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// // This file is auto-generated. Please don't modify it! ///* w ww .j a v a 2 s .c o m*/ package org.opencv.ml; import org.opencv.core.Mat; // C++: class CvSVM /** * <p>Support Vector Machines.</p> * * <p>Note:</p> * <ul> * <li> (Python) An example of digit recognition using SVM can be found at * opencv_source/samples/python2/digits.py * <li> (Python) An example of grid search digit recognition using SVM can be * found at opencv_source/samples/python2/digits_adjust.py * <li> (Python) An example of video digit recognition using SVM can be found * at opencv_source/samples/python2/digits_video.py * </ul> * * @see <a href="http://docs.opencv.org/modules/ml/doc/support_vector_machines.html#cvsvm">org.opencv.ml.CvSVM : public CvStatModel</a> */ public class CvSVM extends CvStatModel { protected CvSVM(long addr) { super(addr); } public static final int C_SVC = 100, NU_SVC = 101, ONE_CLASS = 102, EPS_SVR = 103, NU_SVR = 104, LINEAR = 0, POLY = 1, RBF = 2, SIGMOID = 3, C = 0, GAMMA = 1, P = 2, NU = 3, COEF = 4, DEGREE = 5; // // C++: CvSVM::CvSVM() // /** * <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/support_vector_machines.html#cvsvm-cvsvm">org.opencv.ml.CvSVM.CvSVM</a> */ public CvSVM() { super( CvSVM_0() ); return; } // // C++: CvSVM::CvSVM(Mat trainData, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat(), CvSVMParams params = CvSVMParams()) // /** * <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 responses a responses * @param varIdx a varIdx * @param sampleIdx a sampleIdx * @param params a params * * @see <a href="http://docs.opencv.org/modules/ml/doc/support_vector_machines.html#cvsvm-cvsvm">org.opencv.ml.CvSVM.CvSVM</a> */ public CvSVM(Mat trainData, Mat responses, Mat varIdx, Mat sampleIdx, CvSVMParams params) { super( CvSVM_1(trainData.nativeObj, responses.nativeObj, varIdx.nativeObj, sampleIdx.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 responses a responses * * @see <a href="http://docs.opencv.org/modules/ml/doc/support_vector_machines.html#cvsvm-cvsvm">org.opencv.ml.CvSVM.CvSVM</a> */ public CvSVM(Mat trainData, Mat responses) { super( CvSVM_2(trainData.nativeObj, responses.nativeObj) ); return; } // // C++: void CvSVM::clear() // public void clear() { clear_0(nativeObj); return; } // // C++: int CvSVM::get_support_vector_count() // public int get_support_vector_count() { int retVal = get_support_vector_count_0(nativeObj); return retVal; } // // C++: int CvSVM::get_var_count() // /** * <p>Returns the number of used features (variables count).</p> * * @see <a href="http://docs.opencv.org/modules/ml/doc/support_vector_machines.html#cvsvm-get-var-count">org.opencv.ml.CvSVM.get_var_count</a> */ public int get_var_count() { int retVal = get_var_count_0(nativeObj); return retVal; } // // C++: float CvSVM::predict(Mat sample, bool returnDFVal = false) // /** * <p>Predicts the response for input sample(s).</p> * * <p>If you pass one sample then prediction result is returned. If you want to get * responses for several samples then you should pass the <code>results</code> * matrix where prediction results will be stored.</p> * * <p>The function is parallelized with the TBB library.</p> * * @param sample Input sample for prediction. * @param returnDFVal Specifies a type of the return value. If <code>true</code> * and the problem is 2-class classification then the method returns the * decision function value that is signed distance to the margin, else the * function returns a class label (classification) or estimated function value * (regression). * * @see <a href="http://docs.opencv.org/modules/ml/doc/support_vector_machines.html#cvsvm-predict">org.opencv.ml.CvSVM.predict</a> */ public float predict(Mat sample, boolean returnDFVal) { float retVal = predict_0(nativeObj, sample.nativeObj, returnDFVal); return retVal; } /** * <p>Predicts the response for input sample(s).</p> * * <p>If you pass one sample then prediction result is returned. If you want to get * responses for several samples then you should pass the <code>results</code> * matrix where prediction results will be stored.