org.opencv.ml.CvSVM Maven / Gradle / Ivy
//
// This file is auto-generated. Please don't modify it!
//
package org.opencv.ml;
import org.opencv.core.Mat;
// C++: class CvSVM
/**
* Support Vector Machines.
*
* @see org.opencv.ml.CvSVM : public CvStatModel
*/
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()
//
/**
* Default and training constructors.
*
* The constructors follow conventions of "CvStatModel.CvStatModel". See
* "CvStatModel.train" for parameters descriptions.
*
* @see org.opencv.ml.CvSVM.CvSVM
*/
public CvSVM()
{
super( CvSVM_0() );
return;
}
//
// C++: CvSVM::CvSVM(Mat trainData, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat(), CvSVMParams params = CvSVMParams())
//
/**
* Default and training constructors.
*
* The constructors follow conventions of "CvStatModel.CvStatModel". See
* "CvStatModel.train" for parameters descriptions.
*
* @param trainData a trainData
* @param responses a responses
* @param varIdx a varIdx
* @param sampleIdx a sampleIdx
* @param params a params
*
* @see org.opencv.ml.CvSVM.CvSVM
*/
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;
}
/**
* Default and training constructors.
*
* The constructors follow conventions of "CvStatModel.CvStatModel". See
* "CvStatModel.train" for parameters descriptions.
*
* @param trainData a trainData
* @param responses a responses
*
* @see org.opencv.ml.CvSVM.CvSVM
*/
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()
//
/**
* Returns the number of used features (variables count).
*
* @see org.opencv.ml.CvSVM.get_var_count
*/
public int get_var_count()
{
int retVal = get_var_count_0(nativeObj);
return retVal;
}
//
// C++: float CvSVM::predict(Mat sample, bool returnDFVal = false)
//
/**
* Predicts the response for input sample(s).
*
* If you pass one sample then prediction result is returned. If you want to get
* responses for several samples then you should pass the results
* matrix where prediction results will be stored.
*
* The function is parallelized with the TBB library.
*
* @param sample Input sample for prediction.
* @param returnDFVal Specifies a type of the return value. If true
* 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 org.opencv.ml.CvSVM.predict
*/
public float predict(Mat sample, boolean returnDFVal)
{
float retVal = predict_0(nativeObj, sample.nativeObj, returnDFVal);
return retVal;
}
/**
* Predicts the response for input sample(s).
*
* If you pass one sample then prediction result is returned. If you want to get
* responses for several samples then you should pass the results
* matrix where prediction results will be stored.
*
* The function is parallelized with the TBB library.
*
* @param sample Input sample for prediction.
*
* @see org.opencv.ml.CvSVM.predict
*/
public float predict(Mat sample)
{
float retVal = predict_1(nativeObj, sample.nativeObj);
return retVal;
}
//
// C++: void CvSVM::predict(Mat samples, Mat& results)
//
/**
* Predicts the response for input sample(s).
*
* If you pass one sample then prediction result is returned. If you want to get
* responses for several samples then you should pass the results
* matrix where prediction results will be stored.
*
* The function is parallelized with the TBB library.
*
* @param samples Input samples for prediction.
* @param results Output prediction responses for corresponding samples.
*
* @see org.opencv.ml.CvSVM.predict
*/
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())
//
/**
* Trains an SVM.
*
* The method trains the SVM model. It follows the conventions of the generic
* "CvStatModel.train" approach with the following limitations:
*
* - Only the
CV_ROW_SAMPLE data layout is supported.
* - Input variables are all ordered.
*
- Output variables can be either categorical (
params.svm_type=CvSVM.C_SVC
* or params.svm_type=CvSVM.NU_SVC), or ordered (params.svm_type=CvSVM.EPS_SVR
* or params.svm_type=CvSVM.NU_SVR), or not required at all
* (params.svm_type=CvSVM.ONE_CLASS).
* - Missing measurements are not supported.
*
*
* All the other parameters are gathered in the "CvSVMParams" structure.
*
* @param trainData a trainData
* @param responses a responses
* @param varIdx a varIdx
* @param sampleIdx a sampleIdx
* @param params a params
*
* @see org.opencv.ml.CvSVM.train
*/
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;
}
/**
* Trains an SVM.
*
* The method trains the SVM model. It follows the conventions of the generic
* "CvStatModel.train" approach with the following limitations:
*
* - Only the
CV_ROW_SAMPLE data layout is supported.
* - Input variables are all ordered.
*
- Output variables can be either categorical (
params.svm_type=CvSVM.C_SVC
* or params.svm_type=CvSVM.NU_SVC), or ordered (params.svm_type=CvSVM.EPS_SVR
* or params.svm_type=CvSVM.NU_SVR), or not required at all
* (params.svm_type=CvSVM.ONE_CLASS).
* - Missing measurements are not supported.
*
*
* All the other parameters are gathered in the "CvSVMParams" structure.
*
* @param trainData a trainData
* @param responses a responses
*
* @see org.opencv.ml.CvSVM.train
*/
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)
//
/**
* Trains an SVM with optimal parameters.
*
* The method trains the SVM model automatically by choosing the optimal
* parameters C, gamma, p,
* nu, coef0, degree from "CvSVMParams".
* Parameters are considered optimal when the cross-validation estimate of the
* test set error is minimal.
*
* 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 gamma, set gamma_grid.step = 0,
* gamma_grid.min_val, gamma_grid.max_val as arbitrary
* numbers. In this case, the value params.gamma is taken for
* gamma.
*
* 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 gamma, call
* CvSVM.get_default_grid(CvSVM.GAMMA).
*
* This function works for the classification (params.svm_type=CvSVM.C_SVC
* or params.svm_type=CvSVM.NU_SVC) as well as for the regression
* (params.svm_type=CvSVM.EPS_SVR or params.svm_type=CvSVM.NU_SVR).
* If params.svm_type=CvSVM.ONE_CLASS, no optimization is made and
* the usual SVM with parameters specified in params is executed.
*
* @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
* k_fold subsets. One subset is used to test the model, the others
* form the train set. So, the SVM algorithm is executed k_fold
* 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 true 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 org.opencv.ml.CvSVM.train_auto
*/
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;
}
/**
* Trains an SVM with optimal parameters.
*
* The method trains the SVM model automatically by choosing the optimal
* parameters C, gamma, p,
* nu, coef0, degree from "CvSVMParams".
* Parameters are considered optimal when the cross-validation estimate of the
* test set error is minimal.
*
* 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 gamma, set gamma_grid.step = 0,
* gamma_grid.min_val, gamma_grid.max_val as arbitrary
* numbers. In this case, the value params.gamma is taken for
* gamma.
*
* 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 gamma, call
* CvSVM.get_default_grid(CvSVM.GAMMA).
*
* This function works for the classification (params.svm_type=CvSVM.C_SVC
* or params.svm_type=CvSVM.NU_SVC) as well as for the regression
* (params.svm_type=CvSVM.EPS_SVR or params.svm_type=CvSVM.NU_SVR).
* If params.svm_type=CvSVM.ONE_CLASS, no optimization is made and
* the usual SVM with parameters specified in params is executed.
*
* @param trainData a trainData
* @param responses a responses
* @param varIdx a varIdx
* @param sampleIdx a sampleIdx
* @param params a params
*
* @see org.opencv.ml.CvSVM.train_auto
*/
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);
}