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Find *k*-nearest neighbors using data

`Idx = knnsearch(X,Y)`

`Idx = knnsearch(X,Y,Name,Value)`

`[Idx,D] = knnsearch(___)`

returns `Idx`

= knnsearch(`X`

,`Y`

,`Name,Value`

)`Idx`

with additional options specified using one or more
name-value pair arguments. For example, you can specify the number of nearest
neighbors to search for and the distance metric used in the search.

For a fixed positive integer

*k*,`knnsearch`

finds the*k*points in`X`

that are the nearest to each point in`Y`

. To find all points in`X`

within a fixed distance of each point in`Y`

, use`rangesearch`

.`knnsearch`

does not save a search object. To create a search object, use`createns`

.

For information on a specific search algorithm, see Distance Metrics.

If you set the `knnsearch`

function's `'NSMethod'`

name-value pair argument to the appropriate value (`'exhaustive'`

for
an exhaustive search algorithm or `'kdtree'`

for a
*K*d-tree algorithm), then the search results are equivalent to the
results obtained by conducting a distance search using the `knnsearch`

object function. Unlike the `knnsearch`

function, the `knnsearch`

object function requires an
`ExhaustiveSearcher`

or a `KDTreeSearcher`

model object.

[1] Friedman, J. H., J. Bentely, and R. A. Finkel. “An Algorithm for Finding Best Matches in Logarithmic Expected Time.” ACM Transactions on Mathematical Software 3, no. 3 (1977): 209–226.

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