Trait opencv::hub_prelude::LogisticRegressionConst

pub trait LogisticRegressionConst: StatModelConst {
    fn as_raw_LogisticRegression(&self) -> *const c_void;

    fn get_learning_rate(&self) -> Result<f64> { ... }
    fn get_iterations(&self) -> Result<i32> { ... }
    fn get_regularization(&self) -> Result<i32> { ... }
    fn get_train_method(&self) -> Result<i32> { ... }
    fn get_mini_batch_size(&self) -> Result<i32> { ... }
    fn get_term_criteria(&self) -> Result<TermCriteria> { ... }
    fn predict(
        &self,
        samples: &dyn ToInputArray,
        results: &mut dyn ToOutputArray,
        flags: i32
    ) -> Result<f32> { ... }
    fn get_learnt_thetas(&self) -> Result<Mat> { ... }
}

Implements Logistic Regression classifier.

See also

@ref ml_intro_lr

Required Methods

fn as_raw_LogisticRegression(&self) -> *const c_void

Provided Methods

fn get_learning_rate(&self) -> Result<f64>

Learning rate.

See also

setLearningRate

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

Number of iterations.

See also

setIterations

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

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

See also

setRegularization

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

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

See also

setTrainMethod

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

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

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

Termination criteria of the algorithm.

See also

setTermCriteria

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

Predicts responses for input samples and returns a float type.

Parameters

• samples: The input data for the prediction algorithm. Matrix [m x n], where each row contains variables (features) of one object being classified. Should have data type CV_32F.
• results: Predicted labels as a column matrix of type CV_32S.
• flags: Not used.

C++ default parameters

• results: noArray()
• flags: 0

fn get_learnt_thetas(&self) -> Result<Mat>

This function returns the trained parameters arranged across rows.

For a two class classification problem, it returns a row matrix. It returns learnt parameters of the Logistic Regression as a matrix of type CV_32F.

Implementors

impl LogisticRegressionConst for PtrOfLogisticRegression