The class implements K-Nearest Neighbors model. More...

Inheritance diagram for OpenCVForUnity.MlModule.KNearest:

Public Member Functions
int	getDefaultK ()

void	setDefaultK (int val)

bool	getIsClassifier ()

void	setIsClassifier (bool val)

int	getEmax ()

void	setEmax (int val)

int	getAlgorithmType ()

void	setAlgorithmType (int val)

float	findNearest (Mat samples, int k, Mat results, Mat neighborResponses, Mat dist)
	Finds the neighbors and predicts responses for input vectors. More...

float	findNearest (Mat samples, int k, Mat results, Mat neighborResponses)
	Finds the neighbors and predicts responses for input vectors. More...

float	findNearest (Mat samples, int k, Mat results)
	Finds the neighbors and predicts responses for input vectors. More...

Public Member Functions inherited from OpenCVForUnity.MlModule.StatModel
int	getVarCount ()
	Returns the number of variables in training samples. More...

override bool	empty ()
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More...

bool	isTrained ()
	Returns true if the model is trained. More...

bool	isClassifier ()
	Returns true if the model is classifier. More...

bool	train (TrainData trainData, int flags)
	Trains the statistical model. More...

bool	train (TrainData trainData)
	Trains the statistical model. More...

bool	train (Mat samples, int layout, Mat responses)
	Trains the statistical model. More...

float	calcError (TrainData data, bool test, Mat resp)
	Computes error on the training or test dataset. More...

virtual float	predict (Mat samples, Mat results, int flags)
	Predicts response(s) for the provided sample(s) More...

virtual float	predict (Mat samples, Mat results)
	Predicts response(s) for the provided sample(s) More...

virtual float	predict (Mat samples)
	Predicts response(s) for the provided sample(s) More...

Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
IntPtr	getNativeObjAddr ()

virtual void	clear ()
	Clears the algorithm state. More...

void	save (string filename)

virtual string	getDefaultName ()

Public Member Functions inherited from OpenCVForUnity.DisposableObject
void	Dispose ()

void	ThrowIfDisposed ()

Static Public Member Functions
static new KNearest	__fromPtr__ (IntPtr addr)

static KNearest	create ()
	Creates the empty model. More...

static KNearest	load (string filepath)
	Loads and creates a serialized knearest from a file. More...

Static Public Member Functions inherited from OpenCVForUnity.MlModule.StatModel
static new StatModel	__fromPtr__ (IntPtr addr)

Static Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
static Algorithm	__fromPtr__ (IntPtr addr)

Static Public Member Functions inherited from OpenCVForUnity.DisposableObject
static IntPtr	ThrowIfNullIntPtr (IntPtr ptr)

Public Attributes
const int	BRUTE_FORCE = 1

const int	KDTREE = 2

Public Attributes inherited from OpenCVForUnity.MlModule.StatModel
const int	UPDATE_MODEL = 1

const int	RAW_OUTPUT = 1

const int	COMPRESSED_INPUT = 2

const int	PREPROCESSED_INPUT = 4

Protected Member Functions
override void	Dispose (bool disposing)

Protected Member Functions inherited from OpenCVForUnity.MlModule.StatModel
override void	Dispose (bool disposing)

Protected Member Functions inherited from OpenCVForUnity.DisposableOpenCVObject
	DisposableOpenCVObject ()

	DisposableOpenCVObject (IntPtr ptr)

	DisposableOpenCVObject (bool isEnabledDispose)

	DisposableOpenCVObject (IntPtr ptr, bool isEnabledDispose)

Protected Member Functions inherited from OpenCVForUnity.DisposableObject
	DisposableObject ()

	DisposableObject (bool isEnabledDispose)

Additional Inherited Members
Properties inherited from OpenCVForUnity.DisposableObject
bool	IsDisposed `[get, protected set]`

bool	IsEnabledDispose `[get, set]`

Detailed Description

The class implements K-Nearest Neighbors model.

See also: ml_intro_knn

Member Function Documentation

◆ fromPtr()

static new KNearest OpenCVForUnity.MlModule.KNearest.__fromPtr__ ( IntPtr addr )

static

◆ create()

static KNearest OpenCVForUnity.MlModule.KNearest.create ( )

static

Creates the empty model.

The static method creates empty KNearest classifier. It should be then trained using StatModel::train method.

◆ Dispose()

override void OpenCVForUnity.MlModule.KNearest.Dispose ( bool disposing )

protectedvirtual

Reimplemented from OpenCVForUnity.CoreModule.Algorithm.

◆ findNearest() [1/3]

float OpenCVForUnity.MlModule.KNearest.findNearest	(	Mat	samples,
		int	k,
		Mat	results,
		Mat	neighborResponses,
		Mat	dist
	)

Finds the neighbors and predicts responses for input vectors.

Parameters

samples	Input samples stored by rows. It is a single-precision floating-point matrix of `<number_of_samples> * k` size.
k	Number of used nearest neighbors. Should be greater than 1.
results	Vector with results of prediction (regression or classification) for each input sample. It is a single-precision floating-point vector with `<number_of_samples>` elements.
neighborResponses	Optional output values for corresponding neighbors. It is a single- precision floating-point matrix of `<number_of_samples> * k` size.
dist	Optional output distances from the input vectors to the corresponding neighbors. It is a single-precision floating-point matrix of `<number_of_samples> * k` size.

