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Java Source Code

//
// This file is auto-generated. Please don't modify it!
////w  w w  .j  a va 2 s.  com
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);
    }



    //
    // native stuff
    //
    static { System.loadLibrary("opencv_java"); }

    // 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);

}




Java Source Code List

org.opencv.android.Utils.java
org.opencv.calib3d.Calib3d.java
org.opencv.calib3d.StereoBM.java
org.opencv.calib3d.StereoSGBM.java
org.opencv.core.Algorithm.java
org.opencv.core.Core.java
org.opencv.core.CvException.java
org.opencv.core.CvType.java
org.opencv.core.MatOfByte.java
org.opencv.core.MatOfDMatch.java
org.opencv.core.MatOfDouble.java
org.opencv.core.MatOfFloat4.java
org.opencv.core.MatOfFloat6.java
org.opencv.core.MatOfFloat.java
org.opencv.core.MatOfInt4.java
org.opencv.core.MatOfInt.java
org.opencv.core.MatOfKeyPoint.java
org.opencv.core.MatOfPoint2f.java
org.opencv.core.MatOfPoint3.java
org.opencv.core.MatOfPoint3f.java
org.opencv.core.MatOfPoint.java
org.opencv.core.MatOfRect.java
org.opencv.core.Mat.java
org.opencv.core.Point3.java
org.opencv.core.Point.java
org.opencv.core.Range.java
org.opencv.core.Rect.java
org.opencv.core.RotatedRect.java
org.opencv.core.Scalar.java
org.opencv.core.Size.java
org.opencv.core.TermCriteria.java
org.opencv.features2d.DMatch.java
org.opencv.features2d.DescriptorExtractor.java
org.opencv.features2d.DescriptorMatcher.java
org.opencv.features2d.FeatureDetector.java
org.opencv.features2d.Features2d.java
org.opencv.features2d.GenericDescriptorMatcher.java
org.opencv.features2d.KeyPoint.java
org.opencv.highgui.Highgui.java
org.opencv.highgui.VideoCapture.java
org.opencv.imgproc.Imgproc.java
org.opencv.imgproc.Moments.java
org.opencv.imgproc.Subdiv2D.java
org.opencv.ml.CvANN_MLP_TrainParams.java
org.opencv.ml.CvANN_MLP.java
org.opencv.ml.CvBoostParams.java
org.opencv.ml.CvBoost.java
org.opencv.ml.CvDTreeParams.java
org.opencv.ml.CvDTree.java
org.opencv.ml.CvERTrees.java
org.opencv.ml.CvGBTreesParams.java
org.opencv.ml.CvGBTrees.java
org.opencv.ml.CvKNearest.java
org.opencv.ml.CvNormalBayesClassifier.java
org.opencv.ml.CvParamGrid.java
org.opencv.ml.CvRTParams.java
org.opencv.ml.CvRTrees.java
org.opencv.ml.CvSVMParams.java
org.opencv.ml.CvSVM.java
org.opencv.ml.CvStatModel.java
org.opencv.ml.EM.java
org.opencv.ml.Ml.java
org.opencv.objdetect.CascadeClassifier.java
org.opencv.objdetect.HOGDescriptor.java
org.opencv.objdetect.Objdetect.java
org.opencv.photo.Photo.java
org.opencv.utils.Converters.java
org.opencv.video.BackgroundSubtractorMOG.java
org.opencv.video.BackgroundSubtractor.java
org.opencv.video.KalmanFilter.java
org.opencv.video.Video.java
valkyrie.colorpicker.ColorPickerDialog.java
valkyrie.colorpicker.ColorPicker.java
valkyrie.file.DecodeBitmaps.java
valkyrie.file.FileManager.java
valkyrie.filter.FilterAssets.java
valkyrie.filter.FilterInternalStorage.java
valkyrie.filter.FilterManager.java
valkyrie.filter.FilterUIPosition.java
valkyrie.filter.IFilter.java
valkyrie.filter.ascii.Ascii.java
valkyrie.filter.ascii.Converter.java
valkyrie.filter.ascii.Font.java
valkyrie.filter.ascii.Options.java
valkyrie.filter.canny.Canny.java
valkyrie.filter.grayscale.Grayscale.java
valkyrie.filter.nofilter.NoFilter.java
valkyrie.ui.IUpdateableUI.java
valkyrie.ui.LayoutManager.java
valkyrie.ui.MainActivity.java
valkyrie.ui.UpdateableRelativeLayout.java
valkyrie.ui.gallery.AboutActivity.java
valkyrie.ui.gallery.GalleryActivity.java
valkyrie.ui.gallery.ImageAdapter.java
valkyrie.ui.gallery.ShowPicActivity.java
valkyrie.ui.preview.CameraPreviewViewCV.java
valkyrie.widget.MultiDirectionSlidingDrawer.java
valkyrie.widget.TouchImageView.java