Package org.opencv.ml
Class EM
 java.lang.Object

 org.opencv.core.Algorithm

 org.opencv.ml.StatModel

 org.opencv.ml.EM

public class EM extends StatModel
The class implements the Expectation Maximization algorithm. SEE: REF: ml_intro_em


Field Summary
Fields Modifier and Type Field Description static int
COV_MAT_DEFAULT
static int
COV_MAT_DIAGONAL
static int
COV_MAT_GENERIC
static int
COV_MAT_SPHERICAL
static int
DEFAULT_MAX_ITERS
static int
DEFAULT_NCLUSTERS
static int
START_AUTO_STEP
static int
START_E_STEP
static int
START_M_STEP

Fields inherited from class org.opencv.ml.StatModel
COMPRESSED_INPUT, PREPROCESSED_INPUT, RAW_OUTPUT, UPDATE_MODEL


Constructor Summary
Constructors Modifier Constructor Description protected
EM(long addr)

Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description static EM
__fromPtr__(long addr)
static EM
create()
Creates empty %EM model.protected void
finalize()
int
getClustersNumber()
SEE: setClustersNumberint
getCovarianceMatrixType()
SEE: setCovarianceMatrixTypevoid
getCovs(java.util.List<Mat> covs)
Returns covariation matrices Returns vector of covariation matrices.Mat
getMeans()
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.TermCriteria
getTermCriteria()
SEE: setTermCriteriaMat
getWeights()
Returns weights of the mixtures Returns vector with the number of elements equal to the number of mixtures.static EM
load(java.lang.String filepath)
Loads and creates a serialized EM from a file Use EM::save to serialize and store an EM to disk.static EM
load(java.lang.String filepath, java.lang.String nodeName)
Loads and creates a serialized EM from a file Use EM::save to serialize and store an EM to disk.float
predict(Mat samples)
Returns posterior probabilities for the provided samplesfloat
predict(Mat samples, Mat results)
Returns posterior probabilities for the provided samplesfloat
predict(Mat samples, Mat results, int flags)
Returns posterior probabilities for the provided samplesdouble[]
predict2(Mat sample, Mat probs)
Returns a likelihood logarithm value and an index of the most probable mixture component for the given sample.void
setClustersNumber(int val)
getClustersNumber SEE: getClustersNumbervoid
setCovarianceMatrixType(int val)
getCovarianceMatrixType SEE: getCovarianceMatrixTypevoid
setTermCriteria(TermCriteria val)
getTermCriteria SEE: getTermCriteriaboolean
trainE(Mat samples, Mat means0)
Estimate the Gaussian mixture parameters from a samples set.boolean
trainE(Mat samples, Mat means0, Mat covs0)
Estimate the Gaussian mixture parameters from a samples set.boolean
trainE(Mat samples, Mat means0, Mat covs0, Mat weights0)
Estimate the Gaussian mixture parameters from a samples set.boolean
trainE(Mat samples, Mat means0, Mat covs0, Mat weights0, Mat logLikelihoods)
Estimate the Gaussian mixture parameters from a samples set.boolean
trainE(Mat samples, Mat means0, Mat covs0, Mat weights0, Mat logLikelihoods, Mat labels)
Estimate the Gaussian mixture parameters from a samples set.boolean
trainE(Mat samples, Mat means0, Mat covs0, Mat weights0, Mat logLikelihoods, Mat labels, Mat probs)
Estimate the Gaussian mixture parameters from a samples set.boolean
trainEM(Mat samples)
Estimate the Gaussian mixture parameters from a samples set.boolean
trainEM(Mat samples, Mat logLikelihoods)
Estimate the Gaussian mixture parameters from a samples set.boolean
trainEM(Mat samples, Mat logLikelihoods, Mat labels)
Estimate the Gaussian mixture parameters from a samples set.boolean
trainEM(Mat samples, Mat logLikelihoods, Mat labels, Mat probs)
Estimate the Gaussian mixture parameters from a samples set.boolean
trainM(Mat samples, Mat probs0)
Estimate the Gaussian mixture parameters from a samples set.boolean
trainM(Mat samples, Mat probs0, Mat logLikelihoods)
Estimate the Gaussian mixture parameters from a samples set.boolean
trainM(Mat samples, Mat probs0, Mat logLikelihoods, Mat labels)
Estimate the Gaussian mixture parameters from a samples set.boolean
trainM(Mat samples, Mat probs0, Mat logLikelihoods, Mat labels, Mat probs)
Estimate the Gaussian mixture parameters from a samples set.
Methods inherited from class org.opencv.ml.StatModel
calcError, empty, getVarCount, isClassifier, isTrained, train, train, train

