org.opencv.ml.CvGBTrees Maven / Gradle / Ivy
//
// 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
/**
* The class implements the Gradient boosted tree model as described in the
* beginning of this section.
*
* @see org.opencv.ml.CvGBTrees : public CvStatModel
*/
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()
//
/**
* Default and training constructors.
*
* The constructors follow conventions of "CvStatModel.CvStatModel". See
* "CvStatModel.train" for parameters descriptions.
*
* @see org.opencv.ml.CvGBTrees.CvGBTrees
*/
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())
//
/**
* Default and training constructors.
*
* The constructors follow conventions of "CvStatModel.CvStatModel". See
* "CvStatModel.train" for parameters descriptions.
*
* @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 org.opencv.ml.CvGBTrees.CvGBTrees
*/
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;
}
/**
* Default and training constructors.
*
* The constructors follow conventions of "CvStatModel.CvStatModel". See
* "CvStatModel.train" for parameters descriptions.
*
* @param trainData a trainData
* @param tflag a tflag
* @param responses a responses
*
* @see org.opencv.ml.CvGBTrees.CvGBTrees
*/
public CvGBTrees(Mat trainData, int tflag, Mat responses)
{
super( CvGBTrees_2(trainData.nativeObj, tflag, responses.nativeObj) );
return;
}
//
// C++: void CvGBTrees::clear()
//
/**
* Clears the model.
*
* 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.
*
* @see org.opencv.ml.CvGBTrees.clear
*/
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)
//
/**
* Predicts a response for an input sample.
*
* 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.
*
* @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,
* sample contains forged values at the appropriate places.
* @param missing Missing values mask, which is a dimensional matrix of the same
* size as sample having the CV_8U type.
* 1 corresponds to the missing value in the same position in the
* sample 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.
* If slice = Range.all(), all trees are used. Use this parameter
* to get predictions of the GBT models with different ensemble sizes learning
* only one model.
* @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 k-th ensemble only. To get the total
* GBT model prediction, k value must be -1. For regression
* problems, k is also equal to -1.
*
* @see org.opencv.ml.CvGBTrees.predict
*/
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;
}
/**
* Predicts a response for an input sample.
*
* 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.
*
* @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,
* sample contains forged values at the appropriate places.
*
* @see org.opencv.ml.CvGBTrees.predict
*/
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)
//
/**
* Trains a Gradient boosted tree model.
*
* The first train method follows the common template (see "CvStatModel.train").
* Both tflag values (CV_ROW_SAMPLE, CV_COL_SAMPLE)
* are supported.
* trainData must be of the CV_32F type.
* responses must be a matrix of type CV_32S or
* CV_32F. In both cases it is converted into the CV_32F
* matrix inside the training procedure. varIdx and
* sampleIdx must be a list of indices (CV_32S) or a
* mask (CV_8U or CV_8S). update is a
* dummy parameter.
*
* The second form of "CvGBTrees.train" function uses "CvMLData" as a data set
* container. update is still a dummy parameter.
*
* All parameters specific to the GBT model are passed into the training
* function as a "CvGBTreesParams" structure.
*
* @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 org.opencv.ml.CvGBTrees.train
*/
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;
}
/**
* Trains a Gradient boosted tree model.
*
* The first train method follows the common template (see "CvStatModel.train").
* Both tflag values (CV_ROW_SAMPLE, CV_COL_SAMPLE)
* are supported.
* trainData must be of the CV_32F type.
* responses must be a matrix of type CV_32S or
* CV_32F. In both cases it is converted into the CV_32F
* matrix inside the training procedure. varIdx and
* sampleIdx must be a list of indices (CV_32S) or a
* mask (CV_8U or CV_8S). update is a
* dummy parameter.
*
* The second form of "CvGBTrees.train" function uses "CvMLData" as a data set
* container. update is still a dummy parameter.
*
* All parameters specific to the GBT model are passed into the training
* function as a "CvGBTreesParams" structure.
*
* @param trainData a trainData
* @param tflag a tflag
* @param responses a responses
*
* @see org.opencv.ml.CvGBTrees.train
*/
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);
}