class cv::ml::SVM
Overview
Support Vector Machines. More…
#include <ml.hpp> class SVM: public cv::ml::StatModel { public: // enums enum KernelTypes; enum ParamTypes; enum Types; // classes class Kernel; // methods virtual double getC() const = 0; virtual cv::Mat getClassWeights() const = 0; virtual double getCoef0() const = 0; virtual double getDecisionFunction( int i, OutputArray alpha, OutputArray svidx ) const = 0; virtual double getDegree() const = 0; virtual double getGamma() const = 0; virtual int getKernelType() const = 0; virtual double getNu() const = 0; virtual double getP() const = 0; virtual Mat getSupportVectors() const = 0; virtual cv::TermCriteria getTermCriteria() const = 0; virtual int getType() const = 0; Mat getUncompressedSupportVectors() const; virtual void setC(double val) = 0; virtual void setClassWeights(const cv::Mat& val) = 0; virtual void setCoef0(double val) = 0; virtual void setCustomKernel(const Ptr<Kernel>& _kernel) = 0; virtual void setDegree(double val) = 0; virtual void setGamma(double val) = 0; virtual void setKernel(int kernelType) = 0; virtual void setNu(double val) = 0; virtual void setP(double val) = 0; virtual void setTermCriteria(const cv::TermCriteria& val) = 0; virtual void setType(int val) = 0; virtual bool trainAuto( const Ptr<TrainData>& data, int kFold = 10, ParamGrid Cgrid = getDefaultGrid(C), ParamGrid gammaGrid = getDefaultGrid(GAMMA), ParamGrid pGrid = getDefaultGrid(P), ParamGrid nuGrid = getDefaultGrid(NU), ParamGrid coeffGrid = getDefaultGrid(COEF), ParamGrid degreeGrid = getDefaultGrid(DEGREE), bool balanced = false ) = 0; bool trainAuto( InputArray samples, int layout, InputArray responses, int kFold = 10, Ptr<ParamGrid> Cgrid = SVM::getDefaultGridPtr(SVM::C), Ptr<ParamGrid> gammaGrid = SVM::getDefaultGridPtr(SVM::GAMMA), Ptr<ParamGrid> pGrid = SVM::getDefaultGridPtr(SVM::P), Ptr<ParamGrid> nuGrid = SVM::getDefaultGridPtr(SVM::NU), Ptr<ParamGrid> coeffGrid = SVM::getDefaultGridPtr(SVM::COEF), Ptr<ParamGrid> degreeGrid = SVM::getDefaultGridPtr(SVM::DEGREE), bool balanced = false ); static Ptr<SVM> create(); static ParamGrid getDefaultGrid(int param_id); static Ptr<ParamGrid> getDefaultGridPtr(int param_id); static Ptr<SVM> load(const String& filepath); };
Inherited Members
public: // enums enum Flags; // methods virtual void clear(); virtual bool empty() const; virtual String getDefaultName() const; virtual void read(const FileNode& fn); virtual void save(const String& filename) const; virtual void write(FileStorage& fs) const; template <typename _Tp> static Ptr<_Tp> load( const String& filename, const String& objname = String() ); template <typename _Tp> static Ptr<_Tp> loadFromString( const String& strModel, const String& objname = String() ); template <typename _Tp> static Ptr<_Tp> read(const FileNode& fn); virtual float calcError( const Ptr<TrainData>& data, bool test, OutputArray resp ) const; virtual bool empty() const; virtual int getVarCount() const = 0; virtual bool isClassifier() const = 0; virtual bool isTrained() const = 0; virtual float predict( InputArray samples, OutputArray results = noArray(), int flags = 0 ) const = 0; virtual bool train( const Ptr<TrainData>& trainData, int flags = 0 ); virtual bool train( InputArray samples, int layout, InputArray responses ); template <typename _Tp> static Ptr<_Tp> train( const Ptr<TrainData>& data, int flags = 0 ); protected: // methods void writeFormat(FileStorage& fs) const;
Detailed Documentation
Support Vector Machines.
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Methods
virtual double getC() const = 0
Parameter C of a SVM optimization problem. For SVM::C_SVC, SVM::EPS_SVR or SVM::NU_SVR. Default value is 0.
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virtual cv::Mat getClassWeights() const = 0
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 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.
