Trait opencv::ml::prelude::LogisticRegression

pub trait LogisticRegression: LogisticRegressionConst + StatModel {
    fn as_raw_mut_LogisticRegression(&mut self) -> *mut c_void;

    fn set_learning_rate(&mut self, val: f64) -> Result<()> { ... }
    fn set_iterations(&mut self, val: i32) -> Result<()> { ... }
    fn set_regularization(&mut self, val: i32) -> Result<()> { ... }
    fn set_train_method(&mut self, val: i32) -> Result<()> { ... }
    fn set_mini_batch_size(&mut self, val: i32) -> Result<()> { ... }
    fn set_term_criteria(&mut self, val: TermCriteria) -> Result<()> { ... }
}

Required methods

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

Provided methods

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

Learning rate.

See also

setLearningRate getLearningRate

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

Number of iterations.

See also

setIterations getIterations

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

Kind of regularization to be applied. See LogisticRegression::RegKinds.

See also

setRegularization getRegularization

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

Kind of training method used. See LogisticRegression::Methods.

See also

setTrainMethod getTrainMethod

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

Specifies the number of training samples taken in each step of Mini-Batch Gradient Descent. Will only be used if using LogisticRegression::MINI_BATCH training algorithm. It has to take values less than the total number of training samples.

See also

setMiniBatchSize getMiniBatchSize

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

Termination criteria of the algorithm.

See also

setTermCriteria getTermCriteria

Implementations

impl dyn LogisticRegression + '_

pub fn create() -> Result<Ptr<dyn LogisticRegression>>

Creates empty model.

Creates Logistic Regression model with parameters given.

pub fn load(
    filepath: &str,
    node_name: &str
) -> Result<Ptr<dyn LogisticRegression>>

Loads and creates a serialized LogisticRegression from a file

Use LogisticRegression::save to serialize and store an LogisticRegression to disk. Load the LogisticRegression 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 LogisticRegression
• nodeName: name of node containing the classifier

C++ default parameters

• node_name: String()

Implementors

impl LogisticRegression for PtrOfLogisticRegression