Trait opencv::ml::SVMSGD[−][src]

pub trait SVMSGD: SVMSGDConst + StatModel {
    fn as_raw_mut_SVMSGD(&mut self) -> *mut c_void;

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

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

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

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

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

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

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

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

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

Required methods

Provided methods

Returns

the weights of the trained model (decision function f(x) = weights * x + shift).

Returns

the shift of the trained model (decision function f(x) = weights * x + shift).

Function sets optimal parameters values for chosen SVM SGD model.

Parameters
  • svmsgdType: is the type of SVMSGD classifier.
  • marginType: is the type of margin constraint.
C++ default parameters
  • svmsgd_type: SVMSGD::ASGD
  • margin_type: SVMSGD::SOFT_MARGIN

%Algorithm type, one of SVMSGD::SvmsgdType.

See also

setSvmsgdType getSvmsgdType

%Margin type, one of SVMSGD::MarginType.

See also

setMarginType getMarginType

Parameter marginRegularization of a %SVMSGD optimization problem.

See also

setMarginRegularization getMarginRegularization

Parameter initialStepSize of a %SVMSGD optimization problem.

See also

setInitialStepSize getInitialStepSize

Parameter stepDecreasingPower of a %SVMSGD optimization problem.

See also

setStepDecreasingPower getStepDecreasingPower

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).

See also

setTermCriteria getTermCriteria

Implementations

Creates empty model. Use StatModel::train to train the model. Since %SVMSGD has several parameters, you may want to find the best parameters for your problem or use setOptimalParameters() to set some default parameters.

Loads and creates a serialized SVMSGD from a file

Use SVMSGD::save to serialize and store an SVMSGD to disk. Load the SVMSGD from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier

Parameters
  • filepath: path to serialized SVMSGD
  • nodeName: name of node containing the classifier
C++ default parameters
  • node_name: String()

Implementors