Methods inherited from class org.opencv.core.Algorithm
clear, getDefaultName, getNativeObjAddr, save




Field Detail

DEFAULT_NCLUSTERS
public static final int DEFAULT_NCLUSTERS
 See Also:
 Constant Field Values

DEFAULT_MAX_ITERS
public static final int DEFAULT_MAX_ITERS
 See Also:
 Constant Field Values

START_E_STEP
public static final int START_E_STEP
 See Also:
 Constant Field Values

START_M_STEP
public static final int START_M_STEP
 See Also:
 Constant Field Values

START_AUTO_STEP
public static final int START_AUTO_STEP
 See Also:
 Constant Field Values

COV_MAT_SPHERICAL
public static final int COV_MAT_SPHERICAL
 See Also:
 Constant Field Values

COV_MAT_DIAGONAL
public static final int COV_MAT_DIAGONAL
 See Also:
 Constant Field Values

COV_MAT_GENERIC
public static final int COV_MAT_GENERIC
 See Also:
 Constant Field Values

COV_MAT_DEFAULT
public static final int COV_MAT_DEFAULT
 See Also:
 Constant Field Values


Method Detail

__fromPtr__
public static EM __fromPtr__(long addr)

getClustersNumber
public int getClustersNumber()
SEE: setClustersNumber Returns:
 automatically generated

setClustersNumber
public void setClustersNumber(int val)
getClustersNumber SEE: getClustersNumber Parameters:
val
 automatically generated

getCovarianceMatrixType
public int getCovarianceMatrixType()
SEE: setCovarianceMatrixType Returns:
 automatically generated

setCovarianceMatrixType
public void setCovarianceMatrixType(int val)
getCovarianceMatrixType SEE: getCovarianceMatrixType Parameters:
val
 automatically generated

getTermCriteria
public TermCriteria getTermCriteria()
SEE: setTermCriteria Returns:
 automatically generated

setTermCriteria
public void setTermCriteria(TermCriteria val)
getTermCriteria SEE: getTermCriteria Parameters:
val
 automatically generated

getWeights
public Mat getWeights()
Returns weights of the mixtures Returns vector with the number of elements equal to the number of mixtures. Returns:
 automatically generated

getMeans
public Mat getMeans()
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. Returns:
 automatically generated

getCovs
public void getCovs(java.util.List<Mat> covs)
Returns covariation matrices Returns vector of covariation matrices. Number of matrices is the number of gaussian mixtures, each matrix is a square floatingpoint matrix NxN, where N is the space dimensionality. Parameters:
covs
 automatically generated

predict
public float predict(Mat samples, Mat results, int flags)
Returns posterior probabilities for the provided samples Overrides:
predict
in classStatModel
 Parameters:
samples
 The input samples, floatingpoint matrixresults
 The optional output \( nSamples \times nClusters\) matrix of results. It contains posterior probabilities for each sample from the inputflags
 This parameter will be ignored Returns:
 automatically generated

predict
public float predict(Mat samples, Mat results)
Returns posterior probabilities for the provided samples

predict
public float predict(Mat samples)
Returns posterior probabilities for the provided samples

predict2
public double[] predict2(Mat sample, Mat probs)
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 onechannel 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 twoelement 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. Returns:
 automatically generated

trainEM
public boolean trainEM(Mat samples, Mat logLikelihoods, Mat labels, Mat probs)
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 kmeans 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 onechannel 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. Returns:
 automatically generated

trainEM
public boolean trainEM(Mat samples, Mat logLikelihoods, Mat labels)
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 kmeans 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 onechannel 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. mixture component given the each sample. It has \(nsamples \times nclusters\) size and CV_64FC1 type. Returns:
 automatically generated

trainEM
public boolean trainEM(Mat samples, Mat logLikelihoods)
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 kmeans 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 onechannel 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. \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type. mixture component given the each sample. It has \(nsamples \times nclusters\) size and CV_64FC1 type. Returns:
 automatically generated

