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// // This file is auto-generated. Please don't modify it! ///* w w w . ja v a2s.c o m*/ 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); } // // native stuff // static { System.loadLibrary("opencv_java"); } // 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); }