//
// This file is auto-generated. Please don't modify it!
//
package org.opencv.ml;
import org.opencv.core.Algorithm;
import org.opencv.core.Mat;
import org.opencv.core.TermCriteria;
// C++: class EM
/**
* <p>The class implements the EM algorithm as described in the beginning of this
* section. It is inherited from "Algorithm".</p>
*
* @see <a href="http://docs.opencv.org/modules/ml/doc/expectation_maximization.html#em">org.opencv.ml.EM : public Algorithm</a>
*/
public class EM extends Algorithm {
protected EM(long addr) { super(addr); }
public static final int
COV_MAT_SPHERICAL = 0,
COV_MAT_DIAGONAL = 1,
COV_MAT_GENERIC = 2,
COV_MAT_DEFAULT = COV_MAT_DIAGONAL,
DEFAULT_NCLUSTERS = 5,
DEFAULT_MAX_ITERS = 100,
START_E_STEP = 1,
START_M_STEP = 2,
START_AUTO_STEP = 0;
//
// C++: EM::EM(int nclusters = EM::DEFAULT_NCLUSTERS, int covMatType = EM::COV_MAT_DIAGONAL, TermCriteria termCrit = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, EM::DEFAULT_MAX_ITERS, FLT_EPSILON))
//
/**
* <p>The constructor of the class</p>
*
* @param nclusters The number of mixture components in the Gaussian mixture
* model. Default value of the parameter is <code>EM.DEFAULT_NCLUSTERS=5</code>.
* 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.
* @param covMatType Constraint on covariance matrices which defines type of
* matrices. Possible values are:
* <ul>
* <li> EM.COV_MAT_SPHERICAL A scaled identity matrix <em>mu_k * I</em>.
* There is the only parameter <em>mu_k</em> 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 <code>covMatType=EM.COV_MAT_DIAGONAL</code>.
* <li> EM.COV_MAT_DIAGONAL A diagonal matrix with positive diagonal
* elements. The number of free parameters is <code>d</code> for each matrix.
* This is most commonly used option yielding good estimation results.
* <li> EM.COV_MAT_GENERIC A symmetric positively defined matrix. The number
* of free parameters in each matrix is about <em>d^2/2</em>. 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.
* </ul>
* @param termCrit The termination criteria of the EM algorithm. The EM
* algorithm can be terminated by the number of iterations <code>termCrit.maxCount</code>
* (number of M-steps) or when relative change of likelihood logarithm is less
* than <code>termCrit.epsilon</code>. Default maximum number of iterations is
* <code>EM.DEFAULT_MAX_ITERS=100</code>.
*
* @see <a href="http://docs.opencv.org/modules/ml/doc/expectation_maximization.html#em-em">org.opencv.ml.EM.EM</a>
*/
public EM(int nclusters, int covMatType, TermCriteria termCrit)
{
super( EM_0(nclusters, covMatType, termCrit.type, termCrit.maxCount, termCrit.epsilon) );
return;
}
/**
* <p>The constructor of the class</p>
*
* @see <a href="http://docs.opencv.org/modules/ml/doc/expectation_maximization.html#em-em">org.opencv.ml.EM.EM</a>
*/
public EM()
{
super( EM_1() );
return;
}
//
// C++: void EM::clear()
//
public void clear()
{
clear_0(nativeObj);
return;
}
//
// C++: bool EM::isTrained()
//
public boolean isTrained()
{
boolean retVal = isTrained_0(nativeObj);
return retVal;
}
//
// C++: Vec2d EM::predict(Mat sample, Mat& probs = Mat())
//
/**
* <p>Returns a likelihood logarithm value and an index of the most probable
* mixture component for the given sample.</p>
*
* <p>The method returns a two-element <code>double</code> 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.</p>
*
* @param sample A sample for classification. It should be a one-channel matrix
* of <em>1 x dims</em> or <em>dims x 1</em> size.
* @param probs Optional output matrix that contains posterior probabilities of
* each component given the sample. It has <em>1 x nclusters</em> size and
* <code>CV_64FC1</code> type.
