less (version 0.1.0)

KDTree: KDTree - Nearest Neighbor Search

Description

Wrapper R6 Class of RANN::nn2 function that can be used for LESSRegressor and LESSClassifier

Value

R6 Class of KDTree

Methods

Public methods

• KDTree$new()

• KDTree$query()

• KDTree$clone()

---

Method new()

Creates a new instance of R6 Class of KDTree

Usage

KDTree$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)
kdt <- KDTree$new(abalone[1:100,])

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Method query()

Finds the p number of near neighbours for each point in an input/output dataset. The advantage of the kd-tree is that it runs in O(M log M) time.

Usage

KDTree$query(query_X = private$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 <- kdt$query(abalone[1:3,], k=2)
print(res)

---

Method clone()

The objects of this class are cloneable with this method.

Usage

KDTree$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

RANN::nn2()

Examples

## ------------------------------------------------
## Method `KDTree$new`
## ------------------------------------------------

data(abalone)
kdt <- KDTree$new(abalone[1:100,])

## ------------------------------------------------
## Method `KDTree$query`
## ------------------------------------------------

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