cv::ml::KNearest Class Reference

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

#include <opencv2/ml.hpp>

Collaboration diagram for cv::ml::KNearest:

Public Types
enum	Types {   BRUTE_FORCE =1 ,   KDTREE =2 }
	Implementations of KNearest algorithm. More...
Public Types inherited from cv::ml::StatModel
enum	Flags {   UPDATE_MODEL = 1 ,   RAW_OUTPUT =1 ,   COMPRESSED_INPUT =2 ,   PREPROCESSED_INPUT =4 }

Public Member Functions
virtual float	findNearest (InputArray samples, int k, OutputArray results, OutputArray neighborResponses=noArray(), OutputArray dist=noArray()) const =0
	Finds the neighbors and predicts responses for input vectors.
virtual int	getAlgorithmType () const =0
virtual int	getDefaultK () const =0
virtual int	getEmax () const =0
virtual bool	getIsClassifier () const =0
virtual void	setAlgorithmType (int val)=0
virtual void	setDefaultK (int val)=0
virtual void	setEmax (int val)=0
virtual void	setIsClassifier (bool val)=0
Public Member Functions inherited from cv::ml::StatModel
virtual float	calcError (const Ptr< TrainData > &data, bool test, OutputArray resp) const
	Computes error on the training or test dataset.
virtual bool	empty () const CV_OVERRIDE
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
virtual int	getVarCount () const =0
	Returns the number of variables in training samples.
virtual bool	isClassifier () const =0
	Returns true if the model is classifier.
virtual bool	isTrained () const =0
	Returns true if the model is trained.
virtual float	predict (InputArray samples, OutputArray results=noArray(), int flags=0) const =0
	Predicts response(s) for the provided sample(s)
virtual bool	train (const Ptr< TrainData > &trainData, int flags=0)
	Trains the statistical model.
virtual bool	train (InputArray samples, int layout, InputArray responses)
	Trains the statistical model.
Public Member Functions inherited from cv::Algorithm
	Algorithm ()
virtual	~Algorithm ()
virtual void	clear ()
	Clears the algorithm state.
virtual String	getDefaultName () const
virtual void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage.
virtual void	save (const String &filename) const
void	write (const Ptr< FileStorage > &fs, const String &name=String()) const
virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage.
void	write (FileStorage &fs, const String &name) const

Static Public Member Functions
static Ptr< KNearest >	create ()
	Creates the empty model.
static Ptr< KNearest >	load (const String &filepath)
	Loads and creates a serialized knearest from a file.
Static Public Member Functions inherited from cv::ml::StatModel
template<typename _Tp >
static Ptr< _Tp >	train (const Ptr< TrainData > &data, int flags=0)
	Create and train model with default parameters.
Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file.
template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String.
template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node.

Additional Inherited Members
Protected Member Functions inherited from cv::Algorithm
void	writeFormat (FileStorage &fs) const

Detailed Description

The class implements K-Nearest Neighbors model.

See also

K-Nearest Neighbors

Member Enumeration Documentation

Types

enum cv::ml::KNearest::Types

Implementations of KNearest algorithm.

Enumerator
BRUTE_FORCE
KDTREE

Member Function Documentation

create()

static Ptr< KNearest > cv::ml::KNearest::create ( )

static

Python:
	cv.ml.KNearest.create(		) ->	retval
	cv.ml.KNearest_create(		) ->	retval

Creates the empty model.

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

findNearest()

virtual float cv::ml::KNearest::findNearest ( InputArray  samples, int  k, OutputArray  results, OutputArray  neighborResponses = noArray(), OutputArray  dist = noArray()  ) const

pure virtual

Python:
	cv.ml.KNearest.findNearest(	samples, k[, results[, neighborResponses[, dist]]]	) ->	retval, results, neighborResponses, 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.

Here is the call graph for this function:

getAlgorithmType()

virtual int cv::ml::KNearest::getAlgorithmType ( ) const

pure virtual

Python:
	cv.ml.KNearest.getAlgorithmType(		) ->	retval

Algorithm type, one of KNearest::Types.

See also

setAlgorithmType

getDefaultK()

virtual int cv::ml::KNearest::getDefaultK ( ) const

pure virtual

Python:
	cv.ml.KNearest.getDefaultK(		) ->	retval

Default number of neighbors to use in predict method.

See also

setDefaultK

getEmax()

virtual int cv::ml::KNearest::getEmax ( ) const

pure virtual

Python:
	cv.ml.KNearest.getEmax(		) ->	retval

Parameter for KDTree implementation.

See also

setEmax

getIsClassifier()

virtual bool cv::ml::KNearest::getIsClassifier ( ) const

pure virtual

Python:
	cv.ml.KNearest.getIsClassifier(		) ->	retval

Whether classification or regression model should be trained.

See also

setIsClassifier

load()

static Ptr< KNearest > cv::ml::KNearest::load ( const String &  filepath )

static

Python:
	cv.ml.KNearest.load(	filepath	) ->	retval
	cv.ml.KNearest_load(	filepath	) ->	retval

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()

virtual void cv::ml::KNearest::setAlgorithmType ( int  val )

pure virtual

Python:
	cv.ml.KNearest.setAlgorithmType(	val	) ->	None

See also

getAlgorithmType

setDefaultK()

virtual void cv::ml::KNearest::setDefaultK ( int  val )

pure virtual

Python:
	cv.ml.KNearest.setDefaultK(	val	) ->	None

See also

getDefaultK

setEmax()

virtual void cv::ml::KNearest::setEmax ( int  val )

pure virtual

Python:
	cv.ml.KNearest.setEmax(	val	) ->	None

See also

getEmax

setIsClassifier()

virtual void cv::ml::KNearest::setIsClassifier ( bool  val )

pure virtual

Python:
	cv.ml.KNearest.setIsClassifier(	val	) ->	None

See also

getIsClassifier

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

• opencv2/ml.hpp