# knnsearch

Find *k*-nearest neighbors using input data

## Description

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.

## Examples

### Find Nearest Neighbors

Find the patients in the `hospital`

data set that most closely resemble the patients in `Y`

, according to age and weight.

Load the `hospital`

data set.

load hospital; X = [hospital.Age hospital.Weight]; Y = [20 162; 30 169; 40 168; 50 170; 60 171]; % New patients

Perform a `knnsearch`

between `X`

and `Y`

to find indices of nearest neighbors.

Idx = knnsearch(X,Y);

Find the patients in `X`

closest in age and weight to those in `Y`

.

X(Idx,:)

`ans = `*5×2*
25 171
25 171
39 164
49 170
50 172

### Find *k*-Nearest Neighbors Using Different Distance Metrics

Find the 10 nearest neighbors in `X`

to each point in `Y`

, first using the Minkowski distance metric and then using the Chebychev distance metric.

Load Fisher's iris data set.

load fisheriris X = meas(:,3:4); % Measurements of original flowers Y = [5 1.45;6 2;2.75 .75]; % New flower data

Perform a `knnsearch`

between `X`

and the query points `Y`

using Minkowski and Chebychev distance metrics.

[mIdx,mD] = knnsearch(X,Y,'K',10,'Distance','minkowski','P',5); [cIdx,cD] = knnsearch(X,Y,'K',10,'Distance','chebychev');

Visualize the results of the two nearest neighbor searches. Plot the training data. Plot the query points with the marker X. Use circles to denote the Minkowski nearest neighbors. Use pentagrams to denote the Chebychev nearest neighbors.

gscatter(X(:,1),X(:,2),species) line(Y(:,1),Y(:,2),'Marker','x','Color','k',... 'Markersize',10,'Linewidth',2,'Linestyle','none') line(X(mIdx,1),X(mIdx,2),'Color',[.5 .5 .5],'Marker','o',... 'Linestyle','none','Markersize',10) line(X(cIdx,1),X(cIdx,2),'Color',[.5 .5 .5],'Marker','p',... 'Linestyle','none','Markersize',10) legend('setosa','versicolor','virginica','query point',... 'minkowski','chebychev','Location','best')

### Accelerate Distance Computation Using `fasteuclidean`

Distance

Create two large matrices of points, and then measure the time used by `knnsearch`

with the default "`euclidean"`

distance metric.

rng default % For reproducibility N = 10000; X = randn(N,1000); Y = randn(N,1000); Idx = knnsearch(X,Y); % Warm up function for more reliable timing information tic Idx = knnsearch(X,Y); standard = toc

standard = 15.0068

Next, measure the time used by `knnsearch`

with the `"fasteuclidean"`

distance metric. Specify a cache size of 100.

Idx2 = knnsearch(X,Y,Distance="fasteuclidean",CacheSize=100); % Warm up function tic Idx2 = knnsearch(X,Y,Distance="fasteuclidean",CacheSize=100); accelerated = toc

accelerated = 1.3934

Evaluate how many times faster the accelerated computation is compared to the standard.

standard/accelerated

ans = 10.7696

The accelerated version is more than three times faster for this example.

## Input Arguments

`X`

— Input data

numeric matrix

Input data, specified as a numeric matrix. Rows of `X`

correspond to observations, and columns correspond to variables.

**Data Types: **`single`

| `double`

`Y`

— Query points

numeric matrix

Query points, specified as a numeric matrix. Rows of
`Y`

correspond to observations, and columns
correspond to variables. `Y`

must have the same number of
columns as `X`

.

**Data Types: **`single`

| `double`

### 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(X,Y,'K',10,'IncludeTies',true,'Distance','cityblock')`

searches for 10 nearest neighbors, including ties and using the city block
distance.

`IncludeTies`

— Flag to include all nearest neighbors

`false`

(`0`

) (default) | `true`

(`1`

)

Flag to include all nearest neighbors that have the same distance from
query points, specified as the comma-separated pair consisting of
`'IncludeTies'`

and `false`

(`0`

) or `true`

(`1`

).

If `'IncludeTies'`

is `false`

, then
`knnsearch`

chooses the observation with the
smallest index among the observations that have the same distance from a
query point.

