Delphi-OpenCV/source/ocv.ml.pas

(*
  **************************************************************************************************
  Project Delphi-OpenCV
  **************************************************************************************************
  Contributors:
  Laentir Valetov
  email:laex@bk.ru
  Mikhail Grigorev
  Email: sleuthhound@gmail.com
  **************************************************************************************************
  You may retrieve the latest version of this file at the GitHub,
  located at git://github.com/Laex/Delphi-OpenCV.git
  **************************************************************************************************
  License:
  The contents of this file are subject to the Mozilla Public License Version 1.1 (the "License");
  you may not use this file except in compliance with the License. You may obtain a copy of the
  License at http://www.mozilla.org/MPL/

  Software distributed under the License is distributed on an "AS IS" basis, WITHOUT WARRANTY OF
  ANY KIND, either express or implied. See the License for the specific language governing rights
  and limitations under the License.

  Alternatively, the contents of this file may be used under the terms of the
  GNU Lesser General Public License (the  "LGPL License"), in which case the
  provisions of the LGPL License are applicable instead of those above.
  If you wish to allow use of your version of this file only under the terms
  of the LGPL License and not to allow others to use your version of this file
  under the MPL, indicate your decision by deleting  the provisions above and
  replace  them with the notice and other provisions required by the LGPL
  License.  If you do not delete the provisions above, a recipient may use
  your version of this file under either the MPL or the LGPL License.

  For more information about the LGPL: http://www.gnu.org/copyleft/lesser.html
  **************************************************************************************************
  The Initial Developer of the Original Code:
  OpenCV: open source computer vision library
  Homepage:    http://ocv.org
  Online docs: http://docs.ocv.org
  Q&A forum:   http://answers.ocv.org
  Dev zone:    http://code.ocv.org
  **************************************************************************************************
  Original file:
  opencv\modules\ml\include\opencv2\ml.hpp
  ************************************************************************************************ *)

unit ocv.ml;

{$POINTERMATH ON}

interface

Uses
  WinApi.Windows,
  ocv.core.types_c;

(* ***************************************************************************************
  *                               Main struct definitions                                *
  *************************************************************************************** *)

const
  (* log(2*PI) *)
  CV_LOG2PI = (1.8378770664093454835606594728112);

  (* columns of <trainData> matrix are training samples *)
  CV_COL_SAMPLE = 0;

  (* rows of <trainData> matrix are training samples *)
  CV_ROW_SAMPLE = 1;

function CV_IS_ROW_SAMPLE(flags: Integer): Boolean; inline;

Type
  (* struct CvVectors
    {
    int type;
    int dims, count;
    CvVectors* next;
    union
    {
    uchar** ptr;
    float** fl;
    double** db;
    } data;
    };
  *)
  pCvVectors = ^TCvVectors;

  TCvVectors = record
    _type: Integer;
    dims, count: Integer;
    next: pCvVectors;
    case data: byte of
      0:
        (ptr: ^pByte);
      1:
        (fl: ^PSingle);
      2:
        (db: ^PDouble);
  end;

  // #if 0
  (* A structure, representing the lattice range of statmodel parameters.
    It is used for optimizing statmodel parameters by cross-validation method.
    The lattice is logarithmic, so <step> must be greater then 1. *)
  (*
    typedef struct CvParamLattice
    {
    double min_val;
    double max_val;
    double step;
    }
    CvParamLattice;

    CV_INLINE CvParamLattice cvParamLattice( double min_val, double max_val,
    double log_step )
    {
    CvParamLattice pl;
    pl.min_val = MIN( min_val, max_val );
    pl.max_val = MAX( min_val, max_val );
    pl.step = MAX( log_step, 1. );
    return pl;
    }

    CV_INLINE CvParamLattice cvDefaultParamLattice( void )
    {
    CvParamLattice pl = {0,0,0};
    return pl;
    }
  *)
  // #endif

const
  (* Variable type *)
  CV_VAR_NUMERICAL   = 0;
  CV_VAR_ORDERED     = 0;
  CV_VAR_CATEGORICAL = 1;

  CV_TYPE_NAME_ML_SVM      = 'opencv-ml-svm';
  CV_TYPE_NAME_ML_KNN      = 'opencv-ml-knn';
  CV_TYPE_NAME_ML_NBAYES   = 'opencv-ml-bayesian';
  CV_TYPE_NAME_ML_EM       = 'opencv-ml-em';
  CV_TYPE_NAME_ML_BOOSTING = 'opencv-ml-boost-tree';
  CV_TYPE_NAME_ML_TREE     = 'opencv-ml-tree';
  CV_TYPE_NAME_ML_ANN_MLP  = 'opencv-ml-ann-mlp';
  CV_TYPE_NAME_ML_CNN      = 'opencv-ml-cnn';
  CV_TYPE_NAME_ML_RTREES   = 'opencv-ml-random-trees';
  CV_TYPE_NAME_ML_ERTREES  = 'opencv-ml-extremely-randomized-trees';
  CV_TYPE_NAME_ML_GBT      = 'opencv-ml-gradient-boosting-trees';

  CV_TRAIN_ERROR = 0;
  CV_TEST_ERROR  = 1;

  (*
    class CV_EXPORTS_W CvStatModel
    {
    public:
    CvStatModel();
    virtual ~CvStatModel();

    virtual void clear();

