible methods are:

`show(object)`

:Display the class and dimensions of a BiocNeighborIndex

`object`

.`dim(x)`

:Return the dimensions of a BiocNeighborIndex

`x`

, in terms of the matrix used to construct it.`dimnames(x)`

:Return the dimension names of a BiocNeighborIndex

`x`

. Only the row names of the input matrix are stored, in the same order.

More advanced methods (intended for developers of other packages) are:

`bndata(object)`

:Return a numeric matrix containing the data used to construct

`object`

. Each column should represent a data point and each row should represent a variable (i.e., it is transposed compared to the usual input, for efficient column-major access in C++ code). Columns may be reordered from the input matrix according to`bnorder(object)`

.`bnorder(object)`

:Return an integer vector specifying the new ordering of columns in

`bndata(object)`

. This generally only needs to be considered if`raw.index=TRUE`

, see`?"BiocNeighbors-raw-index"`

.`bndistance(object)`

:Return a string specifying the distance metric to be used for searching, usually

`"Euclidean"`

or`"Manhattan"`

. Obviously, this should be the same as the distance metric used for constructing the index.

Aaron Lun

`KmknnIndex`

,
`VptreeIndex`

,
`AnnoyIndex`

,
and `HnswIndex`

for direct constructors.

`buildIndex`

for construction on an actual data set.

`findKNN`

and `queryKNN`

for dispatch.

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