NaiveNearestNeighbour

class NaiveNearestNeighbour(*args)

Brute force algorithm for nearest-neighbour lookup.

Parameters:

sample2-d sequence of float

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])]

Methods

getClassName()	Accessor to the object's name.
getId()	Accessor to the object's id.
getName()	Accessor to the object's name.
getSample()	Get the points which have been used to build this nearest neighbour algorithm.
getShadowedId()	Accessor to the object's shadowed id.
getVisibility()	Accessor to the object's visibility state.
hasName()	Test if the object is named.
hasVisibleName()	Test if the object has a distinguishable name.
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.
setShadowedId(id)	Accessor to the object's shadowed id.
setVisibility(visible)	Accessor to the object's visibility state.

__init__(*args)

getClassName()

Accessor to the object’s name.

Returns:

class_namestr

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

getId()

Accessor to the object’s id.

Returns:

idint

Internal unique identifier.

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.

getShadowedId()

Accessor to the object’s shadowed id.

Returns:

idint

Internal unique identifier.

getVisibility()

Accessor to the object’s visibility state.

Returns:

visiblebool

Visibility flag.

hasName()

Test if the object is named.

Returns:

hasNamebool

True if the name is not empty.

hasVisibleName()

Test if the object has a distinguishable name.

Returns:

hasVisibleNamebool

True if the name is not empty and not the default one.

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.

setShadowedId(id)

Accessor to the object’s shadowed id.

Parameters:

idint

Internal unique identifier.

setVisibility(visible)

Accessor to the object’s visibility state.

Parameters:

visiblebool

Visibility flag.