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

#include "ml.hpp"

Inheritance 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. More...

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. More...

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

virtual int	getVarCount () const =0
	Returns the number of variables in training samples. More...

virtual bool	isClassifier () const =0
	Returns true if the model is classifier. More...

virtual bool	isTrained () const =0
	Returns true if the model is trained. More...

virtual float	predict (InputArray samples, OutputArray results=noArray(), int flags=0) const =0
	Predicts response(s) for the provided sample(s) More...

virtual bool	train (const Ptr< TrainData > &trainData, int flags=0)
	Trains the statistical model. More...

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

Public Member Functions inherited from cv::Algorithm
	Algorithm ()

virtual	~Algorithm ()

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

virtual String	getDefaultName () const

virtual void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage. More...

virtual void	save (const String &filename) const

virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage. More...

Static Public Member Functions
static Ptr< KNearest >	create ()
	Creates the empty model. More...

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. More...

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. More...

template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String. More...

template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node. More...

Detailed Description

The class implements K-Nearest Neighbors model.

See also: K-Nearest Neighbors

Member Enumeration Documentation

enum cv::ml::KNearest::Types

Implementations of KNearest algorithm.

Enumerator
BRUTE_FORCE
KDTREE

Member Function Documentation

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

static

Creates the empty model.

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

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

pure virtual

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.

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

pure virtual

Algorithm type, one of KNearest::Types.