Implements Logistic Regression classifier. More...

#include <opencv2/ml.hpp>

Inheritance diagram for cv::ml::LogisticRegression:

Collaboration diagram for cv::ml::LogisticRegression:

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

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 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 Mat	get_learnt_thetas () const =0
	This function returns the trained parameters arranged across rows. More...

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

virtual int	getIterations () const =0
	Number of iterations. More...

virtual double	getLearningRate () const =0
	Learning rate. More...

virtual int	getMiniBatchSize () const =0
	Specifies the number of training samples taken in each step of Mini-Batch Gradient Descent. More...

virtual int	getRegularization () const =0
	Kind of regularization to be applied. More...

virtual TermCriteria	getTermCriteria () const =0
	Termination criteria of the algorithm. More...

virtual int	getTrainMethod () const =0
	Kind of training method used. 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 CV_OVERRIDE=0
	Predicts responses for input samples and returns a float type. 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	setIterations (int val)=0
	Number of iterations. More...

virtual void	setLearningRate (double val)=0
	Learning rate. More...

virtual void	setMiniBatchSize (int val)=0
	Specifies the number of training samples taken in each step of Mini-Batch Gradient Descent. More...

virtual void	setRegularization (int val)=0
	Kind of regularization to be applied. More...

virtual void	setTermCriteria (TermCriteria val)=0
	Termination criteria of the algorithm. More...

virtual void	setTrainMethod (int val)=0
	Kind of training method used. 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< 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...

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

Implements Logistic Regression classifier.

See also: Logistic Regression

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

◆ 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

◆ 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<LogisticRegression> cv::ml::LogisticRegression::create ( )

static

Creates empty model.

Creates Logistic Regression model with parameters given.

◆ 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.

◆ get_learnt_thetas()

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

pure virtual

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.

◆ 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.

◆ getIterations()

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

pure virtual

Number of iterations.

See also: setIterations

◆ getLearningRate()

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

pure virtual

Learning rate.

See also: setLearningRate

◆ getMiniBatchSize()

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

◆ getRegularization()

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

pure virtual

Kind of regularization to be applied.

See LogisticRegression::RegKinds.

See also: setRegularization

◆ getTermCriteria()

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

pure virtual

Termination criteria of the algorithm.

See also: setTermCriteria

◆ getTrainMethod()

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

pure virtual

Kind of training method used.

See LogisticRegression::Methods.

See also: setTrainMethod

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

static

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

◆ 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::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.

◆ 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).

◆ setIterations()

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

pure virtual

Number of iterations.

See also: getIterations

◆ setLearningRate()

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

pure virtual

Learning rate.

See also: getLearningRate

◆ setMiniBatchSize()

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

pure virtual

Specifies the number of training samples taken in each step of Mini-Batch Gradient Descent.

See also: getMiniBatchSize

◆ setRegularization()

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

pure virtual

Kind of regularization to be applied.

See also: getRegularization

◆ setTermCriteria()

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

pure virtual

Termination criteria of the algorithm.

See also: getTermCriteria

◆ setTrainMethod()

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

pure virtual

Kind of training method used.

See also: getTrainMethod

◆ 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

◆ Methods

◆ RegKinds

Member Function Documentation

◆ calcError()

◆ clear()

◆ create()

◆ empty()

◆ get_learnt_thetas()

◆ getDefaultName()

◆ getIterations()

◆ getLearningRate()

◆ getMiniBatchSize()

◆ getRegularization()

◆ getTermCriteria()

◆ getTrainMethod()

◆ getVarCount()

◆ isClassifier()

◆ isTrained()

◆ load()

◆ loadFromString()

◆ predict()

◆ read() [1/2]

◆ read() [2/2]

◆ save()

◆ setIterations()

◆ setLearningRate()

◆ setMiniBatchSize()

◆ setRegularization()

◆ setTermCriteria()

◆ setTrainMethod()

◆ train() [1/3]

◆ train() [2/3]

◆ train() [3/3]

◆ write() [1/2]

◆ write() [2/2]

◆ writeFormat()