Type Definition opencv::types::PtrOfLogisticRegression

pub type PtrOfLogisticRegression = Ptr<dyn LogisticRegression>;

Implementations

impl PtrOfLogisticRegression

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

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

Trait Implementations

impl AlgorithmTrait for PtrOfLogisticRegression

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

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

Clears the algorithm state

fn read(&mut self, fn_: &FileNode) -> Result<()>

Reads algorithm parameters from a file storage

impl AlgorithmTraitConst for PtrOfLogisticRegression

fn as_raw_Algorithm(&self) -> *const c_void

fn write(&self, fs: &mut FileStorage) -> Result<()>

Stores algorithm parameters in a file storage

fn write_with_name(&self, fs: &Ptr<FileStorage>, name: &str) -> Result<()>

simplified API for language bindings Stores algorithm parameters in a file storage

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_mut_LogisticRegression(&mut self) -> *mut c_void

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

Learning rate.

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

Number of iterations.

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

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

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

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

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.

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

Termination criteria of the algorithm.

impl LogisticRegressionConst for PtrOfLogisticRegression

fn as_raw_LogisticRegression(&self) -> *const c_void

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

Learning rate.

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

Number of iterations.

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

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

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

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

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.

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

Termination criteria of the algorithm.

fn predict(
    &self,
    samples: &dyn ToInputArray,
    results: &mut dyn ToOutputArray,
    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_mut_StatModel(&mut self) -> *mut c_void

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

Trains the statistical model

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

Trains the statistical model

impl StatModelConst for PtrOfLogisticRegression

fn as_raw_StatModel(&self) -> *const 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 calc_error(
    &self,
    data: &Ptr<dyn TrainData>,
    test: bool,
    resp: &mut dyn ToOutputArray
) -> Result<f32>

Computes error on the training or test dataset

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

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