    CV_WRAP virtual void save( const char* filename, const char* name=0 ) const;
    CV_WRAP virtual void load( const char* filename, const char* name=0 );

    virtual void write( CvFileStorage* storage, const char* name ) const;
    virtual void read( CvFileStorage* storage, CvFileNode* node );

    protected:
    const char* default_model_name;
    };
  *)
  (* ***************************************************************************************
    *                                 Normal Bayes Classifier                              *
    *************************************************************************************** *)

  (* The structure, representing the grid range of statmodel parameters.
    It is used for optimizing statmodel accuracy by varying model parameters,
    the accuracy estimate being computed by cross-validation.
    The grid is logarithmic, so <step> must be greater then 1. *)

  // class CvMLData;
  //
  // struct CV_EXPORTS_W_MAP CvParamGrid
  // {
  // // SVM params type
  // enum { SVM_C=0, SVM_GAMMA=1, SVM_P=2, SVM_NU=3, SVM_COEF=4, SVM_DEGREE=5 };
  //
  // CvParamGrid()
  // {
  // min_val = max_val = step = 0;
  // }
  //
  // CvParamGrid( double min_val, double max_val, double log_step );
  // //CvParamGrid( int param_id );
  // bool check() const;
  //
  // CV_PROP_RW double min_val;
  // CV_PROP_RW double max_val;
  // CV_PROP_RW double step;
  // };
  //
  // inline CvParamGrid::CvParamGrid( double _min_val, double _max_val, double _log_step )
  // {
  // min_val = _min_val;
  // max_val = _max_val;
  // step = _log_step;
  // }
  //
  // class CV_EXPORTS_W CvNormalBayesClassifier : public CvStatModel
  // {
  // public:
  // CV_WRAP CvNormalBayesClassifier();
  // virtual ~CvNormalBayesClassifier();
  //
  // CvNormalBayesClassifier( const CvMat* trainData, const CvMat* responses,
  // const CvMat* varIdx=0, const CvMat* sampleIdx=0 );
  //
  // virtual bool train( const CvMat* trainData, const CvMat* responses,
  // const CvMat* varIdx = 0, const CvMat* sampleIdx=0, bool update=false );
  //
  // virtual float predict( const CvMat* samples, CV_OUT CvMat* results=0 ) const;
  // CV_WRAP virtual void clear();
  //
  // CV_WRAP CvNormalBayesClassifier( const cv::Mat& trainData, const cv::Mat& responses,
  // const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat() );
  // CV_WRAP virtual bool train( const cv::Mat& trainData, const cv::Mat& responses,
  // const cv::Mat& varIdx = cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
  // bool update=false );
  // CV_WRAP virtual float predict( const cv::Mat& samples, CV_OUT cv::Mat* results=0 ) const;
  //
  // virtual void write( CvFileStorage* storage, const char* name ) const;
  // virtual void read( CvFileStorage* storage, CvFileNode* node );
  //
  // protected:
  // int     var_count, var_all;
  // CvMat*  var_idx;
  // CvMat*  cls_labels;
  // CvMat** count;
  // CvMat** sum;
  // CvMat** productsum;
  // CvMat** avg;
  // CvMat** inv_eigen_values;
  // CvMat** cov_rotate_mats;
  // CvMat*  c;
  // };
  //
  //
  (* ***************************************************************************************\
    // *                          K-Nearest Neighbour Classifier                                *
    // \*************************************************************************************** *)
Type
  TCvKNearest = class(TObject)
    function train(const trainData: pCvMat; const responses: pCvMat; const sampleIdx: pCvMat = nil;
      is_regression: bool = false; maxK: Integer = 32; updateBase: bool = false): bool; virtual; stdcall; abstract;
    function find_nearest(const samples: pCvMat; k: Integer; results: pCvMat = nil; const neighbors: PSingle = nil;
      neighborResponses: pCvMat = nil; dist: pCvMat = nil): float; virtual; stdcall; abstract;
    // -----------------------------------
    class function Create: TCvKNearest; overload;
    class function Create(const trainData: pCvMat; const responses: pCvMat; const sampleIdx: pCvMat = nil;
      isRegression: bool = false; max_k: Integer = 32): TCvKNearest; overload;
    procedure Free; reintroduce;
  end;

