Type Definition opencv::types::PtrOfEM

pub type PtrOfEM = Ptr<dyn EM>;

Implementations

impl PtrOfEM

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

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

Trait Implementations

impl AlgorithmTrait for PtrOfEM

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 PtrOfEM

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 EM for PtrOfEM

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

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

The number of mixture components in the Gaussian mixture model. Default value of the parameter is EM::DEFAULT_NCLUSTERS=5. Some of %EM implementation could determine the optimal number of mixtures within a specified value range, but that is not the case in ML yet.

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

Constraint on covariance matrices which defines type of matrices. See EM::Types.

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

The termination criteria of the %EM algorithm. The %EM algorithm can be terminated by the number of iterations termCrit.maxCount (number of M-steps) or when relative change of likelihood logarithm is less than termCrit.epsilon. Default maximum number of iterations is EM::DEFAULT_MAX_ITERS=100.

fn train_em(
    &mut self,
    samples: &dyn ToInputArray,
    log_likelihoods: &mut dyn ToOutputArray,
    labels: &mut dyn ToOutputArray,
    probs: &mut dyn ToOutputArray
) -> Result<bool>

Estimate the Gaussian mixture parameters from a samples set.

fn train_e(
    &mut self,
    samples: &dyn ToInputArray,
    means0: &dyn ToInputArray,
    covs0: &dyn ToInputArray,
    weights0: &dyn ToInputArray,
    log_likelihoods: &mut dyn ToOutputArray,
    labels: &mut dyn ToOutputArray,
    probs: &mut dyn ToOutputArray
) -> Result<bool>

Estimate the Gaussian mixture parameters from a samples set.

fn train_m(
    &mut self,
    samples: &dyn ToInputArray,
    probs0: &dyn ToInputArray,
    log_likelihoods: &mut dyn ToOutputArray,
    labels: &mut dyn ToOutputArray,
    probs: &mut dyn ToOutputArray
) -> Result<bool>

Estimate the Gaussian mixture parameters from a samples set.

impl EMConst for PtrOfEM

fn as_raw_EM(&self) -> *const c_void

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

The number of mixture components in the Gaussian mixture model. Default value of the parameter is EM::DEFAULT_NCLUSTERS=5. Some of %EM implementation could determine the optimal number of mixtures within a specified value range, but that is not the case in ML yet.

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

Constraint on covariance matrices which defines type of matrices. See EM::Types.

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

The termination criteria of the %EM algorithm. The %EM algorithm can be terminated by the number of iterations termCrit.maxCount (number of M-steps) or when relative change of likelihood logarithm is less than termCrit.epsilon. Default maximum number of iterations is EM::DEFAULT_MAX_ITERS=100.

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

Returns weights of the mixtures

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

Returns the cluster centers (means of the Gaussian mixture)

fn get_covs(&self, covs: &mut Vector<Mat>) -> Result<()>

Returns covariation matrices

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

Returns posterior probabilities for the provided samples

fn predict2(
    &self,
    sample: &dyn ToInputArray,
    probs: &mut dyn ToOutputArray
) -> Result<Vec2d>

Returns a likelihood logarithm value and an index of the most probable mixture component for the given sample.

impl StatModel for PtrOfEM

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 PtrOfEM

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)