If `'IncludeTies'`

is `true`

, then:

`knnsearch`

includes all nearest neighbors whose distances are equal to the*k*th smallest distance in the output arguments. To specify*k*, use the`'K'`

name-value pair argument.`Idx`

and`D`

are*m*-by-`1`

cell arrays such that each cell contains a vector of at least*k*indices and distances, respectively. Each vector in`D`

contains distances arranged in ascending order. Each row in`Idx`

contains the indices of the nearest neighbors corresponding to the distances in`D`

.

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

`NSMethod`

— Nearest neighbor search method

`'kdtree'`

| `'exhaustive'`

Nearest neighbor search method, specified as the comma-separated pair
consisting of `'NSMethod'`

and one of these values.

`'kdtree'`

— Creates and uses a*K*d-tree to find nearest neighbors.`'kdtree'`

is the default value when the number of columns in`X`

is less than or equal to 10,`X`

is not sparse, and the distance metric is`'euclidean'`

,`'cityblock'`

,`'chebychev'`

, or`'minkowski'`

. Otherwise, the default value is`'exhaustive'`

.The value

`'kdtree'`

is valid only when the distance metric is one of the four metrics noted above.`'exhaustive'`

— Uses the exhaustive search algorithm by computing the distance values from all the points in`X`

to each point in`Y`

.

**Example: **`'NSMethod','exhaustive'`

`Distance`

— Distance metric

`'euclidean'`

(default) | `'seuclidean'`

| `'fasteuclidean'`

| `'fastseuclidean'`

| `'cityblock'`

| `'chebychev'`

| `'minkowski'`

| `'mahalanobis'`

| `'cosine'`

| `'correlation'`

| `'spearman'`

| `'hamming'`

| `'jaccard'`

| function handle | ...

Distance metric `knnsearch`

uses, specified as one
of the values in this table or a function handle.

Value | Description |
---|---|

`'euclidean'` | Euclidean distance |

`'seuclidean'` | Standardized Euclidean distance. Each coordinate
difference between the rows in
`X` and the query matrix
`Y` is scaled by dividing by
the corresponding element of the standard deviation
computed from `X` . To specify a
different scaling, use the
`'Scale'` name-value
argument. |

`'fasteuclidean'` | Euclidean distance computed by using an
alternative algorithm that saves time when the
number of predictors is at least 10. In some cases,
this faster algorithm can reduce accuracy. This
distance metric is available only when
`NSMethod` is
`'exhaustive'` . Algorithms
starting with `'fast'` do not
support sparse data. For details, see Algorithms. |

`'fastseuclidean'` | Standardized Euclidean distance computed by using
an alternative algorithm that saves time when the
number of predictors is at least 10. In some cases,
this faster algorithm can reduce accuracy. This
distance metric is available only when
`NSMethod` is
`'exhaustive'` . Algorithms
starting with `'fast'` do not
support sparse data. For details, see Algorithms. |

`'cityblock'` | City block distance |

`'chebychev'` | Chebychev distance (maximum coordinate difference) |

`'minkowski'` | Minkowski distance. The default exponent is 2. To
specify a different exponent, use the
`'P'` name-value
argument. |

`'mahalanobis'` | Mahalanobis distance, computed using a positive
definite covariance matrix. To change the value of
the covariance matrix, use the
`'Cov'` name-value
argument. |

`'cosine'` | One minus the cosine of the included angle between observations (treated as vectors) |

`'correlation'` | One minus the sample linear correlation between observations (treated as sequences of values) |

`'spearman'` | One minus the sample Spearman's rank correlation between observations (treated as sequences of values) |

`'hamming'` | Hamming distance, which is the percentage of coordinates that differ |

`'jaccard'` | One minus the Jaccard coefficient, which is the percentage of nonzero coordinates that differ |

You can also specify a function handle for a custom
distance metric by using `@`

(for example,
`@distfun`

). A custom distance function must:

Have the form

`function D2 = distfun(ZI,ZJ)`

.Take as arguments:

A 1-by-

*n*vector`ZI`

containing a single row from`X`

or from the query points`Y`

.An

*m*-by-_{2}*n*matrix`ZJ`

containing multiple rows of`X`

or`Y`

.

Return an

*m*-by-1 vector of distances_{2}`D2`

, whose`j`

th element is the distance between the observations`ZI`

and`ZJ(j,:)`

.

