org.bytedeco.opencv.opencv_ml.SVM Maven / Gradle / Ivy
// Targeted by JavaCPP version 1.5.7: DO NOT EDIT THIS FILE
package org.bytedeco.opencv.opencv_ml;
import java.nio.*;
import org.bytedeco.javacpp.*;
import org.bytedeco.javacpp.annotation.*;
import static org.bytedeco.javacpp.presets.javacpp.*;
import static org.bytedeco.openblas.global.openblas_nolapack.*;
import static org.bytedeco.openblas.global.openblas.*;
import org.bytedeco.opencv.opencv_core.*;
import static org.bytedeco.opencv.global.opencv_core.*;
import static org.bytedeco.opencv.global.opencv_ml.*;
/****************************************************************************************\
* Support Vector Machines *
\****************************************************************************************/
/** \brief Support Vector Machines.
@see \ref ml_intro_svm
*/
@Namespace("cv::ml") @Properties(inherit = org.bytedeco.opencv.presets.opencv_ml.class)
public class SVM extends StatModel {
static { Loader.load(); }
/** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */
public SVM(Pointer p) { super(p); }
public static class Kernel extends Algorithm {
static { Loader.load(); }
/** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */
public Kernel(Pointer p) { super(p); }
public native int getType();
public native void calc( int vcount, int n, @Const FloatPointer vecs, @Const FloatPointer another, FloatPointer results );
public native void calc( int vcount, int n, @Const FloatBuffer vecs, @Const FloatBuffer another, FloatBuffer results );
public native void calc( int vcount, int n, @Const float[] vecs, @Const float[] another, float[] results );
}
/** Type of a %SVM formulation.
See SVM::Types. Default value is SVM::C_SVC. */
/** @see setType */
public native int getType();
/** \copybrief getType @see getType */
public native void setType(int val);
/** Parameter {@code \gamma} of a kernel function.
For SVM::POLY, SVM::RBF, SVM::SIGMOID or SVM::CHI2. Default value is 1. */
/** @see setGamma */
public native double getGamma();
/** \copybrief getGamma @see getGamma */
public native void setGamma(double val);
/** Parameter _coef0_ of a kernel function.
For SVM::POLY or SVM::SIGMOID. Default value is 0.*/
/** @see setCoef0 */
public native double getCoef0();
/** \copybrief getCoef0 @see getCoef0 */
public native void setCoef0(double val);
/** Parameter _degree_ of a kernel function.
For SVM::POLY. Default value is 0. */
/** @see setDegree */
public native double getDegree();
/** \copybrief getDegree @see getDegree */
public native void setDegree(double val);
/** Parameter _C_ of a %SVM optimization problem.
For SVM::C_SVC, SVM::EPS_SVR or SVM::NU_SVR. Default value is 0. */
/** @see setC */
public native double getC();
/** \copybrief getC @see getC */
public native void setC(double val);
/** Parameter {@code \nu} of a %SVM optimization problem.
For SVM::NU_SVC, SVM::ONE_CLASS or SVM::NU_SVR. Default value is 0. */
/** @see setNu */
public native double getNu();
/** \copybrief getNu @see getNu */
public native void setNu(double val);
/** Parameter {@code \epsilon} of a %SVM optimization problem.
For SVM::EPS_SVR. Default value is 0. */
/** @see setP */
public native double getP();
/** \copybrief getP @see getP */
public native void setP(double val);
/** Optional weights in the SVM::C_SVC problem, assigned to particular classes.
They are multiplied by _C_ so the parameter _C_ of class _i_ becomes {@code classWeights(i) * C}. Thus
these weights affect the misclassification penalty for different classes. The larger weight,
the larger penalty on misclassification of data from the corresponding class. Default value is
empty Mat. */
/** @see setClassWeights */
public native @ByVal Mat getClassWeights();
/** \copybrief getClassWeights @see getClassWeights */
public native void setClassWeights(@Const @ByRef Mat val);
/** Termination criteria of the iterative %SVM training procedure which solves a partial
case of constrained quadratic optimization problem.
You can specify tolerance and/or the maximum number of iterations. Default value is
{@code TermCriteria( TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, FLT_EPSILON )}; */
/** @see setTermCriteria */
public native @ByVal TermCriteria getTermCriteria();
/** \copybrief getTermCriteria @see getTermCriteria */
public native void setTermCriteria(@Const @ByRef TermCriteria val);
/** Type of a %SVM kernel.
