content: 
  //
  // 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 CvBoost
  /**
   * <p>Boosted tree classifier derived from "CvStatModel".</p>
   *
   * @see <a href="http://docs.opencv.org/modules/ml/doc/boosting.html#cvboost">org.opencv.ml.CvBoost : public CvStatModel</a>
   */
  public class CvBoost extends CvStatModel {
  
      protected CvBoost(long addr) { super(addr); }
  
  
      public static final int
              DISCRETE = 0,
              REAL = 1,
              LOGIT = 2,
              GENTLE = 3,
              DEFAULT = 0,
              GINI = 1,
              MISCLASS = 3,
              SQERR = 4;
  
  
      //
      // C++:   CvBoost::CvBoost()
      //
  
  /**
   * <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/boosting.html#cvboost-cvboost">org.opencv.ml.CvBoost.CvBoost</a>
   */
      public   CvBoost()
      {
  
          super( CvBoost_0() );
  
          return;
      }
  
  
      //
      // C++:   CvBoost::CvBoost(Mat trainData, int tflag, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat(), Mat varType = cv::Mat(), Mat missingDataMask = cv::Mat(), CvBoostParams params = CvBoostParams())
      //
  
  /**
   * <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/boosting.html#cvboost-cvboost">org.opencv.ml.CvBoost.CvBoost</a>
   */
      public   CvBoost(Mat trainData, int tflag, Mat responses, Mat varIdx, Mat sampleIdx, Mat varType, Mat missingDataMask, CvBoostParams params)
      {
  
          super( CvBoost_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/boosting.html#cvboost-cvboost">org.opencv.ml.CvBoost.CvBoost</a>
   */
      public   CvBoost(Mat trainData, int tflag, Mat responses)
      {
  
          super( CvBoost_2(trainData.nativeObj, tflag, responses.nativeObj) );
  
          return;
      }
  
  
      //
      // C++:  void CvBoost::clear()
      //
  
      public  void clear()
      {
  
          clear_0(nativeObj);
  
          return;
      }
  
  
      //
      // C++:  float CvBoost::predict(Mat sample, Mat missing = cv::Mat(), Range slice = cv::Range::all(), bool rawMode = false, bool returnSum = false)
      //
  
  /**
   * <p>Predicts a response for an input sample.</p>
   *
   * <p>The method runs the sample through the trees in the ensemble and returns the
   * output class label based on the weighted voting.</p>
   *
   * @param sample Input sample.
   * @param missing Optional mask of missing measurements. To handle missing
   * measurements, the weak classifiers must include surrogate splits (see
   * <code>CvDTreeParams.use_surrogates</code>).
   * @param slice Continuous subset of the sequence of weak classifiers to be used
   * for prediction. By default, all the weak classifiers are used.
   * @param rawMode Normally, it should be set to <code>false</code>.
   * @param returnSum If <code>true</code> then return sum of votes instead of the
   * class label.
   *
   * @see <a href="http://docs.opencv.org/modules/ml/doc/boosting.html#cvboost-predict">org.opencv.ml.CvBoost.predict</a>
   */
      public  float predict(Mat sample, Mat missing, Range slice, boolean rawMode, boolean returnSum)
      {
  
          float retVal = predict_0(nativeObj, sample.nativeObj, missing.nativeObj, slice.start, slice.end, rawMode, returnSum);
  
          return retVal;
      }
  
  /**
   * <p>Predicts a response for an input sample.</p>
   *
   * <p>The method runs the sample through the trees in the ensemble and returns the
   * output class label based on the weighted voting.</p>
   *
   * @param sample Input sample.
   *
   * @see <a href="http://docs.opencv.org/modules/ml/doc/boosting.html#cvboost-predict">org.opencv.ml.CvBoost.predict</a>
   */
      public  float predict(Mat sample)
      {
  
          float retVal = predict_1(nativeObj, sample.nativeObj);
  
          return retVal;
      }
  
  
      //
      // C++:  void CvBoost::prune(CvSlice slice)
      //
  
  /**
   * <p>Removes the specified weak classifiers.</p>
   *
   * <p>The method removes the specified weak classifiers from the sequence.</p>
   *
   * <p>Note: Do not confuse this method with the pruning of individual decision
   * trees, which is currently not supported.</p>
   *
   * @param slice Continuous subset of the sequence of weak classifiers to be
   * removed.
   *
   * @see <a href="http://docs.opencv.org/modules/ml/doc/boosting.html#cvboost-prune">org.opencv.ml.CvBoost.prune</a>
   */
      public  void prune(Range slice)
      {
  
          prune_0(nativeObj, slice.start, slice.end);
  
          return;
      }
  
  
      //
      // C++:  bool CvBoost::train(Mat trainData, int tflag, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat(), Mat varType = cv::Mat(), Mat missingDataMask = cv::Mat(), CvBoostParams params = CvBoostParams(), bool update = false)
      //
  
  /**
   * <p>Trains a boosted tree classifier.</p>
   *
   * <p>The train method follows the common template of "CvStatModel.train". The
   * responses must be categorical, which means that boosted trees cannot be built
   * for regression, and there should be two classes.</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 Specifies whether the classifier needs to be updated
   * (<code>true</code>, the new weak tree classifiers added to the existing
   * ensemble) or the classifier needs to be rebuilt from scratch
   * (<code>false</code>).
   *
   * @see <a href="http://docs.opencv.org/modules/ml/doc/boosting.html#cvboost-train">org.opencv.ml.CvBoost.train</a>
   */
      public  boolean train(Mat trainData, int tflag, Mat responses, Mat varIdx, Mat sampleIdx, Mat varType, Mat missingDataMask, CvBoostParams 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 boosted tree classifier.</p>
   *
   * <p>The train method follows the common template of "CvStatModel.train". The
   * responses must be categorical, which means that boosted trees cannot be built
   * for regression, and there should be two classes.</p>
   *
   * @param trainData a trainData
   * @param tflag a tflag
   * @param responses a responses
   *
   * @see <a href="http://docs.opencv.org/modules/ml/doc/boosting.html#cvboost-train">org.opencv.ml.CvBoost.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++:   CvBoost::CvBoost()
      private static native long CvBoost_0();
  
      // C++:   CvBoost::CvBoost(Mat trainData, int tflag, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat(), Mat varType = cv::Mat(), Mat missingDataMask = cv::Mat(), CvBoostParams params = CvBoostParams())
      private static native long CvBoost_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 CvBoost_2(long trainData_nativeObj, int tflag, long responses_nativeObj);
  
      // C++:  void CvBoost::clear()
      private static native void clear_0(long nativeObj);
  
      // C++:  float CvBoost::predict(Mat sample, Mat missing = cv::Mat(), Range slice = cv::Range::all(), bool rawMode = false, bool returnSum = false)
      private static native float predict_0(long nativeObj, long sample_nativeObj, long missing_nativeObj, int slice_start, int slice_end, boolean rawMode, boolean returnSum);
      private static native float predict_1(long nativeObj, long sample_nativeObj);
  
      // C++:  void CvBoost::prune(CvSlice slice)
      private static native void prune_0(long nativeObj, int slice_start, int slice_end);
  
      // C++:  bool CvBoost::train(Mat trainData, int tflag, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat(), Mat varType = cv::Mat(), Mat missingDataMask = cv::Mat(), CvBoostParams params = CvBoostParams(), 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);
  
  }
  