Android Open Source - Operation-Valkyrie Cv Normal Bayes Classifier






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Java Source Code

//
// This file is auto-generated. Please don't modify it!
////from w w  w  .  j av a  2s.c om
package org.opencv.ml;

import org.opencv.core.Mat;

// C++: class CvNormalBayesClassifier
/**
 * <p>Bayes classifier for normally distributed data.</p>
 *
 * @see <a href="http://docs.opencv.org/modules/ml/doc/normal_bayes_classifier.html#cvnormalbayesclassifier">org.opencv.ml.CvNormalBayesClassifier : public CvStatModel</a>
 */
public class CvNormalBayesClassifier extends CvStatModel {

    protected CvNormalBayesClassifier(long addr) { super(addr); }


    //
    // C++:   CvNormalBayesClassifier::CvNormalBayesClassifier()
    //

/**
 * <p>Default and training constructors.</p>
 *
 * <p>The constructors follow conventions of "CvStatModel.CvStatModel". See
 * "CvStatModel.train" for parameters descriptions.</p>
 *
 * @see <a href="http://docs.opencv.org/modules/ml/doc/normal_bayes_classifier.html#cvnormalbayesclassifier-cvnormalbayesclassifier">org.opencv.ml.CvNormalBayesClassifier.CvNormalBayesClassifier</a>
 */
    public   CvNormalBayesClassifier()
    {

        super( CvNormalBayesClassifier_0() );

        return;
    }


    //
    // C++:   CvNormalBayesClassifier::CvNormalBayesClassifier(Mat trainData, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat())
    //

/**
 * <p>Default and training constructors.</p>
 *
 * <p>The constructors follow conventions of "CvStatModel.CvStatModel". See
 * "CvStatModel.train" for parameters descriptions.</p>
 *
 * @param trainData a trainData
 * @param responses a responses
 * @param varIdx a varIdx
 * @param sampleIdx a sampleIdx
 *
 * @see <a href="http://docs.opencv.org/modules/ml/doc/normal_bayes_classifier.html#cvnormalbayesclassifier-cvnormalbayesclassifier">org.opencv.ml.CvNormalBayesClassifier.CvNormalBayesClassifier</a>
 */
    public   CvNormalBayesClassifier(Mat trainData, Mat responses, Mat varIdx, Mat sampleIdx)
    {

        super( CvNormalBayesClassifier_1(trainData.nativeObj, responses.nativeObj, varIdx.nativeObj, sampleIdx.nativeObj) );

        return;
    }

/**
 * <p>Default and training constructors.</p>
 *
 * <p>The constructors follow conventions of "CvStatModel.CvStatModel". See
 * "CvStatModel.train" for parameters descriptions.</p>
 *
 * @param trainData a trainData
 * @param responses a responses
 *
 * @see <a href="http://docs.opencv.org/modules/ml/doc/normal_bayes_classifier.html#cvnormalbayesclassifier-cvnormalbayesclassifier">org.opencv.ml.CvNormalBayesClassifier.CvNormalBayesClassifier</a>
 */
    public   CvNormalBayesClassifier(Mat trainData, Mat responses)
    {

        super( CvNormalBayesClassifier_2(trainData.nativeObj, responses.nativeObj) );

        return;
    }


    //
    // C++:  void CvNormalBayesClassifier::clear()
    //

    public  void clear()
    {

        clear_0(nativeObj);

        return;
    }


    //
    // C++:  float CvNormalBayesClassifier::predict(Mat samples, Mat* results = 0)
    //

/**
 * <p>Predicts the response for sample(s).</p>
 *
 * <p>The method estimates the most probable classes for input vectors. Input
 * vectors (one or more) are stored as rows of the matrix <code>samples</code>.
 * In case of multiple input vectors, there should be one output vector
 * <code>results</code>. The predicted class for a single input vector is
 * returned by the method.</p>
 *
 * <p>The function is parallelized with the TBB library.</p>
 *
 * @param samples a samples
 * @param results a results
 *
 * @see <a href="http://docs.opencv.org/modules/ml/doc/normal_bayes_classifier.html#cvnormalbayesclassifier-predict">org.opencv.ml.CvNormalBayesClassifier.predict</a>
 */
    public  float predict(Mat samples, Mat results)
    {

        float retVal = predict_0(nativeObj, samples.nativeObj, results.nativeObj);