  // k Nearest Neighbors
  // class CV_EXPORTS_W CvKNearest : public CvStatModel
  // {
  // public:
  //
  // CV_WRAP CvKNearest();
  // virtual ~CvKNearest();
  //
  // CvKNearest( const CvMat* trainData, const CvMat* responses, const CvMat* sampleIdx=0, bool isRegression=false, int max_k=32 );
  //
  // virtual bool train( const CvMat* trainData, const CvMat* responses,
  // const CvMat* sampleIdx=0, bool is_regression=false,
  // int maxK=32, bool updateBase=false );
  //
  // virtual float find_nearest( const CvMat* samples, int k, CV_OUT CvMat* results=0,
  // const float** neighbors=0, CV_OUT CvMat* neighborResponses=0, CV_OUT CvMat* dist=0 ) const;
  //
  // CV_WRAP CvKNearest( const cv::Mat& trainData, const cv::Mat& responses,
  // const cv::Mat& sampleIdx=cv::Mat(), bool isRegression=false, int max_k=32 );
  //
  // CV_WRAP virtual bool train( const cv::Mat& trainData, const cv::Mat& responses,
  // const cv::Mat& sampleIdx=cv::Mat(), bool isRegression=false,
  // int maxK=32, bool updateBase=false );
  //
  // virtual float find_nearest( const cv::Mat& samples, int k, cv::Mat* results=0,
  // const float** neighbors=0, cv::Mat* neighborResponses=0,
  // cv::Mat* dist=0 ) const;
  // CV_WRAP virtual float find_nearest( const cv::Mat& samples, int k, CV_OUT cv::Mat& results,
  // CV_OUT cv::Mat& neighborResponses, CV_OUT cv::Mat& dists) const;
  //
  // virtual void clear();
  // int get_max_k() const;
  // int get_var_count() const;
  // int get_sample_count() const;
  // bool is_regression() const;
  //
  // virtual float write_results( int k, int k1, int start, int end,
  // const float* neighbor_responses, const float* dist, CvMat* _results,
  // CvMat* _neighbor_responses, CvMat* _dist, Cv32suf* sort_buf ) const;
  //
  // virtual void find_neighbors_direct( const CvMat* _samples, int k, int start, int end,
  // float* neighbor_responses, const float** neighbors, float* dist ) const;
  //
  // protected:
  //
  // int max_k, var_count;
  // int total;
  // bool regression;
  // CvVectors* samples;
  // };

  (* ***************************************************************************************
    *                                   Support Vector Machines                            *
    *************************************************************************************** *)

