# knnsearch

Find nearest neighbors by edit distance

## Syntax

## Description

## Examples

### Find Nearest Words

Create an edit distance searcher.

vocabulary = ["Text" "Analytics" "Toolbox"]; eds = editDistanceSearcher(vocabulary,2);

Find the nearest words to `"Test"`

and `"Analysis"`

.

words = ["Test" "Analysis"]; idx = knnsearch(eds,words)

`idx = `*2×1*
1
2

Get the words from the vocabulary using the returned indices.

nearestWords = eds.Vocabulary(idx)

`nearestWords = `*1x2 string*
"Text" "Analytics"

### Find Edit Distances to Nearest Words

Create an edit distance searcher.

vocabulary = ["MATLAB" "Text" "Analytics" "Toolbox"]; eds = editDistanceSearcher(vocabulary,2);

Find the nearest words and their edit distances to `"Test"`

and `"Analysis"`

.

words = ["Test" "Analysis"]; [idx,d] = knnsearch(eds,words)

`idx = `*2×1*
2
3

`d = `*2×1*
1
2

Get the words from the vocabulary using the returned indices.

nearestWords = eds.Vocabulary(idx)

`nearestWords = `*1x2 string*
"Text" "Analytics"

Changing the word `"Test"`

to `"Text"`

requires one edit: a substitution. Changing the word `"Analysis"`

into `"Analytics"`

requires two edits: a substitution and an insertion.

### Find Multiple Neighbors

Create an edit distance searcher.

vocabulary = ["MathWorks" "MATLAB" "Analytics"]; eds = editDistanceSearcher(vocabulary,5);

Find the two nearest words and their edit distances to `"Math"`

and `"Analysis"`

.

words = ["Math" "Analysis"]; idx = knnsearch(eds,words,'K',2)

`idx = `*2×2*
1 2
3 NaN

View the two closest words to `"Math"`

.

idxMath = idx(1,:); newWords = eds.Vocabulary(idxMath)

`newWords = `*1x2 string*
"MathWorks" "MATLAB"

There is only one word within the maximum edit distance from `"Analysis"`

, so the function returns `NaN`

for the other indices. View the nearest words with valid indices.

idxAnalysis = idx(2,:); idxAnalysis(isnan(idxAnalysis)) = []; newWords = eds.Vocabulary(idxAnalysis)

newWords = "Analytics"

## Input Arguments

`eds`

— Edit distance searcher

`editDistanceSearcher`

Edit distance searcher, specified as an `editDistanceSearcher`

object.

`words`

— Input words

string vector | character vector | cell array of character vectors

Input words, specified as a string vector, character vector, or cell array of character vectors. If you specify `words`

as a character vector, then the function treats the argument as a single word.

**Data Types: **`string`

| `char`

| `cell`

### Name-Value Arguments

Specify optional pairs of arguments as
`Name1=Value1,...,NameN=ValueN`

, where `Name`

is
the argument name and `Value`

is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.

*
Before R2021a, use commas to separate each name and value, and enclose*
`Name`

*in quotes.*

**Example: **`knnsearch(eds,words,'K',3)`

finds the nearest three neighbors
in `eds`

to the elements of `words`

.

`K`

— Number of nearest neighbors to find

1 (default) | positive integer

Number of nearest neighbors to find for each element in
`words`

, specified as a positive integer.

**Example: **`'K',3`

**Data Types: **`single`

| `double`

| `int8`

| `int16`

| `int32`

| `int64`

| `uint8`

| `uint16`

| `uint32`

| `uint64`

`IncludeTies`

— Option to include neighbors whose distance values are equal

`false`

(default) | `true`

Option to return neighbors whose distance values are equal, specified as
`true`

or `false`

.

If `'IncludeTies'`

is `false`

, then the function
returns the *K* neighbors with the shortest edit distance, where
*K* is the number of neighbors to find. In this case, the function
outputs *N*-by-*K* matrices, where *N* is
the number of input words. To specify *K*, use the
`'K'`

name-value pair argument.

If `'IncludeTies'`

is `true`

, then the function also
returns the neighbors whose distances are equal to the *K*th smallest
distance in the output. In this case, the function outputs cell arrays of size
*N*-by-1, where *N* is the number of input words. The
elements of the cell arrays are vectors with at least *K* elements. The
function sorts the neighbors in each vector in ascending order of distance.

**Example: **`'IncludeTies',true`

**Data Types: **`logical`

## Output Arguments

`idx`

— Indices of nearest neighbors in searcher

matrix | cell array of vectors

Indices of nearest neighbors in the searcher, returned as a matrix or a cell array of vectors.

If `'IncludeTies'`

is `false`

, then the function
returns the *K* neighbors with the shortest edit distance, where
*K* is the number of neighbors to find. In this case, the function
outputs *N*-by-*K* matrices, where *N* is
the number of input words. To specify *K*, use the
`'K'`

name-value pair argument.

If `'IncludeTies'`

is `true`

, then the function also
returns the neighbors whose distances are equal to the *K*th smallest
distance in the output. In this case, the function outputs cell arrays of size
*N*-by-1, where *N* is the number of input words. The
elements of the cell arrays are vectors with at least *K* elements. The
function sorts the neighbors in each vector in ascending order of distance.

**Data Types: **`double`

| `cell`

`d`

— Edit distances to neighbors

matrix | cell array of vectors

Edit distances to neighbors, returned as a matrix or a cell array of vectors.

If `'IncludeTies'`

is `false`

, then the function
returns the *K* neighbors with the shortest edit distance, where
*K* is the number of neighbors to find. In this case, the function
outputs *N*-by-*K* matrices, where *N* is
the number of input words. To specify *K*, use the
`'K'`

name-value pair argument.

If `'IncludeTies'`

is `true`

, then the function also
returns the neighbors whose distances are equal to the *K*th smallest
distance in the output. In this case, the function outputs cell arrays of size
*N*-by-1, where *N* is the number of input words. The
elements of the cell arrays are vectors with at least *K* elements. The
function sorts the neighbors in each vector in ascending order of distance.

**Data Types: **`double`

| `cell`

## Version History

**Introduced in R2019a**

## See Also

`correctSpelling`

| `editDistance`

| `editDistanceSearcher`

| `rangesearch`

| `splitGraphemes`

| `tokenizedDocument`

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