[−][src]Trait opencv::ml::LogisticRegression

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

    fn get_learning_rate(&self) -> Result<f64> { ... }

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

    fn get_iterations(&self) -> Result<i32> { ... }

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

    fn get_regularization(&self) -> Result<i32> { ... }

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

    fn get_train_method(&self) -> Result<i32> { ... }

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

    fn get_mini_batch_size(&self) -> Result<i32> { ... }

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

    fn get_term_criteria(&self) -> Result<TermCriteria> { ... }

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

    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) -> *mut c_void

Loading content...

Provided methods

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

@see setLearningRate

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

@copybrief getLearningRate @see getLearningRate

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

@see setIterations

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

@copybrief getIterations @see getIterations

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

@see setRegularization

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

@copybrief getRegularization @see getRegularization

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

@see setTrainMethod

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

@copybrief getTrainMethod @see getTrainMethod

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

@see setMiniBatchSize

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

@copybrief getMiniBatchSize @see getMiniBatchSize

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

@see setTermCriteria

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

@copybrief getTermCriteria @see getTermCriteria

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 classifcation problem, it returns a row matrix. It returns learnt parameters of the Logistic Regression as a matrix of type CV_32F.

Loading content...

Methods

impl<'_> dyn LogisticRegression + '_[src]

pub fn create() -> Result<PtrOfLogisticRegression>[src]

Creates empty model.

Creates Logistic Regression model with parameters given.

pub fn load(filepath: &str, node_name: &str) -> Result<PtrOfLogisticRegression>[src]

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

Loading content...