| OpenCV 3.4.8 Open Source Computer Vision

Classes | Public Types | Public Member Functions | Static Public Member Functions | List of all members
cv::ml::SVM Class Referenceabstract
Machine Learning

Support Vector Machines. More...

`#include <opencv2/ml.hpp>`

Inheritance diagram for cv::ml::SVM:

## Classes
class | Kernel

## Public Types
enum | KernelTypes { CUSTOM =-1, LINEAR =0, POLY =1, RBF =2, SIGMOID =3, CHI2 =4, INTER =5 }
| SVM kernel type More...
enum | ParamTypes { C =0, GAMMA =1, P =2, NU =3, COEF =4, DEGREE =5 }
| SVM params type More...
enum | Types { C_SVC =100, NU_SVC =101, ONE_CLASS =102, EPS_SVR =103, NU_SVR =104 }
| SVM type More...
Public Types inherited from cv::ml::StatModel
enum | Flags { UPDATE_MODEL = 1, RAW_OUTPUT =1, COMPRESSED_INPUT =2, PREPROCESSED_INPUT =4 }

## Public Member Functions
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
| Retrieves the decision function. More...
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
| Retrieves all the support vectors. More...
virtual cv::TermCriteria | getTermCriteria () const =0
virtual int | getType () const =0
Mat | getUncompressedSupportVectors () const
| Retrieves all the uncompressed support vectors of a linear SVM. More...
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
| Trains an SVM with optimal parameters. More...
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. More...
Public Member Functions inherited from cv::ml::StatModel
virtual float | calcError (const Ptr< TrainData > &data, bool test, OutputArray resp) const
| Computes error on the training or test dataset. More...
virtual bool | empty () const CV_OVERRIDE
| Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More...
virtual int | getVarCount () const =0
| Returns the number of variables in training samples. More...
virtual bool | isClassifier () const =0
| Returns true if the model is classifier. More...
virtual bool | isTrained () const =0
| Returns true if the model is trained. More...
virtual float | predict (InputArray samples, OutputArray results=noArray(), int flags=0) const =0
| Predicts response(s) for the provided sample(s) More...
virtual bool | train (const Ptr< TrainData > &trainData, int flags=0)
| Trains the statistical model. More...
virtual bool | train (InputArray samples, int layout, InputArray responses)
| Trains the statistical model. More...
Public Member Functions inherited from cv::Algorithm
| Algorithm ()
virtual | ~Algorithm ()
virtual void | clear ()
| Clears the algorithm state. More...
virtual String | getDefaultName () const
virtual void | read (const FileNode &fn)
| Reads algorithm parameters from a file storage. More...
virtual void | save (const String &filename) const
virtual void | write (FileStorage &fs) const
| Stores algorithm parameters in a file storage. More...
void | write (const Ptr< FileStorage > &fs, const String &name=String()) const
| simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. More...

## Static Public Member Functions
static Ptr< SVM > | create ()
static ParamGrid | getDefaultGrid (int param_id)
| Generates a grid for SVM parameters. More...
static Ptr< ParamGrid > | getDefaultGridPtr (int param_id)
| Generates a grid for SVM parameters. More...
static Ptr< SVM > | load (const String &filepath)
| Loads and creates a serialized svm from a file. More...
Static Public Member Functions inherited from cv::ml::StatModel
template<typename _Tp >
static Ptr< _Tp > | train (const Ptr< TrainData > &data, int flags=0)
| Create and train model with default parameters. More...
Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tp > | load (const String &filename, const String &objname=String())
| Loads algorithm from the file. More...
template<typename _Tp >
static Ptr< _Tp > | loadFromString (const String &strModel, const String &objname=String())
| Loads algorithm from a String. More...
template<typename _Tp >
static Ptr< _Tp > | read (const FileNode &fn)
| Reads algorithm from the file node. More...

## Additional Inherited Members
Protected Member Functions inherited from cv::Algorithm
void | writeFormat (FileStorage &fs) const

## Detailed Description

Support Vector Machines.

See also
  Support Vector Machines

## Member Enumeration Documentation

## § KernelTypes

enum cv::ml::SVM::KernelTypes

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.

image

Enumerator
CUSTOM | Returned by SVM::getKernelType in case when custom kernel has been set
LINEAR | Linear kernel. No mapping is done, linear discrimination (or regression) is done in the original feature space. It is the fastest option. \(K(x_i, x_j) = x_i^T x_j\).
POLY | Polynomial kernel: \(K(x_i, x_j) = (\gamma x_i^T x_j + coef0)^{degree}, \gamma > 0\).
RBF | Radial basis function (RBF), a good choice in most cases. \(K(x_i, x_j) = e^{-\gamma ||x_i - x_j||^2}, \gamma > 0\).
SIGMOID | Sigmoid kernel: \(K(x_i, x_j) = \tanh(\gamma x_i^T x_j + coef0)\).
CHI2 | Exponential Chi2 kernel, similar to the RBF kernel: \(K(x_i, x_j) = e^{-\gamma \chi^2(x_i,x_j)}, \chi^2(x_i,x_j) = (x_i-x_j)^2/(x_i+x_j), \gamma > 0\).
INTER | Histogram intersection kernel. A fast kernel. \(K(x_i, x_j) = min(x_i,x_j)\).

