classdef SVM < handle %SVM Support Vector Machines % % Originally, support vector machines (SVM) was a technique for building an % optimal binary (2-class) classifier. Later the technique was extended to % regression and clustering problems. SVM is a partial case of kernel-based % methods. It maps feature vectors into a higher-dimensional space using a % kernel function and builds an optimal linear discriminating function in % this space or an optimal hyper-plane that fits into the training data. In % case of SVM, the kernel is not defined explicitly. Instead, a distance % between any 2 points in the hyper-space needs to be defined. % % The solution is optimal, which means that the margin between the % separating hyper-plane and the nearest feature vectors from both classes % (in case of 2-class classifier) is maximal. The feature vectors that are % the closest to the hyper-plane are called support vectors, which means % that the position of other vectors does not affect the hyper-plane (the % decision function). % % SVM implementation in OpenCV is based on [LibSVM]. % http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf % % See also cv.SVM.SVM cv.SVM.train cv.SVM.predict cv.SVM.train_auto % properties (SetAccess = private) % Object ID id end properties (SetAccess = private, Dependent) % Object parameters Params % Variable count VarCount % Support vector count SupportVectorCount end methods function this = SVM(varargin) %SVM Creates a new SVM instance % % classifier = cv.SVM % classifier = cv.SVM(...) % % See also cv.SVM cv.SVM.train % this.id = SVM_(); if nargin>0, this.train(varargin{:}); end end function delete(this) %DELETE Destructor % % See also cv.SVM % SVM_(this.id, 'delete'); end function clear(this) %CLEAR Deallocates memory and resets the model state % % classifier.clear() % % The method clear does the same job as the destructor: it % deallocates all the memory occupied by the class members. But % the object itself is not destructed and can be reused % further. This method is called from the destructor, from the % train() methods of the derived classes, from the methods % load(), or even explicitly by the user. % % See also cv.SVM % SVM_(this.id, 'clear'); end function save(this, filename) %SAVE Saves the model to a file % % classifier.save(filename) % % See also cv.SVM % SVM_(this.id, 'save', filename); end function load(this, filename) %LOAD Loads the model from a file % % classifier.load(filename) % % See also cv.SVM % SVM_(this.id, 'load', filename); end function status = train(this, trainData, responses, varargin) %TRAIN Trains the model % % classifier.train(trainData, responses) % classifier.train(trainData, responses, 'OptionName', optionValue, ...) % % ## Input % * __trainData__ Row vectors of feature. % * __responses__ Output of the corresponding feature vectors. % % ## Options % * __VarIdx__ Indicator variables (features) of interest. % Must have the same size to responses. % * __SampleIdx__ Indicator samples of interest. Must have the % the same size to responses. % * __SvmType__ Type of a SVM formulation. Possible values are: % * 'C_SVC' C-Support Vector Classification. n-class % classification (n 2), allows imperfect separation % of classes with penalty multiplier C for outliers. % This is the default. % * 'NU_SVC' Nu-Support Vector Classification. n-class % classification with possible imperfect separation. % Parameter (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' 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 [LibSVM] for details. % * __KernelType__ Type of a SVM kernel. Possible values are: % * 'Linear' Linear kernel. No mapping is done, linear discrimination % (or regression) is done in the original feature space. % It is the fastest option. % * 'Poly' Polynomial kernel. % * 'RBF' Radial basis function (RBF), a good choice in most cases. % This is the default kernel. % * 'Sigmoid' Sigmoid kernel. % * __Degree__ Parameter degree of a kernel function (`POLY`). default 0. % * __Gamma__ Parameter of a kernel function (`POLY` / `RBF` / `SIGMOID`). % The default is 1. % * __Coef0__ Parameter coef0 of a kernel function (`POLY` / `SIGMOID`). % The default is 0. % * __C__ Parameter C of a SVM optimiazation problem (`C_SVC` / `EPS_SVR` / `NU_SVR`). % The default is 1. % * __Nu__ Parameter nu of a SVM optimization problem (`NU_SVC` / `ONE_CLASS` / `NU_SVR`). % The default is 0. % * __P__ Parameter p of a SVM optimization problem (`EPS_SVR`). % The default is 0. % * __ClassWeights__ Optional weights in the `C_SVC` problem, assigned % to particular classes. They are multiplied by C so the parameter % C of class #i becomes . 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 none. % * __TermCrit__ 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. A struct with the % following fields are accepted: % * 'type' one of {'Count','EPS','Count+EPS'} % * 'maxCount' maximum number of iterations % * 'epsilon' tolerance value % % The method trains the SVM model. % % See also cv.SVM cv.SVM.train_auto % status = SVM_(this.id, 'train', trainData, responses, varargin{:}); end function status = train_auto(this, trainData, responses, varargin) %TRAIN_AUTO Trains an SVM with optimal parameters % % classifier.train_auto(trainData, responses) % classifier.train_auto(trainData, responses, 'OptionName', optionValue, ...) % % ## Input % * __trainData__ Row vectors of feature. % * __responses__ Output of the corresponding feature vectors. % % ## Options % All the options in cv.SVM.train are supported. In addition: % * __KFold__ Cross-validation parameter. The training set is divided % into KFold subsets. One subset is used to train the model, % the others form the test set. So, the SVM algorithm is executed % KFold times. % * __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. % * __CGrid__, __GammaGrid__, __NuGrid__, __PGrid__, __CoeffGrid__, __DegreeGrid__ % Iteration grid for the corresponding SVM parameter. It accepts % a struct having {`min_val`, `max_val`, `log_step`} fields, or % a 3-element vector in which each parameter is specified in % the same order to the struct is supported. % % See also cv.SVM cv.SVM.train % status = SVM_(this.id, 'train_auto', trainData, responses, varargin{:}); end function results = predict(this, samples) %PREDICT Predicts the response for input samples. % % results = classifier.predict(samples) % % ## Options % * __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). % % The method estimates the most probable classes for input vectors. % Input vectors (one or more) are stored as rows of the matrix % samples. % % See also cv.SVM % results = SVM_(this.id, 'predict', samples); end function value = get.Params(this) %PARAMS value = SVM_(this.id, 'get_params'); end function value = get.VarCount(this) %VARCOUNT value = SVM_(this.id, 'get_var_count'); end function value = get.SupportVectorCount(this) %SUPPORTVECTORCOUNT value = SVM_(this.id, 'get_support_vector_count'); end end end