Android Open Source - Operation-Valkyrie Cv R Trees






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Java Source Code

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

import org.opencv.core.Mat;

// C++: class CvRTrees
/**
 * <p>The class implements the random forest predictor as described in the
 * beginning of this section.</p>
 *
 * @see <a href="http://docs.opencv.org/modules/ml/doc/random_trees.html#cvrtrees">org.opencv.ml.CvRTrees : public CvStatModel</a>
 */
public class CvRTrees extends CvStatModel {

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


    //
    // C++:   CvRTrees::CvRTrees()
    //

    public   CvRTrees()
    {

        super( CvRTrees_0() );

        return;
    }


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

    public  void clear()
    {

        clear_0(nativeObj);

        return;
    }


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

/**
 * <p>Returns the variable importance array.</p>
 *
 * <p>The method returns the variable importance vector, computed at the training
 * stage when <code>CvRTParams.calc_var_importance</code> is set to true. If
 * this flag was set to false, the <code>NULL</code> pointer is returned. This
 * differs from the decision trees where variable importance can be computed
 * anytime after the training.</p>
 *
 * @see <a href="http://docs.opencv.org/modules/ml/doc/random_trees.html#cvrtrees-getvarimportance">org.opencv.ml.CvRTrees.getVarImportance</a>
 */
    public  Mat getVarImportance()
    {

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

        return retVal;
    }


    //
    // C++:  float CvRTrees::predict(Mat sample, Mat missing = cv::Mat())
    //

/**
 * <p>Predicts the output for an input sample.</p>
 *
 * <p>The input parameters of the prediction method are the same as in
 * "CvDTree.predict" but the return value type is different. This method
 * returns the cumulative result from all the trees in the forest (the class
 * that receives the majority of voices, or the mean of the regression function
 * estimates).</p>
 *
 * @param sample Sample for classification.
 * @param missing Optional missing measurement mask of the sample.
 *
 * @see <a href="http://docs.opencv.org/modules/ml/doc/random_trees.html#cvrtrees-predict">org.opencv.ml.CvRTrees.predict</a>
 */
    public  float predict(Mat sample, Mat missing)
    {

        float retVal = predict_0(nativeObj, sample.nativeObj, missing.nativeObj);

        return retVal;
    }

/**
 * <p>Predicts the output for an input sample.</p>
 *
 * <p>The input parameters of the prediction method are the same as in
 * "CvDTree.predict" but the return value type is different. This method
 * returns the cumulative result from all the trees in the forest (the class
 * that receives the majority of voices, or the mean of the regression function
 * estimates).</p>
 *
 * @param sample Sample for classification.
 *
 * @see <a href="http://docs.opencv.org/modules/ml/doc/random_trees.html#cvrtrees-predict">org.opencv.ml.CvRTrees.predict</a>
 */
    public  float predict(Mat sample)
    {

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

        return retVal;
    }


    //
    // C++:  float CvRTrees::predict_prob(Mat sample, Mat missing = cv::Mat())
    //

/**
 * <p>Returns a fuzzy-predicted class label.</p>
 *
 * <p>The function works for binary classification problems only. It returns the
 * number between 0 and 1. This number represents probability or confidence of
 * the sample belonging to the second class. It is calculated as the proportion
 * of decision trees that classified the sample to the second class.</p>
 *
 * @param sample Sample for classification.
 * @param missing Optional missing measurement mask of the sample.
 *
 * @see <a href="http://docs.opencv.org/modules/ml/doc/random_trees.html#cvrtrees-predict-prob">org.opencv.ml.CvRTrees.predict_prob</a>
 */
    public  float predict_prob(Mat sample, Mat missing)
    {

        float retVal = predict_prob_0(nativeObj, sample.nativeObj, missing.nativeObj);

        return retVal;
    }

/**
 * <p>Returns a fuzzy-predicted class label.</p>
 *
 * <p>The function works for binary classification problems only. It returns the
 * number between 0 and 1. This number represents probability or confidence of
 * the sample belonging to the second class. It is calculated as the proportion
 * of decision trees that classified the sample to the second class.</p>
 *
 * @param sample Sample for classification.
 *
 * @see <a href="http://docs.opencv.org/modules/ml/doc/random_trees.html#cvrtrees-predict-prob">org.opencv.ml.CvRTrees.predict_prob</a>
 */
    public  float predict_prob(Mat sample)
    {

        float retVal = predict_prob_1(nativeObj, sample.nativeObj);

        return retVal;
    }


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

/**
 * <p>Trains the Random Trees model.</p>
 *
 * <p>The method "CvRTrees.train" is very similar to the method "CvDTree.train"
 * and follows the generic method "CvStatModel.train" conventions. All the
 * parameters specific to the algorithm training are passed as a "CvRTParams"
 * instance. The estimate of the training error (<code>oob-error</code>) is
 * stored in the protected class member <code>oob_error</code>.</p>
 *
 * <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/random_trees.html#cvrtrees-train">org.opencv.ml.CvRTrees.train</a>
 */
    public  boolean train(Mat trainData, int tflag, Mat responses, Mat varIdx, Mat sampleIdx, Mat varType, Mat missingDataMask, CvRTParams 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 the Random Trees model.</p>
 *
 * <p>The method "CvRTrees.train" is very similar to the method "CvDTree.train"
 * and follows the generic method "CvStatModel.train" conventions. All the
 * parameters specific to the algorithm training are passed as a "CvRTParams"
 * instance. The estimate of the training error (<code>oob-error</code>) is
 * stored in the protected class member <code>oob_error</code>.</p>
 *
 * <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/random_trees.html#cvrtrees-train">org.opencv.ml.CvRTrees.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++:   CvRTrees::CvRTrees()
    private static native long CvRTrees_0();

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

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

    // C++:  float CvRTrees::predict(Mat sample, Mat missing = cv::Mat())
    private static native float predict_0(long nativeObj, long sample_nativeObj, long missing_nativeObj);
    private static native float predict_1(long nativeObj, long sample_nativeObj);

    // C++:  float CvRTrees::predict_prob(Mat sample, Mat missing = cv::Mat())
    private static native float predict_prob_0(long nativeObj, long sample_nativeObj, long missing_nativeObj);
    private static native float predict_prob_1(long nativeObj, long sample_nativeObj);

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