NaiveNearestNeighbour

class NaiveNearestNeighbour(*args)

Brute force algorithm for nearest-neighbour lookup.

Parameters:

sample2-d sequence of float

Points.

Methods

getClassName()	Accessor to the object's name.
getName()	Accessor to the object's name.
getSample()	Get the points which have been used to build this nearest neighbour algorithm.
hasName()	Test if the object is named.
query(*args)	Get the index of the nearest neighbour of the given point.
queryK(x, k[, sorted])	Get the indices of nearest neighbours of the given point.
setName(name)	Accessor to the object's name.
setSample(sample)	Build a NearestNeighbourAlgorithm from these points.

See also

NearestNeighbourAlgorithm

Notes

This algorithm compares distance to all points in input sample. It can be used when sample size is very small, or in high dimension. In other cases, KDTree is much faster.

Examples

>>> import openturns as ot
>>> sample = ot.Normal(2).getSample(10)
>>> tree = ot.NaiveNearestNeighbour(sample)
>>> neighbour = sample[tree.query([0.1, 0.2])]

__init__(*args)

getClassName()

Accessor to the object’s name.

Returns:

class_namestr

The object class name (object.__class__.__name__).

getName()

Accessor to the object’s name.

Returns:

namestr

The name of the object.

getSample()

Get the points which have been used to build this nearest neighbour algorithm.

Returns:

sampleSample

Input points.

hasName()

Test if the object is named.

Returns:

hasNamebool

True if the name is not empty.

query(*args)

Get the index of the nearest neighbour of the given point.

Available usages:

query(point)

query(sample)

Parameters:

pointsequence of float

Given point.

sample2-d sequence of float

Given points.

Returns:

indexint

Index of the nearest neighbour of the given point.

indicesIndices

Index of the nearest neighbour of the given points.

queryK(x, k, sorted=False)

Get the indices of nearest neighbours of the given point.

Parameters:

xsequence of float

Given point.

kint

Number of indices to return.

sortedbool, optional

Boolean to tell whether returned indices are sorted according to the distance to the given point.

Returns:

indicessequence of int

Indices of the k nearest neighbours of the given point.

setName(name)

Accessor to the object’s name.

Parameters:

namestr

The name of the object.

setSample(sample)

Build a NearestNeighbourAlgorithm from these points.

Parameters:

sampleSample

Input points.