Struct opencv::types::PtrOfSVM

pub struct PtrOfSVM { /* fields omitted */ }

Methods

impl PtrOfSVM

pub fn as_raw_PtrOfSVM(&self) -> *mut c_void

pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self

Trait Implementations

impl Algorithm for PtrOfSVM

fn as_raw_Algorithm(&self) -> *mut c_void

fn clear(&mut self) -> Result<()>

Clears the algorithm state

fn empty(&self) -> Result<bool>

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

fn save(&self, filename: &str) -> Result<()>

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

fn get_default_name(&self) -> Result<String>

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

impl SVM for PtrOfSVM

fn as_raw_SVM(&self) -> *mut c_void

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

@see setType

fn set_type(&mut self, val: i32) -> Result<()>

@copybrief getType @see getType

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

@see setGamma

fn set_gamma(&mut self, val: f64) -> Result<()>

@copybrief getGamma @see getGamma

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

@see setCoef0

fn set_coef0(&mut self, val: f64) -> Result<()>

@copybrief getCoef0 @see getCoef0

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

@see setDegree

fn set_degree(&mut self, val: f64) -> Result<()>

@copybrief getDegree @see getDegree

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

@see setC

fn set_c(&mut self, val: f64) -> Result<()>

@copybrief getC @see getC

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

@see setNu

fn set_nu(&mut self, val: f64) -> Result<()>

@copybrief getNu @see getNu

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

@see setP

fn set_p(&mut self, val: f64) -> Result<()>

@copybrief getP @see getP

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

@see setClassWeights

fn set_class_weights(&mut self, val: &Mat) -> Result<()>

@copybrief getClassWeights @see getClassWeights

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

@see setTermCriteria

fn set_term_criteria(&mut self, val: &TermCriteria) -> Result<()>

@copybrief getTermCriteria @see getTermCriteria

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

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

fn set_kernel(&mut self, kernel_type: i32) -> Result<()>

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

fn set_custom_kernel(&mut self, _kernel: &PtrOfKernel) -> Result<()>

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

fn train_auto(
    &mut self,
    data: &PtrOfTrainData,
    k_fold: i32,
    cgrid: &ParamGrid,
    gamma_grid: &ParamGrid,
    p_grid: &ParamGrid,
    nu_grid: &ParamGrid,
    coeff_grid: &ParamGrid,
    degree_grid: &ParamGrid,
    balanced: bool
) -> Result<bool>

Trains an %SVM with optimal parameters.

fn train_auto_1(
    &mut self,
    samples: &Mat,
    layout: i32,
    responses: &Mat,
    k_fold: i32,
    cgrid: &PtrOfParamGrid,
    gamma_grid: &PtrOfParamGrid,
    p_grid: &PtrOfParamGrid,
    nu_grid: &PtrOfParamGrid,
    coeff_grid: &PtrOfParamGrid,
    degree_grid: &PtrOfParamGrid,
    balanced: bool
) -> Result<bool>

Trains an %SVM with optimal parameters

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

Retrieves all the support vectors

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

Retrieves all the uncompressed support vectors of a linear %SVM

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

Retrieves the decision function

impl StatModel for PtrOfSVM

fn as_raw_StatModel(&self) -> *mut c_void

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

Returns the number of variables in training samples

fn empty(&self) -> Result<bool>

fn is_trained(&self) -> Result<bool>

Returns true if the model is trained

fn is_classifier(&self) -> Result<bool>

Returns true if the model is classifier

fn train_with_data(
    &mut self,
    train_data: &PtrOfTrainData,
    flags: i32
) -> Result<bool>

Trains the statistical model

fn train(&mut self, samples: &Mat, layout: i32, responses: &Mat) -> Result<bool>

Trains the statistical model

fn calc_error(
    &self,
    data: &PtrOfTrainData,
    test: bool,
    resp: &mut Mat
) -> Result<f32>

Computes error on the training or test dataset

fn predict(&self, samples: &Mat, results: &mut Mat, flags: i32) -> Result<f32>

Predicts response(s) for the provided sample(s)

impl Send for PtrOfSVM

impl Drop for PtrOfSVM

fn drop(&mut self)

Executes the destructor for this type.