  // SVM training parameters
  // struct CV_EXPORTS_W_MAP CvSVMParams
  // {
  // CvSVMParams();
  // CvSVMParams( int svm_type, int kernel_type,
  // double degree, double gamma, double coef0,
  // double Cvalue, double nu, double p,
  // CvMat* class_weights, CvTermCriteria term_crit );
  //
  // CV_PROP_RW int         svm_type;
  // CV_PROP_RW int         kernel_type;
  // CV_PROP_RW double      degree; // for poly
  // CV_PROP_RW double      gamma;  // for poly/rbf/sigmoid/chi2
  // CV_PROP_RW double      coef0;  // for poly/sigmoid
  //
  // CV_PROP_RW double      C;  // for CV_SVM_C_SVC, CV_SVM_EPS_SVR and CV_SVM_NU_SVR
  // CV_PROP_RW double      nu; // for CV_SVM_NU_SVC, CV_SVM_ONE_CLASS, and CV_SVM_NU_SVR
  // CV_PROP_RW double      p; // for CV_SVM_EPS_SVR
  // CvMat*      class_weights; // for CV_SVM_C_SVC
  // CV_PROP_RW CvTermCriteria term_crit; // termination criteria
  // };
  //
  //
  // struct CV_EXPORTS CvSVMKernel
  // {
  // typedef void (CvSVMKernel::*Calc)( int vec_count, int vec_size, const float** vecs,
  // const float* another, float* results );
  // CvSVMKernel();
  // CvSVMKernel( const CvSVMParams* params, Calc _calc_func );
  // virtual bool create( const CvSVMParams* params, Calc _calc_func );
  // virtual ~CvSVMKernel();
  //
  // virtual void clear();
  // virtual void calc( int vcount, int n, const float** vecs, const float* another, float* results );
  //
  // const CvSVMParams* params;
  // Calc calc_func;
  //
  // virtual void calc_non_rbf_base( int vec_count, int vec_size, const float** vecs,
  // const float* another, float* results,
  // double alpha, double beta );
  // virtual void calc_intersec( int vcount, int var_count, const float** vecs,
  // const float* another, float* results );
  // virtual void calc_chi2( int vec_count, int vec_size, const float** vecs,
  // const float* another, float* results );
  // virtual void calc_linear( int vec_count, int vec_size, const float** vecs,
  // const float* another, float* results );
  // virtual void calc_rbf( int vec_count, int vec_size, const float** vecs,
  // const float* another, float* results );
  // virtual void calc_poly( int vec_count, int vec_size, const float** vecs,
  // const float* another, float* results );
  // virtual void calc_sigmoid( int vec_count, int vec_size, const float** vecs,
  // const float* another, float* results );
  // };
  //
  //
  // struct CvSVMKernelRow
  // {
  // CvSVMKernelRow* prev;
  // CvSVMKernelRow* next;
  // float* data;
  // };
  //
  //
  // struct CvSVMSolutionInfo
  // {
  // double obj;
  // double rho;
  // double upper_bound_p;
  // double upper_bound_n;
  // double r;   // for Solver_NU
  // };
  //
  // class CV_EXPORTS CvSVMSolver
  // {
  // public:
  // typedef bool (CvSVMSolver::*SelectWorkingSet)( int& i, int& j );
  // typedef float* (CvSVMSolver::*GetRow)( int i, float* row, float* dst, bool existed );
  // typedef void (CvSVMSolver::*CalcRho)( double& rho, double& r );
  //
  // CvSVMSolver();
  //
  // CvSVMSolver( int count, int var_count, const float** samples, schar* y,
  // int alpha_count, double* alpha, double Cp, double Cn,
  // CvMemStorage* storage, CvSVMKernel* kernel, GetRow get_row,
  // SelectWorkingSet select_working_set, CalcRho calc_rho );
  // virtual bool create( int count, int var_count, const float** samples, schar* y,
  // int alpha_count, double* alpha, double Cp, double Cn,
  // CvMemStorage* storage, CvSVMKernel* kernel, GetRow get_row,
  // SelectWorkingSet select_working_set, CalcRho calc_rho );
  // virtual ~CvSVMSolver();
  //
  // virtual void clear();
  // virtual bool solve_generic( CvSVMSolutionInfo& si );
  //
  // virtual bool solve_c_svc( int count, int var_count, const float** samples, schar* y,
  // double Cp, double Cn, CvMemStorage* storage,
  // CvSVMKernel* kernel, double* alpha, CvSVMSolutionInfo& si );
  // virtual bool solve_nu_svc( int count, int var_count, const float** samples, schar* y,
  // CvMemStorage* storage, CvSVMKernel* kernel,
  // double* alpha, CvSVMSolutionInfo& si );
  // virtual bool solve_one_class( int count, int var_count, const float** samples,
  // CvMemStorage* storage, CvSVMKernel* kernel,
  // double* alpha, CvSVMSolutionInfo& si );
  //
  // virtual bool solve_eps_svr( int count, int var_count, const float** samples, const float* y,
  // CvMemStorage* storage, CvSVMKernel* kernel,
  // double* alpha, CvSVMSolutionInfo& si );
  //
  // virtual bool solve_nu_svr( int count, int var_count, const float** samples, const float* y,
  // CvMemStorage* storage, CvSVMKernel* kernel,
  // double* alpha, CvSVMSolutionInfo& si );
  //
  // virtual float* get_row_base( int i, bool* _existed );
  // virtual float* get_row( int i, float* dst );
  //
  // int sample_count;
  // int var_count;
  // int cache_size;
  // int cache_line_size;
  // const float** samples;
  // const CvSVMParams* params;
  // CvMemStorage* storage;
  // CvSVMKernelRow lru_list;
  // CvSVMKernelRow* rows;
  //
  // int alpha_count;
  //
  // double* G;
  // double* alpha;
  //
  // // -1 - lower bound, 0 - free, 1 - upper bound
  // schar* alpha_status;
  //
  // schar* y;
  // double* b;
  // float* buf[2];
  // double eps;
  // int max_iter;
  // double C[2];  // C[0] == Cn, C[1] == Cp
  // CvSVMKernel* kernel;
  //
  // SelectWorkingSet select_working_set_func;
  // CalcRho calc_rho_func;
  // GetRow get_row_func;
  //
  // virtual bool select_working_set( int& i, int& j );
  // virtual bool select_working_set_nu_svm( int& i, int& j );
  // virtual void calc_rho( double& rho, double& r );
  // virtual void calc_rho_nu_svm( double& rho, double& r );
  //
  // virtual float* get_row_svc( int i, float* row, float* dst, bool existed );
  // virtual float* get_row_one_class( int i, float* row, float* dst, bool existed );
  // virtual float* get_row_svr( int i, float* row, float* dst, bool existed );
  // };
  //
  //
  // struct CvSVMDecisionFunc
  // {
  // double rho;
  // int sv_count;
  // double* alpha;
  // int* sv_index;
  // };
  //
  //
  /// / SVM model
  // class CV_EXPORTS_W CvSVM : public CvStatModel
  // {
  // public:
  // // SVM type
  // enum { C_SVC=100, NU_SVC=101, ONE_CLASS=102, EPS_SVR=103, NU_SVR=104 };
  //
  // // SVM kernel type
  // enum { LINEAR=0, POLY=1, RBF=2, SIGMOID=3, CHI2=4, INTER=5 };
  //
  // // SVM params type
  // enum { C=0, GAMMA=1, P=2, NU=3, COEF=4, DEGREE=5 };
  //
  // CV_WRAP CvSVM();
  // virtual ~CvSVM();
  //
  // CvSVM( const CvMat* trainData, const CvMat* responses,
  // const CvMat* varIdx=0, const CvMat* sampleIdx=0,
  // CvSVMParams params=CvSVMParams() );
  //
  // virtual bool train( const CvMat* trainData, const CvMat* responses,
  // const CvMat* varIdx=0, const CvMat* sampleIdx=0,
  // CvSVMParams params=CvSVMParams() );
  //
  // virtual bool train_auto( const CvMat* trainData, const CvMat* responses,
  // const CvMat* varIdx, const CvMat* sampleIdx, CvSVMParams params,
  // int kfold = 10,
  // CvParamGrid Cgrid      = get_default_grid(CvSVM::C),
  // CvParamGrid gammaGrid  = get_default_grid(CvSVM::GAMMA),
  // CvParamGrid pGrid      = get_default_grid(CvSVM::P),
  // CvParamGrid nuGrid     = get_default_grid(CvSVM::NU),
  // CvParamGrid coeffGrid  = get_default_grid(CvSVM::COEF),
  // CvParamGrid degreeGrid = get_default_grid(CvSVM::DEGREE),
  // bool balanced=false );
  //
  // virtual float predict( const CvMat* sample, bool returnDFVal=false ) const;
  // virtual float predict( const CvMat* samples, CV_OUT CvMat* results ) const;
  //
  // CV_WRAP CvSVM( const cv::Mat& trainData, const cv::Mat& responses,
  // const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
  // CvSVMParams params=CvSVMParams() );
  //
  // CV_WRAP virtual bool train( const cv::Mat& trainData, const cv::Mat& responses,
  // const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
  // CvSVMParams params=CvSVMParams() );
  //
  // CV_WRAP virtual bool train_auto( const cv::Mat& trainData, const cv::Mat& responses,
  // const cv::Mat& varIdx, const cv::Mat& sampleIdx, CvSVMParams params,
  // int k_fold = 10,
  // CvParamGrid Cgrid      = CvSVM::get_default_grid(CvSVM::C),
  // CvParamGrid gammaGrid  = CvSVM::get_default_grid(CvSVM::GAMMA),
  // CvParamGrid pGrid      = CvSVM::get_default_grid(CvSVM::P),
  // CvParamGrid nuGrid     = CvSVM::get_default_grid(CvSVM::NU),
  // CvParamGrid coeffGrid  = CvSVM::get_default_grid(CvSVM::COEF),
  // CvParamGrid degreeGrid = CvSVM::get_default_grid(CvSVM::DEGREE),
  // bool balanced=false);
  // CV_WRAP virtual float predict( const cv::Mat& sample, bool returnDFVal=false ) const;
  // CV_WRAP_AS(predict_all) virtual void predict( cv::InputArray samples, cv::OutputArray results ) const;
  //
  // CV_WRAP virtual int get_support_vector_count() const;
  // virtual const float* get_support_vector(int i) const;
  // virtual CvSVMParams get_params() const { return params; };
  // CV_WRAP virtual void clear();
  //
  // static CvParamGrid get_default_grid( int param_id );
  //
  // virtual void write( CvFileStorage* storage, const char* name ) const;
  // virtual void read( CvFileStorage* storage, CvFileNode* node );
  // CV_WRAP int get_var_count() const { return var_idx ? var_idx->cols : var_all; }
  //
  // protected:
  //
  // virtual bool set_params( const CvSVMParams& params );
  // virtual bool train1( int sample_count, int var_count, const float** samples,
  // const void* responses, double Cp, double Cn,
  // CvMemStorage* _storage, double* alpha, double& rho );
  // virtual bool do_train( int svm_type, int sample_count, int var_count, const float** samples,
  // const CvMat* responses, CvMemStorage* _storage, double* alpha );
  // virtual void create_kernel();
  // virtual void create_solver();
  //
  // virtual float predict( const float* row_sample, int row_len, bool returnDFVal=false ) const;
  //
  // virtual void write_params( CvFileStorage* fs ) const;
  // virtual void read_params( CvFileStorage* fs, CvFileNode* node );
  //
  // void optimize_linear_svm();
  //
  // CvSVMParams params;
  // CvMat* class_labels;
  // int var_all;
  // float** sv;
  // int sv_total;
  // CvMat* var_idx;
  // CvMat* class_weights;
  // CvSVMDecisionFunc* decision_func;
  // CvMemStorage* storage;
  //
  // CvSVMSolver* solver;
  // CvSVMKernel* kernel;
  // };