*
* @see <a href="http://docs.opencv.org/modules/ml/doc/expectation_maximization.html#em-predict">org.opencv.ml.EM.predict</a>
*/
public double[] predict(Mat sample, Mat probs)
{
double[] retVal = predict_0(nativeObj, sample.nativeObj, probs.nativeObj);
return retVal;
}
/**
* <p>Returns a likelihood logarithm value and an index of the most probable
* mixture component for the given sample.</p>
*
* <p>The method returns a two-element <code>double</code> 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.</p>
*
* @param sample A sample for classification. It should be a one-channel matrix
* of <em>1 x dims</em> or <em>dims x 1</em> size.
*
* @see <a href="http://docs.opencv.org/modules/ml/doc/expectation_maximization.html#em-predict">org.opencv.ml.EM.predict</a>
*/
public double[] predict(Mat sample)
{
double[] retVal = predict_1(nativeObj, sample.nativeObj);
return retVal;
}
//
// C++: bool EM::train(Mat samples, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
//
/**
* <p>Estimates the Gaussian mixture parameters from a samples set.</p>
*
* <p>Three versions of training method differ in the initialization of Gaussian
* mixture model parameters and start step:</p>
* <ul>
* <li> train - Starts with Expectation step. Initial values of the model
* parameters will be estimated by the k-means algorithm.
* <li> trainE - Starts with Expectation step. You need to provide initial
* means <em>a_k</em> of mixture components. Optionally you can pass initial
* weights <em>pi_k</em> and covariance matrices <em>S_k</em> of mixture
* components.
* <li> trainM - Starts with Maximization step. You need to provide initial
* probabilities <em>p_(i,k)</em> to use this option.
* </ul>
*
* <p>The methods return <code>true</code> if the Gaussian mixture model was
* trained successfully, otherwise it returns <code>false</code>.</p>
*
* <p>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: <em>p_(i,k)</em> in <code>probs</code>, <em>a_k</em> in
* <code>means</code>, <em>S_k</em> in <code>covs[k]</code>, <em>pi_k</em> in
* <code>weights</code>, and optionally computes the output "class label" for
* each sample: <em>labels_i=arg max_k(p_(i,k)), i=1..N</em> (indices of the
* most probable mixture component for each sample).</p>
*
* <p>The trained model can be used further for prediction, just like any other
* classifier. The trained model is similar to the "CvNormalBayesClassifier".</p>
*
* @param 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 <code>CV_64F</code> type it will be converted to
* the inner matrix of such type for the further computing.
* @param logLikelihoods The optional output matrix that contains a likelihood
* logarithm value for each sample. It has <em>nsamples x 1</em> size and
* <code>CV_64FC1</code> type.
* @param labels The optional output "class label" for each sample:
* <em>labels_i=arg max_k(p_(i,k)), i=1..N</em> (indices of the most probable
* mixture component for each sample). It has <em>nsamples x 1</em> size and
* <code>CV_32SC1</code> type.
* @param probs The optional output matrix that contains posterior probabilities
* of each Gaussian mixture component given the each sample. It has <em>nsamples
* x nclusters</em> size and <code>CV_64FC1</code> type.
*
* @see <a href="http://docs.opencv.org/modules/ml/doc/expectation_maximization.html#em-train">org.opencv.ml.EM.train</a>
*/
public boolean train(Mat samples, Mat logLikelihoods, Mat labels, Mat probs)
{
boolean retVal = train_0(nativeObj, samples.nativeObj, logLikelihoods.nativeObj, labels.nativeObj, probs.nativeObj);
return retVal;
}
/**
* <p>Estimates the Gaussian mixture parameters from a samples set.</p>
*
* <p>Three versions of training method differ in the initialization of Gaussian
* mixture model parameters and start step:</p>
* <ul>
* <li> train - Starts with Expectation step. Initial values of the model
* parameters will be estimated by the k-means algorithm.
* <li> trainE - Starts with Expectation step. You need to provide initial
* means <em>a_k</em> of mixture components. Optionally you can pass initial
* weights <em>pi_k</em> and covariance matrices <em>S_k</em> of mixture
* components.
* <li> trainM - Starts with Maximization step. You need to provide initial
* probabilities <em>p_(i,k)</em> to use this option.
