The class implements the Expectation Maximization algorithm. More...

#include <opencv2/ml.hpp>

Inheritance diagram for cv::ml::EM:

Collaboration diagram for cv::ml::EM:

Public Types
enum	{ DEFAULT_NCLUSTERS =5, DEFAULT_MAX_ITERS =100 }
	Default parameters. More...

enum	{ START_E_STEP =1, START_M_STEP =2, START_AUTO_STEP =0 }
	The initial step. More...

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

enum	Types { COV_MAT_SPHERICAL =0, COV_MAT_DIAGONAL =1, COV_MAT_GENERIC =2, COV_MAT_DEFAULT =COV_MAT_DIAGONAL }
	Type of covariation matrices. 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 int	getClustersNumber () const =0
	The number of mixture components in the Gaussian mixture model. More...

virtual int	getCovarianceMatrixType () const =0
	Constraint on covariance matrices which defines type of matrices. More...

virtual void	getCovs (std::vector< Mat > &covs) const =0
	Returns covariation matrices. More...

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

virtual Mat	getMeans () const =0
	Returns the cluster centers (means of the Gaussian mixture) More...

virtual TermCriteria	getTermCriteria () const =0
	The termination criteria of the EM algorithm. More...

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

virtual Mat	getWeights () const =0
	Returns weights of the mixtures. 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
	Returns posterior probabilities for the provided samples. More...

virtual Vec2d	predict2 (InputArray sample, OutputArray probs) const =0
	Returns a likelihood logarithm value and an index of the most probable mixture component for the given sample. 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	setClustersNumber (int val)=0
	The number of mixture components in the Gaussian mixture model. More...

virtual void	setCovarianceMatrixType (int val)=0
	Constraint on covariance matrices which defines type of matrices. More...

virtual void	setTermCriteria (const TermCriteria &val)=0
	The termination criteria of the EM 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 bool	trainE (InputArray samples, InputArray means0, InputArray covs0=noArray(), InputArray weights0=noArray(), OutputArray logLikelihoods=noArray(), OutputArray labels=noArray(), OutputArray probs=noArray())=0
	Estimate the Gaussian mixture parameters from a samples set. More...

virtual bool	trainEM (InputArray samples, OutputArray logLikelihoods=noArray(), OutputArray labels=noArray(), OutputArray probs=noArray())=0
	Estimate the Gaussian mixture parameters from a samples set. More...

virtual bool	trainM (InputArray samples, InputArray probs0, OutputArray logLikelihoods=noArray(), OutputArray labels=noArray(), OutputArray probs=noArray())=0
	Estimate the Gaussian mixture parameters from a samples set. 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< EM >	create ()
	Creates empty EM model. More...

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

The class implements the Expectation Maximization algorithm.

See also: Expectation Maximization

Member Enumeration Documentation

◆ anonymous enum

anonymous enum

Default parameters.

Enumerator
DEFAULT_NCLUSTERS
DEFAULT_MAX_ITERS

◆ anonymous enum

anonymous enum

The initial step.

Enumerator
START_E_STEP
START_M_STEP
START_AUTO_STEP

◆ 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

◆ Types

enum cv::ml::EM::Types

Type of covariation matrices.

Enumerator
COV_MAT_SPHERICAL	A scaled identity matrix \(\mu_k * I\). There is the only parameter \(\mu_k\) to be estimated for each matrix. The option may be used in special cases, when the constraint is relevant, or as a first step in the optimization (for example in case when the data is preprocessed with PCA). The results of such preliminary estimation may be passed again to the optimization procedure, this time with covMatType=EM::COV_MAT_DIAGONAL.
COV_MAT_DIAGONAL	A diagonal matrix with positive diagonal elements. The number of free parameters is d for each matrix. This is most commonly used option yielding good estimation results.
COV_MAT_GENERIC	A symmetric positively defined matrix. The number of free parameters in each matrix is about \(d^2/2\). It is not recommended to use this option, unless there is pretty accurate initial estimation of the parameters and/or a huge number of training samples.
COV_MAT_DEFAULT

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

static

Creates empty EM model.

The model should be trained then using StatModel::train(traindata, flags) method. Alternatively, you can use one of the EM::train* methods or load it from file using Algorithm::load<EM>(filename).

