class cv::ml::EM
Overview
The class implements the Expectation Maximization algorithm. More…
#include <ml.hpp> class EM: public cv::ml::StatModel { public: // enums enum { DEFAULT_NCLUSTERS =5, DEFAULT_MAX_ITERS =100, }; enum { START_E_STEP =1, START_M_STEP =2, START_AUTO_STEP =0, }; enum Types; // methods virtual int getClustersNumber() const = 0; virtual int getCovarianceMatrixType() const = 0; virtual void getCovs(std::vector<Mat>& covs) const = 0; virtual Mat getMeans() const = 0; virtual TermCriteria getTermCriteria() const = 0; virtual Mat getWeights() const = 0; virtual float predict( InputArray samples, OutputArray results = noArray(), int flags = 0 ) const = 0; virtual Vec2d predict2( InputArray sample, OutputArray probs ) const = 0; virtual void setClustersNumber(int val) = 0; virtual void setCovarianceMatrixType(int val) = 0; virtual void setTermCriteria(const TermCriteria& val) = 0; virtual bool trainE( InputArray samples, InputArray means0, InputArray covs0 = noArray(), InputArray weights0 = noArray(), OutputArray logLikelihoods = noArray(), OutputArray labels = noArray(), OutputArray probs = noArray() ) = 0; virtual bool trainEM( InputArray samples, OutputArray logLikelihoods = noArray(), OutputArray labels = noArray(), OutputArray probs = noArray() ) = 0; virtual bool trainM( InputArray samples, InputArray probs0, OutputArray logLikelihoods = noArray(), OutputArray labels = noArray(), OutputArray probs = noArray() ) = 0; static Ptr<EM> create(); static Ptr<EM> load( const String& filepath, const String& nodeName = String() ); };
Inherited Members
public: // enums enum Flags; // methods virtual void clear(); virtual bool empty() const; virtual String getDefaultName() const; virtual void read(const FileNode& fn); virtual void save(const String& filename) const; virtual void write(FileStorage& fs) const; template <typename _Tp> static Ptr<_Tp> load( const String& filename, const String& objname = String() ); template <typename _Tp> static Ptr<_Tp> loadFromString( const String& strModel, const String& objname = String() ); template <typename _Tp> static Ptr<_Tp> read(const FileNode& fn); virtual float calcError( const Ptr<TrainData>& data, bool test, OutputArray resp ) const; virtual bool empty() const; virtual int getVarCount() const = 0; virtual bool isClassifier() const = 0; virtual bool isTrained() const = 0; virtual float predict( InputArray samples, OutputArray results = noArray(), int flags = 0 ) const = 0; virtual bool train( const Ptr<TrainData>& trainData, int flags = 0 ); virtual bool train( InputArray samples, int layout, InputArray responses ); template <typename _Tp> static Ptr<_Tp> train( const Ptr<TrainData>& data, int flags = 0 ); protected: // methods void writeFormat(FileStorage& fs) const;
Detailed Documentation
The class implements the Expectation Maximization algorithm.
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Methods
virtual int getClustersNumber() const = 0
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.
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virtual int getCovarianceMatrixType() const = 0
Constraint on covariance matrices which defines type of matrices. See EM::Types.
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virtual void getCovs(std::vector<Mat>& covs) const = 0
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.
virtual Mat getMeans() const = 0
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.
virtual TermCriteria getTermCriteria() const = 0
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.
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virtual Mat getWeights() const = 0
Returns weights of the mixtures.
Returns vector with the number of elements equal to the number of mixtures.
virtual float predict( InputArray samples, OutputArray results = noArray(), int flags = 0 ) const = 0
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 |
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.
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.
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. |
virtual void setClustersNumber(int val) = 0
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virtual void setCovarianceMatrixType(int val) = 0
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virtual void setTermCriteria(const TermCriteria& val) = 0
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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.
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. |
virtual bool trainEM( InputArray samples, OutputArray logLikelihoods = noArray(), OutputArray labels = noArray(), OutputArray probs = noArray() ) = 0
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. |
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.
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 | |
| 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. |
static Ptr<EM> create()
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).
static Ptr<EM> load( const String& filepath, const String& nodeName = String() )
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 |