Type Definition opencv::types::PtrOfKNearest

pub type PtrOfKNearest = Ptr<dyn KNearest>;

Implementations

impl PtrOfKNearest

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

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

Trait Implementations

impl AlgorithmTrait for PtrOfKNearest

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 PtrOfKNearest

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 KNearest for PtrOfKNearest

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

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

Default number of neighbors to use in predict method.

fn set_is_classifier(&mut self, val: bool) -> Result<()>

Whether classification or regression model should be trained.

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

Parameter for KDTree implementation.

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

%Algorithm type, one of KNearest::Types.

impl KNearestConst for PtrOfKNearest

fn as_raw_KNearest(&self) -> *const c_void

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

Default number of neighbors to use in predict method.

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

Whether classification or regression model should be trained.

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

Parameter for KDTree implementation.

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

%Algorithm type, one of KNearest::Types.

fn find_nearest(
    &self,
    samples: &dyn ToInputArray,
    k: i32,
    results: &mut dyn ToOutputArray,
    neighbor_responses: &mut dyn ToOutputArray,
    dist: &mut dyn ToOutputArray
) -> Result<f32>

Finds the neighbors and predicts responses for input vectors.

impl StatModel for PtrOfKNearest

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 PtrOfKNearest

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)