Struct opencv::core::LDA [−][src]
pub struct LDA { /* fields omitted */ }
Expand description
Linear Discriminant Analysis @todo document this class
Implementations
constructor Initializes a LDA with num_components (default 0).
C++ default parameters
- num_components: 0
pub fn new_with_data(
src: &dyn ToInputArray,
labels: &dyn ToInputArray,
num_components: i32
) -> Result<LDA>
pub fn new_with_data(
src: &dyn ToInputArray,
labels: &dyn ToInputArray,
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 subspace_project(
w: &dyn ToInputArray,
mean: &dyn ToInputArray,
src: &dyn ToInputArray
) -> Result<Mat>
pub fn subspace_reconstruct(
w: &dyn ToInputArray,
mean: &dyn ToInputArray,
src: &dyn ToInputArray
) -> Result<Mat>
Trait Implementations
Deserializes this object from a given filename.
Deserializes this object from a given cv::FileStorage.
Compute the discriminants for data in src (row aligned) and labels.
Projects samples into the LDA subspace. src may be one or more row aligned samples. Read more
Reconstructs projections from the LDA subspace. src may be one or more row aligned projections. Read more
Serializes this object to a given cv::FileStorage.
Returns the eigenvectors of this LDA.
Returns the eigenvalues of this LDA.