Struct opencv::ml::NormalBayesClassifier

pub struct NormalBayesClassifier { /* private fields */ }

Bayes classifier for normally distributed data.

See also

[ml_intro_bayes]

Implementations

impl NormalBayesClassifier

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

Creates empty model Use StatModel::train to train the model after creation.

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

Loads and creates a serialized NormalBayesClassifier from a file

Use NormalBayesClassifier::save to serialize and store an NormalBayesClassifier to disk. Load the NormalBayesClassifier 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 NormalBayesClassifier
• nodeName: name of node containing the classifier

C++ default parameters

• node_name: String()

Trait Implementations

impl AlgorithmTrait for NormalBayesClassifier

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 NormalBayesClassifier

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 NormalBayesClassifier

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 Debug for NormalBayesClassifier

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

Formats the value using the given formatter.

impl Drop for NormalBayesClassifier

fn drop(&mut self)

Executes the destructor for this type.

impl From<NormalBayesClassifier> for Algorithm

fn from(s: NormalBayesClassifier) -> Self

Converts to this type from the input type.

impl From<NormalBayesClassifier> for StatModel

fn from(s: NormalBayesClassifier) -> Self

Converts to this type from the input type.

impl NormalBayesClassifierTrait for NormalBayesClassifier

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

impl NormalBayesClassifierTraitConst for NormalBayesClassifier

fn as_raw_NormalBayesClassifier(&self) -> *const c_void

fn predict_prob( &self, inputs: &impl ToInputArray, outputs: &mut impl ToOutputArray, output_probs: &mut impl ToOutputArray, flags: i32 ) -> Result<f32>

Predicts the response for sample(s).

impl StatModelTrait for NormalBayesClassifier

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 NormalBayesClassifier

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 TryFrom<StatModel> for NormalBayesClassifier

type Error = Error

The type returned in the event of a conversion error.

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

Performs the conversion.

impl Send for NormalBayesClassifier