</p> * * <p>The function is parallelized with the TBB library.</p> * * @param sample Input sample for prediction. * * @see <a href="http://docs.opencv.org/modules/ml/doc/support_vector_machines.html#cvsvm-predict">org.opencv.ml.CvSVM.predict</a> */ public float predict(Mat sample) { float retVal = predict_1(nativeObj, sample.nativeObj); return retVal; } // // C++: void CvSVM::predict(Mat samples, Mat& results) // /** * <p>Predicts the response for input sample(s).</p> * * <p>If you pass one sample then prediction result is returned. If you want to get * responses for several samples then you should pass the <code>results</code> * matrix where prediction results will be stored.</p> * * <p>The function is parallelized with the TBB library.</p> * * @param samples Input samples for prediction. * @param results Output prediction responses for corresponding samples. * * @see <a href="http://docs.opencv.org/modules/ml/doc/support_vector_machines.html#cvsvm-predict">org.opencv.ml.CvSVM.predict</a> */ public void predict_all(Mat samples, Mat results) { predict_all_0(nativeObj, samples.nativeObj, results.nativeObj); return; } // // C++: bool CvSVM::train(Mat trainData, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat(), CvSVMParams params = CvSVMParams()) // /** * <p>Trains an SVM.</p> * * <p>The method trains the SVM model. It follows the conventions of the generic * "CvStatModel.train" approach with the following limitations:</p> * <ul> * <li> Only the <code>CV_ROW_SAMPLE</code> data layout is supported. * <li> Input variables are all ordered. * <li> Output variables can be either categorical (<code>params.svm_type=CvSVM.C_SVC</code> * or <code>params.svm_type=CvSVM.NU_SVC</code>), or ordered (<code>params.svm_type=CvSVM.EPS_SVR</code> * or <code>params.svm_type=CvSVM.NU_SVR</code>), or not required at all * (<code>params.svm_type=CvSVM.ONE_CLASS</code>). * <li> Missing measurements are not supported. * </ul> * * <p>All the other parameters are gathered in the "CvSVMParams" structure.</p> * * @param trainData a trainData * @param responses a responses * @param varIdx a varIdx * @param sampleIdx a sampleIdx * @param params a params * * @see <a href="http://docs.opencv.org/modules/ml/doc/support_vector_machines.html#cvsvm-train">org.opencv.ml.CvSVM.train</a> */ public boolean train(Mat trainData, Mat responses, Mat varIdx, Mat sampleIdx, CvSVMParams params) { boolean retVal = train_0(nativeObj, trainData.nativeObj, responses.nativeObj, varIdx.nativeObj, sampleIdx.nativeObj, params.nativeObj); return retVal; } /** * <p>Trains an SVM.</p> * * <p>The method trains the SVM model. It follows the conventions of the generic * "CvStatModel.train" approach with the following limitations:</p> * <ul> * <li> Only the <code>CV_ROW_SAMPLE</code> data layout is supported. * <li> Input variables are all ordered. * <li> Output variables can be either categorical (<code>params.svm_type=CvSVM.C_SVC</code> * or <code>params.svm_type=CvSVM.NU_SVC</code>), or ordered (<code>params.svm_type=CvSVM.EPS_SVR</code> * or <code>params.svm_type=CvSVM.NU_SVR</code>), or not required at all * (<code>params.svm_type=CvSVM.ONE_CLASS</code>). * <li> Missing measurements are not supported. * </ul> * * <p>All the other parameters are gathered in the "CvSVMParams" structure.</p> * * @param trainData a trainData * @param responses a responses * * @see <a href="http://docs.opencv.org/modules/ml/doc/support_vector_machines.html#cvsvm-train">org.opencv.ml.CvSVM.train</a> */ public boolean train(Mat trainData, Mat responses) { boolean retVal = train_1(nativeObj, trainData.nativeObj, responses.nativeObj); return retVal; } // // C++: bool CvSVM::train_auto(Mat trainData, Mat responses, Mat varIdx, Mat sampleIdx, CvSVMParams params, int k_fold = 10, CvParamGrid Cgrid = CvSVM::get_default_grid(CvSVM::C), CvParamGrid gammaGrid = CvSVM::get_default_grid(CvSVM::GAMMA), CvParamGrid pGrid = CvSVM::get_default_grid(CvSVM::P), CvParamGrid nuGrid = CvSVM::get_default_grid(CvSVM::NU), CvParamGrid coeffGrid = CvSVM::get_default_grid(CvSVM::COEF), CvParamGrid degreeGrid = CvSVM::get_default_grid(CvSVM::DEGREE), bool balanced = false) // /** * <p>Trains an SVM with optimal parameters.