Struct opencv::types::PtrOfLogisticRegression

pub struct PtrOfLogisticRegression { /* fields omitted */ }

Methods

impl PtrOfLogisticRegression

pub fn as_raw_PtrOfLogisticRegression(&self) -> *mut c_void

pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self

Trait Implementations

impl Algorithm for PtrOfLogisticRegression

fn as_raw_Algorithm(&self) -> *mut c_void

fn clear(&mut self) -> Result<()>

Clears the algorithm state

fn empty(&self) -> Result<bool>

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

fn save(&self, filename: &str) -> Result<()>

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

fn get_default_name(&self) -> Result<String>

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

impl LogisticRegression for PtrOfLogisticRegression

fn as_raw_LogisticRegression(&self) -> *mut c_void

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: &Mat, results: &mut Mat, flags: i32) -> Result<f32>

Predicts responses for input samples and returns a float type.

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

This function returns the trained parameters arranged across rows.

impl StatModel for PtrOfLogisticRegression

fn as_raw_StatModel(&self) -> *mut c_void

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

Returns the number of variables in training samples

fn empty(&self) -> Result<bool>

fn is_trained(&self) -> Result<bool>

Returns true if the model is trained

fn is_classifier(&self) -> Result<bool>

Returns true if the model is classifier

fn train_with_data(
    &mut self,
    train_data: &PtrOfTrainData,
    flags: i32
) -> Result<bool>

Trains the statistical model

fn train(&mut self, samples: &Mat, layout: i32, responses: &Mat) -> Result<bool>

Trains the statistical model

fn calc_error(
    &self,
    data: &PtrOfTrainData,
    test: bool,
    resp: &mut Mat
) -> Result<f32>

Computes error on the training or test dataset

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

Predicts response(s) for the provided sample(s)

impl Send for PtrOfLogisticRegression

impl Drop for PtrOfLogisticRegression

fn drop(&mut self)

Executes the destructor for this type.