Bayes classifier for normally distributed data. More...

#include <opencv2/ml.hpp>

Inheritance diagram for cv::ml::NormalBayesClassifier:

Collaboration diagram for cv::ml::NormalBayesClassifier:

Public Types
enum	Flags { UPDATE_MODEL = 1, RAW_OUTPUT =1, COMPRESSED_INPUT =2, PREPROCESSED_INPUT =4 }
	Predict options. 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 String	getDefaultName () const
	Returns the algorithm string identifier. 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 float	predictProb (InputArray inputs, OutputArray outputs, OutputArray outputProbs, int flags=0) const =0
	Predicts the response for 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 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< NormalBayesClassifier >	create ()
	Creates empty model Use StatModel::train to train the model after creation. More...

static Ptr< NormalBayesClassifier >	load (const String &filepath, const String &nodeName=String())
	Loads and creates a serialized NormalBayesClassifier from a 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

Bayes classifier for normally distributed data.

See also: Normal Bayes Classifier

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

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<NormalBayesClassifier> cv::ml::NormalBayesClassifier::create ( )

static

Creates empty model Use StatModel::train to train the model after creation.

◆ 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.

◆ 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.

◆ 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()

static Ptr<NormalBayesClassifier> cv::ml::NormalBayesClassifier::load	(	const String &	filepath,
		const String &	nodeName = `String()`
	)

static

Loads and creates a serialized NormalBayesClassifier from a file.

Use NormalBayesClassifier::save to serialize and store an NormalBayesClassifier to disk. Load the NormalBayesClassifier from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier

Parameters

filepath	path to serialized NormalBayesClassifier
nodeName	name of node containing the classifier

◆ 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.

◆ predictProb()

virtual float cv::ml::NormalBayesClassifier::predictProb	(	InputArray	inputs,
		OutputArray	outputs,
		OutputArray	outputProbs,
		int	flags = `0`
	)		const

pure virtual

Predicts the response for sample(s).

The method estimates the most probable classes for input vectors. Input vectors (one or more) are stored as rows of the matrix inputs. In case of multiple input vectors, there should be one output vector outputs. The predicted class for a single input vector is returned by the method. The vector outputProbs contains the output probabilities corresponding to each element of result.

◆ 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).

◆ 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

Member Function Documentation

◆ calcError()

◆ clear()

◆ create()

◆ empty()

◆ getDefaultName()

◆ getVarCount()

◆ isClassifier()

◆ isTrained()

◆ load()

◆ loadFromString()

◆ predict()

◆ predictProb()

◆ read() [1/2]

◆ read() [2/2]

◆ save()

◆ train() [1/3]

◆ train() [2/3]

◆ train() [3/3]

◆ write() [1/2]

◆ write() [2/2]

◆ writeFormat()