Struct opencv::ml::LogisticRegression

pub struct LogisticRegression { /* private fields */ }

Implements Logistic Regression classifier.

See also

[ml_intro_lr]

Implementations

impl LogisticRegression

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

Creates empty model.

Creates Logistic Regression model with parameters given.

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

Trait Implementations

impl AlgorithmTrait for LogisticRegression

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 LogisticRegression

fn as_raw_Algorithm(&self) -> *const c_void

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

Stores algorithm parameters in a file storage

fn write_1(&self, fs: &mut FileStorage, name: &str) -> Result<()>

Stores algorithm parameters in a file storage

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

@deprecated

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 Boxed for LogisticRegression

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

Wrap the specified raw pointer

fn into_raw(self) -> *mut c_void

Return an the underlying raw pointer while consuming this wrapper.

fn as_raw(&self) -> *const c_void

Return the underlying raw pointer.

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

Return the underlying mutable raw pointer

impl Drop for LogisticRegression

fn drop(&mut self)

Executes the destructor for this type.

impl From<LogisticRegression> for Algorithm

fn from(s: LogisticRegression) -> Self

Converts to this type from the input type.

impl LogisticRegressionTrait for LogisticRegression

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 LogisticRegressionTraitConst for LogisticRegression

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: &impl ToInputArray, results: &mut impl 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 StatModelTrait for LogisticRegression

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

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

Trains the statistical model

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

Trains the statistical model

impl StatModelTraitConst for LogisticRegression

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<TrainData>, test: bool, resp: &mut impl ToOutputArray ) -> Result<f32>

Computes error on the training or test dataset

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

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

impl Send for LogisticRegression