Stochastic Gradient Descent SVM classifier. More...

#include <opencv2/ml.hpp>

Inheritance diagram for cv::ml::SVMSGD:

Collaboration diagram for cv::ml::SVMSGD:

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

enum	MarginType { SOFT_MARGIN, HARD_MARGIN }
	Margin type. More...

enum	SvmsgdType { SGD, ASGD }
	SVMSGD type. 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 float	getInitialStepSize () const =0
	Parameter initialStepSize of a SVMSGD optimization problem. More...

virtual float	getMarginRegularization () const =0
	Parameter marginRegularization of a SVMSGD optimization problem. More...

virtual int	getMarginType () const =0
	Margin type, one of SVMSGD::MarginType. More...

virtual float	getShift ()=0

virtual float	getStepDecreasingPower () const =0
	Parameter stepDecreasingPower of a SVMSGD optimization problem. More...

virtual int	getSvmsgdType () const =0
	Algorithm type, one of SVMSGD::SvmsgdType. More...

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

virtual int	getVarCount () const =0
	Returns the number of variables in training samples. More...

virtual Mat	getWeights ()=0

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 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	setInitialStepSize (float InitialStepSize)=0
	Parameter initialStepSize of a SVMSGD optimization problem. More...

virtual void	setMarginRegularization (float marginRegularization)=0
	Parameter marginRegularization of a SVMSGD optimization problem. More...

virtual void	setMarginType (int marginType)=0
	Margin type, one of SVMSGD::MarginType. More...

virtual void	setOptimalParameters (int svmsgdType=SVMSGD::ASGD, int marginType=SVMSGD::SOFT_MARGIN)=0
	Function sets optimal parameters values for chosen SVM SGD model. More...

virtual void	setStepDecreasingPower (float stepDecreasingPower)=0
	Parameter stepDecreasingPower of a SVMSGD optimization problem. More...

virtual void	setSvmsgdType (int svmsgdType)=0
	Algorithm type, one of SVMSGD::SvmsgdType. More...

virtual void	setTermCriteria (const cv::TermCriteria &val)=0
	Termination criteria of the training algorithm. 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< SVMSGD >	create ()
	Creates empty model. More...

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

Stochastic Gradient Descent SVM classifier.

SVMSGD provides a fast and easy-to-use implementation of the SVM classifier using the Stochastic Gradient Descent approach, as presented in [9].

The classifier has following parameters:

model type,
margin type,
margin regularization ( \(\lambda\)),
initial step size ( \(\gamma_0\)),
step decreasing power ( \(c\)),
and termination criteria.

The model type may have one of the following values: SGD and ASGD.

SGD is the classic version of SVMSGD classifier: every next step is calculated by the formula
\[w_{t+1} = w_t - \gamma(t) \frac{dQ_i}{dw} |_{w = w_t}\]
where
- \(w_t\) is the weights vector for decision function at step \(t\),
- \(\gamma(t)\) is the step size of model parameters at the iteration \(t\), it is decreased on each step by the formula \(\gamma(t) = \gamma_0 (1 + \lambda \gamma_0 t) ^ {-c}\)
- \(Q_i\) is the target functional from SVM task for sample with number \(i\), this sample is chosen stochastically on each step of the algorithm.
ASGD is Average Stochastic Gradient Descent SVM Classifier. ASGD classifier averages weights vector on each step of algorithm by the formula \(\widehat{w}_{t+1} = \frac{t}{1+t}\widehat{w}_{t} + \frac{1}{1+t}w_{t+1}\)

The recommended model type is ASGD (following [9]).

The margin type may have one of the following values: SOFT_MARGIN or HARD_MARGIN.

You should use HARD_MARGIN type, if you have linearly separable sets.
You should use SOFT_MARGIN type, if you have non-linearly separable sets or sets with outliers.
In the general case (if you know nothing about linear separability of your sets), use SOFT_MARGIN.

The other parameters may be described as follows:

Margin regularization parameter is responsible for weights decreasing at each step and for the strength of restrictions on outliers (the less the parameter, the less probability that an outlier will be ignored). Recommended value for SGD model is 0.0001, for ASGD model is 0.00001.
Initial step size parameter is the initial value for the step size \(\gamma(t)\). You will have to find the best initial step for your problem.
Step decreasing power is the power parameter for \(\gamma(t)\) decreasing by the formula, mentioned above. Recommended value for SGD model is 1, for ASGD model is 0.75.
Termination criteria can be TermCriteria::COUNT, TermCriteria::EPS or TermCriteria::COUNT + TermCriteria::EPS. You will have to find the best termination criteria for your problem.

Note that the parameters margin regularization, initial step size, and step decreasing power should be positive.

To use SVMSGD algorithm do as follows:

first, create the SVMSGD object. The algoorithm will set optimal parameters by default, but you can set your own parameters via functions setSvmsgdType(), setMarginType(), setMarginRegularization(), setInitialStepSize(), and setStepDecreasingPower().
then the SVM model can be trained using the train features and the correspondent labels by the method train().
after that, the label of a new feature vector can be predicted using the method predict().

// Create empty object
cv::Ptr<SVMSGD> svmsgd = SVMSGD::create();
// Train the Stochastic Gradient Descent SVM
svmsgd->train(trainData);
// Predict labels for the new samples
svmsgd->predict(samples, responses);

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

◆ MarginType

enum cv::ml::SVMSGD::MarginType

Margin type.