For more information, see Distance Metrics.

**Example: **`'Distance','chebychev'`

**Data Types: **`char`

| `string`

| `function_handle`

`CacheSize`

— Size of Gram matrix in megabytes

`1e3`

(default) | positive scalar | `"maximal"`

Size of the Gram matrix in megabytes, specified as a positive scalar
or `"maximal"`

. The `knnsearch`

function can use `CacheSize`

only when the
`Distance`

name-value argument begins with
`fast`

and the `NSMethod`

name-value argument is set to `'exhaustive'`

.

If you set `CacheSize`

to
`"maximal"`

, `knnsearch`

tries
to allocate enough memory for an entire intermediate matrix whose size
is `MX`

-by-`MY`

, where
`MX`

is the number of rows of the input data
`X`

, and `MY`

is the number of
rows of the input data `Y`

. The cache size does not
have to be large enough for an entire intermediate matrix, but must be
at least large enough to hold an `MX`

-by-1 vector.
Otherwise, `knnsearch`

uses the standard algorithm
for computing Euclidean distance.

If the value of the `Distance`

argument begins with
`fast`

, the value of `NSMethod`

is `'exhaustive'`

, and the value of
`CacheSize`

is too large or
`"maximal"`

, `knnsearch`

might
try to allocate a Gram matrix that exceeds the available memory. In this
case, MATLAB^{®} issues an error.

**Example: **`CacheSize="maximal"`

**Data Types: **`double`

| `char`

| `string`

`P`

— Exponent for Minkowski distance metric

`2`

(default) | positive scalar

Exponent for the Minkowski distance metric, specified as the comma-separated pair consisting of `'P'`

and a positive scalar.

This argument is valid only if `'Distance'`

is `'minkowski'`

.

**Example: **`'P',3`

**Data Types: **`single`

| `double`

`Cov`

— Covariance matrix for Mahalanobis distance metric

`cov(X,'omitrows')`

(default) | positive definite matrix

Covariance matrix for the Mahalanobis distance metric, specified as the comma-separated pair
consisting of `'Cov'`

and a positive definite matrix.

This argument is valid only if `'Distance'`

is `'mahalanobis'`

.

**Example: **`'Cov',eye(4)`

**Data Types: **`single`

| `double`

`Scale`

— Scale parameter value for standardized Euclidean distance metric

`std(X,'omitnan')`

(default) | nonnegative numeric vector

Scale parameter value for the standardized Euclidean distance metric,
specified as the comma-separated pair consisting of
`'Scale'`

and a nonnegative numeric vector.
`'Scale'`

has length equal to the number of columns
in `X`

. When `knnsearch`

computes
the standardized Euclidean distance, each coordinate of
`X`

is scaled by the corresponding element of
`'Scale'`

, as is each query point. This argument is
valid only when `'Distance'`

is
`'seuclidean'`

.