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virtual double getCoef0() const = 0
Parameter coef0 of a kernel function. For SVM::POLY or SVM::SIGMOID. Default value is 0.
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virtual double getDecisionFunction( int i, OutputArray alpha, OutputArray svidx ) const = 0
Retrieves the decision function.
The method returns rho parameter of the decision function, a scalar subtracted from the weighted sum of kernel responses.
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. |
virtual double getDegree() const = 0
Parameter degree of a kernel function. For SVM::POLY. Default value is 0.
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virtual double getGamma() const = 0
Parameter \(\gamma\) of a kernel function. For SVM::POLY, SVM::RBF, SVM::SIGMOID or SVM::CHI2. Default value is 1.
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virtual int getKernelType() const = 0
Type of a SVM kernel. See SVM::KernelTypes. Default value is SVM::RBF.
virtual double getNu() const = 0
Parameter \(\nu\) of a SVM optimization problem. For SVM::NU_SVC, SVM::ONE_CLASS or SVM::NU_SVR. Default value is 0.
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virtual double getP() const = 0
Parameter \(\epsilon\) of a SVM optimization problem. For SVM::EPS_SVR. Default value is 0.
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virtual Mat getSupportVectors() const = 0
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.
virtual cv::TermCriteria getTermCriteria() const = 0
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 TermCriteria( TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, FLT_EPSILON );
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virtual int getType() const = 0
Type of a SVM formulation. See SVM::Types. Default value is SVM::C_SVC.
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Mat getUncompressedSupportVectors() const
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.
virtual void setC(double val) = 0
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virtual void setClassWeights(const cv::Mat& val) = 0
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virtual void setCoef0(double val) = 0
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virtual void setCustomKernel(const Ptr<Kernel>& _kernel) = 0
Initialize with custom kernel. See SVM::Kernel class for implementation details
virtual void setDegree(double val) = 0
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virtual void setGamma(double val) = 0
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virtual void setKernel(int kernelType) = 0
Initialize with one of predefined kernels. See SVM::KernelTypes.
virtual void setNu(double val) = 0
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virtual void setP(double val) = 0
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virtual void setTermCriteria(const cv::TermCriteria& val) = 0
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virtual void setType(int val) = 0
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virtual bool trainAuto( const Ptr<TrainData>& data, int kFold = 10, ParamGrid Cgrid = getDefaultGrid(C), ParamGrid gammaGrid = getDefaultGrid(GAMMA), ParamGrid pGrid = getDefaultGrid(P), ParamGrid nuGrid = getDefaultGrid(NU), ParamGrid coeffGrid = getDefaultGrid(COEF), ParamGrid degreeGrid = getDefaultGrid(DEGREE), bool balanced = false ) = 0
Trains an SVM with optimal parameters.
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 gammaGrid.step = 0, gammaGrid.minVal, gamma_grid.maxVal as arbitrary numbers. In this case, the value 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 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.
Parameters:
| data | the training data that can be constructed using TrainData::create or TrainData::loadFromCSV. |
| 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. |
| 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. |
bool trainAuto( InputArray samples, int layout, InputArray responses, int kFold = 10, Ptr<ParamGrid> Cgrid = SVM::getDefaultGridPtr(SVM::C), Ptr<ParamGrid> gammaGrid = SVM::getDefaultGridPtr(SVM::GAMMA), Ptr<ParamGrid> pGrid = SVM::getDefaultGridPtr(SVM::P), Ptr<ParamGrid> nuGrid = SVM::getDefaultGridPtr(SVM::NU), Ptr<ParamGrid> coeffGrid = SVM::getDefaultGridPtr(SVM::COEF), Ptr<ParamGrid> degreeGrid = SVM::getDefaultGridPtr(SVM::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. 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.
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. |
static Ptr<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.
static ParamGrid getDefaultGrid(int param_id)
Generates a grid for SVM parameters.
The function generates a grid for the specified parameter of the SVM algorithm. The grid may be passed to the function SVM::trainAuto.
Parameters:
| param_id | SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is generated for the parameter with this ID. |
static Ptr<ParamGrid> getDefaultGridPtr(int param_id)
Generates a grid for SVM parameters.
The function generates a grid pointer for the specified parameter of the SVM algorithm. The grid may be passed to the function SVM::trainAuto.
Parameters:
| param_id | SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is generated for the parameter with this ID. |
static Ptr<SVM> load(const 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 |