## § ParamTypes

enum cv::ml::SVM::ParamTypes

SVM params type

Enumerator
C |
GAMMA |
P |
NU |
COEF |
DEGREE |

## § Types

enum cv::ml::SVM::Types

SVM type

Enumerator
C_SVC | C-Support Vector Classification. n-class classification (n \(\geq\) 2), allows imperfect separation of classes with penalty multiplier C for outliers.
NU_SVC | \(\nu\)-Support Vector Classification. n-class classification with possible imperfect separation. Parameter \(\nu\) (in the range 0..1, the larger the value, the smoother the decision boundary) is used instead of C.
ONE_CLASS | 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.
EPS_SVR | \(\epsilon\)-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.
NU_SVR | \(\nu\)-Support Vector Regression. \(\nu\) is used instead of p. See [35] for details.

## Member Function Documentation

## § create()

static Ptr<SVM> cv::ml::SVM::create | ( |  | ) |
| static

Python:
| retval | = | cv.ml.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.

Examples:
  samples/cpp/train_HOG.cpp.

## § getC()

virtual double cv::ml::SVM::getC | ( |  | ) | const
| pure virtual

Python:
| retval | = | cv.ml_SVM.getC( |  | )

Parameter C of a SVM optimization problem. For SVM::C_SVC, SVM::EPS_SVR or SVM::NU_SVR. Default value is 0.

See also
  setC

## § getClassWeights()

virtual cv::Mat cv::ml::SVM::getClassWeights | ( |  | ) | const
| pure virtual

Python:
| retval | = | cv.ml_SVM.getClassWeights( |  | )

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.

See also
  setClassWeights

## § getCoef0()

virtual double cv::ml::SVM::getCoef0 | ( |  | ) | const
| pure virtual

Python:
| retval | = | cv.ml_SVM.getCoef0( |  | )

Parameter coef0 of a kernel function. For SVM::POLY or SVM::SIGMOID. Default value is 0.

See also
  setCoef0

## § getDecisionFunction()

virtual double cv::ml::SVM::getDecisionFunction | ( | int | i,
|  | OutputArray | alpha,
|  | OutputArray | svidx
| ) |  | const
| pure virtual

Python:
| retval, alpha, svidx | = | cv.ml_SVM.getDecisionFunction( | i[, alpha[, 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.

Examples:
  samples/cpp/train_HOG.cpp.

## § getDefaultGrid()

static ParamGrid cv::ml::SVM::getDefaultGrid | ( | int | param_id | ) |
| static

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 for the specified parameter of the SVM algorithm. The grid may be passed to the function SVM::trainAuto.

## § getDefaultGridPtr()

static Ptr<ParamGrid> cv::ml::SVM::getDefaultGridPtr | ( | int | param_id | ) |
| static

Python:
| retval | = | cv.ml.SVM_getDefaultGridPtr( | 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.

## § getDegree()

virtual double cv::ml::SVM::getDegree | ( |  | ) | const
| pure virtual

Python:
| retval | = | cv.ml_SVM.getDegree( |  | )

Parameter degree of a kernel function. For SVM::POLY. Default value is 0.

See also
  setDegree

## § getGamma()

virtual double cv::ml::SVM::getGamma | ( |  | ) | const
| pure virtual

Python:
| retval | = | cv.ml_SVM.getGamma( |  | )

Parameter \(\gamma\) of a kernel function. For SVM::POLY, SVM::RBF, SVM::SIGMOID or SVM::CHI2. Default value is 1.

See also
  setGamma

## § getKernelType()

virtual int cv::ml::SVM::getKernelType | ( |  | ) | const
| pure virtual

Python:
| retval | = | cv.ml_SVM.getKernelType( |  | )

Type of a SVM kernel. See SVM::KernelTypes. Default value is SVM::RBF.

## § getNu()

virtual double cv::ml::SVM::getNu | ( |  | ) | const
| pure virtual

Python:
| retval | = | cv.ml_SVM.getNu( |  | )

Parameter \(\nu\) of a SVM optimization problem. For SVM::NU_SVC, SVM::ONE_CLASS or SVM::NU_SVR. Default value is 0.

See also
  setNu

## § getP()

virtual double cv::ml::SVM::getP | ( |  | ) | const
| pure virtual

Python:
| retval | = | cv.ml_SVM.getP( |  | )

Parameter \(\epsilon\) of a SVM optimization problem. For SVM::EPS_SVR. Default value is 0.