**Example: **```
'Scale',quantile(X,0.75) -
quantile(X,0.25)
```

**Data Types: **`single`

| `double`

`BucketSize`

— Maximum number of data points in leaf node of *K*d-tree

`50`

(default) | positive integer

Maximum number of data points in the leaf node of the
*K*d-tree, specified as the comma-separated pair
consisting of `'BucketSize'`

and a positive integer.
This argument is valid only when `NSMethod`

is
`'kdtree'`

.

**Example: **`'BucketSize',20`

**Data Types: **`single`

| `double`

`SortIndices`

— Flag to sort returned indices according to distance

`true`

(`1`

) (default) | `false`

(`0`

)

Flag to sort returned indices according to distance, specified as the
comma-separated pair consisting of `'SortIndices'`

and
either `true`

(`1`

) or
`false`

(`0`

).

For faster performance, you can set `SortIndices`

to `false`

when the following are true:

`Y`

contains many observations that have many nearest neighbors in`X`

.`NSMethod`

is`'kdtree'`

.`IncludeTies`

is`false`

.

In this case, `knnsearch`

returns the
indices of the nearest neighbors in no particular order. When
`SortIndices`

is `true`

, the
function arranges the nearest neighbor indices in ascending order by
distance.

`SortIndices`

is `true`

by
default. When `NSMethod`

is
`'exhaustive'`

or `IncludeTies`

is `true`

, the function always sorts the
indices.

**Example: **`'SortIndices',false`

**Data Types: **`logical`

## Output Arguments

`Idx`

— Input data indices of nearest neighbors

numeric matrix | cell array of numeric vectors

Input data indices of the nearest neighbors, returned as a numeric matrix or cell array of numeric vectors.

If you do not specify

`IncludeTies`

(`false`

by default), then`Idx`

is an*m*-by-*k*numeric matrix, where*m*is the number of rows in`Y`

and*k*is the number of searched nearest neighbors.`Idx(j,i)`

indicates that`X(Idx(j,i),:)`

is one of the*k*closest observations in`X`

to the query point`Y(j,:)`

.If you specify

`'IncludeTies',true`

, then`Idx`

is an*m*-by-`1`

cell array such that cell`j`

(`Idx{j}`

) contains a vector of at least*k*indices of the closest observations in`X`

to the query point`Y(j,:)`

.

If `SortIndices`

is `true`

, then
`knnsearch`

arranges the indices in ascending order
by distance.

`D`

— Distances of nearest neighbors

numeric matrix | cell array of numeric vectors

Distances of the nearest neighbors to the query points, returned as a numeric matrix or cell array of numeric vectors.

If you do not specify

`IncludeTies`

(`false`

by default), then`D`

is an*m*-by-*k*numeric matrix, where*m*is the number of rows in`Y`

and*k*is the number of searched nearest neighbors.`D(j,i)`

is the distance between`X(Idx(j,i),:)`

and`Y(j,:)`

with respect to the distance metric.If you specify

`'IncludeTies',true`

, then`D`

is an*m*-by-`1`

cell array such that cell`j`

(`D{j}`

) contains a vector of at least*k*distances of the closest observations in`X`

to the query point`Y(j,:)`

.

If `SortIndices`

is `true`

, then
`knnsearch`

arranges the distances in ascending
order.

## Tips

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`

.

## Algorithms

For information on a specific search algorithm, see k-Nearest Neighbor Search and Radius Search.

### Fast Euclidean Distance Algorithm

The values of the `Distance`

argument that begin `fast`

(such as `'fasteuclidean'`

and `'fastseuclidean'`

)
calculate Euclidean distances using an algorithm that uses extra memory to save
computational time. This algorithm is named "Euclidean Distance Matrix Trick" in Albanie
[1] and elsewhere. Internal
testing shows that this algorithm saves time when the number of predictors is at least 10.
Algorithms starting with `'fast'`

do not support sparse data.

To find the matrix D of distances between all the points
*x _{i}* and

*x*, where each

_{j}*x*has

_{i}*n*variables, the algorithm computes distance using the final line in the following equations:

$$\begin{array}{c}{D}_{i,j}^{2}=\Vert {x}_{i}-{x}_{j}{\Vert}^{2}\\ ={(}^{{x}_{i}}({x}_{i}-{x}_{j})\\ =\Vert {x}_{i}{\Vert}^{2}-2{x}_{i}^{T}{x}_{j}+\Vert {x}_{j}{\Vert}^{2}.\end{array}$$

The matrix $${x}_{i}^{T}{x}_{j}$$ in the last line of the equations is called the *Gram
matrix*. Computing the set of squared distances is faster, but slightly less
numerically stable, when you compute and use the Gram matrix instead of computing the
squared distances by squaring and summing. For a discussion, see Albanie [1].

To store the Gram matrix, the software uses a cache with the default size of
`1e3`

megabytes. You can set the cache size using the
`CacheSize`

name-value argument. If the value of
`CacheSize`

is too large or `"maximal"`

,
`knnsearch`

might try to allocate a Gram matrix that exceeds the
available memory. In this case, MATLAB issues an error.

## References

[1] Albanie, Samuel. *Euclidean Distance Matrix Trick.* June, 2019. Available at https://www.robots.ox.ac.uk/%7Ealbanie/notes/Euclidean_distance_trick.pdf.

## Alternative Functionality

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.

### Simulink Block

To integrate a `k`

