Struct opencv::ml::EM

pub struct EM { /* private fields */ }

The class implements the Expectation Maximization algorithm.

See also

[ml_intro_em]

Implementations

impl EM

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

Creates empty %EM model. The model should be trained then using StatModel::train(traindata, flags) method. Alternatively, you can use one of the EM::train* methods or load it from file using Algorithm::load<EM>(filename).

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

Loads and creates a serialized EM from a file

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

C++ default parameters

• node_name: String()

Trait Implementations

impl AlgorithmTrait for EM

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 EM

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 EM

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 Drop for EM

fn drop(&mut self)

Executes the destructor for this type.

impl EMTrait for EM

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: &impl ToInputArray, log_likelihoods: &mut impl ToOutputArray, labels: &mut impl ToOutputArray, probs: &mut impl ToOutputArray ) -> Result<bool>

Estimate the Gaussian mixture parameters from a samples set.

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

Estimate the Gaussian mixture parameters from a samples set.

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

Estimate the Gaussian mixture parameters from a samples set.

impl EMTraitConst for EM

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: &impl ToInputArray, results: &mut impl ToOutputArray, flags: i32 ) -> Result<f32>

Returns posterior probabilities for the provided samples

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

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

impl From<EM> for Algorithm

fn from(s: EM) -> Self

Converts to this type from the input type.

impl StatModelTrait for EM

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 EM

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 Send for EM