        return retVal;
    }

/**
 * <p>Predicts the response for sample(s).</p>
 *
 * <p>The method estimates the most probable classes for input vectors. Input
 * vectors (one or more) are stored as rows of the matrix <code>samples</code>.
 * In case of multiple input vectors, there should be one output vector
 * <code>results</code>. The predicted class for a single input vector is
 * returned by the method.</p>
 *
 * <p>The function is parallelized with the TBB library.</p>
 *
 * @param samples a samples
 *
 * @see <a href="http://docs.opencv.org/modules/ml/doc/normal_bayes_classifier.html#cvnormalbayesclassifier-predict">org.opencv.ml.CvNormalBayesClassifier.predict</a>
 */
    public  float predict(Mat samples)
    {

        float retVal = predict_1(nativeObj, samples.nativeObj);

        return retVal;
    }


    //
    // C++:  bool CvNormalBayesClassifier::train(Mat trainData, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat(), bool update = false)
    //

/**
 * <p>Trains the model.</p>
 *
 * <p>The method trains the Normal Bayes classifier. It follows the conventions of
 * the generic "CvStatModel.train" approach with the following limitations:</p>
 * <ul>
 *   <li> Only <code>CV_ROW_SAMPLE</code> data layout is supported.
 *   <li> Input variables are all ordered.
 *   <li> Output variable is categorical, which means that elements of
 * <code>responses</code> must be integer numbers, though the vector may have
 * the <code>CV_32FC1</code> type.
 *   <li> Missing measurements are not supported.
 * </ul>
 *
 * @param trainData a trainData
 * @param responses a responses
 * @param varIdx a varIdx
 * @param sampleIdx a sampleIdx
 * @param update Identifies whether the model should be trained from scratch
 * (<code>update=false</code>) or should be updated using the new training data
 * (<code>update=true</code>).
 *
 * @see <a href="http://docs.opencv.org/modules/ml/doc/normal_bayes_classifier.html#cvnormalbayesclassifier-train">org.opencv.ml.CvNormalBayesClassifier.train</a>
 */
    public  boolean train(Mat trainData, Mat responses, Mat varIdx, Mat sampleIdx, boolean update)
    {

        boolean retVal = train_0(nativeObj, trainData.nativeObj, responses.nativeObj, varIdx.nativeObj, sampleIdx.nativeObj, update);

        return retVal;
    }

/**
 * <p>Trains the model.</p>
 *
 * <p>The method trains the Normal Bayes classifier. It follows the conventions of
 * the generic "CvStatModel.train" approach with the following limitations:</p>
 * <ul>
 *   <li> Only <code>CV_ROW_SAMPLE</code> data layout is supported.
 *   <li> Input variables are all ordered.
 *   <li> Output variable is categorical, which means that elements of
 * <code>responses</code> must be integer numbers, though the vector may have
 * the <code>CV_32FC1</code> type.
 *   <li> Missing measurements are not supported.
 * </ul>
 *
 * @param trainData a trainData
 * @param responses a responses
 *
 * @see <a href="http://docs.opencv.org/modules/ml/doc/normal_bayes_classifier.html#cvnormalbayesclassifier-train">org.opencv.ml.CvNormalBayesClassifier.train</a>
 */
    public  boolean train(Mat trainData, Mat responses)
    {

        boolean retVal = train_1(nativeObj, trainData.nativeObj, responses.nativeObj);

        return retVal;
    }


    @Override
    protected void finalize() throws Throwable {
        delete(nativeObj);
    }



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

    // C++:   CvNormalBayesClassifier::CvNormalBayesClassifier()
    private static native long CvNormalBayesClassifier_0();

    // C++:   CvNormalBayesClassifier::CvNormalBayesClassifier(Mat trainData, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat())
    private static native long CvNormalBayesClassifier_1(long trainData_nativeObj, long responses_nativeObj, long varIdx_nativeObj, long sampleIdx_nativeObj);
    private static native long CvNormalBayesClassifier_2(long trainData_nativeObj, long responses_nativeObj);

    // C++:  void CvNormalBayesClassifier::clear()
    private static native void clear_0(long nativeObj);

    // C++:  float CvNormalBayesClassifier::predict(Mat samples, Mat* results = 0)
    private static native float predict_0(long nativeObj, long samples_nativeObj, long results_nativeObj);
    private static native float predict_1(long nativeObj, long samples_nativeObj);

    // C++:  bool CvNormalBayesClassifier::train(Mat trainData, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat(), bool update = false)
    private static native boolean train_0(long nativeObj, long trainData_nativeObj, long responses_nativeObj, long varIdx_nativeObj, long sampleIdx_nativeObj, boolean update);
    private static native boolean train_1(long nativeObj, long trainData_nativeObj, long responses_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