Enumerator
SOFT_MARGIN	General case, suits to the case of non-linearly separable sets, allows outliers.
HARD_MARGIN	More accurate for the case of linearly separable sets.

◆ SvmsgdType

enum cv::ml::SVMSGD::SvmsgdType

SVMSGD type.

ASGD is often the preferable choice.

Enumerator
SGD	Stochastic Gradient Descent.
ASGD	Average Stochastic Gradient Descent.

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

static

Creates empty model.

Use StatModel::train to train the model. Since SVMSGD has several parameters, you may want to find the best parameters for your problem or use setOptimalParameters() to set some default parameters.

◆ 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.

◆ getInitialStepSize()

virtual float cv::ml::SVMSGD::getInitialStepSize ( ) const

pure virtual

Parameter initialStepSize of a SVMSGD optimization problem.

See also: setInitialStepSize

◆ getMarginRegularization()

virtual float cv::ml::SVMSGD::getMarginRegularization ( ) const

pure virtual

Parameter marginRegularization of a SVMSGD optimization problem.

See also: setMarginRegularization

◆ getMarginType()

virtual int cv::ml::SVMSGD::getMarginType ( ) const

pure virtual

Margin type, one of SVMSGD::MarginType.

See also: setMarginType

◆ getShift()

virtual float cv::ml::SVMSGD::getShift ( )

pure virtual

Returns: the shift of the trained model (decision function f(x) = weights * x + shift).

◆ getStepDecreasingPower()

virtual float cv::ml::SVMSGD::getStepDecreasingPower ( ) const

pure virtual

Parameter stepDecreasingPower of a SVMSGD optimization problem.

See also: setStepDecreasingPower

◆ getSvmsgdType()

virtual int cv::ml::SVMSGD::getSvmsgdType ( ) const

pure virtual

Algorithm type, one of SVMSGD::SvmsgdType.

See also: setSvmsgdType

◆ getTermCriteria()

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

pure virtual

Termination criteria of the training algorithm.

You can specify the maximum number of iterations (maxCount) and/or how much the error could change between the iterations to make the algorithm continue (epsilon).

See also: setTermCriteria

◆ getVarCount()

virtual int cv::ml::StatModel::getVarCount ( ) const

pure virtualinherited

Returns the number of variables in training samples.

◆ getWeights()

virtual Mat cv::ml::SVMSGD::getWeights ( )

pure virtual

Returns: the weights of the trained model (decision function f(x) = weights * x + shift).

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

static

Loads and creates a serialized SVMSGD from a file.

Use SVMSGD::save to serialize and store an SVMSGD to disk. Load the SVMSGD 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 SVMSGD
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.

◆ 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).

◆ setInitialStepSize()

virtual void cv::ml::SVMSGD::setInitialStepSize ( float InitialStepSize )

pure virtual

Parameter initialStepSize of a SVMSGD optimization problem.

See also: getInitialStepSize

◆ setMarginRegularization()

virtual void cv::ml::SVMSGD::setMarginRegularization ( float marginRegularization )

pure virtual

Parameter marginRegularization of a SVMSGD optimization problem.

See also: getMarginRegularization

◆ setMarginType()

virtual void cv::ml::SVMSGD::setMarginType ( int marginType )

pure virtual

Margin type, one of SVMSGD::MarginType.

See also: getMarginType

◆ setOptimalParameters()

virtual void cv::ml::SVMSGD::setOptimalParameters	(	int	svmsgdType = `SVMSGD::ASGD`,
		int	marginType = `SVMSGD::SOFT_MARGIN`
	)

pure virtual

Function sets optimal parameters values for chosen SVM SGD model.

Parameters

svmsgdType	is the type of SVMSGD classifier.
marginType	is the type of margin constraint.

◆ setStepDecreasingPower()

virtual void cv::ml::SVMSGD::setStepDecreasingPower ( float stepDecreasingPower )

pure virtual

Parameter stepDecreasingPower of a SVMSGD optimization problem.

See also: getStepDecreasingPower

◆ setSvmsgdType()

virtual void cv::ml::SVMSGD::setSvmsgdType ( int svmsgdType )

pure virtual

Algorithm type, one of SVMSGD::SvmsgdType.

See also: getSvmsgdType

◆ setTermCriteria()

virtual void cv::ml::SVMSGD::setTermCriteria ( const cv::TermCriteria & val )

pure virtual

Termination criteria of the training algorithm.

See also: getTermCriteria

◆ 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

◆ MarginType

◆ SvmsgdType

Member Function Documentation

◆ calcError()

◆ clear()

◆ create()

◆ empty()

◆ getDefaultName()

◆ getInitialStepSize()

◆ getMarginRegularization()

◆ getMarginType()

◆ getShift()

◆ getStepDecreasingPower()

◆ getSvmsgdType()

◆ getTermCriteria()

◆ getVarCount()

◆ getWeights()

◆ isClassifier()

◆ isTrained()

◆ load()

◆ loadFromString()

◆ predict()

◆ read() [1/2]

◆ read() [2/2]

◆ save()

◆ setInitialStepSize()

◆ setMarginRegularization()

◆ setMarginType()

◆ setOptimalParameters()

◆ setStepDecreasingPower()

◆ setSvmsgdType()

◆ setTermCriteria()

◆ train() [1/3]

◆ train() [2/3]

◆ train() [3/3]

◆ write() [1/2]

◆ write() [2/2]

◆ writeFormat()