text: 
  //
  // This file is auto-generated. Please don't modify it!
  //
  package org.opencv.ml;
  
  import org.opencv.core.Mat;
  import org.opencv.core.Range;
  
  // C++: class CvGBTrees
  /**
   * <p>The class implements the Gradient boosted tree model as described in the
   * beginning of this section.</p>
   *
   * @see <a href="http://docs.opencv.org/modules/ml/doc/gradient_boosted_trees.html#cvgbtrees">org.opencv.ml.CvGBTrees : public CvStatModel</a>
   */
  public class CvGBTrees extends CvStatModel {
  
      protected CvGBTrees(long addr) { super(addr); }
  
  
      public static final int
              SQUARED_LOSS = 0,
              ABSOLUTE_LOSS = 0+1,
              HUBER_LOSS = 3,
              DEVIANCE_LOSS = 3+1;
  
  
      //
      // C++:   CvGBTrees::CvGBTrees()
      //
  
  /**
   * <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/gradient_boosted_trees.html#cvgbtrees-cvgbtrees">org.opencv.ml.CvGBTrees.CvGBTrees</a>
   */
      public   CvGBTrees()
      {
  
          super( CvGBTrees_0() );
  
          return;
      }
  
  
      //
      // C++:   CvGBTrees::CvGBTrees(Mat trainData, int tflag, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat(), Mat varType = cv::Mat(), Mat missingDataMask = cv::Mat(), CvGBTreesParams params = CvGBTreesParams())
      //
  
  /**
   * <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 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/gradient_boosted_trees.html#cvgbtrees-cvgbtrees">org.opencv.ml.CvGBTrees.CvGBTrees</a>
   */
      public   CvGBTrees(Mat trainData, int tflag, Mat responses, Mat varIdx, Mat sampleIdx, Mat varType, Mat missingDataMask, CvGBTreesParams params)
      {
  
          super( CvGBTrees_1(trainData.nativeObj, tflag, responses.nativeObj, varIdx.nativeObj, sampleIdx.nativeObj, varType.nativeObj, missingDataMask.nativeObj, params.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 tflag a tflag
   * @param responses a responses
   *
   * @see <a href="http://docs.opencv.org/modules/ml/doc/gradient_boosted_trees.html#cvgbtrees-cvgbtrees">org.opencv.ml.CvGBTrees.CvGBTrees</a>
   */
      public   CvGBTrees(Mat trainData, int tflag, Mat responses)
      {
  
          super( CvGBTrees_2(trainData.nativeObj, tflag, responses.nativeObj) );
  
          return;
      }
  
  
      //
      // C++:  void CvGBTrees::clear()
      //
  
  /**
   * <p>Clears the model.</p>
   *
   * <p>The function deletes the data set information and all the weak models and
   * sets all internal variables to the initial state. The function is called in
   * "CvGBTrees.train" and in the destructor.</p>
   *
   * @see <a href="http://docs.opencv.org/modules/ml/doc/gradient_boosted_trees.html#cvgbtrees-clear">org.opencv.ml.CvGBTrees.clear</a>
   */
      public  void clear()
      {
  
          clear_0(nativeObj);
  
          return;
      }
  
  
      //
      // C++:  float CvGBTrees::predict(Mat sample, Mat missing = cv::Mat(), Range slice = cv::Range::all(), int k = -1)
      //
  
  /**
   * <p>Predicts a response for an input sample.</p>
   *
   * <p>The method predicts the response corresponding to the given sample (see
   * "Predicting with GBT").
   * The result is either the class label or the estimated function value. The
   * "CvGBTrees.predict" method enables using the parallel version of the GBT
   * model prediction if the OpenCV is built with the TBB library. In this case,
   * predictions of single trees are computed in a parallel fashion.</p>
   *
   * @param sample Input feature vector that has the same format as every training
   * set element. If not all the variables were actually used during training,
   * <code>sample</code> contains forged values at the appropriate places.
   * @param missing Missing values mask, which is a dimensional matrix of the same
   * size as <code>sample</code> having the <code>CV_8U</code> type.
   * <code>1</code> corresponds to the missing value in the same position in the
   * <code>sample</code> vector. If there are no missing values in the feature
   * vector, an empty matrix can be passed instead of the missing mask.
   * @param slice Parameter defining the part of the ensemble used for prediction.
   * <p>If <code>slice = Range.all()</code>, all trees are used. Use this parameter
   * to get predictions of the GBT models with different ensemble sizes learning
   * only one model.</p>
   * @param k Number of tree ensembles built in case of the classification problem
   * (see "Training GBT"). Use this parameter to change the output to sum of the
   * trees' predictions in the <code>k</code>-th ensemble only. To get the total
   * GBT model prediction, <code>k</code> value must be -1. For regression
   * problems, <code>k</code> is also equal to -1.
   *
   * @see <a href="http://docs.opencv.org/modules/ml/doc/gradient_boosted_trees.html#cvgbtrees-predict">org.opencv.ml.CvGBTrees.predict</a>
   */
      public  float predict(Mat sample, Mat missing, Range slice, int k)
      {
  
          float retVal = predict_0(nativeObj, sample.nativeObj, missing.nativeObj, slice.start, slice.end, k);
  
