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Java Source Code

//
// This file is auto-generated. Please don't modify it!
///*w ww. j av a 2s .c  om*/
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);

}
Java Source Code List