See SVM::KernelTypes. Default value is SVM::RBF. */
public native int getKernelType();
/** Initialize with one of predefined kernels.
See SVM::KernelTypes. */
public native void setKernel(int kernelType);
/** Initialize with custom kernel.
See SVM::Kernel class for implementation details */
public native void setCustomKernel(@Ptr Kernel _kernel);
/** %SVM type */
/** enum cv::ml::SVM::Types */
public static final int
/** C-Support Vector Classification. n-class classification (n {@code \geq} 2), allows
imperfect separation of classes with penalty multiplier C for outliers. */
C_SVC = 100,
/** {@code \nu}-Support Vector Classification. n-class classification with possible
imperfect separation. Parameter {@code \nu} (in the range 0..1, the larger the value, the smoother
the decision boundary) is used instead of C. */
NU_SVC = 101,
/** Distribution Estimation (One-class %SVM). All the training data are from
the same class, %SVM builds a boundary that separates the class from the rest of the feature
space. */
ONE_CLASS = 102,
/** {@code \epsilon}-Support Vector Regression. The distance between feature vectors
from the training set and the fitting hyper-plane must be less than p. For outliers the
penalty multiplier C is used. */
EPS_SVR = 103,
/** {@code \nu}-Support Vector Regression. {@code \nu} is used instead of p.
See \cite LibSVM for details. */
NU_SVR = 104;
/** \brief %SVM kernel type
A comparison of different kernels on the following 2D test case with four classes. Four
SVM::C_SVC SVMs have been trained (one against rest) with auto_train. Evaluation on three
different kernels (SVM::CHI2, SVM::INTER, SVM::RBF). The color depicts the class with max score.
Bright means max-score \> 0, dark means max-score \< 0.
*/
/** enum cv::ml::SVM::KernelTypes */
public static final int
/** Returned by SVM::getKernelType in case when custom kernel has been set */
CUSTOM = -1,
/** Linear kernel. No mapping is done, linear discrimination (or regression) is
done in the original feature space. It is the fastest option. {@code K(x_i, x_j) = x_i^T x_j}. */
LINEAR = 0,
/** Polynomial kernel:
{@code K(x_i, x_j) = (\gamma x_i^T x_j + coef0)^{degree}, \gamma > 0}. */
POLY = 1,
/** Radial basis function (RBF), a good choice in most cases.
{@code K(x_i, x_j) = e^{-\gamma ||x_i - x_j||^2}, \gamma > 0}. */
RBF = 2,
/** Sigmoid kernel: {@code K(x_i, x_j) = \tanh(\gamma x_i^T x_j + coef0)}. */
SIGMOID = 3,
/** Exponential Chi2 kernel, similar to the RBF kernel:
{@code K(x_i, x_j) = e^{-\gamma \chi^2(x_i,x_j)}, \chi^2(x_i,x_j) = (x_i-x_j)^2/(x_i+x_j), \gamma > 0}. */
CHI2 = 4,
/** Histogram intersection kernel. A fast kernel. {@code K(x_i, x_j) = min(x_i,x_j)}. */
INTER = 5;
/** %SVM params type */
/** enum cv::ml::SVM::ParamTypes */
public static final int
C = 0,
GAMMA = 1,
P = 2,
NU = 3,
COEF = 4,
DEGREE = 5;
/** \brief Trains an %SVM with optimal parameters.
@param data the training data that can be constructed using TrainData::create or
TrainData::loadFromCSV.
@param kFold Cross-validation parameter. The training set is divided into kFold subsets. One
subset is used to test the model, the others form the train set. So, the %SVM algorithm is
executed kFold times.
@param Cgrid grid for C
@param gammaGrid grid for gamma
@param pGrid grid for p
@param nuGrid grid for nu
@param coeffGrid grid for coeff
@param degreeGrid grid for degree
@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.
The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p,
nu, coef0, degree. 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 {@code gammaGrid.step
= 0}, {@code gammaGrid.minVal}, {@code gamma_grid.maxVal} as arbitrary numbers. In this case, the value
{@code 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 SVM::getDefaultGrid. To generate a grid, for example, for
gamma, call {@code SVM::getDefaultGrid(SVM::GAMMA)}.
