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

#include <opencv2/ml.hpp>

Inheritance diagram for cv::ml::KNearest:

Collaboration diagram for cv::ml::KNearest:

Public Types
enum	Flags { UPDATE_MODEL = 1, RAW_OUTPUT =1, COMPRESSED_INPUT =2, PREPROCESSED_INPUT =4 }
	Predict options. More...

enum	Types { BRUTE_FORCE =1, KDTREE =2 }
	Implementations of KNearest algorithm. More...

Public Member Functions
virtual float	calcError (const Ptr< TrainData > &data, bool test, OutputArray resp) const
	Computes error on the training or test dataset. More...

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

virtual bool	empty () const CV_OVERRIDE
	Returns true if the Algorithm is empty (e.g. More...

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
	Algorithm type, one of KNearest::Types. More...

virtual int	getDefaultK () const =0
	Default number of neighbors to use in predict method. More...

virtual String	getDefaultName () const
	Returns the algorithm string identifier. More...

virtual int	getEmax () const =0
	Parameter for KDTree implementation. More...

virtual bool	getIsClassifier () const =0
	Whether classification or regression model should be trained. 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 void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage. More...

virtual void	save (const String &filename) const
	Saves the algorithm to a file. More...

virtual void	setAlgorithmType (int val)=0
	Algorithm type, one of KNearest::Types. More...

virtual void	setDefaultK (int val)=0
	Default number of neighbors to use in predict method. More...

virtual void	setEmax (int val)=0
	Parameter for KDTree implementation. More...

virtual void	setIsClassifier (bool val)=0
	Whether classification or regression model should be trained. 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...

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

void	write (const Ptr< FileStorage > &fs, const String &name=String()) const
	simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. More...

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

static Ptr< KNearest >	load (const String &filepath)
	Loads and creates a serialized knearest from a file. More...

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...

template<typename _Tp >
static Ptr< _Tp >	train (const Ptr< TrainData > &data, int flags=0)
	Create and train model with default parameters. More...

Protected Member Functions
void	writeFormat (FileStorage &fs) const

Detailed Description

The class implements K-Nearest Neighbors model.

See also: K-Nearest Neighbors

Member Enumeration Documentation

◆ Flags

enum cv::ml::StatModel::Flags

inherited

Predict options.

Enumerator
UPDATE_MODEL
RAW_OUTPUT	makes the method return the raw results (the sum), not the class label
COMPRESSED_INPUT
PREPROCESSED_INPUT

◆ Types

enum cv::ml::KNearest::Types

Implementations of KNearest algorithm.

Enumerator
BRUTE_FORCE
KDTREE

Member Function Documentation

◆ calcError()

virtual float cv::ml::StatModel::calcError	(	const Ptr< TrainData > &	data,
		bool	test,
		OutputArray	resp
	)		const

virtualinherited

Computes error on the training or test dataset.

Parameters

data	the training data
test	if true, the error is computed over the test subset of the data, otherwise it's computed over the training subset of the data. Please note that if you loaded a completely different dataset to evaluate already trained classifier, you will probably want not to set the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so that the error is computed for the whole new set. Yes, this sounds a bit confusing.
resp	the optional output responses.

The method uses StatModel::predict to compute the error. For regression models the error is computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%).

◆ clear()

virtual void cv::Algorithm::clear ( )

inlinevirtualinherited

Clears the algorithm state.

Reimplemented in cv::FlannBasedMatcher, and cv::DescriptorMatcher.

◆ create()

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.

◆ empty()

virtual bool cv::ml::StatModel::empty ( ) const

virtualinherited

Returns true if the Algorithm is empty (e.g.

in the very beginning or after unsuccessful read

Reimplemented from cv::Algorithm.

◆ findNearest()

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.

◆ getAlgorithmType()

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

pure virtual

Algorithm type, one of KNearest::Types.

See also: setAlgorithmType

◆ getDefaultK()

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

pure virtual

Default number of neighbors to use in predict method.

See also: setDefaultK

◆ getDefaultName()

virtual String cv::Algorithm::getDefaultName ( ) const

virtualinherited

Returns the algorithm string identifier.

This string is used as top level xml/yml node tag when the object is saved to a file or string.

Reimplemented in cv::AKAZE, cv::KAZE, cv::SimpleBlobDetector, cv::GFTTDetector, cv::AgastFeatureDetector, cv::FastFeatureDetector, cv::MSER, cv::ORB, cv::BRISK, and cv::Feature2D.

◆ getEmax()

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

pure virtual

Parameter for KDTree implementation.

See also: setEmax

◆ getIsClassifier()

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

pure virtual

Whether classification or regression model should be trained.

See also: setIsClassifier

◆ getVarCount()

virtual int cv::ml::StatModel::getVarCount ( ) const

pure virtualinherited

Returns the number of variables in training samples.

◆ isClassifier()

virtual bool cv::ml::StatModel::isClassifier ( ) const

pure virtualinherited

Returns true if the model is classifier.

◆ isTrained()

virtual bool cv::ml::StatModel::isTrained ( ) const

pure virtualinherited

Returns true if the model is trained.

◆ load() [1/2]

static Ptr<KNearest> cv::ml::KNearest::load ( const 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

◆ load() [2/2]

template<typename _Tp >

static Ptr<_Tp> cv::Algorithm::load	(	const String &	filename,
		const String &	objname = `String()`
	)

inlinestaticinherited

Loads algorithm from the file.

