Implements Logistic Regression classifier. More...

#include "ml.hpp"

Inheritance diagram for cv::ml::LogisticRegression:

Public Types
enum	Methods { BATCH = 0, MINI_BATCH = 1 }
	Training methods. More...

enum	RegKinds { REG_DISABLE = -1, REG_L1 = 0, REG_L2 = 1 }
	Regularization kinds. 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 Mat	get_learnt_thetas () const =0
	This function returns the trained parameters arranged across rows. More...

virtual int	getIterations () const =0

virtual double	getLearningRate () const =0

virtual int	getMiniBatchSize () const =0

virtual int	getRegularization () const =0

virtual TermCriteria	getTermCriteria () const =0

virtual int	getTrainMethod () const =0

virtual float	predict (InputArray samples, OutputArray results=noArray(), int flags=0) const CV_OVERRIDE=0
	Predicts responses for input samples and returns a float type. More...

virtual void	setIterations (int val)=0

virtual void	setLearningRate (double val)=0

virtual void	setMiniBatchSize (int val)=0

virtual void	setRegularization (int val)=0

virtual void	setTermCriteria (TermCriteria val)=0

virtual void	setTrainMethod (int 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 CV_OVERRIDE
	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 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...

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< LogisticRegression >	create ()
	Creates empty model. More...

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

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

Detailed Description

Implements Logistic Regression classifier.

See also: Logistic Regression

Member Enumeration Documentation

§ Methods

enum cv::ml::LogisticRegression::Methods

Training methods.

Enumerator
BATCH
MINI_BATCH	Set MiniBatchSize to a positive integer when using this method.

§ RegKinds

enum cv::ml::LogisticRegression::RegKinds

Regularization kinds.

Enumerator
REG_DISABLE	Regularization disabled.
REG_L1	L1 norm
REG_L2	L2 norm

Member Function Documentation

§ create()

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

static

Python:
	retval	=	cv.ml.LogisticRegression_create(		)

Creates empty model.

Creates Logistic Regression model with parameters given.

§ get_learnt_thetas()

virtual Mat cv::ml::LogisticRegression::get_learnt_thetas ( ) const

pure virtual

Python:
	retval	=	cv.ml_LogisticRegression.get_learnt_thetas(		)

This function returns the trained parameters arranged across rows.

For a two class classifcation problem, it returns a row matrix. It returns learnt parameters of the Logistic Regression as a matrix of type CV_32F.

§ getIterations()

virtual int cv::ml::LogisticRegression::getIterations ( ) const

pure virtual

Python:
	retval	=	cv.ml_LogisticRegression.getIterations(		)

Number of iterations.

See also: setIterations

§ getLearningRate()

virtual double cv::ml::LogisticRegression::getLearningRate ( ) const

pure virtual

Python:
	retval	=	cv.ml_LogisticRegression.getLearningRate(		)

Learning rate.

See also: setLearningRate

§ getMiniBatchSize()

virtual int cv::ml::LogisticRegression::getMiniBatchSize ( ) const

pure virtual

Python:
	retval	=	cv.ml_LogisticRegression.getMiniBatchSize(		)

Specifies the number of training samples taken in each step of Mini-Batch Gradient Descent. Will only be used if using LogisticRegression::MINI_BATCH training algorithm. It has to take values less than the total number of training samples.

See also: setMiniBatchSize

§ getRegularization()

virtual int cv::ml::LogisticRegression::getRegularization ( ) const

pure virtual

Python:
	retval	=	cv.ml_LogisticRegression.getRegularization(		)

Kind of regularization to be applied. See LogisticRegression::RegKinds.

See also: setRegularization

§ getTermCriteria()

virtual TermCriteria cv::ml::LogisticRegression::getTermCriteria ( ) const

pure virtual

Python:
	retval	=	cv.ml_LogisticRegression.getTermCriteria(		)

Termination criteria of the algorithm.

See also: setTermCriteria

§ getTrainMethod()

virtual int cv::ml::LogisticRegression::getTrainMethod ( ) const

pure virtual

Python:
	retval	=	cv.ml_LogisticRegression.getTrainMethod(		)

Kind of training method used. See LogisticRegression::Methods.

See also: setTrainMethod

§ load()

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

static

Python:
	retval	=	cv.ml.LogisticRegression_load(	filepath[, nodeName]	)

Loads and creates a serialized LogisticRegression from a file.

Use LogisticRegression::save to serialize and store an LogisticRegression to disk. Load the LogisticRegression 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 LogisticRegression
nodeName	name of node containing the classifier

§ predict()

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

pure virtual

Python:
	retval, results	=	cv.ml_LogisticRegression.predict(	samples[, results[, flags]]	)

Predicts responses for input samples and returns a float type.

Parameters

samples	The input data for the prediction algorithm. Matrix [m x n], where each row contains variables (features) of one object being classified. Should have data type CV_32F.
results	Predicted labels as a column matrix of type CV_32S.
flags	Not used.

Implements cv::ml::StatModel.

§ setIterations()

virtual void cv::ml::LogisticRegression::setIterations ( int val )

pure virtual

Python:
	None	=	cv.ml_LogisticRegression.setIterations(	val	)

See also: getIterations

§ setLearningRate()

virtual void cv::ml::LogisticRegression::setLearningRate ( double val )

pure virtual

Python:
	None	=	cv.ml_LogisticRegression.setLearningRate(	val	)

See also: getLearningRate

§ setMiniBatchSize()

virtual void cv::ml::LogisticRegression::setMiniBatchSize ( int val )

pure virtual

Python:
	None	=	cv.ml_LogisticRegression.setMiniBatchSize(	val	)

See also: getMiniBatchSize

§ setRegularization()

virtual void cv::ml::LogisticRegression::setRegularization ( int val )

pure virtual

Python:
	None	=	cv.ml_LogisticRegression.setRegularization(	val	)

See also: getRegularization

§ setTermCriteria()

virtual void cv::ml::LogisticRegression::setTermCriteria ( TermCriteria val )

pure virtual

Python:
	None	=	cv.ml_LogisticRegression.setTermCriteria(	val	)

See also: getTermCriteria

§ setTrainMethod()

virtual void cv::ml::LogisticRegression::setTrainMethod ( int val )

pure virtual

Python:
	None	=	cv.ml_LogisticRegression.setTrainMethod(	val	)

See also: getTrainMethod

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

ml/include/opencv2/ml.hpp

Public Types

Public Member Functions

Static Public Member Functions

Additional Inherited Members

Detailed Description

Member Enumeration Documentation

§ Methods

§ RegKinds

Member Function Documentation

§ create()

§ get_learnt_thetas()

§ getIterations()

§ getLearningRate()

§ getMiniBatchSize()

§ getRegularization()

§ getTermCriteria()

§ getTrainMethod()

§ load()

§ predict()

§ setIterations()

§ setLearningRate()

§ setMiniBatchSize()

§ setRegularization()

§ setTermCriteria()

§ setTrainMethod()