Struct opencv::core::LDA

pub struct LDA { /* fields omitted */ }

Linear Discriminant Analysis @todo document this class

Methods

impl LDA

pub fn new(num_components: i32) -> Result<LDA>

constructor Initializes a LDA with num_components (default 0).

C++ default parameters

• num_components: 0

pub fn new_with_data(
    src: &VectorOfMat,
    labels: &Mat,
    num_components: i32
) -> Result<LDA>

Initializes and performs a Discriminant Analysis with Fisher's Optimization Criterion on given data in src and corresponding labels in labels. If 0 (or less) number of components are given, they are automatically determined for given data in computation.

C++ default parameters

• num_components: 0

pub fn save(&self, filename: &str) -> Result<()>

Serializes this object to a given filename.

pub fn load(&mut self, filename: &str) -> Result<()>

Deserializes this object from a given filename.

pub fn compute(&mut self, src: &VectorOfMat, labels: &Mat) -> Result<()>

Compute the discriminants for data in src (row aligned) and labels.

pub fn project(&mut self, src: &Mat) -> Result<Mat>

Projects samples into the LDA subspace. src may be one or more row aligned samples.

pub fn reconstruct(&mut self, src: &Mat) -> Result<Mat>

Reconstructs projections from the LDA subspace. src may be one or more row aligned projections.

pub fn eigenvectors(&self) -> Result<Mat>

Returns the eigenvectors of this LDA.

pub fn eigenvalues(&self) -> Result<Mat>

Returns the eigenvalues of this LDA.

pub fn subspace_project(w: &Mat, mean: &Mat, src: &Mat) -> Result<Mat>

pub fn subspace_reconstruct(w: &Mat, mean: &Mat, src: &Mat) -> Result<Mat>

Trait Implementations

impl Drop for LDA

fn drop(&mut self)

Executes the destructor for this type.