Android Open Source - Operation-Valkyrie Cv D Tree






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License

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Java Source Code

//
// This file is auto-generated. Please don't modify it!
///*from  w ww.  j  a va  2s  .c o  m*/
package org.opencv.ml;

import org.opencv.core.Mat;

// C++: class CvDTree
/**
 * <p>The class implements a decision tree as described in the beginning of this
 * section.</p>
 *
 * @see <a href="http://docs.opencv.org/modules/ml/doc/decision_trees.html#cvdtree">org.opencv.ml.CvDTree : public CvStatModel</a>
 */
public class CvDTree extends CvStatModel {

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


    //
    // C++:   CvDTree::CvDTree()
    //

    public   CvDTree()
    {

        super( CvDTree_0() );

        return;
    }


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

    public  void clear()
    {

        clear_0(nativeObj);

        return;
    }


    //
    // C++:  Mat CvDTree::getVarImportance()
    //

/**
 * <p>Returns the variable importance array.</p>
 *
 * @see <a href="http://docs.opencv.org/modules/ml/doc/decision_trees.html#cvdtree-getvarimportance">org.opencv.ml.CvDTree.getVarImportance</a>
 */
    public  Mat getVarImportance()
    {

        Mat retVal = new Mat(getVarImportance_0(nativeObj));

        return retVal;
    }


    //
    // C++:  CvDTreeNode* CvDTree::predict(Mat sample, Mat missingDataMask = cv::Mat(), bool preprocessedInput = false)
    //

    // Return type 'CvDTreeNode*' is not supported, skipping the function


    //
    // C++:  bool CvDTree::train(Mat trainData, int tflag, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat(), Mat varType = cv::Mat(), Mat missingDataMask = cv::Mat(), CvDTreeParams params = CvDTreeParams())
    //

/**
 * <p>Trains a decision tree.</p>
 *
 * <p>There are four <code>train</code> methods in "CvDTree":</p>
 * <ul>
 *   <li> The first two methods follow the generic "CvStatModel.train"
 * conventions. It is the most complete form. Both data layouts
 * (<code>tflag=CV_ROW_SAMPLE</code> and <code>tflag=CV_COL_SAMPLE</code>) are
 * supported, as well as sample and variable subsets, missing measurements,
 * arbitrary combinations of input and output variable types, and so on. The
 * last parameter contains all of the necessary training parameters (see the
 * "CvDTreeParams" description).
 *   <li> The third method uses "CvMLData" to pass training data to a decision
 * tree.
 *   <li> The last method <code>train</code> is mostly used for building tree
 * ensembles. It takes the pre-constructed "CvDTreeTrainData" instance and an
 * optional subset of the training set. The indices in <code>subsampleIdx</code>
 * are counted relatively to the <code>_sample_idx</code>, passed to the
 * <code>CvDTreeTrainData</code> constructor. For example, if <code>_sample_idx=[1,
 * 5, 7, 100]</code>, then <code>subsampleIdx=[0,3]</code> means that the
 * samples <code>[1, 100]</code> of the original training set are used.
 * </ul>
 *
 * <p>The function is parallelized with the TBB library.</p>
 *
 * @param trainData a trainData
 * @param tflag a tflag
 * @param responses a responses
 * @param varIdx a varIdx
 * @param sampleIdx a sampleIdx
 * @param varType a varType
 * @param missingDataMask a missingDataMask
 * @param params a params
 *
 * @see <a href="http://docs.opencv.org/modules/ml/doc/decision_trees.html#cvdtree-train">org.opencv.ml.CvDTree.train</a>
 */
    public  boolean train(Mat trainData, int tflag, Mat responses, Mat varIdx, Mat sampleIdx, Mat varType, Mat missingDataMask, CvDTreeParams params)
    {

        boolean retVal = train_0(nativeObj, trainData.nativeObj, tflag, responses.nativeObj, varIdx.nativeObj, sampleIdx.nativeObj, varType.nativeObj, missingDataMask.nativeObj, params.nativeObj);

        return retVal;
    }

/**
 * <p>Trains a decision tree.</p>
 *
 * <p>There are four <code>train</code> methods in "CvDTree":</p>
 * <ul>
 *   <li> The first two methods follow the generic "CvStatModel.train"
 * conventions. It is the most complete form. Both data layouts
 * (<code>tflag=CV_ROW_SAMPLE</code> and <code>tflag=CV_COL_SAMPLE</code>) are
 * supported, as well as sample and variable subsets, missing measurements,
 * arbitrary combinations of input and output variable types, and so on. The
 * last parameter contains all of the necessary training parameters (see the
 * "CvDTreeParams" description).
 *   <li> The third method uses "CvMLData" to pass training data to a decision
 * tree.
 *   <li> The last method <code>train</code> is mostly used for building tree
 * ensembles. It takes the pre-constructed "CvDTreeTrainData" instance and an
 * optional subset of the training set. The indices in <code>subsampleIdx</code>
 * are counted relatively to the <code>_sample_idx</code>, passed to the
 * <code>CvDTreeTrainData</code> constructor. For example, if <code>_sample_idx=[1,
 * 5, 7, 100]</code>, then <code>subsampleIdx=[0,3]</code> means that the
 * samples <code>[1, 100]</code> of the original training set are used.
 * </ul>
 *
 * <p>The function is parallelized with the TBB library.</p>
 *
 * @param trainData a trainData
 * @param tflag a tflag
 * @param responses a responses
 *
 * @see <a href="http://docs.opencv.org/modules/ml/doc/decision_trees.html#cvdtree-train">org.opencv.ml.CvDTree.train</a>
 */
    public  boolean train(Mat trainData, int tflag, Mat responses)
    {

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

        return retVal;
    }


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



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

    // C++:   CvDTree::CvDTree()
    private static native long CvDTree_0();

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

    // C++:  Mat CvDTree::getVarImportance()
    private static native long getVarImportance_0(long nativeObj);

    // C++:  bool CvDTree::train(Mat trainData, int tflag, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat(), Mat varType = cv::Mat(), Mat missingDataMask = cv::Mat(), CvDTreeParams params = CvDTreeParams())
    private static native boolean train_0(long nativeObj, long trainData_nativeObj, int tflag, long responses_nativeObj, long varIdx_nativeObj, long sampleIdx_nativeObj, long varType_nativeObj, long missingDataMask_nativeObj, long params_nativeObj);
    private static native boolean train_1(long nativeObj, long trainData_nativeObj, int tflag, 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