Parameters

filename	Name of the file to read.
objname	The optional name of the node to read (if empty, the first top-level node will be used)

This is static template method of Algorithm. It's usage is following (in the case of SVM):

Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");

In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn).

References CV_Assert, cv::FileNode::empty(), cv::FileStorage::getFirstTopLevelNode(), cv::FileStorage::isOpened(), and cv::FileStorage::READ.

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◆ loadFromString()

template<typename _Tp >

static Ptr<_Tp> cv::Algorithm::loadFromString	(	const String &	strModel,
		const String &	objname = `String()`
	)

inlinestaticinherited

Loads algorithm from a String.

Parameters

strModel	The string variable containing the model you want to load.
objname	The optional name of the node to read (if empty, the first top-level node will be used)

This is static template method of Algorithm. It's usage is following (in the case of SVM):

Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);

References CV_WRAP, cv::FileNode::empty(), cv::FileStorage::getFirstTopLevelNode(), cv::FileStorage::MEMORY, and cv::FileStorage::READ.

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◆ predict()

virtual float cv::ml::StatModel::predict	(	InputArray	samples,
		OutputArray	results = `noArray()`,
		int	flags = `0`
	)		const

pure virtualinherited

Predicts response(s) for the provided sample(s)

Parameters

samples	The input samples, floating-point matrix
results	The optional output matrix of results.
flags	The optional flags, model-dependent. See cv::ml::StatModel::Flags.

Implemented in cv::ml::LogisticRegression, and cv::ml::EM.

◆ read() [1/2]

virtual void cv::Algorithm::read ( const FileNode & fn )

inlinevirtualinherited

Reads algorithm parameters from a file storage.

Reimplemented in cv::FlannBasedMatcher, cv::DescriptorMatcher, and cv::Feature2D.

◆ read() [2/2]

template<typename _Tp >

static Ptr<_Tp> cv::Algorithm::read ( const FileNode & fn )

inlinestaticinherited

Reads algorithm from the file node.

This is static template method of Algorithm. It's usage is following (in the case of SVM):

cv::FileStorage fsRead("example.xml", FileStorage::READ);

Ptr<SVM> svm = Algorithm::read<SVM>(fsRead.root());

In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn) and also have static create() method without parameters (or with all the optional parameters)

◆ save()

virtual void cv::Algorithm::save ( const String & filename ) const

virtualinherited

Saves the algorithm to a file.

In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

◆ setAlgorithmType()

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

pure virtual

Algorithm type, one of KNearest::Types.

See also: getAlgorithmType

◆ setDefaultK()

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

pure virtual

Default number of neighbors to use in predict method.

See also: getDefaultK

◆ setEmax()

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

pure virtual

Parameter for KDTree implementation.

See also: getEmax

◆ setIsClassifier()

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

pure virtual

Whether classification or regression model should be trained.

See also: getIsClassifier

◆ train() [1/3]

virtual bool cv::ml::StatModel::train	(	const Ptr< TrainData > &	trainData,
		int	flags = `0`
	)

virtualinherited

Trains the statistical model.

Parameters

trainData	training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create.
flags	optional flags, depending on the model. Some of the models can be updated with the new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).

◆ train() [2/3]

virtual bool cv::ml::StatModel::train	(	InputArray	samples,
		int	layout,
		InputArray	responses
	)

virtualinherited

Trains the statistical model.

Parameters

samples	training samples
layout	See ml::SampleTypes.
responses	vector of responses associated with the training samples.

◆ train() [3/3]

template<typename _Tp >

static Ptr<_Tp> cv::ml::StatModel::train	(	const Ptr< TrainData > &	data,
		int	flags = `0`
	)

inlinestaticinherited

Create and train model with default parameters.

The class must implement static create() method with no parameters or with all default parameter values

◆ write() [1/2]

virtual void cv::Algorithm::write ( FileStorage & fs ) const

inlinevirtualinherited

Stores algorithm parameters in a file storage.

Reimplemented in cv::FlannBasedMatcher, cv::DescriptorMatcher, and cv::Feature2D.

References CV_WRAP.

Referenced by cv::Feature2D::write(), and cv::DescriptorMatcher::write().

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◆ write() [2/2]

void cv::Algorithm::write	(	const Ptr< FileStorage > &	fs,
		const String &	name = `String()`
	)		const

inherited

simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

◆ writeFormat()

void cv::Algorithm::writeFormat ( FileStorage & fs ) const

protectedinherited

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

opencv2/ml.hpp

Public Types

Public Member Functions

Static Public Member Functions

Protected Member Functions

Detailed Description

Member Enumeration Documentation

◆ Flags

◆ Types

Member Function Documentation

◆ calcError()

◆ clear()

◆ create()

◆ empty()

◆ findNearest()

◆ getAlgorithmType()

◆ getDefaultK()

◆ getDefaultName()

◆ getEmax()

◆ getIsClassifier()

◆ getVarCount()

◆ isClassifier()

◆ isTrained()

◆ load() [1/2]

◆ load() [2/2]

◆ loadFromString()

◆ predict()

◆ read() [1/2]

◆ read() [2/2]

◆ save()

◆ setAlgorithmType()

◆ setDefaultK()

◆ setEmax()

◆ setIsClassifier()

◆ train() [1/3]

◆ train() [2/3]

◆ train() [3/3]

◆ write() [1/2]

◆ write() [2/2]

◆ writeFormat()