Type Definition opencv::types::PtrOfSVMSGD

pub type PtrOfSVMSGD = Ptr<dyn SVMSGD>;

Implementations

impl PtrOfSVMSGD

pub fn as_raw_PtrOfSVMSGD(&self) -> *const c_void

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

Trait Implementations

impl AlgorithmTrait for PtrOfSVMSGD

fn as_raw_mut_Algorithm(&mut self) -> *mut c_void

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

Clears the algorithm state

fn read(&mut self, fn_: &FileNode) -> Result<()>

Reads algorithm parameters from a file storage

impl AlgorithmTraitConst for PtrOfSVMSGD

fn as_raw_Algorithm(&self) -> *const c_void

fn write(&self, fs: &mut FileStorage) -> Result<()>

Stores algorithm parameters in a file storage

fn write_with_name(&self, fs: &Ptr<FileStorage>, name: &str) -> Result<()>

simplified API for language bindings Stores algorithm parameters in a file storage

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 SVMSGD for PtrOfSVMSGD

fn as_raw_mut_SVMSGD(&mut self) -> *mut c_void

fn get_weights(&mut self) -> Result<Mat>

Returns

fn get_shift(&mut self) -> Result<f32>

Returns

fn set_optimal_parameters(
    &mut self,
    svmsgd_type: i32,
    margin_type: i32
) -> Result<()>

Function sets optimal parameters values for chosen SVM SGD model.

fn set_svmsgd_type(&mut self, svmsgd_type: i32) -> Result<()>

%Algorithm type, one of SVMSGD::SvmsgdType.

fn set_margin_type(&mut self, margin_type: i32) -> Result<()>

%Margin type, one of SVMSGD::MarginType.

fn set_margin_regularization(
    &mut self,
    margin_regularization: f32
) -> Result<()>

Parameter marginRegularization of a %SVMSGD optimization problem.

fn set_initial_step_size(&mut self, initial_step_size: f32) -> Result<()>

Parameter initialStepSize of a %SVMSGD optimization problem.

fn set_step_decreasing_power(
    &mut self,
    step_decreasing_power: f32
) -> Result<()>

Parameter stepDecreasingPower of a %SVMSGD optimization problem.

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

Termination criteria of the training algorithm. You can specify the maximum number of iterations (maxCount) and/or how much the error could change between the iterations to make the algorithm continue (epsilon).

impl SVMSGDConst for PtrOfSVMSGD

fn as_raw_SVMSGD(&self) -> *const c_void

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

%Algorithm type, one of SVMSGD::SvmsgdType.

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

%Margin type, one of SVMSGD::MarginType.

fn get_margin_regularization(&self) -> Result<f32>

Parameter marginRegularization of a %SVMSGD optimization problem.

fn get_initial_step_size(&self) -> Result<f32>

Parameter initialStepSize of a %SVMSGD optimization problem.

fn get_step_decreasing_power(&self) -> Result<f32>

Parameter stepDecreasingPower of a %SVMSGD optimization problem.

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

Termination criteria of the training algorithm. You can specify the maximum number of iterations (maxCount) and/or how much the error could change between the iterations to make the algorithm continue (epsilon).

impl StatModel for PtrOfSVMSGD

fn as_raw_mut_StatModel(&mut self) -> *mut c_void

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

Trains the statistical model

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

Trains the statistical model

impl StatModelConst for PtrOfSVMSGD

fn as_raw_StatModel(&self) -> *const 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 calc_error(
    &self,
    data: &Ptr<dyn TrainData>,
    test: bool,
    resp: &mut dyn ToOutputArray
) -> Result<f32>

Computes error on the training or test dataset

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

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