Package org.opencv.ml

## Class 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
• ### Fields inherited from class org.opencv.core.Algorithm

nativeObj
• ### Constructor Summary

Constructors
Modifier Constructor Description
protected  EM​(long addr)
• ### Method Summary

All Methods
Modifier and Type Method Description
static EM __fromPtr__​(long addr)
static EM create()
Creates empty %EM model.
protected void finalize()
int getClustersNumber()
SEE: setClustersNumber
int getCovarianceMatrixType()
SEE: setCovarianceMatrixType
void 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: setTermCriteria
Mat 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 samples
float predict​(Mat samples, Mat results)
Returns posterior probabilities for the provided samples
float predict​(Mat samples, Mat results, int flags)
Returns posterior probabilities for the provided samples
double[] 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: getClustersNumber
void setCovarianceMatrixType​(int val)
getCovarianceMatrixType SEE: getCovarianceMatrixType
void setTermCriteria​(TermCriteria val)
getTermCriteria SEE: getTermCriteria
boolean 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
• ### Methods inherited from class java.lang.Object

clone, equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
• ### Field Detail

• #### DEFAULT_NCLUSTERS

public static final int DEFAULT_NCLUSTERS
Constant Field Values
• #### DEFAULT_MAX_ITERS

public static final int DEFAULT_MAX_ITERS
Constant Field Values
• #### START_E_STEP

public static final int START_E_STEP
Constant Field Values
• #### START_M_STEP

public static final int START_M_STEP
Constant Field Values
• #### START_AUTO_STEP

public static final int START_AUTO_STEP
Constant Field Values
• #### COV_MAT_SPHERICAL

public static final int COV_MAT_SPHERICAL
Constant Field Values
• #### COV_MAT_DIAGONAL

public static final int COV_MAT_DIAGONAL
Constant Field Values
• #### COV_MAT_GENERIC

public static final int COV_MAT_GENERIC
Constant Field Values
• #### COV_MAT_DEFAULT

public static final int COV_MAT_DEFAULT
Constant Field Values
• ### Constructor Detail

• #### EM

protected EM​(long addr)
• ### 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 floating-point 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 class StatModel
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
Returns:
automatically generated
• #### predict

public float predict​(Mat samples,
Mat results)
Returns posterior probabilities for the provided samples
Overrides:
predict in class StatModel
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
Returns:
automatically generated
• #### predict

public float predict​(Mat samples)
Returns posterior probabilities for the provided samples
Overrides:
predict in class StatModel
Parameters:
samples - The input samples, floating-point matrix posterior probabilities for each sample from the input
Returns:
automatically generated
• #### 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 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.
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 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.
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 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. 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 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. $$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N$$ (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 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. 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 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.
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 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. 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 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. $$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N$$ (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 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. 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 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. floating-point 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 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. 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. floating-point 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 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.
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 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. 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 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. $$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N$$ (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 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 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

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 EM
nodeName - name of node containing the classifier
Returns:
automatically generated

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
• #### finalize

protected void finalize()
throws java.lang.Throwable
Overrides:
finalize in class StatModel
Throws:
java.lang.Throwable