  (* ***************************************************************************************
    *                              Expectation - Maximization                                *
    *************************************************************************************** *)
  // namespace cv
  // {
  // class CV_EXPORTS_W EM : public Algorithm
  // {
  // public:
  // // Type of covariation matrices
  // enum {COV_MAT_SPHERICAL=0, COV_MAT_DIAGONAL=1, COV_MAT_GENERIC=2, COV_MAT_DEFAULT=COV_MAT_DIAGONAL};
  //
  // // Default parameters
  // enum {DEFAULT_NCLUSTERS=5, DEFAULT_MAX_ITERS=100};
  //
  // // The initial step
  // enum {START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0};
  //
  // CV_WRAP EM(int nclusters=EM::DEFAULT_NCLUSTERS, int covMatType=EM::COV_MAT_DIAGONAL,
  // const TermCriteria& termCrit=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,
  // EM::DEFAULT_MAX_ITERS, FLT_EPSILON));
  //
  // virtual ~EM();
  // CV_WRAP virtual void clear();
  //
  // CV_WRAP virtual bool train(InputArray samples,
  // OutputArray logLikelihoods=noArray(),
  // OutputArray labels=noArray(),
  // OutputArray probs=noArray());
  //
  // CV_WRAP virtual bool trainE(InputArray samples,
  // InputArray means0,
  // InputArray covs0=noArray(),
  // InputArray weights0=noArray(),
  // OutputArray logLikelihoods=noArray(),
  // OutputArray labels=noArray(),
  // OutputArray probs=noArray());
  //
  // CV_WRAP virtual bool trainM(InputArray samples,
  // InputArray probs0,
  // OutputArray logLikelihoods=noArray(),
  // OutputArray labels=noArray(),
  // OutputArray probs=noArray());
  //
  // CV_WRAP Vec2d predict(InputArray sample,
  // OutputArray probs=noArray()) const;
  //
  // CV_WRAP bool isTrained() const;
  //
  // AlgorithmInfo* info() const;
  // virtual void read(const FileNode& fn);
  //
  // protected:
  //
  // virtual void setTrainData(int startStep, const Mat& samples,
  // const Mat* probs0,
  // const Mat* means0,
  // const std::vector<Mat>* covs0,
  // const Mat* weights0);
  //
  // bool doTrain(int startStep,
  // OutputArray logLikelihoods,
  // OutputArray labels,
  // OutputArray probs);
  // virtual void eStep();
  // virtual void mStep();
  //
  // void clusterTrainSamples();
  // void decomposeCovs();
  // void computeLogWeightDivDet();
  //
  // Vec2d computeProbabilities(const Mat& sample, Mat* probs) const;
  //
  // // all inner matrices have type CV_64FC1
  // CV_PROP_RW int nclusters;
  // CV_PROP_RW int covMatType;
  // CV_PROP_RW int maxIters;
  // CV_PROP_RW double epsilon;
  //
  // Mat trainSamples;
  // Mat trainProbs;
  // Mat trainLogLikelihoods;
  // Mat trainLabels;
  //
  // CV_PROP Mat weights;
  // CV_PROP Mat means;
  // CV_PROP std::vector<Mat> covs;
  //
  // std::vector<Mat> covsEigenValues;
  // std::vector<Mat> covsRotateMats;
  // std::vector<Mat> invCovsEigenValues;
  // Mat logWeightDivDet;
  // };
  // } // namespace cv

