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
fn as_raw_mut_SVMSGD(&mut self) -> *mut c_void
Provided Methods
sourcefn get_weights(&mut self) -> Result<Mat>
fn get_weights(&mut self) -> Result<Mat>
Returns
the weights of the trained model (decision function f(x) = weights * x + shift).
sourcefn get_shift(&mut self) -> Result<f32>
fn get_shift(&mut self) -> Result<f32>
Returns
the shift of the trained model (decision function f(x) = weights * x + shift).
sourcefn set_optimal_parameters(
&mut self,
svmsgd_type: i32,
margin_type: i32
) -> Result<()>
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
sourcefn set_svmsgd_type(&mut self, svmsgd_type: i32) -> Result<()>
fn set_svmsgd_type(&mut self, svmsgd_type: i32) -> Result<()>
sourcefn set_margin_type(&mut self, margin_type: i32) -> Result<()>
fn set_margin_type(&mut self, margin_type: i32) -> Result<()>
sourcefn set_margin_regularization(&mut self, margin_regularization: f32) -> Result<()>
fn set_margin_regularization(&mut self, margin_regularization: f32) -> Result<()>
Parameter marginRegularization of a %SVMSGD optimization problem.
See also
setMarginRegularization getMarginRegularization
sourcefn set_initial_step_size(&mut self, initial_step_size: f32) -> Result<()>
fn set_initial_step_size(&mut self, initial_step_size: f32) -> Result<()>
Parameter initialStepSize of a %SVMSGD optimization problem.
See also
setInitialStepSize getInitialStepSize
sourcefn set_step_decreasing_power(&mut self, step_decreasing_power: f32) -> Result<()>
fn set_step_decreasing_power(&mut self, step_decreasing_power: f32) -> Result<()>
Parameter stepDecreasingPower of a %SVMSGD optimization problem.
See also
setStepDecreasingPower getStepDecreasingPower
sourcefn set_term_criteria(&mut self, val: TermCriteria) -> Result<()>
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
Implementations
sourceimpl dyn SVMSGD + '_
impl dyn SVMSGD + '_
sourcepub fn create() -> Result<Ptr<dyn SVMSGD>>
pub fn create() -> Result<Ptr<dyn SVMSGD>>
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.
sourcepub fn load(filepath: &str, node_name: &str) -> Result<Ptr<dyn SVMSGD>>
pub fn load(filepath: &str, node_name: &str) -> Result<Ptr<dyn SVMSGD>>
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()