org.bytedeco.opencv.opencv_ml

Class EM

java.lang.Object

org.bytedeco.javacpp.Pointer

org.bytedeco.opencv.opencv_core.Algorithm

org.bytedeco.opencv.opencv_ml.AbstractStatModel

org.bytedeco.opencv.opencv_ml.StatModel

org.bytedeco.opencv.opencv_ml.EM

All Implemented Interfaces:

AutoCloseable

@Namespace(value="cv::ml")
 @Properties(inherit=opencv_ml.class)
public class EM
extends StatModel

\brief The class implements the Expectation Maximization algorithm.

See Also:

ml_intro_em

Nested Class Summary

Nested classes/interfaces inherited from class org.bytedeco.javacpp.Pointer

Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator, Pointer.ReferenceCounter

Field Summary

Fields

Modifier and Type	Field and Description
static int	COV_MAT_DEFAULT enum cv::ml::EM::Types
static int	COV_MAT_DIAGONAL enum cv::ml::EM::Types
static int	COV_MAT_GENERIC enum cv::ml::EM::Types
static int	COV_MAT_SPHERICAL enum cv::ml::EM::Types
static int	DEFAULT_MAX_ITERS enum cv::ml::EM::
static int	DEFAULT_NCLUSTERS enum cv::ml::EM::
static int	START_AUTO_STEP enum cv::ml::EM::
static int	START_E_STEP enum cv::ml::EM::
static int	START_M_STEP enum cv::ml::EM::

Fields inherited from class org.bytedeco.opencv.opencv_ml.StatModel

COMPRESSED_INPUT, PREPROCESSED_INPUT, RAW_OUTPUT, UPDATE_MODEL

Fields inherited from class org.bytedeco.javacpp.Pointer

address, capacity, limit, position

Constructor Summary

Constructors

Constructor and Description
EM(Pointer p) Pointer cast constructor.

Method Summary

Modifier and Type	Method and Description
static EM	create() Creates empty %EM model.
int	getClustersNumber()
int	getCovarianceMatrixType()
void	getCovs(MatVector covs) \brief Returns covariation matrices
Mat	getMeans() \brief Returns the cluster centers (means of the Gaussian mixture)
TermCriteria	getTermCriteria()
Mat	getWeights() \brief Returns weights of the mixtures
static EM	load(BytePointer filepath)
static EM	load(BytePointer filepath, BytePointer nodeName) \brief Loads and creates a serialized EM from a file Use EM::save to serialize and store an EM to disk.
static EM	load(String filepath)
static EM	load(String filepath, String nodeName)
float	predict(GpuMat samples)
float	predict(GpuMat samples, GpuMat results, int flags)
float	predict(Mat samples)
float	predict(Mat samples, Mat results, int flags) \brief Returns posterior probabilities for the provided samples
float	predict(UMat samples)
float	predict(UMat samples, UMat results, int flags)
Point2d	predict2(GpuMat sample, GpuMat probs)
Point2d	predict2(Mat sample, Mat probs) \brief Returns a likelihood logarithm value and an index of the most probable mixture component for the given sample.
Point2d	predict2(UMat sample, UMat probs)
void	setClustersNumber(int val) \copybrief getClustersNumber @see getClustersNumber
void	setCovarianceMatrixType(int val) \copybrief getCovarianceMatrixType @see getCovarianceMatrixType
void	setTermCriteria(TermCriteria val) \copybrief getTermCriteria @see getTermCriteria
boolean	trainE(GpuMat samples, GpuMat means0)
boolean	trainE(GpuMat samples, GpuMat means0, GpuMat covs0, GpuMat weights0, GpuMat logLikelihoods, GpuMat labels, GpuMat probs)
boolean	trainE(Mat samples, Mat means0)
boolean	trainE(Mat samples, Mat means0, Mat covs0, Mat weights0, Mat logLikelihoods, Mat labels, Mat probs) \brief Estimate the Gaussian mixture parameters from a samples set.
boolean	trainE(UMat samples, UMat means0)
boolean	trainE(UMat samples, UMat means0, UMat covs0, UMat weights0, UMat logLikelihoods, UMat labels, UMat probs)
boolean	trainEM(GpuMat samples)
boolean	trainEM(GpuMat samples, GpuMat logLikelihoods, GpuMat labels, GpuMat probs)
boolean	trainEM(Mat samples)
boolean	trainEM(Mat samples, Mat logLikelihoods, Mat labels, Mat probs) \brief Estimate the Gaussian mixture parameters from a samples set.
boolean	trainEM(UMat samples)
boolean	trainEM(UMat samples, UMat logLikelihoods, UMat labels, UMat probs)
boolean	trainM(GpuMat samples, GpuMat probs0)
boolean	trainM(GpuMat samples, GpuMat probs0, GpuMat logLikelihoods, GpuMat labels, GpuMat probs)
boolean	trainM(Mat samples, Mat probs0)
boolean	trainM(Mat samples, Mat probs0, Mat logLikelihoods, Mat labels, Mat probs) \brief Estimate the Gaussian mixture parameters from a samples set.
boolean	trainM(UMat samples, UMat probs0)
boolean	trainM(UMat samples, UMat probs0, UMat logLikelihoods, UMat labels, UMat probs)