  (* ***************************************************************************************
    *                                      Decision Tree                                     *
    *************************************************************************************** *)
  // struct CvPair16u32s
  // {
  // unsigned short* u;
  // int* i;
  // };

  // #define CV_DTREE_CAT_DIR(idx,subset) \
  // (2*((subset[(idx)>>5]&(1 << ((idx) & 31)))==0)-1)
Type
  pCvDTreeSplit = ^TCvDTreeSplit;

  TCvDTreeSplit = record // struct CvDTreeSplit
    var_idx: Integer; // int var_idx;
    condensed_idx: Integer; // int condensed_idx;
    inversed: Integer; // int inversed;
    quality: float; // float quality;
    next: pCvDTreeSplit; // CvDTreeSplit* next;
    case byte of
      0:
        (subset: array [0 .. 1] of Integer);
      1:
        (c: Single;
          split_point: Integer;
        );
  end;

  pCvDTreeNode = ^TCvDTreeNode;

  TCvDTreeNode = record // struct CvDTreeNode
    class_idx: Integer; // int class_idx;
    Tn: Integer; // int Tn;
    value: Double; // double value;
    //
    parent: pCvDTreeNode; // CvDTreeNode* parent;
    left: pCvDTreeNode; // CvDTreeNode* left;
    right: pCvDTreeNode; // CvDTreeNode* right;
    //
    split: pCvDTreeSplit; // CvDTreeSplit* split;
    //
    sample_count: Integer; // int sample_count;
    depth: Integer; // int depth;
    num_valid: pInteger; // int* num_valid;
    offset: Integer; // int offset;
    buf_idx: Integer; // int buf_idx;
    maxlr: Double; // double maxlr;
    //
    // // global pruning data
    complexity: Integer; // int complexity;
    alpha: Double; // double alpha;
    node_risk, tree_risk, tree_error: Double; // double node_risk, tree_risk, tree_error;
    //
    // // cross-validation pruning data
    cv_Tn: pInteger; // int* cv_Tn;
    cv_node_risk: PDouble; // double* cv_node_risk;
    cv_node_error: PDouble; // double* cv_node_error;

    //
    // int get_num_valid(int vi) { return num_valid ? num_valid[vi] : sample_count; }
    function get_num_valid(vi: Integer): Integer;
    // void set_num_valid(int vi, int n) { if( num_valid ) num_valid[vi] = n; }
    procedure set_num_valid(vi: Integer; n: Integer);
  end;

  TCvDTreeParams = record // struct CV_EXPORTS_W_MAP CvDTreeParams
    max_categories: Integer; // CV_PROP_RW int   max_categories;
    max_depth: Integer; // CV_PROP_RW int   max_depth;
    min_sample_count: Integer; // CV_PROP_RW int   min_sample_count;
    cv_folds: Integer; // CV_PROP_RW int   cv_folds;
    use_surrogates: ByteBool; // CV_PROP_RW bool  use_surrogates;
    use_1se_rule: ByteBool; // CV_PROP_RW bool  use_1se_rule;
    truncate_pruned_tree: ByteBool; // CV_PROP_RW bool  truncate_pruned_tree;
    regression_accuracy: float; // CV_PROP_RW float regression_accuracy;
    priors: pFloat; // const float* priors;
    //
    // CvDTreeParams();
    {
      CvDTreeParams( int max_depth, int min_sample_count, float regression_accuracy,
      bool use_surrogates, int max_categories, int cv_folds,
      bool use_1se_rule, bool truncate_pruned_tree, const float* priors );
    }
  end;

