Trait opencv::ml::SVMSGDTrait

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pub trait SVMSGDTrait: SVMSGDTraitConst + StatModelTrait {
    // Required method
    fn as_raw_mut_SVMSGD(&mut self) -> *mut c_void;

    // Provided methods
    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<()> { ... }
}
Expand description

Mutable methods for crate::ml::SVMSGD

Required Methods§

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

Provided Methods§

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

Returns

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

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

Returns

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

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

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

%Algorithm type, one of SVMSGD::SvmsgdType.

See also

setSvmsgdType getSvmsgdType

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

%Margin type, one of SVMSGD::MarginType.

See also

setMarginType getMarginType

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

Parameter marginRegularization of a %SVMSGD optimization problem.

See also

setMarginRegularization getMarginRegularization

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

Parameter initialStepSize of a %SVMSGD optimization problem.

See also

setInitialStepSize getInitialStepSize

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

Parameter stepDecreasingPower of a %SVMSGD optimization problem.

See also

setStepDecreasingPower getStepDecreasingPower

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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).

See also

setTermCriteria getTermCriteria

Implementors§