CoverTree {less}

CoverTree - Nearest Neighbor Search

Description

Wrapper R6 Class of FNN::get.knnx function that can be used for LESSRegressor and LESSClassifier

Details

The cover tree is O(n) space data structure which allows us to answer queries in the same O(log(n)) time as kd tree given a fixed intrinsic dimensionality. Templated code from https://hunch.net/~jl/projects/cover_tree/cover_tree.html is used.

Value

R6 Class of CoverTree

Methods

Public methods

• CoverTree$new()

• CoverTree$query()

• CoverTree$clone()

Method new()

Creates a new instance of R6 Class of CoverTree

Usage

CoverTree$new(X = NULL)

Arguments

X

An M x d data.frame or matrix, where each of the M rows is a point or a (column) vector (where d=1).

Examples

data(abalone)
ct <- CoverTree$new(abalone[1:100,])

Method query()

Finds the p number of near neighbours for each point in an input/output dataset.

Usage

CoverTree$query(query_X = private$X, k = 1)

Arguments

query_X

A set of N x d points that will be queried against X. d, the number of columns, must be the same as X. If missing, defaults to X.

k

The maximum number of nearest neighbours to compute (deafults to 1).

Returns

A list of length 2 with elements:

nn.idx	A N x k integer matrix returning the near neighbour indices.
nn.dists	A N x k matrix returning the near neighbour Euclidean distances

Examples

res <- ct$query(abalone[1:3,], k=2)
print(res)

Method clone()

The objects of this class are cloneable with this method.

Usage

CoverTree$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

FNN::get.knnx()

Examples

## ------------------------------------------------
## Method `CoverTree$new`
## ------------------------------------------------

data(abalone)
ct <- CoverTree$new(abalone[1:100,])

## ------------------------------------------------
## Method `CoverTree$query`
## ------------------------------------------------

res <- ct$query(abalone[1:3,], k=2)
print(res)