This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the
regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and
the usual %SVM with parameters specified in params is executed.
*/
public native @Cast("bool") boolean trainAuto( @Ptr TrainData data, int kFold/*=10*/,
@ByVal(nullValue = "cv::ml::ParamGrid(cv::ml::SVM::getDefaultGrid(cv::ml::SVM::C))") ParamGrid Cgrid,
@ByVal(nullValue = "cv::ml::ParamGrid(cv::ml::SVM::getDefaultGrid(cv::ml::SVM::GAMMA))") ParamGrid gammaGrid,
@ByVal(nullValue = "cv::ml::ParamGrid(cv::ml::SVM::getDefaultGrid(cv::ml::SVM::P))") ParamGrid pGrid,
@ByVal(nullValue = "cv::ml::ParamGrid(cv::ml::SVM::getDefaultGrid(cv::ml::SVM::NU))") ParamGrid nuGrid,
@ByVal(nullValue = "cv::ml::ParamGrid(cv::ml::SVM::getDefaultGrid(cv::ml::SVM::COEF))") ParamGrid coeffGrid,
@ByVal(nullValue = "cv::ml::ParamGrid(cv::ml::SVM::getDefaultGrid(cv::ml::SVM::DEGREE))") ParamGrid degreeGrid,
@Cast("bool") boolean balanced/*=false*/);
public native @Cast("bool") boolean trainAuto( @Ptr TrainData data);
/** \brief Trains an %SVM with optimal parameters
@param samples training samples
@param layout See ml::SampleTypes.
@param responses vector of responses associated with the training samples.
@param kFold Cross-validation parameter. The training set is divided into kFold subsets. One
subset is used to test the model, the others form the train set. So, the %SVM algorithm is
@param Cgrid grid for C
@param gammaGrid grid for gamma
@param pGrid grid for p
@param nuGrid grid for nu
@param coeffGrid grid for coeff
@param degreeGrid grid for degree
@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.
The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p,
nu, coef0, degree. Parameters are considered optimal when the cross-validation
estimate of the test set error is minimal.
This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only
offers rudimentary parameter options.
This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the
regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and
the usual %SVM with parameters specified in params is executed.
*/
public native @Cast("bool") boolean trainAuto(@ByVal Mat samples,
int layout,
@ByVal Mat responses,
int kFold/*=10*/,
@Ptr ParamGrid Cgrid/*=cv::ml::SVM::getDefaultGridPtr(cv::ml::SVM::C)*/,
@Ptr ParamGrid gammaGrid/*=cv::ml::SVM::getDefaultGridPtr(cv::ml::SVM::GAMMA)*/,
@Ptr ParamGrid pGrid/*=cv::ml::SVM::getDefaultGridPtr(cv::ml::SVM::P)*/,
@Ptr ParamGrid nuGrid/*=cv::ml::SVM::getDefaultGridPtr(cv::ml::SVM::NU)*/,
@Ptr ParamGrid coeffGrid/*=cv::ml::SVM::getDefaultGridPtr(cv::ml::SVM::COEF)*/,
@Ptr ParamGrid degreeGrid/*=cv::ml::SVM::getDefaultGridPtr(cv::ml::SVM::DEGREE)*/,
@Cast("bool") boolean balanced/*=false*/);
public native @Cast("bool") boolean trainAuto(@ByVal Mat samples,
int layout,
@ByVal Mat responses);
public native @Cast("bool") boolean trainAuto(@ByVal UMat samples,
int layout,
@ByVal UMat responses,
int kFold/*=10*/,
@Ptr ParamGrid Cgrid/*=cv::ml::SVM::getDefaultGridPtr(cv::ml::SVM::C)*/,
@Ptr ParamGrid gammaGrid/*=cv::ml::SVM::getDefaultGridPtr(cv::ml::SVM::GAMMA)*/,
@Ptr ParamGrid pGrid/*=cv::ml::SVM::getDefaultGridPtr(cv::ml::SVM::P)*/,
@Ptr ParamGrid nuGrid/*=cv::ml::SVM::getDefaultGridPtr(cv::ml::SVM::NU)*/,
@Ptr ParamGrid coeffGrid/*=cv::ml::SVM::getDefaultGridPtr(cv::ml::SVM::COEF)*/,
@Ptr ParamGrid degreeGrid/*=cv::ml::SVM::getDefaultGridPtr(cv::ml::SVM::DEGREE)*/,
@Cast("bool") boolean balanced/*=false*/);
public native @Cast("bool") boolean trainAuto(@ByVal UMat samples,
int layout,
@ByVal UMat responses);
public native @Cast("bool") boolean trainAuto(@ByVal GpuMat samples,
int layout,
@ByVal GpuMat responses,
int kFold/*=10*/,