* </ul>
*
* <p>The methods return <code>true</code> if the Gaussian mixture model was
* trained successfully, otherwise it returns <code>false</code>.</p>
*
* <p>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: <em>p_(i,k)</em> in <code>probs</code>, <em>a_k</em> in
* <code>means</code>, <em>S_k</em> in <code>covs[k]</code>, <em>pi_k</em> in
* <code>weights</code>, and optionally computes the output "class label" for
* each sample: <em>labels_i=arg max_k(p_(i,k)), i=1..N</em> (indices of the
* most probable mixture component for each sample).</p>
*
* <p>The trained model can be used further for prediction, just like any other
* classifier. The trained model is similar to the "CvNormalBayesClassifier".</p>
*
* @param 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 <code>CV_64F</code> type it will be converted to
* the inner matrix of such type for the further computing.
*
* @see <a href="http://docs.opencv.org/modules/ml/doc/expectation_maximization.html#em-train">org.opencv.ml.EM.train</a>
*/
public boolean train(Mat samples)
{
boolean retVal = train_1(nativeObj, samples.nativeObj);
return retVal;
}
//
// C++: bool EM::trainE(Mat samples, Mat means0, Mat covs0 = Mat(), Mat weights0 = Mat(), Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
//
public boolean trainE(Mat samples, Mat means0, Mat covs0, Mat weights0, Mat logLikelihoods, Mat labels, Mat probs)
{
boolean retVal = trainE_0(nativeObj, samples.nativeObj, means0.nativeObj, covs0.nativeObj, weights0.nativeObj, logLikelihoods.nativeObj, labels.nativeObj, probs.nativeObj);
return retVal;
}
public boolean trainE(Mat samples, Mat means0)
{
boolean retVal = trainE_1(nativeObj, samples.nativeObj, means0.nativeObj);
return retVal;
}
//
// C++: bool EM::trainM(Mat samples, Mat probs0, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
//
public boolean trainM(Mat samples, Mat probs0, Mat logLikelihoods, Mat labels, Mat probs)
{
boolean retVal = trainM_0(nativeObj, samples.nativeObj, probs0.nativeObj, logLikelihoods.nativeObj, labels.nativeObj, probs.nativeObj);
return retVal;
}
public boolean trainM(Mat samples, Mat probs0)
{
boolean retVal = trainM_1(nativeObj, samples.nativeObj, probs0.nativeObj);
return retVal;
}
@Override
protected void finalize() throws Throwable {
delete(nativeObj);
}
// C++: EM::EM(int nclusters = EM::DEFAULT_NCLUSTERS, int covMatType = EM::COV_MAT_DIAGONAL, TermCriteria termCrit = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, EM::DEFAULT_MAX_ITERS, FLT_EPSILON))
private static native long EM_0(int nclusters, int covMatType, int termCrit_type, int termCrit_maxCount, double termCrit_epsilon);
private static native long EM_1();
// C++: void EM::clear()
private static native void clear_0(long nativeObj);
// C++: bool EM::isTrained()
private static native boolean isTrained_0(long nativeObj);
// C++: Vec2d EM::predict(Mat sample, Mat& probs = Mat())
private static native double[] predict_0(long nativeObj, long sample_nativeObj, long probs_nativeObj);
private static native double[] predict_1(long nativeObj, long sample_nativeObj);
// C++: bool EM::train(Mat samples, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
private static native boolean train_0(long nativeObj, long samples_nativeObj, long logLikelihoods_nativeObj, long labels_nativeObj, long probs_nativeObj);
private static native boolean train_1(long nativeObj, long samples_nativeObj);
// C++: bool EM::trainE(Mat samples, Mat means0, Mat covs0 = Mat(), Mat weights0 = Mat(), Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
private static native boolean trainE_0(long nativeObj, long samples_nativeObj, long means0_nativeObj, long covs0_nativeObj, long weights0_nativeObj, long logLikelihoods_nativeObj, long labels_nativeObj, long probs_nativeObj);
private static native boolean trainE_1(long nativeObj, long samples_nativeObj, long means0_nativeObj);
// C++: bool EM::trainM(Mat samples, Mat probs0, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
private static native boolean trainM_0(long nativeObj, long samples_nativeObj, long probs0_nativeObj, long logLikelihoods_nativeObj, long labels_nativeObj, long probs_nativeObj);
private static native boolean trainM_1(long nativeObj, long samples_nativeObj, long probs0_nativeObj);
// native support for java finalize()
private static native void delete(long nativeObj);
}