cv::ml::NormalBayesClassifier Class Reference

Bayes classifier for normally distributed data. More...

#include <opencv2/ml.hpp>

Collaboration diagram for cv::ml::NormalBayesClassifier:

Public Member Functions
virtual float	predictProb (InputArray inputs, OutputArray outputs, OutputArray outputProbs, int flags=0) const =0
	Predicts the response for sample(s).
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< NormalBayesClassifier >	create ()
static Ptr< NormalBayesClassifier >	load (const String &filepath, const String &nodeName=String())
	Loads and creates a serialized NormalBayesClassifier 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
Public Types inherited from cv::ml::StatModel
enum	Flags {   UPDATE_MODEL = 1 ,   RAW_OUTPUT =1 ,   COMPRESSED_INPUT =2 ,   PREPROCESSED_INPUT =4 }
Protected Member Functions inherited from cv::Algorithm
void	writeFormat (FileStorage &fs) const

Detailed Description

Bayes classifier for normally distributed data.

See also

Normal Bayes Classifier

Member Function Documentation

create()

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

static

Python:
	cv.ml.NormalBayesClassifier.create(		) ->	retval
	cv.ml.NormalBayesClassifier_create(		) ->	retval

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

load()

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

static

Python:
	cv.ml.NormalBayesClassifier.load(	filepath[, nodeName]	) ->	retval
	cv.ml.NormalBayesClassifier_load(	filepath[, nodeName]	) ->	retval

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

predictProb()

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

pure virtual

Python:
	cv.ml.NormalBayesClassifier.predictProb(	inputs[, outputs[, outputProbs[, flags]]]	) ->	retval, outputs, outputProbs

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.

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

• opencv2/ml.hpp