Trait opencv::ml::prelude::SVMConst

pub trait SVMConst: StatModelConst {
    fn as_raw_SVM(&self) -> *const c_void;

    fn get_type(&self) -> Result<i32> { ... }
    fn get_gamma(&self) -> Result<f64> { ... }
    fn get_coef0(&self) -> Result<f64> { ... }
    fn get_degree(&self) -> Result<f64> { ... }
    fn get_c(&self) -> Result<f64> { ... }
    fn get_nu(&self) -> Result<f64> { ... }
    fn get_p(&self) -> Result<f64> { ... }
    fn get_class_weights(&self) -> Result<Mat> { ... }
    fn get_term_criteria(&self) -> Result<TermCriteria> { ... }
    fn get_kernel_type(&self) -> Result<i32> { ... }
    fn get_support_vectors(&self) -> Result<Mat> { ... }
    fn get_uncompressed_support_vectors(&self) -> Result<Mat> { ... }
    fn get_decision_function(
        &self, 
        i: i32, 
        alpha: &mut dyn ToOutputArray, 
        svidx: &mut dyn ToOutputArray
    ) -> Result<f64> { ... }
}

Support Vector Machines.

See also

@ref ml_intro_svm

Required methods

fn as_raw_SVM(&self) -> *const c_void

Provided methods

fn get_type(&self) -> Result<i32>

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

See also

setType

fn get_gamma(&self) -> Result<f64>

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

See also

setGamma

fn get_coef0(&self) -> Result<f64>

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

See also

setCoef0

fn get_degree(&self) -> Result<f64>

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

See also

setDegree

fn get_c(&self) -> Result<f64>

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

fn get_nu(&self) -> Result<f64>

Parameter of a %SVM optimization problem. For SVM::NU_SVC, SVM::ONE_CLASS or SVM::NU_SVR. Default value is 0.

See also

setNu

fn get_p(&self) -> Result<f64>

Parameter of a %SVM optimization problem. For SVM::EPS_SVR. Default value is 0.

See also

setP

fn get_class_weights(&self) -> Result<Mat>

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

fn get_term_criteria(&self) -> Result<TermCriteria>

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

fn get_kernel_type(&self) -> Result<i32>

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

fn get_support_vectors(&self) -> Result<Mat>

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.

fn get_uncompressed_support_vectors(&self) -> Result<Mat>

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.

fn get_decision_function(
    &self,
    i: i32,
    alpha: &mut dyn ToOutputArray,
    svidx: &mut dyn ToOutputArray
) -> Result<f64>

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 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.

Implementors

impl SVMConst for PtrOfSVM