Trait opencv::prelude::EM

pub trait EM: EMConst + StatModel {
    // Required method
    fn as_raw_mut_EM(&mut self) -> *mut c_void;

    // Provided methods
    fn set_clusters_number(&mut self, val: i32) -> Result<()> { ... }
    fn set_covariance_matrix_type(&mut self, val: i32) -> Result<()> { ... }
    fn set_term_criteria(&mut self, val: TermCriteria) -> Result<()> { ... }
    fn train_em(
        &mut self,
        samples: &dyn ToInputArray,
        log_likelihoods: &mut dyn ToOutputArray,
        labels: &mut dyn ToOutputArray,
        probs: &mut dyn ToOutputArray
    ) -> Result<bool> { ... }
    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> { ... }
    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> { ... }
}

The class implements the Expectation Maximization algorithm.

See also

[ml_intro_em]

Required Methods

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

Provided Methods

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.

See also

setClustersNumber getClustersNumber

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

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

See also

setCovarianceMatrixType getCovarianceMatrixType

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.

See also

setTermCriteria getTermCriteria

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.

This variation starts with Expectation step. Initial values of the model parameters will be estimated by the k-means algorithm.

Unlike many of the ML models, %EM is an unsupervised learning algorithm and it does not take responses (class labels or function values) as input. Instead, it computes the Maximum Likelihood Estimate of the Gaussian mixture parameters from an input sample set, stores all the parameters inside the structure: in probs, in means , in covs[k], in weights , and optionally computes the output “class label” for each sample: (indices of the most probable mixture component for each sample).

The trained model can be used further for prediction, just like any other classifier. The trained model is similar to the NormalBayesClassifier.

Parameters

• samples: Samples from which the Gaussian mixture model will be estimated. It should be a one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type it will be converted to the inner matrix of such type for the further computing.
• logLikelihoods: The optional output matrix that contains a likelihood logarithm value for each sample. It has size and CV_64FC1 type.
• labels: The optional output “class label” for each sample: (indices of the most probable mixture component for each sample). It has size and CV_32SC1 type.
• probs: The optional output matrix that contains posterior probabilities of each Gaussian mixture component given the each sample. It has size and CV_64FC1 type.

C++ default parameters

• log_likelihoods: noArray()
• labels: noArray()
• probs: noArray()

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.

This variation starts with Expectation step. You need to provide initial means of mixture components. Optionally you can pass initial weights and covariance matrices of mixture components.

Parameters

• samples: Samples from which the Gaussian mixture model will be estimated. It should be a one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type it will be converted to the inner matrix of such type for the further computing.
• means0: Initial means of mixture components. It is a one-channel matrix of size. If the matrix does not have CV_64F type it will be converted to the inner matrix of such type for the further computing.
• covs0: The vector of initial covariance matrices of mixture components. Each of covariance matrices is a one-channel matrix of size. If the matrices do not have CV_64F type they will be converted to the inner matrices of such type for the further computing.
• weights0: Initial weights of mixture components. It should be a one-channel floating-point matrix with or size.
• logLikelihoods: The optional output matrix that contains a likelihood logarithm value for each sample. It has size and CV_64FC1 type.
• labels: The optional output “class label” for each sample: (indices of the most probable mixture component for each sample). It has size and CV_32SC1 type.
• probs: The optional output matrix that contains posterior probabilities of each Gaussian mixture component given the each sample. It has size and CV_64FC1 type.

C++ default parameters

• covs0: noArray()
• weights0: noArray()
• log_likelihoods: noArray()
• labels: noArray()
• probs: noArray()

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.

This variation starts with Maximization step. You need to provide initial probabilities to use this option.

Parameters

• samples: Samples from which the Gaussian mixture model will be estimated. It should be a one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type it will be converted to the inner matrix of such type for the further computing.
• probs0: the probabilities
• logLikelihoods: The optional output matrix that contains a likelihood logarithm value for each sample. It has size and CV_64FC1 type.
• labels: The optional output “class label” for each sample: (indices of the most probable mixture component for each sample). It has size and CV_32SC1 type.
• probs: The optional output matrix that contains posterior probabilities of each Gaussian mixture component given the each sample. It has size and CV_64FC1 type.

C++ default parameters

• log_likelihoods: noArray()
• labels: noArray()
• probs: noArray()

Implementations

impl dyn EM + '_

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

Implementors

impl EM for Ptr<dyn EM>