See also
  setP

## § getSupportVectors()

virtual Mat cv::ml::SVM::getSupportVectors | ( |  | ) | const
| pure virtual

Python:
| retval | = | cv.ml_SVM.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.

Examples:
  samples/cpp/train_HOG.cpp.

## § getTermCriteria()

virtual cv::TermCriteria cv::ml::SVM::getTermCriteria | ( |  | ) | const
| pure virtual

Python:
| retval | = | cv.ml_SVM.getTermCriteria( |  | )

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 )`;

See also
  setTermCriteria

## § getType()

virtual int cv::ml::SVM::getType | ( |  | ) | const
| pure virtual

Python:
| retval | = | cv.ml_SVM.getType( |  | )

Type of a SVM formulation. See SVM::Types. Default value is SVM::C_SVC.

See also
  setType

## § getUncompressedSupportVectors()

Mat cv::ml::SVM::getUncompressedSupportVectors | ( |  | ) | const

Python:
| retval | = | cv.ml_SVM.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.

## § load()

static Ptr<SVM> cv::ml::SVM::load | ( | const String & | filepath | ) |
| static

Python:
| retval | = | cv.ml.SVM_load( | 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

## § setC()

virtual void cv::ml::SVM::setC | ( | double | val | ) |
| pure virtual

Python:
| None | = | cv.ml_SVM.setC( | val | )

See also
  getC

Examples:
  samples/cpp/train_HOG.cpp.

## § setClassWeights()

virtual void cv::ml::SVM::setClassWeights | ( | const cv::Mat & | val | ) |
| pure virtual

Python:
| None | = | cv.ml_SVM.setClassWeights( | val | )

See also
  getClassWeights

## § setCoef0()

virtual void cv::ml::SVM::setCoef0 | ( | double | val | ) |
| pure virtual

Python:
| None | = | cv.ml_SVM.setCoef0( | val | )

See also
  getCoef0

Examples:
  samples/cpp/train_HOG.cpp.

## § setCustomKernel()

virtual void cv::ml::SVM::setCustomKernel | ( | const Ptr< Kernel > & | _kernel | ) |
| pure virtual

Initialize with custom kernel. See SVM::Kernel class for implementation details

## § setDegree()

virtual void cv::ml::SVM::setDegree | ( | double | val | ) |
| pure virtual

Python:
| None | = | cv.ml_SVM.setDegree( | val | )

See also
  getDegree

Examples:
  samples/cpp/train_HOG.cpp.

## § setGamma()

virtual void cv::ml::SVM::setGamma | ( | double | val | ) |
| pure virtual

Python:
| None | = | cv.ml_SVM.setGamma( | val | )

See also
  getGamma

Examples:
  samples/cpp/train_HOG.cpp.

## § setKernel()

virtual void cv::ml::SVM::setKernel | ( | int | kernelType | ) |
| pure virtual

Python:
| None | = | cv.ml_SVM.setKernel( | kernelType | )

Initialize with one of predefined kernels. See SVM::KernelTypes.

Examples:
  samples/cpp/train_HOG.cpp.

## § setNu()

virtual void cv::ml::SVM::setNu | ( | double | val | ) |
| pure virtual

Python:
| None | = | cv.ml_SVM.setNu( | val | )

See also
  getNu

Examples:
  samples/cpp/train_HOG.cpp.

## § setP()

virtual void cv::ml::SVM::setP | ( | double | val | ) |
| pure virtual

Python:
| None | = | cv.ml_SVM.setP( | val | )

See also
  getP

Examples:
  samples/cpp/train_HOG.cpp.

## § setTermCriteria()

virtual void cv::ml::SVM::setTermCriteria | ( | const cv::TermCriteria & | val | ) |
| pure virtual

Python:
| None | = | cv.ml_SVM.setTermCriteria( | val | )

See also
  getTermCriteria

Examples:
  samples/cpp/train_HOG.cpp.

## § setType()

virtual void cv::ml::SVM::setType | ( | int | val | ) |
| pure virtual

Python:
| None | = | cv.ml_SVM.setType( | val | )

See also
  getType

Examples:
  samples/cpp/train_HOG.cpp.

## § trainAuto() [1/2]

virtual bool cv::ml::SVM::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`
| ) |  |
| pure virtual

Python:
| retval | = | cv.ml_SVM.trainAuto( | samples, layout, responses[, kFold[, Cgrid[, gammaGrid[, pGrid[, nuGrid[, coeffGrid[, degreeGrid[, balanced]]]]]]]] | )

Trains an SVM with optimal parameters.

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.

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.

## § trainAuto() [2/2]

bool cv::ml::SVM::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`
| ) |  |

Python:
| retval | = | cv.ml_SVM.trainAuto( | samples, layout, responses[, kFold[, Cgrid[, gammaGrid[, pGrid[, nuGrid[, coeffGrid[, degreeGrid[, 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.

The documentation for this class was generated from the following file:

- opencv2/ml.hpp