EM Class Reference
[Machine Learning]

Default parameters.

Definition at line 757 of file ml.hpp.

The initial step.

Definition at line 760 of file ml.hpp.

Predict options.

Enumerator:

RAW_OUTPUT

makes the method return the raw results (the sum), not the class label

Reimplemented in DTrees.

Definition at line 296 of file ml.hpp.

Type of covariation matrices.

Enumerator:

COV_MAT_SPHERICAL	A scaled identity matrix . There is the only parameter 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=EMCOV_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 . 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.

Definition at line 736 of file ml.hpp.

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%).

Clears the algorithm state.

Reimplemented in DescriptorMatcher, and FlannBasedMatcher.

Definition at line 2984 of file core.hpp.

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).

Returns true if the Algorithm is empty (e.g.

in the very beginning or after unsuccessful read

Reimplemented from Algorithm.

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

Constraint on covariance matrices which defines type of matrices.

See EM::Types.

See also:

setCovarianceMatrixType

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.

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.

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.

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

Returns the number of variables in training samples.

Returns weights of the mixtures.

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

Returns true if the model is classifier.

Returns true if the model is trained.

Loads algorithm from the file.

Parameters:

filename	Name of the file to read.
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::load<SVM>("my_svm_model.xml");

In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn).

Definition at line 3027 of file core.hpp.

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);

Definition at line 3046 of file core.hpp.

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 LogisticRegression.

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 or size.
probs	Optional output matrix that contains posterior probabilities of each component given the sample. It has 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.

Reads algorithm parameters from a file storage.

Reimplemented in DescriptorMatcher, and FlannBasedMatcher.

Definition at line 2992 of file core.hpp.

Reads algorithm from the file node.

This is static template method of Algorithm. It's usage is following (in the case of SVM):

     Ptr<SVM> svm = Algorithm::read<SVM>(fn);

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)

Reimplemented in DescriptorMatcher, and FlannBasedMatcher.

Definition at line 3008 of file core.hpp.

Saves the algorithm to a file.

In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

The number of mixture components in the Gaussian mixture model.

See also:

getClustersNumber

Constraint on covariance matrices which defines type of matrices.

See also:

getCovarianceMatrixType

The termination criteria of the EM algorithm.

See also:

getTermCriteria

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).

Trains the statistical model.

Parameters:

samples	training samples
layout	See ml::SampleTypes.
responses	vector of responses associated with the training samples.

Create and train model with default parameters.

The class must implement static `create()` method with no parameters or with all default parameter values

Definition at line 357 of file ml.hpp.

Estimate the Gaussian mixture parameters from a samples set.

This variation starts with Expectation step. You need to provide initial means of mixture components. Optionally you can pass initial weights and covariance matrices 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 of mixture components. It is a one-channel matrix of 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 of mixture components. Each of covariance matrices is a one-channel matrix of 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 of mixture components. It should be a one-channel floating-point matrix with or size.
logLikelihoods	The optional output matrix that contains a likelihood logarithm value for each sample. It has size and CV_64FC1 type.
labels	The optional output "class label" for each sample: (indices of the most probable mixture component for each sample). It has 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 size and CV_64FC1 type.

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: in probs, in means , in covs[k], in weights , and optionally computes the output "class label" for each sample: (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 size and CV_64FC1 type.
labels	The optional output "class label" for each sample: (indices of the most probable mixture component for each sample). It has 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 size and CV_64FC1 type.

Estimate the Gaussian mixture parameters from a samples set.

This variation starts with Maximization step. You need to provide initial probabilities 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 size and CV_64FC1 type.
labels	The optional output "class label" for each sample: (indices of the most probable mixture component for each sample). It has 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 size and CV_64FC1 type.

Stores algorithm parameters in a file storage.

Reimplemented in DescriptorMatcher, and FlannBasedMatcher.

Definition at line 2988 of file core.hpp.