@Ptr ParamGrid Cgrid/*=cv::ml::SVM::getDefaultGridPtr(cv::ml::SVM::C)*/,
@Ptr ParamGrid gammaGrid/*=cv::ml::SVM::getDefaultGridPtr(cv::ml::SVM::GAMMA)*/,
@Ptr ParamGrid pGrid/*=cv::ml::SVM::getDefaultGridPtr(cv::ml::SVM::P)*/,
@Ptr ParamGrid nuGrid/*=cv::ml::SVM::getDefaultGridPtr(cv::ml::SVM::NU)*/,
@Ptr ParamGrid coeffGrid/*=cv::ml::SVM::getDefaultGridPtr(cv::ml::SVM::COEF)*/,
@Ptr ParamGrid degreeGrid/*=cv::ml::SVM::getDefaultGridPtr(cv::ml::SVM::DEGREE)*/,
@Cast("bool") boolean balanced/*=false*/);
public native @Cast("bool") boolean trainAuto(@ByVal GpuMat samples,
int layout,
@ByVal GpuMat responses);
/** \brief Retrieves all the support vectors
The method returns all the support vectors as a floating-point matrix, where support vectors are
stored as matrix rows.
*/
public native @ByVal Mat getSupportVectors();
/** \brief Retrieves all the uncompressed support vectors of a linear %SVM
The method returns all the uncompressed support vectors of a linear %SVM that the compressed
support vector, used for prediction, was derived from. They are returned in a floating-point
matrix, where the support vectors are stored as matrix rows.
*/
public native @ByVal Mat getUncompressedSupportVectors();
/** \brief Retrieves the decision function
@param i the index of the decision function. If the problem solved is regression, 1-class or
2-class classification, then there will be just one decision function and the index should
always be 0. Otherwise, in the case of N-class classification, there will be {@code N(N-1)/2}
decision functions.
@param alpha the optional output vector for weights, corresponding to different support vectors.
In the case of linear %SVM all the alpha's will be 1's.
@param svidx the optional output vector of indices of support vectors within the matrix of
support vectors (which can be retrieved by SVM::getSupportVectors). In the case of linear
%SVM each decision function consists of a single "compressed" support vector.
The method returns rho parameter of the decision function, a scalar subtracted from the weighted
sum of kernel responses.
*/
public native double getDecisionFunction(int i, @ByVal Mat alpha, @ByVal Mat svidx);
public native double getDecisionFunction(int i, @ByVal UMat alpha, @ByVal UMat svidx);
public native double getDecisionFunction(int i, @ByVal GpuMat alpha, @ByVal GpuMat svidx);
/** \brief Generates a grid for %SVM parameters.
@param param_id %SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is
generated for the parameter with this ID.
The function generates a grid for the specified parameter of the %SVM algorithm. The grid may be
passed to the function SVM::trainAuto.
*/
public static native @ByVal ParamGrid getDefaultGrid( int param_id );
/** \brief Generates a grid for %SVM parameters.
@param param_id %SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is
generated for the parameter with this ID.
The function generates a grid pointer for the specified parameter of the %SVM algorithm.
The grid may be passed to the function SVM::trainAuto.
*/
public static native @Ptr ParamGrid getDefaultGridPtr( int param_id );
/** Creates empty model.
Use StatModel::train to train the model. Since %SVM has several parameters, you may want to
find the best parameters for your problem, it can be done with SVM::trainAuto. */
public static native @Ptr SVM create();
/** \brief Loads and creates a serialized svm from a file
*
* Use SVM::save to serialize and store an SVM to disk.
* Load the SVM from this file again, by calling this function with the path to the file.
*
* @param filepath path to serialized svm
*/
public static native @Ptr SVM load(@Str BytePointer filepath);
public static native @Ptr SVM load(@Str String filepath);
}