[−][src]Trait opencv::ml::KNearest

pub trait KNearest: StatModel {
    fn as_raw_KNearest(&self) -> *mut c_void;

    fn get_default_k(&self) -> Result<i32> { ... }

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

    fn get_is_classifier(&self) -> Result<bool> { ... }

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

    fn get_emax(&self) -> Result<i32> { ... }

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

    fn get_algorithm_type(&self) -> Result<i32> { ... }

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

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

The class implements K-Nearest Neighbors model

See also

@ref ml_intro_knn

Required methods

fn as_raw_KNearest(&self) -> *mut c_void

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Provided methods

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

@see setDefaultK

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

@copybrief getDefaultK @see getDefaultK

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

@see setIsClassifier

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

@copybrief getIsClassifier @see getIsClassifier

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

@see setEmax

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

@copybrief getEmax @see getEmax

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

@see setAlgorithmType

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

@copybrief getAlgorithmType @see getAlgorithmType

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.

Parameters

  • samples: Input samples stored by rows. It is a single-precision floating-point matrix of <number_of_samples> * k size.
  • k: Number of used nearest neighbors. Should be greater than 1.
  • results: Vector with results of prediction (regression or classification) for each input sample. It is a single-precision floating-point vector with <number_of_samples> elements.
  • neighborResponses: Optional output values for corresponding neighbors. It is a single- precision floating-point matrix of <number_of_samples> * k size.
  • dist: Optional output distances from the input vectors to the corresponding neighbors. It is a single-precision floating-point matrix of <number_of_samples> * k size.

For each input vector (a row of the matrix samples), the method finds the k nearest neighbors. In case of regression, the predicted result is a mean value of the particular vector's neighbor responses. In case of classification, the class is determined by voting.

For each input vector, the neighbors are sorted by their distances to the vector.

In case of C++ interface you can use output pointers to empty matrices and the function will allocate memory itself.

If only a single input vector is passed, all output matrices are optional and the predicted value is returned by the method.

The function is parallelized with the TBB library.

C++ default parameters

  • neighbor_responses: noArray()
  • dist: noArray()
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Methods

impl<'_> dyn KNearest + '_[src]

pub fn create() -> Result<PtrOfKNearest>[src]

Creates the empty model

The static method creates empty %KNearest classifier. It should be then trained using StatModel::train method.

pub fn load(filepath: &str) -> Result<PtrOfKNearest>[src]

Loads and creates a serialized knearest from a file

Use KNearest::save to serialize and store an KNearest to disk. Load the KNearest from this file again, by calling this function with the path to the file.

Parameters

  • filepath: path to serialized KNearest

Implementors

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