  // struct CV_EXPORTS CvDTreeTrainData
  // {
  // CvDTreeTrainData();
  // CvDTreeTrainData( const CvMat* trainData, int tflag,
  // const CvMat* responses, const CvMat* varIdx=0,
  // const CvMat* sampleIdx=0, const CvMat* varType=0,
  // const CvMat* missingDataMask=0,
  // const CvDTreeParams& params=CvDTreeParams(),
  // bool _shared=false, bool _add_labels=false );
  // virtual ~CvDTreeTrainData();
  //
  // virtual void set_data( const CvMat* trainData, int tflag,
  // const CvMat* responses, const CvMat* varIdx=0,
  // const CvMat* sampleIdx=0, const CvMat* varType=0,
  // const CvMat* missingDataMask=0,
  // const CvDTreeParams& params=CvDTreeParams(),
  // bool _shared=false, bool _add_labels=false,
  // bool _update_data=false );
  // virtual void do_responses_copy();
  //
  // virtual void get_vectors( const CvMat* _subsample_idx,
  // float* values, uchar* missing, float* responses, bool get_class_idx=false );
  //
  // virtual CvDTreeNode* subsample_data( const CvMat* _subsample_idx );
  //
  // virtual void write_params( CvFileStorage* fs ) const;
  // virtual void read_params( CvFileStorage* fs, CvFileNode* node );
  //
  // // release all the data
  // virtual void clear();
  //
  // int get_num_classes() const;
  // int get_var_type(int vi) const;
  // int get_work_var_count() const {return work_var_count;}
  //
  // virtual const float* get_ord_responses( CvDTreeNode* n, float* values_buf, int* sample_indices_buf );
  // virtual const int* get_class_labels( CvDTreeNode* n, int* labels_buf );
  // virtual const int* get_cv_labels( CvDTreeNode* n, int* labels_buf );
  // virtual const int* get_sample_indices( CvDTreeNode* n, int* indices_buf );
  // virtual const int* get_cat_var_data( CvDTreeNode* n, int vi, int* cat_values_buf );
  // virtual void get_ord_var_data( CvDTreeNode* n, int vi, float* ord_values_buf, int* sorted_indices_buf,
  // const float** ord_values, const int** sorted_indices, int* sample_indices_buf );
  // virtual int get_child_buf_idx( CvDTreeNode* n );
  //
  // ////////////////////////////////////
  //
  // virtual bool set_params( const CvDTreeParams& params );
  // virtual CvDTreeNode* new_node( CvDTreeNode* parent, int count,
  // int storage_idx, int offset );
  //
  // virtual CvDTreeSplit* new_split_ord( int vi, float cmp_val,
  // int split_point, int inversed, float quality );
  // virtual CvDTreeSplit* new_split_cat( int vi, float quality );
  // virtual void free_node_data( CvDTreeNode* node );
  // virtual void free_train_data();
  // virtual void free_node( CvDTreeNode* node );
  //
  // int sample_count, var_all, var_count, max_c_count;
  // int ord_var_count, cat_var_count, work_var_count;
  // bool have_labels, have_priors;
  // bool is_classifier;
  // int tflag;
  //
  // const CvMat* train_data;
  // const CvMat* responses;
  // CvMat* responses_copy; // used in Boosting
  //
  // int buf_count, buf_size; // buf_size is obsolete, please do not use it, use expression ((int64)buf->rows * (int64)buf->cols / buf_count) instead
  // bool shared;
  // int is_buf_16u;
  //
  // CvMat* cat_count;
  // CvMat* cat_ofs;
  // CvMat* cat_map;
  //
  // CvMat* counts;
  // CvMat* buf;
  // inline size_t get_length_subbuf() const
  // {
  // size_t res = (size_t)(work_var_count + 1) * (size_t)sample_count;
  // return res;
  // }
  //
  // CvMat* direction;
  // CvMat* split_buf;
  //
  // CvMat* var_idx;
  // CvMat* var_type; // i-th element =
  // //   k<0  - ordered
  // //   k>=0 - categorical, see k-th element of cat_* arrays
  // CvMat* priors;
  // CvMat* priors_mult;
  //
  // CvDTreeParams params;
  //
  // CvMemStorage* tree_storage;
  // CvMemStorage* temp_storage;
  //
  // CvDTreeNode* data_root;
  //
  // CvSet* node_heap;
  // CvSet* split_heap;
  // CvSet* cv_heap;
  // CvSet* nv_heap;
  //
  // cv::RNG* rng;
  // };
  //
  // class CvDTree;
  // class CvForestTree;
  //
  // namespace cv
  // {
  // struct DTreeBestSplitFinder;
  // struct ForestTreeBestSplitFinder;
  // }
  //
  // class CV_EXPORTS_W CvDTree : public CvStatModel
  // {
  // public:
  // CV_WRAP CvDTree();
  // virtual ~CvDTree();
  //
  // virtual bool train( const CvMat* trainData, int tflag,
  // const CvMat* responses, const CvMat* varIdx=0,
  // const CvMat* sampleIdx=0, const CvMat* varType=0,
  // const CvMat* missingDataMask=0,
  // CvDTreeParams params=CvDTreeParams() );
  //
  // virtual bool train( CvMLData* trainData, CvDTreeParams params=CvDTreeParams() );
  //
  // // type in {CV_TRAIN_ERROR, CV_TEST_ERROR}
  // virtual float calc_error( CvMLData* trainData, int type, std::vector<float> *resp = 0 );
  //
  // virtual bool train( CvDTreeTrainData* trainData, const CvMat* subsampleIdx );
  //
  // virtual CvDTreeNode* predict( const CvMat* sample, const CvMat* missingDataMask=0,
  // bool preprocessedInput=false ) const;
  //
  // CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
  // const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
  // const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
  // const cv::Mat& missingDataMask=cv::Mat(),
  // CvDTreeParams params=CvDTreeParams() );
  //
  // CV_WRAP virtual CvDTreeNode* predict( const cv::Mat& sample, const cv::Mat& missingDataMask=cv::Mat(),
  // bool preprocessedInput=false ) const;
  // CV_WRAP virtual cv::Mat getVarImportance();
  //
  // virtual const CvMat* get_var_importance();
  // CV_WRAP virtual void clear();
  //
  // virtual void read( CvFileStorage* fs, CvFileNode* node );
  // virtual void write( CvFileStorage* fs, const char* name ) const;
  //
  // // special read & write methods for trees in the tree ensembles
  // virtual void read( CvFileStorage* fs, CvFileNode* node,
  // CvDTreeTrainData* data );
  // virtual void write( CvFileStorage* fs ) const;
  //
  // const CvDTreeNode* get_root() const;
  // int get_pruned_tree_idx() const;
  // CvDTreeTrainData* get_data();
  //
  // protected:
  // friend struct cv::DTreeBestSplitFinder;
  //
  // virtual bool do_train( const CvMat* _subsample_idx );
  //
  // virtual void try_split_node( CvDTreeNode* n );
  // virtual void split_node_data( CvDTreeNode* n );
  // virtual CvDTreeSplit* find_best_split( CvDTreeNode* n );
  // virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi,
  // float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
  // virtual CvDTreeSplit* find_split_cat_class( CvDTreeNode* n, int vi,
  // float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
  // virtual CvDTreeSplit* find_split_ord_reg( CvDTreeNode* n, int vi,
  // float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
  // virtual CvDTreeSplit* find_split_cat_reg( CvDTreeNode* n, int vi,
  // float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
  // virtual CvDTreeSplit* find_surrogate_split_ord( CvDTreeNode* n, int vi, uchar* ext_buf = 0 );
  // virtual CvDTreeSplit* find_surrogate_split_cat( CvDTreeNode* n, int vi, uchar* ext_buf = 0 );
  // virtual double calc_node_dir( CvDTreeNode* node );
  // virtual void complete_node_dir( CvDTreeNode* node );
  // virtual void cluster_categories( const int* vectors, int vector_count,
  // int var_count, int* sums, int k, int* cluster_labels );
  //
  // virtual void calc_node_value( CvDTreeNode* node );
  //
  // virtual void prune_cv();
  // virtual double update_tree_rnc( int T, int fold );
  // virtual int cut_tree( int T, int fold, double min_alpha );
  // virtual void free_prune_data(bool cut_tree);
  // virtual void free_tree();
  //
  // virtual void write_node( CvFileStorage* fs, CvDTreeNode* node ) const;
  // virtual void write_split( CvFileStorage* fs, CvDTreeSplit* split ) const;
  // virtual CvDTreeNode* read_node( CvFileStorage* fs, CvFileNode* node, CvDTreeNode* parent );
  // virtual CvDTreeSplit* read_split( CvFileStorage* fs, CvFileNode* node );
  // virtual void write_tree_nodes( CvFileStorage* fs ) const;
  // virtual void read_tree_nodes( CvFileStorage* fs, CvFileNode* node );
  //
  // CvDTreeNode* root;
  // CvMat* var_importance;
  // CvDTreeTrainData* data;
  //
  // public:
  // int pruned_tree_idx;
  // };
  //
  //
  (* ***************************************************************************************
    *                                   Random Trees Classifier                              *
    *************************************************************************************** *)