◆ 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.

◆ getClustersNumber()

virtual int cv::ml::EM::getClustersNumber ( ) const

pure virtual

The number of mixture components in the Gaussian mixture model.

Default value of the parameter is EM::DEFAULT_NCLUSTERS=5. Some of EM implementation could determine the optimal number of mixtures within a specified value range, but that is not the case in ML yet.

See also: setClustersNumber

◆ getCovarianceMatrixType()

virtual int cv::ml::EM::getCovarianceMatrixType ( ) const

pure virtual

Constraint on covariance matrices which defines type of matrices.

See EM::Types.

See also: setCovarianceMatrixType

◆ getCovs()

virtual void cv::ml::EM::getCovs ( std::vector< Mat > & covs ) const

pure virtual

Returns covariation matrices.

Returns vector of covariation matrices. Number of matrices is the number of gaussian mixtures, each matrix is a square floating-point matrix NxN, where N is the space dimensionality.

◆ 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.

◆ getMeans()

virtual Mat cv::ml::EM::getMeans ( ) const

pure virtual

Returns the cluster centers (means of the Gaussian mixture)

Returns matrix with the number of rows equal to the number of mixtures and number of columns equal to the space dimensionality.

◆ getTermCriteria()

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

pure virtual

The termination criteria of the EM algorithm.

The EM algorithm can be terminated by the number of iterations termCrit.maxCount (number of M-steps) or when relative change of likelihood logarithm is less than termCrit.epsilon. Default maximum number of iterations is EM::DEFAULT_MAX_ITERS=100.

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::EM::getWeights ( ) const

pure virtual

Returns weights of the mixtures.

Returns vector with the number of elements equal to the number of mixtures.

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

static

Loads and creates a serialized EM from a file.

Use EM::save to serialize and store an EM to disk. Load the EM 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 EM
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::EM::predict	(	InputArray	samples,
		OutputArray	results = `noArray()`,
		int	flags = `0`
	)		const

pure virtual

Returns posterior probabilities for the provided samples.

Parameters

samples	The input samples, floating-point matrix
results	The optional output \( nSamples \times nClusters\) matrix of results. It contains posterior probabilities for each sample from the input
flags	This parameter will be ignored

Implements cv::ml::StatModel.

◆ predict2()

virtual Vec2d cv::ml::EM::predict2	(	InputArray	sample,
		OutputArray	probs
	)		const

pure virtual

Returns a likelihood logarithm value and an index of the most probable mixture component for the given sample.

Parameters

sample	A sample for classification. It should be a one-channel matrix of \(1 \times dims\) or \(dims \times 1\) size.
probs	Optional output matrix that contains posterior probabilities of each component given the sample. It has \(1 \times nclusters\) size and CV_64FC1 type.

The method returns a two-element double vector. Zero element is a likelihood logarithm value for the sample. First element is an index of the most probable mixture component for the given sample.

◆ 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).

◆ setClustersNumber()

virtual void cv::ml::EM::setClustersNumber ( int val )

pure virtual

The number of mixture components in the Gaussian mixture model.

See also: getClustersNumber

◆ setCovarianceMatrixType()

virtual void cv::ml::EM::setCovarianceMatrixType ( int val )

pure virtual

Constraint on covariance matrices which defines type of matrices.

See also: getCovarianceMatrixType

◆ setTermCriteria()

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

pure virtual

The termination criteria of the EM 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

◆ trainE()

virtual bool cv::ml::EM::trainE	(	InputArray	samples,
		InputArray	means0,
		InputArray	covs0 = `noArray()`,
		InputArray	weights0 = `noArray()`,
		OutputArray	logLikelihoods = `noArray()`,
		OutputArray	labels = `noArray()`,
		OutputArray	probs = `noArray()`
	)

pure virtual

Estimate the Gaussian mixture parameters from a samples set.

This variation starts with Expectation step. You need to provide initial means \(a_k\) of mixture components. Optionally you can pass initial weights \(\pi_k\) and covariance matrices \(S_k\) of mixture components.