          return retVal;
      }
  
  /**
   * <p>Predicts a response for an input sample.</p>
   *
   * <p>The method predicts the response corresponding to the given sample (see
   * "Predicting with GBT").
   * The result is either the class label or the estimated function value. The
   * "CvGBTrees.predict" method enables using the parallel version of the GBT
   * model prediction if the OpenCV is built with the TBB library. In this case,
   * predictions of single trees are computed in a parallel fashion.</p>
   *
   * @param sample Input feature vector that has the same format as every training
   * set element. If not all the variables were actually used during training,
   * <code>sample</code> contains forged values at the appropriate places.
   *
   * @see <a href="http://docs.opencv.org/modules/ml/doc/gradient_boosted_trees.html#cvgbtrees-predict">org.opencv.ml.CvGBTrees.predict</a>
   */
      public  float predict(Mat sample)
      {
  
          float retVal = predict_1(nativeObj, sample.nativeObj);
  
          return retVal;
      }
  
  
      //
      // C++:  bool CvGBTrees::train(Mat trainData, int tflag, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat(), Mat varType = cv::Mat(), Mat missingDataMask = cv::Mat(), CvGBTreesParams params = CvGBTreesParams(), bool update = false)
      //
  
  /**
   * <p>Trains a Gradient boosted tree model.</p>
   *
   * <p>The first train method follows the common template (see "CvStatModel.train").
   * Both <code>tflag</code> values (<code>CV_ROW_SAMPLE</code>, <code>CV_COL_SAMPLE</code>)
   * are supported.
   * <code>trainData</code> must be of the <code>CV_32F</code> type.
   * <code>responses</code> must be a matrix of type <code>CV_32S</code> or
   * <code>CV_32F</code>. In both cases it is converted into the <code>CV_32F</code>
   * matrix inside the training procedure. <code>varIdx</code> and
   * <code>sampleIdx</code> must be a list of indices (<code>CV_32S</code>) or a
   * mask (<code>CV_8U</code> or <code>CV_8S</code>). <code>update</code> is a
   * dummy parameter.</p>
   *
   * <p>The second form of "CvGBTrees.train" function uses "CvMLData" as a data set
   * container. <code>update</code> is still a dummy parameter.</p>
   *
   * <p>All parameters specific to the GBT model are passed into the training
   * function as a "CvGBTreesParams" structure.</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
   * @param update a update
   *
   * @see <a href="http://docs.opencv.org/modules/ml/doc/gradient_boosted_trees.html#cvgbtrees-train">org.opencv.ml.CvGBTrees.train</a>
   */
      public  boolean train(Mat trainData, int tflag, Mat responses, Mat varIdx, Mat sampleIdx, Mat varType, Mat missingDataMask, CvGBTreesParams params, boolean update)
      {
  
          boolean retVal = train_0(nativeObj, trainData.nativeObj, tflag, responses.nativeObj, varIdx.nativeObj, sampleIdx.nativeObj, varType.nativeObj, missingDataMask.nativeObj, params.nativeObj, update);
  
          return retVal;
      }
  
  /**
   * <p>Trains a Gradient boosted tree model.</p>
   *
   * <p>The first train method follows the common template (see "CvStatModel.train").
   * Both <code>tflag</code> values (<code>CV_ROW_SAMPLE</code>, <code>CV_COL_SAMPLE</code>)
   * are supported.
   * <code>trainData</code> must be of the <code>CV_32F</code> type.
   * <code>responses</code> must be a matrix of type <code>CV_32S</code> or
   * <code>CV_32F</code>. In both cases it is converted into the <code>CV_32F</code>
   * matrix inside the training procedure. <code>varIdx</code> and
   * <code>sampleIdx</code> must be a list of indices (<code>CV_32S</code>) or a
   * mask (<code>CV_8U</code> or <code>CV_8S</code>). <code>update</code> is a
   * dummy parameter.</p>
   *
   * <p>The second form of "CvGBTrees.train" function uses "CvMLData" as a data set
   * container. <code>update</code> is still a dummy parameter.</p>
   *
   * <p>All parameters specific to the GBT model are passed into the training
   * function as a "CvGBTreesParams" structure.</p>
   *
   * @param trainData a trainData
   * @param tflag a tflag
   * @param responses a responses
   *
   * @see <a href="http://docs.opencv.org/modules/ml/doc/gradient_boosted_trees.html#cvgbtrees-train">org.opencv.ml.CvGBTrees.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);
      }
  
  
  
      // C++:   CvGBTrees::CvGBTrees()
      private static native long CvGBTrees_0();
  
      // C++:   CvGBTrees::CvGBTrees(Mat trainData, int tflag, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat(), Mat varType = cv::Mat(), Mat missingDataMask = cv::Mat(), CvGBTreesParams params = CvGBTreesParams())
      private static native long CvGBTrees_1(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 long CvGBTrees_2(long trainData_nativeObj, int tflag, long responses_nativeObj);
  
      // C++:  void CvGBTrees::clear()
      private static native void clear_0(long nativeObj);
  
      // C++:  float CvGBTrees::predict(Mat sample, Mat missing = cv::Mat(), Range slice = cv::Range::all(), int k = -1)
      private static native float predict_0(long nativeObj, long sample_nativeObj, long missing_nativeObj, int slice_start, int slice_end, int k);
      private static native float predict_1(long nativeObj, long sample_nativeObj);
  
      // C++:  bool CvGBTrees::train(Mat trainData, int tflag, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat(), Mat varType = cv::Mat(), Mat missingDataMask = cv::Mat(), CvGBTreesParams params = CvGBTreesParams(), bool update = false)
      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, boolean update);
      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);
  
  }
  