Package org.opencv.ml

Class SVM

java.lang.Object
org.opencv.core.Algorithm
org.opencv.ml.StatModel
org.opencv.ml.SVM
public class SVM
extends StatModel
Support Vector Machines. SEE: REF: ml_intro_svm

  • Field Details

  • Constructor Details

    • SVM

      protected SVM​(long addr)

  • Method Details

    • __fromPtr__

      public static SVM __fromPtr__​(long addr)

    • getType

      public int getType()
      SEE: setType
      Returns:
      automatically generated

    • setType

      public void setType​(int val)
      getType SEE: getType
      Parameters:
      val - automatically generated

    • getGamma

      public double getGamma()
      SEE: setGamma
      Returns:
      automatically generated

    • setGamma

      public void setGamma​(double val)
      getGamma SEE: getGamma
      Parameters:
      val - automatically generated

    • getCoef0

      public double getCoef0()
      SEE: setCoef0
      Returns:
      automatically generated

    • setCoef0

      public void setCoef0​(double val)
      getCoef0 SEE: getCoef0
      Parameters:
      val - automatically generated

    • getDegree

      public double getDegree()
      SEE: setDegree
      Returns:
      automatically generated

    • setDegree

      public void setDegree​(double val)
      getDegree SEE: getDegree
      Parameters:
      val - automatically generated

    • getC

      public double getC()
      SEE: setC
      Returns:
      automatically generated

    • setC

      public void setC​(double val)
      getC SEE: getC
      Parameters:
      val - automatically generated

    • getNu

      public double getNu()
      SEE: setNu
      Returns:
      automatically generated

    • setNu

      public void setNu​(double val)
      getNu SEE: getNu
      Parameters:
      val - automatically generated

    • getP

      public double getP()
      SEE: setP
      Returns:
      automatically generated

    • setP

      public void setP​(double val)
      getP SEE: getP
      Parameters:
      val - automatically generated

    • getClassWeights

      public Mat getClassWeights()
      SEE: setClassWeights
      Returns:
      automatically generated

    • setClassWeights

      public void setClassWeights​(Mat val)
      getClassWeights SEE: getClassWeights
      Parameters:
      val - automatically generated

    • getTermCriteria

      public TermCriteria getTermCriteria()
      SEE: setTermCriteria
      Returns:
      automatically generated

    • setTermCriteria

      public void setTermCriteria​(TermCriteria val)
      getTermCriteria SEE: getTermCriteria
      Parameters:
      val - automatically generated

    • getKernelType

      public int getKernelType()
      Type of a %SVM kernel. See SVM::KernelTypes. Default value is SVM::RBF.
      Returns:
      automatically generated

    • setKernel

      public void setKernel​(int kernelType)
      Initialize with one of predefined kernels. See SVM::KernelTypes.
      Parameters:
      kernelType - automatically generated

    • trainAuto

      public boolean trainAuto​(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid, ParamGrid coeffGrid, ParamGrid degreeGrid, boolean balanced)
      Trains an %SVM with optimal parameters
      Parameters:
      samples - training samples
      layout - See ml::SampleTypes.
      responses - vector of responses associated with the training samples.
      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
      Cgrid - grid for C
      gammaGrid - grid for gamma
      pGrid - grid for p
      nuGrid - grid for nu
      coeffGrid - grid for coeff
      degreeGrid - grid for degree
      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.
      Returns:
      automatically generated

    • trainAuto

      public boolean trainAuto​(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid, ParamGrid coeffGrid, ParamGrid degreeGrid)
      Trains an %SVM with optimal parameters
      Parameters:
      samples - training samples
      layout - See ml::SampleTypes.
      responses - vector of responses associated with the training samples.
      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
      Cgrid - grid for C
      gammaGrid - grid for gamma
      pGrid - grid for p
      nuGrid - grid for nu
      coeffGrid - grid for coeff
      degreeGrid - grid for degree 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.
      Returns:
      automatically generated

    • trainAuto

      public boolean trainAuto​(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid, ParamGrid coeffGrid)
      Trains an %SVM with optimal parameters
      Parameters:
      samples - training samples
      layout - See ml::SampleTypes.
      responses - vector of responses associated with the training samples.
      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
      Cgrid - grid for C
      gammaGrid - grid for gamma
      pGrid - grid for p
      nuGrid - grid for nu
      coeffGrid - grid for coeff 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.
      Returns:
      automatically generated

    • trainAuto

      public boolean trainAuto​(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid)
      Trains an %SVM with optimal parameters
      Parameters:
      samples - training samples
      layout - See ml::SampleTypes.
      responses - vector of responses associated with the training samples.
      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
      Cgrid - grid for C
      gammaGrid - grid for gamma
      pGrid - grid for p
      nuGrid - grid for nu 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.
      Returns:
      automatically generated

    • trainAuto

      public boolean trainAuto​(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid)
      Trains an %SVM with optimal parameters
      Parameters:
      samples - training samples
      layout - See ml::SampleTypes.
      responses - vector of responses associated with the training samples.
      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
      Cgrid - grid for C
      gammaGrid - grid for gamma
      pGrid - grid for p 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.
      Returns:
      automatically generated

    • trainAuto

      public boolean trainAuto​(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid)
      Trains an %SVM with optimal parameters
      Parameters:
      samples - training samples
      layout - See ml::SampleTypes.
      responses - vector of responses associated with the training samples.
      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
      Cgrid - grid for C
      gammaGrid - grid for gamma 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.
      Returns:
      automatically generated

    • trainAuto

      public boolean trainAuto​(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid)
      Trains an %SVM with optimal parameters
      Parameters:
      samples - training samples
      layout - See ml::SampleTypes.
      responses - vector of responses associated with the training samples.
      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
      Cgrid - grid for C 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.
      Returns:
      automatically generated

    • trainAuto

      public boolean trainAuto​(Mat samples, int layout, Mat responses, int kFold)
      Trains an %SVM with optimal parameters
      Parameters:
      samples - training samples
      layout - See ml::SampleTypes.
      responses - vector of responses associated with the training samples.
      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 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.
      Returns:
      automatically generated

    • trainAuto

      public boolean trainAuto​(Mat samples, int layout, Mat responses)
      Trains an %SVM with optimal parameters
      Parameters:
      samples - training samples
      layout - See ml::SampleTypes.
      responses - vector of responses associated with the training samples. subset is used to test the model, the others form the train set. So, the %SVM algorithm is 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.
      Returns:
      automatically generated

    • getSupportVectors

      public Mat getSupportVectors()
      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.
      Returns:
      automatically generated

    • getUncompressedSupportVectors

      public Mat getUncompressedSupportVectors()
      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.
      Returns:
      automatically generated

    • getDecisionFunction

      public double getDecisionFunction​(int i, Mat alpha, Mat svidx)
      Retrieves the decision function
      Parameters:
      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 \(N(N-1)/2\) decision functions.
      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.
      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.
      Returns:
      automatically generated

    • getDefaultGridPtr

      public static ParamGrid getDefaultGridPtr​(int param_id)
      Generates a grid for %SVM parameters.
      Parameters:
      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.
      Returns:
      automatically generated

    • create

      public static SVM create()
      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.
      Returns:
      automatically generated

    • load

      public static SVM load​(String filepath)
      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.
      Parameters:
      filepath - path to serialized svm
      Returns:
      automatically generated

    • finalize

      protected void finalize() throws Throwable
      Overrides:
      finalize in class StatModel
      Throws:
      Throwable