LDA wrapped (minimally) to python/java

core.hpp

class CV_EXPORTS_W LDA
{
public:
    /** @brief constructor
    Initializes a LDA with num_components (default 0).
    */
    explicit LDA(int num_components = 0);

    /** 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.
    */
    CV_WRAP LDA(InputArrayOfArrays src, InputArray labels, int num_components = 0);

    /** Serializes this object to a given filename.
      */
    CV_WRAP void save(const String& filename) const;

    /** Deserializes this object from a given filename.
      */
    CV_WRAP void load(const String& filename);

    /** Serializes this object to a given cv::FileStorage.
      */
    void save(FileStorage& fs) const;

    /** Deserializes this object from a given cv::FileStorage.
      */
    void load(const FileStorage& node);

    /** destructor
      */
    ~LDA();

    /** Compute the discriminants for data in src (row aligned) and labels.
      */
    void compute(InputArrayOfArrays src, InputArray labels);

    /** Projects samples into the LDA subspace.
        src may be one or more row aligned samples.
      */
    CV_WRAP Mat project(InputArray src);

    /** Reconstructs projections from the LDA subspace.
        src may be one or more row aligned projections.
      */
    Mat reconstruct(InputArray src);

    /** Returns the eigenvectors of this LDA.
      */
    Mat eigenvectors() const { return _eigenvectors; }

    /** Returns the eigenvalues of this LDA.
      */
    Mat eigenvalues() const { return _eigenvalues; }

    static Mat subspaceProject(InputArray W, InputArray mean, InputArray src);
    static Mat subspaceReconstruct(InputArray W, InputArray mean, InputArray src);

protected:
    int _num_components;
    Mat _eigenvectors;
    Mat _eigenvalues;
    void lda(InputArrayOfArrays src, InputArray labels);
};