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// // This file is auto-generated. Please don't modify it! ///*from www. ja v a 2s .c om*/ package org.opencv.ml; import org.opencv.core.Mat; // C++: class CvRTrees /** * <p>The class implements the random forest predictor as described in the * beginning of this section.</p> * * @see <a href="http://docs.opencv.org/modules/ml/doc/random_trees.html#cvrtrees">org.opencv.ml.CvRTrees : public CvStatModel</a> */ public class CvRTrees extends CvStatModel { protected CvRTrees(long addr) { super(addr); } // // C++: CvRTrees::CvRTrees() // public CvRTrees() { super( CvRTrees_0() ); return; } // // C++: void CvRTrees::clear() // public void clear() { clear_0(nativeObj); return; } // // C++: Mat CvRTrees::getVarImportance() // /** * <p>Returns the variable importance array.</p> * * <p>The method returns the variable importance vector, computed at the training * stage when <code>CvRTParams.calc_var_importance</code> is set to true. If * this flag was set to false, the <code>NULL</code> pointer is returned. This * differs from the decision trees where variable importance can be computed * anytime after the training.</p> * * @see <a href="http://docs.opencv.org/modules/ml/doc/random_trees.html#cvrtrees-getvarimportance">org.opencv.ml.CvRTrees.getVarImportance</a> */ public Mat getVarImportance() { Mat retVal = new Mat(getVarImportance_0(nativeObj)); return retVal; } // // C++: float CvRTrees::predict(Mat sample, Mat missing = cv::Mat()) // /** * <p>Predicts the output for an input sample.</p> * * <p>The input parameters of the prediction method are the same as in * "CvDTree.predict" but the return value type is different. This method * returns the cumulative result from all the trees in the forest (the class * that receives the majority of voices, or the mean of the regression function * estimates).</p> * * @param sample Sample for classification. * @param missing Optional missing measurement mask of the sample. * * @see <a href="http://docs.opencv.org/modules/ml/doc/random_trees.html#cvrtrees-predict">org.opencv.ml.CvRTrees.predict</a> */ public float predict(Mat sample, Mat missing) { float retVal = predict_0(nativeObj, sample.nativeObj, missing.nativeObj); return retVal; } /** * <p>Predicts the output for an input sample.</p> * * <p>The input parameters of the prediction method are the same as in * "CvDTree.predict" but the return value type is different. This method * returns the cumulative result from all the trees in the forest (the class * that receives the majority of voices, or the mean of the regression function * estimates).</p> * * @param sample Sample for classification. * * @see <a href="http://docs.opencv.org/modules/ml/doc/random_trees.html#cvrtrees-predict">org.opencv.ml.CvRTrees.predict</a> */ public float predict(Mat sample) { float retVal = predict_1(nativeObj, sample.nativeObj); return retVal; } // // C++: float CvRTrees::predict_prob(Mat sample, Mat missing = cv::Mat()) // /** * <p>Returns a fuzzy-predicted class label.</p> * * <p>The function works for binary classification problems only. It returns the * number between 0 and 1. This number represents probability or confidence of * the sample belonging to the second class. It is calculated as the proportion * of decision trees that classified the sample to the second class.</p> * * @param sample Sample for classification. * @param missing Optional missing measurement mask of the sample. * * @see <a href="http://docs.opencv.org/modules/ml/doc/random_trees.html#cvrtrees-predict-prob">org.opencv.ml.CvRTrees.predict_prob</a> */ public float predict_prob(Mat sample, Mat missing) { float retVal = predict_prob_0(nativeObj, sample.nativeObj, missing.nativeObj); return retVal; } /** * <p>Returns a fuzzy-predicted class label.</p> * * <p>The function works for binary classification problems only. It returns the * number between 0 and 1. This number represents probability or confidence of * the sample belonging to the second class. It is calculated as the proportion * of decision trees that classified the sample to the second class.</p> * * @param sample Sample for classification. * * @see <a href="http://docs.opencv.org/modules/ml/doc/random_trees.html#cvrtrees-predict-prob">org.opencv.ml.CvRTrees.predict_prob</a> */ public float predict_prob(Mat sample) { float retVal = predict_prob_1(nativeObj, sample.nativeObj); return retVal; } // // C++: bool CvRTrees::train(Mat trainData, int tflag, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat(), Mat varType = cv::Mat(), Mat missingDataMask = cv::Mat(), CvRTParams params = CvRTParams()) // /** * <p>Trains the Random Trees model.</p> * * <p>The method "CvRTrees.train" is very similar to the method "CvDTree.train" * and follows the generic method "CvStatModel.train" conventions. All the * parameters specific to the algorithm training are passed as a "CvRTParams" * instance. The estimate of the training error (<code>oob-error</code>) is * stored in the protected class member <code>oob_error</code>.</p> * * <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/random_trees.html#cvrtrees-train">org.opencv.ml.CvRTrees.train</a> */ public boolean train(Mat trainData, int tflag, Mat responses, Mat varIdx, Mat sampleIdx, Mat varType, Mat missingDataMask, CvRTParams 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 the Random Trees model.</p> * * <p>The method "CvRTrees.train" is very similar to the method "CvDTree.train" * and follows the generic method "CvStatModel.train" conventions. All the * parameters specific to the algorithm training are passed as a "CvRTParams" * instance. The estimate of the training error (<code>oob-error</code>) is * stored in the protected class member <code>oob_error</code>.</p> * * <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/random_trees.html#cvrtrees-train">org.opencv.ml.CvRTrees.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++: CvRTrees::CvRTrees() private static native long CvRTrees_0(); // C++: void CvRTrees::clear() private static native void clear_0(long nativeObj); // C++: Mat CvRTrees::getVarImportance() private static native long getVarImportance_0(long nativeObj); // C++: float CvRTrees::predict(Mat sample, Mat missing = cv::Mat()) private static native float predict_0(long nativeObj, long sample_nativeObj, long missing_nativeObj); private static native float predict_1(long nativeObj, long sample_nativeObj); // C++: float CvRTrees::predict_prob(Mat sample, Mat missing = cv::Mat()) private static native float predict_prob_0(long nativeObj, long sample_nativeObj, long missing_nativeObj); private static native float predict_prob_1(long nativeObj, long sample_nativeObj); // C++: bool CvRTrees::train(Mat trainData, int tflag, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat(), Mat varType = cv::Mat(), Mat missingDataMask = cv::Mat(), CvRTParams params = CvRTParams()) 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); }