</p> * * <p>The method trains the SVM model automatically by choosing the optimal * parameters <code>C</code>, <code>gamma</code>, <code>p</code>, * <code>nu</code>, <code>coef0</code>, <code>degree</code> from "CvSVMParams". * Parameters are considered optimal when the cross-validation estimate of the * test set error is minimal.</p> * * <p>If there is no need to optimize a parameter, the corresponding grid step * should be set to any value less than or equal to 1. For example, to avoid * optimization in <code>gamma</code>, set <code>gamma_grid.step = 0</code>, * <code>gamma_grid.min_val</code>, <code>gamma_grid.max_val</code> as arbitrary * numbers. In this case, the value <code>params.gamma</code> is taken for * <code>gamma</code>.</p> * * <p>And, finally, if the optimization in a parameter is required but the * corresponding grid is unknown, you may call the function "CvSVM.get_default_grid". * To generate a grid, for example, for <code>gamma</code>, call * <code>CvSVM.get_default_grid(CvSVM.GAMMA)</code>.</p> * * <p>This function works for the classification (<code>params.svm_type=CvSVM.C_SVC</code> * or <code>params.svm_type=CvSVM.NU_SVC</code>) as well as for the regression * (<code>params.svm_type=CvSVM.EPS_SVR</code> or <code>params.svm_type=CvSVM.NU_SVR</code>). * If <code>params.svm_type=CvSVM.ONE_CLASS</code>, no optimization is made and * the usual SVM with parameters specified in <code>params</code> is executed.</p> * * @param trainData a trainData * @param responses a responses * @param varIdx a varIdx * @param sampleIdx a sampleIdx * @param params a params * @param k_fold Cross-validation parameter. The training set is divided into * <code>k_fold</code> subsets. One subset is used to test the model, the others * form the train set. So, the SVM algorithm is executed <code>k_fold</code> * times. * @param Cgrid a Cgrid * @param gammaGrid Iteration grid for the corresponding SVM parameter. * @param pGrid Iteration grid for the corresponding SVM parameter. * @param nuGrid Iteration grid for the corresponding SVM parameter. * @param coeffGrid Iteration grid for the corresponding SVM parameter. * @param degreeGrid Iteration grid for the corresponding SVM parameter. * @param balanced If <code>true</code> and the problem is 2-class * classification then the method creates more balanced cross-validation subsets * that is proportions between classes in subsets are close to such proportion * in the whole train dataset. * * @see <a href="http://docs.opencv.org/modules/ml/doc/support_vector_machines.html#cvsvm-train-auto">org.opencv.ml.CvSVM.train_auto</a> */ public boolean train_auto(Mat trainData, Mat responses, Mat varIdx, Mat sampleIdx, CvSVMParams params, int k_fold, CvParamGrid Cgrid, CvParamGrid gammaGrid, CvParamGrid pGrid, CvParamGrid nuGrid, CvParamGrid coeffGrid, CvParamGrid degreeGrid, boolean balanced) { boolean retVal = train_auto_0(nativeObj, trainData.nativeObj, responses.nativeObj, varIdx.nativeObj, sampleIdx.nativeObj, params.nativeObj, k_fold, Cgrid.nativeObj, gammaGrid.nativeObj, pGrid.nativeObj, nuGrid.nativeObj, coeffGrid.nativeObj, degreeGrid.nativeObj, balanced); return retVal; } /** * <p>Trains an SVM with optimal parameters.</p> * * <p>The method trains the SVM model automatically by choosing the optimal * parameters <code>C</code>, <code>gamma</code>, <code>p</code>, * <code>nu</code>, <code>coef0</code>, <code>degree</code> from "CvSVMParams". * Parameters are considered optimal when the cross-validation estimate of the * test set error is minimal.</p> * * <p>If there is no need to optimize a parameter, the corresponding grid step * should be set to any value less than or equal to 1. For example, to avoid * optimization in <code>gamma</code>, set <code>gamma_grid.step = 0</code>, * <code>gamma_grid.min_val</code>, <code>gamma_grid.max_val</code> as arbitrary * numbers. In this case, the value <code>params.gamma</code> is taken for * <code>gamma</code>.