-nearest neighbor search into Simulink^{®}, you can use the KNN Search
block in the Statistics and Machine Learning Toolbox™ library or a MATLAB Function block with the `knnsearch`

function. For
an example, see Predict Class Labels Using MATLAB Function Block.

When deciding which approach to use, consider the following:

If you use the Statistics and Machine Learning Toolbox library block, you can use the Fixed-Point Tool (Fixed-Point Designer) to convert a floating-point model to fixed point.

Support for variable-size arrays must be enabled for a MATLAB Function block with the

`knnsearch`

function.

## References

[1] Friedman, J. H., J. Bentley, 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.

## Extended Capabilities

### Tall Arrays

Calculate with arrays that have more rows than fit in memory.

The
`knnsearch`

function supports tall arrays with the following usage
notes and limitations:

If

`X`

is a tall array, then`Y`

cannot be a tall array. Similarly, if`Y`

is a tall array, then`X`

cannot be a tall array.

For more information, see Tall Arrays.

### C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

For code generation, the default value of the

`'NSMethod'`

name-value pair argument is`'exhaustive'`

when the number of columns in`X`

is greater than 7.The value of the

`'Distance'`

name-value pair argument must be a compile-time constant and cannot be a custom distance function.The value of the

`'IncludeTies'`

name-value pair argument must be a compile-time constant.The

`'SortIndices'`

name-value pair argument is not supported. The output arguments are always sorted.`knnsearch`

does not support code generation for fast Euclidean distance computations, meaning those distance metrics whose names begin with`fast`

(for example,`'fasteuclidean'`

).Names in name-value arguments must be compile-time constants. For example, to allow a user-defined exponent for the Minkowski distance in the generated code, include

`{coder.Constant('Distance'),coder.Constant('Minkowski'),coder.Constant('P'),0}`

in the`-args`

value of`codegen`

(MATLAB Coder).When you specify

`'IncludeTies'`

as`true`

, the sorted order of tied distances in the generated code can be different from the order in MATLAB due to numerical precision.When

`knnsearch`

uses the*k*d-tree search algorithm, and the code generation build type is a MEX function,`codegen`

(MATLAB Coder) generates a MEX function using Intel^{®}Threading Building Blocks (TBB) for parallel computation. Otherwise,`codegen`

generates code using`parfor`

(MATLAB Coder).MEX function for the

*k*d-tree search algorithm —`codegen`

generates an optimized MEX function using Intel TBB for parallel computation on multicore platforms. You can use the MEX function to accelerate MATLAB algorithms. For details on Intel TBB, see https://www.intel.com/content/www/us/en/developer/tools/oneapi/onetbb.html.If you generate the MEX function to test the generated code of the

`parfor`

version, you can disable the usage of Intel TBB. Set the`ExtrinsicCalls`

property of the MEX configuration object to`false`

. For details, see`coder.MexCodeConfig`

(MATLAB Coder).MEX function for the exhaustive search algorithm and standalone C/C++ code for both algorithms — The generated code of

`knnsearch`

uses`parfor`

(MATLAB Coder) to create loops that run in parallel on supported shared-memory multicore platforms in the generated code. If your compiler does not support the Open Multiprocessing (OpenMP) application interface or you disable OpenMP library, MATLAB Coder™ treats the`parfor`

-loops as`for`

-loops. To find supported compilers, see Supported Compilers. To disable OpenMP library, set the`EnableOpenMP`

property of the configuration object to`false`

. For details, see`coder.CodeConfig`

(MATLAB Coder).

`knnsearch`

returns integer-type (`int32`

) indices in generated standalone C/C++ code. Therefore, the function allows for strict single-precision support when you use single-precision inputs. For MEX code generation, the function still returns double-precision indices to match the MATLAB behavior.*Before R2020a:*`knnsearch`

returns double-precision indices in generated standalone C/C++ code.

For more information on code generation, see Introduction to Code Generation and General Code Generation Workflow.

### GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Usage notes and limitations:

The

`NSMethod`

name-value argument must be specified as`"exhaustive"`

.The

`IncludeTies`

and`SortIndices`

name-value arguments must be specified as their default values.You cannot specify the

`Distance`

name-value argument as`"fasteuclidean"`

or`"fastseuclidean"`

.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

## Version History

**Introduced in R2010a**

### R2023a: Fast Euclidean distance using a cache

The `'fasteuclidean'`

and `'fastseuclidean'`

distance metrics accelerate the computation of Euclidean distances by using a cache
and a different algorithm (see Algorithms). Set the size
of the cache using the `CacheSize`

name-value argument.

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