Struct LogisticRegression

Source
pub struct LogisticRegression { /* private fields */ }
Expand description

Implements Logistic Regression classifier.

§See also

[ml_intro_lr]

Implementations§

Source§

impl LogisticRegression

Source

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

Creates empty model.

Creates Logistic Regression model with parameters given.

Source

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()
Source

pub fn load_def(filepath: &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
§Note

This alternative version of LogisticRegression::load function uses the following default values for its arguments:

  • node_name: String()

Trait Implementations§

Source§

impl AlgorithmTrait for LogisticRegression

Source§

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

Source§

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

Clears the algorithm state
Source§

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

Reads algorithm parameters from a file storage
Source§

impl AlgorithmTraitConst for LogisticRegression

Source§

fn as_raw_Algorithm(&self) -> *const c_void

Source§

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

Stores algorithm parameters in a file storage
Source§

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

Stores algorithm parameters in a file storage Read more
Source§

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

@deprecated Read more
Source§

fn write_with_name_def(&self, fs: &Ptr<FileStorage>) -> Result<()>

👎Deprecated:

§Note

Deprecated: ## Note This alternative version of AlgorithmTraitConst::write_with_name function uses the following default values for its arguments: Read more
Source§

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

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

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).
Source§

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.
Source§

impl Boxed for LogisticRegression

Source§

unsafe fn from_raw( ptr: <LogisticRegression as OpenCVFromExtern>::ExternReceive, ) -> Self

Wrap the specified raw pointer Read more
Source§

fn into_raw( self, ) -> <LogisticRegression as OpenCVTypeExternContainer>::ExternSendMut

Return the underlying raw pointer while consuming this wrapper. Read more
Source§

fn as_raw( &self, ) -> <LogisticRegression as OpenCVTypeExternContainer>::ExternSend

Return the underlying raw pointer. Read more
Source§

fn as_raw_mut( &mut self, ) -> <LogisticRegression as OpenCVTypeExternContainer>::ExternSendMut

Return the underlying mutable raw pointer Read more
Source§

impl Debug for LogisticRegression

Source§

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter. Read more
Source§

impl Drop for LogisticRegression

Source§

fn drop(&mut self)

Executes the destructor for this type. Read more
Source§

impl From<LogisticRegression> for Algorithm

Source§

fn from(s: LogisticRegression) -> Self

Converts to this type from the input type.
Source§

impl From<LogisticRegression> for StatModel

Source§

fn from(s: LogisticRegression) -> Self

Converts to this type from the input type.
Source§

impl LogisticRegressionTrait for LogisticRegression

Source§

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

Source§

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

Learning rate. Read more
Source§

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

Number of iterations. Read more
Source§

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

Kind of regularization to be applied. See LogisticRegression::RegKinds. Read more
Source§

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

Kind of training method used. See LogisticRegression::Methods. Read more
Source§

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. Read more
Source§

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

Termination criteria of the algorithm. Read more
Source§

impl LogisticRegressionTraitConst for LogisticRegression

Source§

fn as_raw_LogisticRegression(&self) -> *const c_void

Source§

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

Learning rate. Read more
Source§

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

Number of iterations. Read more
Source§

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

Kind of regularization to be applied. See LogisticRegression::RegKinds. Read more
Source§

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

Kind of training method used. See LogisticRegression::Methods. Read more
Source§

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. Read more
Source§

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

Termination criteria of the algorithm. Read more
Source§

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

Predicts responses for input samples and returns a float type. Read more
Source§

fn predict_def(&self, samples: &impl ToInputArray) -> Result<f32>

Predicts responses for input samples and returns a float type. Read more
Source§

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

This function returns the trained parameters arranged across rows. Read more
Source§

impl StatModelTrait for LogisticRegression

Source§

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

Source§

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

Trains the statistical model Read more
Source§

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

Trains the statistical model Read more
Source§

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

Trains the statistical model Read more
Source§

impl StatModelTraitConst for LogisticRegression

Source§

fn as_raw_StatModel(&self) -> *const c_void

Source§

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

Returns the number of variables in training samples
Source§

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

Source§

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

Returns true if the model is trained
Source§

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

Returns true if the model is classifier
Source§

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

Computes error on the training or test dataset Read more
Source§

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

Predicts response(s) for the provided sample(s) Read more
Source§

fn predict_def(&self, samples: &impl ToInputArray) -> Result<f32>

Predicts response(s) for the provided sample(s) Read more
Source§

impl TryFrom<StatModel> for LogisticRegression

Source§

type Error = Error

The type returned in the event of a conversion error.
Source§

fn try_from(s: StatModel) -> Result<Self>

Performs the conversion.
Source§

impl Send for LogisticRegression

Auto Trait Implementations§

Blanket Implementations§

Source§

impl<T> Any for T
where T: 'static + ?Sized,

Source§

fn type_id(&self) -> TypeId

Gets the TypeId of self. Read more
Source§

impl<T> Borrow<T> for T
where T: ?Sized,

Source§

fn borrow(&self) -> &T

Immutably borrows from an owned value. Read more
Source§

impl<T> BorrowMut<T> for T
where T: ?Sized,

Source§

fn borrow_mut(&mut self) -> &mut T

Mutably borrows from an owned value. Read more
Source§

impl<T> From<T> for T

Source§

fn from(t: T) -> T

Returns the argument unchanged.

Source§

impl<T, U> Into<U> for T
where U: From<T>,

Source§

fn into(self) -> U

Calls U::from(self).

That is, this conversion is whatever the implementation of From<T> for U chooses to do.

Source§

impl<Mat> ModifyInplace for Mat
where Mat: Boxed,

Source§

unsafe fn modify_inplace<Res>( &mut self, f: impl FnOnce(&Mat, &mut Mat) -> Res, ) -> Res

Helper function to call OpenCV functions that allow in-place modification of a Mat or another similar object. By passing a mutable reference to the Mat to this function your closure will get called with the read reference and a write references to the same Mat. This is unsafe in a general case as it leads to having non-exclusive mutable access to the internal data, but it can be useful for some performance sensitive operations. One example of an OpenCV function that allows such in-place modification is imgproc::threshold. Read more
Source§

impl<T, U> TryFrom<U> for T
where U: Into<T>,

Source§

type Error = Infallible

The type returned in the event of a conversion error.
Source§

fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>

Performs the conversion.
Source§

impl<T, U> TryInto<U> for T
where U: TryFrom<T>,

Source§

type Error = <U as TryFrom<T>>::Error

The type returned in the event of a conversion error.
Source§

fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>

Performs the conversion.