</p> * * <p>And, finally, if the optimization in a parameter is required but the * corresponding grid is unknown, you may call the function "CvSVM.get_default_grid". * To generate a grid, for example, for <code>gamma</code>, call * <code>CvSVM.get_default_grid(CvSVM.GAMMA)</code>.</p> * * <p>This function works for the classification (<code>params.svm_type=CvSVM.C_SVC</code> * or <code>params.svm_type=CvSVM.NU_SVC</code>) as well as for the regression * (<code>params.svm_type=CvSVM.EPS_SVR</code> or <code>params.svm_type=CvSVM.NU_SVR</code>). * If <code>params.svm_type=CvSVM.ONE_CLASS</code>, no optimization is made and * the usual SVM with parameters specified in <code>params</code> is executed.</p> * * @param trainData a trainData * @param responses a responses * @param varIdx a varIdx * @param sampleIdx a sampleIdx * @param params a params * * @see <a href="http://docs.opencv.org/modules/ml/doc/support_vector_machines.html#cvsvm-train-auto">org.opencv.ml.CvSVM.train_auto</a> */ public boolean train_auto(Mat trainData, Mat responses, Mat varIdx, Mat sampleIdx, CvSVMParams params) { boolean retVal = train_auto_1(nativeObj, trainData.nativeObj, responses.nativeObj, varIdx.nativeObj, sampleIdx.nativeObj, params.nativeObj); return retVal; } @Override protected void finalize() throws Throwable { delete(nativeObj); } // C++: CvSVM::CvSVM() private static native long CvSVM_0(); // C++: CvSVM::CvSVM(Mat trainData, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat(), CvSVMParams params = CvSVMParams()) private static native long CvSVM_1(long trainData_nativeObj, long responses_nativeObj, long varIdx_nativeObj, long sampleIdx_nativeObj, long params_nativeObj); private static native long CvSVM_2(long trainData_nativeObj, long responses_nativeObj); // C++: void CvSVM::clear() private static native void clear_0(long nativeObj); // C++: int CvSVM::get_support_vector_count() private static native int get_support_vector_count_0(long nativeObj); // C++: int CvSVM::get_var_count() private static native int get_var_count_0(long nativeObj); // C++: float CvSVM::predict(Mat sample, bool returnDFVal = false) private static native float predict_0(long nativeObj, long sample_nativeObj, boolean returnDFVal); private static native float predict_1(long nativeObj, long sample_nativeObj); // C++: void CvSVM::predict(Mat samples, Mat& results) private static native void predict_all_0(long nativeObj, long samples_nativeObj, long results_nativeObj); // C++: bool CvSVM::train(Mat trainData, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat(), CvSVMParams params = CvSVMParams()) private static native boolean train_0(long nativeObj, long trainData_nativeObj, long responses_nativeObj, long varIdx_nativeObj, long sampleIdx_nativeObj, long params_nativeObj); private static native boolean train_1(long nativeObj, long trainData_nativeObj, long responses_nativeObj); // C++: bool CvSVM::train_auto(Mat trainData, Mat responses, Mat varIdx, Mat sampleIdx, CvSVMParams params, int k_fold = 10, CvParamGrid Cgrid = CvSVM::get_default_grid(CvSVM::C), CvParamGrid gammaGrid = CvSVM::get_default_grid(CvSVM::GAMMA), CvParamGrid pGrid = CvSVM::get_default_grid(CvSVM::P), CvParamGrid nuGrid = CvSVM::get_default_grid(CvSVM::NU), CvParamGrid coeffGrid = CvSVM::get_default_grid(CvSVM::COEF), CvParamGrid degreeGrid = CvSVM::get_default_grid(CvSVM::DEGREE), bool balanced = false) private static native boolean train_auto_0(long nativeObj, long trainData_nativeObj, long responses_nativeObj, long varIdx_nativeObj, long sampleIdx_nativeObj, long params_nativeObj, int k_fold, long Cgrid_nativeObj, long gammaGrid_nativeObj, long pGrid_nativeObj, long nuGrid_nativeObj, long coeffGrid_nativeObj, long degreeGrid_nativeObj, boolean balanced); private static native boolean train_auto_1(long nativeObj, long trainData_nativeObj, long responses_nativeObj, long varIdx_nativeObj, long sampleIdx_nativeObj, long params_nativeObj); // native support for java finalize() private static native void delete(long nativeObj); }