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// // This file is auto-generated. Please don't modify it! //// w w w.j ava 2s.co m package org.opencv.ml; import org.opencv.core.Mat; // C++: class CvDTree /** * <p>The class implements a decision tree as described in the beginning of this * section.</p> * * @see <a href="http://docs.opencv.org/modules/ml/doc/decision_trees.html#cvdtree">org.opencv.ml.CvDTree : public CvStatModel</a> */ public class CvDTree extends CvStatModel { protected CvDTree(long addr) { super(addr); } // // C++: CvDTree::CvDTree() // public CvDTree() { super( CvDTree_0() ); return; } // // C++: void CvDTree::clear() // public void clear() { clear_0(nativeObj); return; } // // C++: Mat CvDTree::getVarImportance() // /** * <p>Returns the variable importance array.</p> * * @see <a href="http://docs.opencv.org/modules/ml/doc/decision_trees.html#cvdtree-getvarimportance">org.opencv.ml.CvDTree.getVarImportance</a> */ public Mat getVarImportance() { Mat retVal = new Mat(getVarImportance_0(nativeObj)); return retVal; } // // C++: CvDTreeNode* CvDTree::predict(Mat sample, Mat missingDataMask = cv::Mat(), bool preprocessedInput = false) // // Return type 'CvDTreeNode*' is not supported, skipping the function // // C++: bool CvDTree::train(Mat trainData, int tflag, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat(), Mat varType = cv::Mat(), Mat missingDataMask = cv::Mat(), CvDTreeParams params = CvDTreeParams()) // /** * <p>Trains a decision tree.</p> * * <p>There are four <code>train</code> methods in "CvDTree":</p> * <ul> * <li> The first two methods follow the generic "CvStatModel.train" * conventions. It is the most complete form. Both data layouts * (<code>tflag=CV_ROW_SAMPLE</code> and <code>tflag=CV_COL_SAMPLE</code>) are * supported, as well as sample and variable subsets, missing measurements, * arbitrary combinations of input and output variable types, and so on. The * last parameter contains all of the necessary training parameters (see the * "CvDTreeParams" description). * <li> The third method uses "CvMLData" to pass training data to a decision * tree. * <li> The last method <code>train</code> is mostly used for building tree * ensembles. It takes the pre-constructed "CvDTreeTrainData" instance and an * optional subset of the training set. The indices in <code>subsampleIdx</code> * are counted relatively to the <code>_sample_idx</code>, passed to the * <code>CvDTreeTrainData</code> constructor. For example, if <code>_sample_idx=[1, * 5, 7, 100]</code>, then <code>subsampleIdx=[0,3]</code> means that the * samples <code>[1, 100]</code> of the original training set are used. * </ul> * * <p>The function is parallelized with the TBB library.</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/decision_trees.html#cvdtree-train">org.opencv.ml.CvDTree.train</a> */ public boolean train(Mat trainData, int tflag, Mat responses, Mat varIdx, Mat sampleIdx, Mat varType, Mat missingDataMask, CvDTreeParams params) { boolean retVal = train_0(nativeObj, trainData.nativeObj, tflag, responses.nativeObj, varIdx.nativeObj, sampleIdx.nativeObj, varType.nativeObj, missingDataMask.nativeObj, params.nativeObj); return retVal; } /** * <p>Trains a decision tree.</p> * * <p>There are four <code>train</code> methods in "CvDTree":</p> * <ul> * <li> The first two methods follow the generic "CvStatModel.train" * conventions. It is the most complete form. Both data layouts * (<code>tflag=CV_ROW_SAMPLE</code> and <code>tflag=CV_COL_SAMPLE</code>) are * supported, as well as sample and variable subsets, missing measurements, * arbitrary combinations of input and output variable types, and so on. The * last parameter contains all of the necessary training parameters (see the * "CvDTreeParams" description). * <li> The third method uses "CvMLData" to pass training data to a decision * tree. * <li> The last method <code>train</code> is mostly used for building tree * ensembles. It takes the pre-constructed "CvDTreeTrainData" instance and an * optional subset of the training set. The indices in <code>subsampleIdx</code> * are counted relatively to the <code>_sample_idx</code>, passed to the * <code>CvDTreeTrainData</code> constructor. For example, if <code>_sample_idx=[1, * 5, 7, 100]</code>, then <code>subsampleIdx=[0,3]</code> means that the * samples <code>[1, 100]</code> of the original training set are used. * </ul> * * <p>The function is parallelized with the TBB library.</p> * * @param trainData a trainData * @param tflag a tflag * @param responses a responses * * @see <a href="http://docs.opencv.org/modules/ml/doc/decision_trees.html#cvdtree-train">org.opencv.ml.CvDTree.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++: CvDTree::CvDTree() private static native long CvDTree_0(); // C++: void CvDTree::clear() private static native void clear_0(long nativeObj); // C++: Mat CvDTree::getVarImportance() private static native long getVarImportance_0(long nativeObj); // C++: bool CvDTree::train(Mat trainData, int tflag, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat(), Mat varType = cv::Mat(), Mat missingDataMask = cv::Mat(), CvDTreeParams params = CvDTreeParams()) 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); 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); }