Methods inherited from class org.bytedeco.opencv.opencv_ml.StatModel

calcError, calcError, calcError, empty, getVarCount, isClassifier, isTrained, train, train, train, train, train

Methods inherited from class org.bytedeco.opencv.opencv_ml.AbstractStatModel

loadANN_MLP, loadANN_MLP, loadBoost, loadBoost, loadDTrees, loadDTrees, loadEM, loadEM, loadKNearest, loadKNearest, loadLogisticRegression, loadLogisticRegression, loadNormalBayesClassifier, loadNormalBayesClassifier, loadRTrees, loadRTrees, loadSVM, loadSVM

Methods inherited from class org.bytedeco.opencv.opencv_core.Algorithm

clear, getDefaultName, position, read, save, save, write, write, write

Methods inherited from class org.bytedeco.javacpp.Pointer

address, asBuffer, asByteBuffer, availablePhysicalBytes, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, formatBytes, free, hashCode, isNull, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, parseBytes, physicalBytes, position, put, realloc, referenceCount, releaseReference, retainReference, setNull, sizeof, toString, totalBytes, totalPhysicalBytes, withDeallocator, zero

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Field Detail

COV_MAT_SPHERICAL

public static final int COV_MAT_SPHERICAL

enum cv::ml::EM::Types

See Also:

Constant Field Values

COV_MAT_DIAGONAL

public static final int COV_MAT_DIAGONAL

enum cv::ml::EM::Types

See Also:

Constant Field Values

COV_MAT_GENERIC

public static final int COV_MAT_GENERIC

enum cv::ml::EM::Types

See Also:

Constant Field Values

COV_MAT_DEFAULT

public static final int COV_MAT_DEFAULT

enum cv::ml::EM::Types

See Also:

Constant Field Values

DEFAULT_NCLUSTERS

public static final int DEFAULT_NCLUSTERS

enum cv::ml::EM::

See Also:

Constant Field Values

DEFAULT_MAX_ITERS

public static final int DEFAULT_MAX_ITERS

enum cv::ml::EM::

See Also:

Constant Field Values

START_E_STEP

public static final int START_E_STEP

enum cv::ml::EM::

See Also:

Constant Field Values

START_M_STEP

public static final int START_M_STEP

enum cv::ml::EM::

See Also:

Constant Field Values

START_AUTO_STEP

public static final int START_AUTO_STEP

enum cv::ml::EM::

See Also:

Constant Field Values

Constructor Detail

EM

public EM(Pointer p)

Pointer cast constructor. Invokes Pointer.Pointer(Pointer).

Method Detail

getClustersNumber

public int getClustersNumber()

See Also:

setClustersNumber

setClustersNumber

public void setClustersNumber(int val)

\copybrief getClustersNumber @see getClustersNumber

getCovarianceMatrixType

public int getCovarianceMatrixType()

See Also:

setCovarianceMatrixType

setCovarianceMatrixType

public void setCovarianceMatrixType(int val)

\copybrief getCovarianceMatrixType @see getCovarianceMatrixType

getTermCriteria

@ByVal
public TermCriteria getTermCriteria()

See Also:

setTermCriteria

setTermCriteria

public void setTermCriteria(@Const @ByRef
                            TermCriteria val)

\copybrief getTermCriteria @see getTermCriteria

getWeights

@ByVal
public Mat getWeights()

\brief Returns weights of the mixtures

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

getMeans

@ByVal
public Mat getMeans()

\brief 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.

getCovs

public void getCovs(@ByRef
                    MatVector covs)

\brief 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.

predict

public float predict(@ByVal
                     Mat samples,
                     @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                     Mat results,
                     int flags)

\brief 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

predict

public float predict(@ByVal
                     Mat samples)

Overrides:

predict in class StatModel

predict

public float predict(@ByVal
                     UMat samples,
                     @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                     UMat results,
                     int flags)

Overrides:

predict in class StatModel

predict

public float predict(@ByVal
                     UMat samples)

Overrides:

predict in class StatModel

predict

public float predict(@ByVal
                     GpuMat samples,
                     @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                     GpuMat results,
                     int flags)

Overrides:

predict in class StatModel

predict

public float predict(@ByVal
                     GpuMat samples)

Overrides:

predict in class StatModel

predict2

@ByVal
 @Cast(value="cv::Vec2d*")
public Point2d predict2(@ByVal
                                                          Mat sample,
                                                          @ByVal
                                                          Mat probs)

\brief 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.