Parameters

samples	Samples from which the Gaussian mixture model will be estimated. It should be a one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type it will be converted to the inner matrix of such type for the further computing.
means0	Initial means \(a_k\) of mixture components. It is a one-channel matrix of \(nclusters \times dims\) size. If the matrix does not have CV_64F type it will be converted to the inner matrix of such type for the further computing.
covs0	The vector of initial covariance matrices \(S_k\) of mixture components. Each of covariance matrices is a one-channel matrix of \(dims \times dims\) size. If the matrices do not have CV_64F type they will be converted to the inner matrices of such type for the further computing.
weights0	Initial weights \(\pi_k\) of mixture components. It should be a one-channel floating-point matrix with \(1 \times nclusters\) or \(nclusters \times 1\) size.
logLikelihoods	The optional output matrix that contains a likelihood logarithm value for each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.
labels	The optional output "class label" for each sample: \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
probs	The optional output matrix that contains posterior probabilities of each Gaussian mixture component given the each sample. It has \(nsamples \times nclusters\) size and CV_64FC1 type.

◆ trainEM()

virtual bool cv::ml::EM::trainEM	(	InputArray	samples,
		OutputArray	logLikelihoods = `noArray()`,
		OutputArray	labels = `noArray()`,
		OutputArray	probs = `noArray()`
	)

pure virtual

Estimate the Gaussian mixture parameters from a samples set.

This variation starts with Expectation step. Initial values of the model parameters will be estimated by the k-means algorithm.

Unlike many of the ML models, EM is an unsupervised learning algorithm and it does not take responses (class labels or function values) as input. Instead, it computes the Maximum Likelihood Estimate of the Gaussian mixture parameters from an input sample set, stores all the parameters inside the structure: \(p_{i,k}\) in probs, \(a_k\) in means , \(S_k\) in covs[k], \(\pi_k\) in weights , and optionally computes the output "class label" for each sample: \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable mixture component for each sample).

The trained model can be used further for prediction, just like any other classifier. The trained model is similar to the NormalBayesClassifier.

Parameters

samples	Samples from which the Gaussian mixture model will be estimated. It should be a one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type it will be converted to the inner matrix of such type for the further computing.
logLikelihoods	The optional output matrix that contains a likelihood logarithm value for each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.
labels	The optional output "class label" for each sample: \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
probs	The optional output matrix that contains posterior probabilities of each Gaussian mixture component given the each sample. It has \(nsamples \times nclusters\) size and CV_64FC1 type.

◆ trainM()

virtual bool cv::ml::EM::trainM	(	InputArray	samples,
		InputArray	probs0,
		OutputArray	logLikelihoods = `noArray()`,
		OutputArray	labels = `noArray()`,
		OutputArray	probs = `noArray()`
	)

pure virtual

Estimate the Gaussian mixture parameters from a samples set.

This variation starts with Maximization step. You need to provide initial probabilities \(p_{i,k}\) to use this option.

Parameters

samples	Samples from which the Gaussian mixture model will be estimated. It should be a one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type it will be converted to the inner matrix of such type for the further computing.
probs0	the probabilities
logLikelihoods	The optional output matrix that contains a likelihood logarithm value for each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.
labels	The optional output "class label" for each sample: \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
probs	The optional output matrix that contains posterior probabilities of each Gaussian mixture component given the each sample. It has \(nsamples \times nclusters\) size and CV_64FC1 type.

◆ 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

◆ anonymous enum

◆ anonymous enum

◆ Flags

◆ Types

Member Function Documentation

◆ calcError()

◆ clear()

◆ create()

◆ empty()

◆ getClustersNumber()

◆ getCovarianceMatrixType()

◆ getCovs()

◆ getDefaultName()

◆ getMeans()

◆ getTermCriteria()

◆ getVarCount()

◆ getWeights()

◆ isClassifier()

◆ isTrained()

◆ load()

◆ loadFromString()

◆ predict()

◆ predict2()

◆ read() [1/2]

◆ read() [2/2]

◆ save()

◆ setClustersNumber()

◆ setCovarianceMatrixType()

◆ setTermCriteria()

◆ train() [1/3]

◆ train() [2/3]

◆ train() [3/3]

◆ trainE()

◆ trainEM()

◆ trainM()

◆ write() [1/2]

◆ write() [2/2]

◆ writeFormat()