NearestNeighbourAlgorithm

class NearestNeighbourAlgorithm(*args)

Nearest neighbour lookup.

Base class to define an algorithm to search for nearest neighbours of a list of points.

Available constructors:
NearestNeighbourAlgorithm(sample)
Parameters:
sample : Sample

Input points.

Notes

Two algorithms can be selected in any dimension:

Two algorithms are specific to 1D input dimension, and much more efficient:

It is recommended to use derived classes in order to select the best algorithm according to your data. If you create a generic NearestNeighbourAlgorithm, here is how the derived class is selected:

Examples

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

Methods

getClassName() Accessor to the object’s name.
getId() Accessor to the object’s id.
getImplementation(*args) Accessor to the underlying implementation.
getName() Accessor to the object’s name.
getSample() Get the points which have been used to build this nearest neighbour algorithm.
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.
__init__(*args)

Initialize self. See help(type(self)) for accurate signature.

getClassName()

Accessor to the object’s name.

Returns:
class_name : str

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

getId()

Accessor to the object’s id.

Returns:
id : int

Internal unique identifier.

getImplementation(*args)

Accessor to the underlying implementation.

Returns:
impl : Implementation

The implementation class.

getName()

Accessor to the object’s name.

Returns:
name : str

The name of the object.

getSample()

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

Returns:
sample : Sample

Input points.

query(*args)

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

Available usages:

query(point)

query(sample)

Parameters:
point : sequence of float

Given point.

sample : 2-d sequence of float

Given points.

Returns:
index : int

Index of the nearest neighbour of the given point.

indices : openturns.Indices

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:
x : sequence of float

Given point.

k : int

Number of indices to return.

sorted : bool, optional

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

Returns:
indices : sequence of int

Indices of the k nearest neighbours of the given point.

setName(name)

Accessor to the object’s name.

Parameters:
name : str

The name of the object.

setSample(sample)

Build a NearestNeighbourAlgorithm from these points.

Parameters:
sample : Sample

Input points.