For each input vector (a row of the matrix samples), the method finds the k nearest neighbors. In case of regression, the predicted result is a mean value of the particular vector's neighbor responses. In case of classification, the class is determined by voting.

For each input vector, the neighbors are sorted by their distances to the vector.

In case of C++ interface you can use output pointers to empty matrices and the function will allocate memory itself.

If only a single input vector is passed, all output matrices are optional and the predicted value is returned by the method.

The function is parallelized with the TBB library.

◆ findNearest() [2/3]

float OpenCVForUnity.MlModule.KNearest.findNearest	(	Mat	samples,
		int	k,
		Mat	results,
		Mat	neighborResponses
	)

Finds the neighbors and predicts responses for input vectors.

Parameters

samples	Input samples stored by rows. It is a single-precision floating-point matrix of `<number_of_samples> * k` size.
k	Number of used nearest neighbors. Should be greater than 1.
results	Vector with results of prediction (regression or classification) for each input sample. It is a single-precision floating-point vector with `<number_of_samples>` elements.
neighborResponses	Optional output values for corresponding neighbors. It is a single- precision floating-point matrix of `<number_of_samples> * k` size.
dist	Optional output distances from the input vectors to the corresponding neighbors. It is a single-precision floating-point matrix of `<number_of_samples> * k` size.

For each input vector (a row of the matrix samples), the method finds the k nearest neighbors. In case of regression, the predicted result is a mean value of the particular vector's neighbor responses. In case of classification, the class is determined by voting.

For each input vector, the neighbors are sorted by their distances to the vector.

In case of C++ interface you can use output pointers to empty matrices and the function will allocate memory itself.

If only a single input vector is passed, all output matrices are optional and the predicted value is returned by the method.

The function is parallelized with the TBB library.

◆ findNearest() [3/3]

float OpenCVForUnity.MlModule.KNearest.findNearest	(	Mat	samples,
		int	k,
		Mat	results
	)

Finds the neighbors and predicts responses for input vectors.

Parameters

samples	Input samples stored by rows. It is a single-precision floating-point matrix of `<number_of_samples> * k` size.
k	Number of used nearest neighbors. Should be greater than 1.
results	Vector with results of prediction (regression or classification) for each input sample. It is a single-precision floating-point vector with `<number_of_samples>` elements.
neighborResponses	Optional output values for corresponding neighbors. It is a single- precision floating-point matrix of `<number_of_samples> * k` size.
dist	Optional output distances from the input vectors to the corresponding neighbors. It is a single-precision floating-point matrix of `<number_of_samples> * k` size.

For each input vector (a row of the matrix samples), the method finds the k nearest neighbors. In case of regression, the predicted result is a mean value of the particular vector's neighbor responses. In case of classification, the class is determined by voting.

For each input vector, the neighbors are sorted by their distances to the vector.

In case of C++ interface you can use output pointers to empty matrices and the function will allocate memory itself.

If only a single input vector is passed, all output matrices are optional and the predicted value is returned by the method.

The function is parallelized with the TBB library.

◆ getAlgorithmType()

int OpenCVForUnity.MlModule.KNearest.getAlgorithmType ( )

See also: setAlgorithmType

◆ getDefaultK()

int OpenCVForUnity.MlModule.KNearest.getDefaultK ( )

See also: setDefaultK

◆ getEmax()

int OpenCVForUnity.MlModule.KNearest.getEmax ( )

See also: setEmax

◆ getIsClassifier()

bool OpenCVForUnity.MlModule.KNearest.getIsClassifier ( )

See also: setIsClassifier

◆ load()

static KNearest OpenCVForUnity.MlModule.KNearest.load ( string filepath )

static

Loads and creates a serialized knearest from a file.

Use KNearest::save to serialize and store an KNearest to disk. Load the KNearest from this file again, by calling this function with the path to the file.

Parameters

filepath path to serialized KNearest

◆ setAlgorithmType()

void OpenCVForUnity.MlModule.KNearest.setAlgorithmType ( int val )

See also: getAlgorithmType

◆ setDefaultK()

void OpenCVForUnity.MlModule.KNearest.setDefaultK ( int val )

See also: getDefaultK

◆ setEmax()

void OpenCVForUnity.MlModule.KNearest.setEmax ( int val )

See also: getEmax

◆ setIsClassifier()

void OpenCVForUnity.MlModule.KNearest.setIsClassifier ( bool val )

See also: getIsClassifier

Member Data Documentation

◆ BRUTE_FORCE

const int OpenCVForUnity.MlModule.KNearest.BRUTE_FORCE = 1

◆ KDTREE

const int OpenCVForUnity.MlModule.KNearest.KDTREE = 2

The documentation for this class was generated from the following file:

OpenCVForUnity/Assets/OpenCVForUnity/org/opencv/ml/KNearest.cs

Public Member Functions

Static Public Member Functions

Public Attributes

Protected Member Functions

Additional Inherited Members

Detailed Description

Member Function Documentation

◆ __fromPtr__()

◆ create()

◆ Dispose()

◆ findNearest() [1/3]

◆ findNearest() [2/3]

◆ findNearest() [3/3]

◆ getAlgorithmType()

◆ getDefaultK()

◆ getEmax()

◆ getIsClassifier()

◆ load()

◆ setAlgorithmType()

◆ setDefaultK()

◆ setEmax()

◆ setIsClassifier()

Member Data Documentation

◆ BRUTE_FORCE

◆ KDTREE

◆ fromPtr()