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 paramters 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 =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
	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...

Static Public Member Functions
static Ptr< LogisticRegression >	create ()
	Creates 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

Implements Logistic Regression classifier.

See also: Logistic Regression

Member Enumeration Documentation

enum cv::ml::LogisticRegression::Methods

Training methods.

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

enum cv::ml::LogisticRegression::RegKinds

Regularization kinds.

Enumerator
REG_DISABLE	Regularization disabled.
REG_L1	L1 norm
REG_L2	L2 norm

Member Function Documentation

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

static

Creates empty model.

Creates Logistic Regression model with parameters given.

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

pure virtual

This function returns the trained paramters arranged across rows.

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

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

pure virtual

Number of iterations.

See also: setIterations

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

pure virtual

Learning rate.

See also: setLearningRate

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

pure virtual

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

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

pure virtual

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

See also: setRegularization

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

pure virtual

Termination criteria of the algorithm.

See also: setTermCriteria

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

pure virtual

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

See also: setTrainMethod

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

pure virtual

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.

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

pure virtual