trainEM
public boolean trainEM(Mat samples)
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 kmeans 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 onechannel 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. each sample. It has \(nsamples \times 1\) size and CV_64FC1 type. \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type. mixture component given the each sample. It has \(nsamples \times nclusters\) size and CV_64FC1 type. Returns:
 automatically generated

trainE
public boolean trainE(Mat samples, Mat means0, Mat covs0, Mat weights0, Mat logLikelihoods, Mat labels, Mat probs)
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 onechannel 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 onechannel 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 onechannel 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 onechannel floatingpoint 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. Returns:
 automatically generated

trainE
public boolean trainE(Mat samples, Mat means0, Mat covs0, Mat weights0, Mat logLikelihoods, Mat labels)
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 onechannel 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 onechannel 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 onechannel 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 onechannel floatingpoint 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. mixture component given the each sample. It has \(nsamples \times nclusters\) size and CV_64FC1 type. Returns:
 automatically generated

trainE
public boolean trainE(Mat samples, Mat means0, Mat covs0, Mat weights0, Mat logLikelihoods)
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 onechannel 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 onechannel 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 onechannel 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 onechannel floatingpoint 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. \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type. mixture component given the each sample. It has \(nsamples \times nclusters\) size and CV_64FC1 type. Returns:
 automatically generated

trainE
public boolean trainE(Mat samples, Mat means0, Mat covs0, Mat weights0)
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 onechannel 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 onechannel 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 onechannel 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 onechannel floatingpoint matrix with \(1 \times nclusters\) or \(nclusters \times 1\) size. each sample. It has \(nsamples \times 1\) size and CV_64FC1 type. \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type. mixture component given the each sample. It has \(nsamples \times nclusters\) size and CV_64FC1 type. Returns:
 automatically generated

trainE
public boolean trainE(Mat samples, Mat means0, Mat covs0)
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 onechannel 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 onechannel 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 onechannel 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. floatingpoint matrix with \(1 \times nclusters\) or \(nclusters \times 1\) size. each sample. It has \(nsamples \times 1\) size and CV_64FC1 type. \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type. mixture component given the each sample. It has \(nsamples \times nclusters\) size and CV_64FC1 type. Returns:
 automatically generated

trainE
public boolean trainE(Mat samples, Mat means0)
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 onechannel 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 onechannel 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. covariance matrices is a onechannel 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. floatingpoint matrix with \(1 \times nclusters\) or \(nclusters \times 1\) size. each sample. It has \(nsamples \times 1\) size and CV_64FC1 type. \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type. mixture component given the each sample. It has \(nsamples \times nclusters\) size and CV_64FC1 type. Returns:
 automatically generated

trainM
public boolean trainM(Mat samples, Mat probs0, Mat logLikelihoods, Mat labels, Mat probs)
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 onechannel 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 probabilitieslogLikelihoods
 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. Returns:
 automatically generated

trainM
public boolean trainM(Mat samples, Mat probs0, Mat logLikelihoods, Mat labels)
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 onechannel 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 probabilitieslogLikelihoods
 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. mixture component given the each sample. It has \(nsamples \times nclusters\) size and CV_64FC1 type. Returns:
 automatically generated

trainM
public boolean trainM(Mat samples, Mat probs0, Mat logLikelihoods)
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 onechannel 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 probabilitieslogLikelihoods
 The optional output matrix that contains a likelihood logarithm value for each sample. It has \(nsamples \times 1\) size and CV_64FC1 type. \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type. mixture component given the each sample. It has \(nsamples \times nclusters\) size and CV_64FC1 type. Returns:
 automatically generated

trainM
public boolean trainM(Mat samples, Mat probs0)
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 onechannel 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 each sample. It has \(nsamples \times 1\) size and CV_64FC1 type. \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type. mixture component given the each sample. It has \(nsamples \times nclusters\) size and CV_64FC1 type. Returns:
 automatically generated

create
public static 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). Returns:
 automatically generated

load
public static EM load(java.lang.String filepath, java.lang.String nodeName)
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 EMnodeName
 name of node containing the classifier Returns:
 automatically generated

load
public static EM load(java.lang.String filepath)
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 Returns:
 automatically generated