predict2

@ByVal
 @Cast(value="cv::Vec2d*")
public Point2d predict2(@ByVal
                                                          UMat sample,
                                                          @ByVal
                                                          UMat probs)

predict2

@ByVal
 @Cast(value="cv::Vec2d*")
public Point2d predict2(@ByVal
                                                          GpuMat sample,
                                                          @ByVal
                                                          GpuMat probs)

trainEM

@Cast(value="bool")
public boolean trainEM(@ByVal
                                           Mat samples,
                                           @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                           Mat logLikelihoods,
                                           @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                           Mat labels,
                                           @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                           Mat probs)

\brief 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.

trainEM

@Cast(value="bool")
public boolean trainEM(@ByVal
                                           Mat samples)

trainEM

@Cast(value="bool")
public boolean trainEM(@ByVal
                                           UMat samples,
                                           @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                           UMat logLikelihoods,
                                           @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                           UMat labels,
                                           @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                           UMat probs)

trainEM

@Cast(value="bool")
public boolean trainEM(@ByVal
                                           UMat samples)

trainEM

@Cast(value="bool")
public boolean trainEM(@ByVal
                                           GpuMat samples,
                                           @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                           GpuMat logLikelihoods,
                                           @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                           GpuMat labels,
                                           @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                           GpuMat probs)

trainEM

@Cast(value="bool")
public boolean trainEM(@ByVal
                                           GpuMat samples)

trainE

@Cast(value="bool")
public boolean trainE(@ByVal
                                          Mat samples,
                                          @ByVal
                                          Mat means0,
                                          @ByVal(nullValue="cv::InputArray(cv::noArray())")
                                          Mat covs0,
                                          @ByVal(nullValue="cv::InputArray(cv::noArray())")
                                          Mat weights0,
                                          @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                          Mat logLikelihoods,
                                          @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                          Mat labels,
                                          @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                          Mat probs)

\brief 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.

trainE

@Cast(value="bool")
public boolean trainE(@ByVal
                                          Mat samples,
                                          @ByVal
                                          Mat means0)

trainE

@Cast(value="bool")
public boolean trainE(@ByVal
                                          UMat samples,
                                          @ByVal
                                          UMat means0,
                                          @ByVal(nullValue="cv::InputArray(cv::noArray())")
                                          UMat covs0,
                                          @ByVal(nullValue="cv::InputArray(cv::noArray())")
                                          UMat weights0,
                                          @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                          UMat logLikelihoods,
                                          @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                          UMat labels,
                                          @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                          UMat probs)

trainE

@Cast(value="bool")
public boolean trainE(@ByVal
                                          UMat samples,
                                          @ByVal
                                          UMat means0)

trainE

@Cast(value="bool")
public boolean trainE(@ByVal
                                          GpuMat samples,
                                          @ByVal
                                          GpuMat means0,
                                          @ByVal(nullValue="cv::InputArray(cv::noArray())")
                                          GpuMat covs0,
                                          @ByVal(nullValue="cv::InputArray(cv::noArray())")
                                          GpuMat weights0,
                                          @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                          GpuMat logLikelihoods,
                                          @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                          GpuMat labels,
                                          @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                          GpuMat probs)

trainE

@Cast(value="bool")
public boolean trainE(@ByVal
                                          GpuMat samples,
                                          @ByVal
                                          GpuMat means0)

trainM

@Cast(value="bool")
public boolean trainM(@ByVal
                                          Mat samples,
                                          @ByVal
                                          Mat probs0,
                                          @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                          Mat logLikelihoods,
                                          @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                          Mat labels,
                                          @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                          Mat probs)

\brief 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.

trainM

@Cast(value="bool")
public boolean trainM(@ByVal
                                          Mat samples,
                                          @ByVal
                                          Mat probs0)

trainM

@Cast(value="bool")
public boolean trainM(@ByVal
                                          UMat samples,
                                          @ByVal
                                          UMat probs0,
                                          @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                          UMat logLikelihoods,
                                          @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                          UMat labels,
                                          @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                          UMat probs)

trainM

@Cast(value="bool")
public boolean trainM(@ByVal
                                          UMat samples,
                                          @ByVal
                                          UMat probs0)

trainM

@Cast(value="bool")
public boolean trainM(@ByVal
                                          GpuMat samples,
                                          @ByVal
                                          GpuMat probs0,
                                          @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                          GpuMat logLikelihoods,
                                          @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                          GpuMat labels,
                                          @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                                          GpuMat probs)

trainM

@Cast(value="bool")
public boolean trainM(@ByVal
                                          GpuMat samples,
                                          @ByVal
                                          GpuMat probs0)

create

@opencv_core.Ptr
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\(filename).

load

@opencv_core.Ptr
public static EM load(@opencv_core.Str
                                       BytePointer filepath,
                                       @opencv_core.Str
                                       BytePointer nodeName)

\brief 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

load

@opencv_core.Ptr
public static EM load(@opencv_core.Str
                                       BytePointer filepath)

load

@opencv_core.Ptr
public static EM load(@opencv_core.Str
                                       String filepath,
                                       @opencv_core.Str
                                       String nodeName)

load

@opencv_core.Ptr
public static EM load(@opencv_core.Str
                                       String filepath)