  // class CvRTrees;
  //
  // class CV_EXPORTS CvForestTree: public CvDTree
  // {
  // public:
  // CvForestTree();
  // virtual ~CvForestTree();
  //
  // virtual bool train( CvDTreeTrainData* trainData, const CvMat* _subsample_idx, CvRTrees* forest );
  //
  // virtual int get_var_count() const {return data ? data->var_count : 0;}
  // virtual void read( CvFileStorage* fs, CvFileNode* node, CvRTrees* forest, CvDTreeTrainData* _data );
  //
  // (* dummy methods to avoid warnings: BEGIN *)
  // virtual bool train( const CvMat* trainData, int tflag,
  // const CvMat* responses, const CvMat* varIdx=0,
  // const CvMat* sampleIdx=0, const CvMat* varType=0,
  // const CvMat* missingDataMask=0,
  // CvDTreeParams params=CvDTreeParams() );
  //
  // virtual bool train( CvDTreeTrainData* trainData, const CvMat* _subsample_idx );
  // virtual void read( CvFileStorage* fs, CvFileNode* node );
  // virtual void read( CvFileStorage* fs, CvFileNode* node,
  // CvDTreeTrainData* data );
  // (* dummy methods to avoid warnings: END *)
  //
  // protected:
  // friend struct cv::ForestTreeBestSplitFinder;
  //
  // virtual CvDTreeSplit* find_best_split( CvDTreeNode* n );
  // CvRTrees* forest;
  // };
  //
  //
  // struct CV_EXPORTS_W_MAP CvRTParams : public CvDTreeParams
  // {
  // //Parameters for the forest
  // CV_PROP_RW bool calc_var_importance; // true <=> RF processes variable importance
  // CV_PROP_RW int nactive_vars;
  // CV_PROP_RW CvTermCriteria term_crit;
  //
  // CvRTParams();
  // CvRTParams( int max_depth, int min_sample_count,
  // float regression_accuracy, bool use_surrogates,
  // int max_categories, const float* priors, bool calc_var_importance,
  // int nactive_vars, int max_num_of_trees_in_the_forest,
  // float forest_accuracy, int termcrit_type );
  // };
  //
  //
  // class CV_EXPORTS_W CvRTrees : public CvStatModel
  // {
  // public:
  // CV_WRAP CvRTrees();
  // virtual ~CvRTrees();
  // virtual bool train( const CvMat* trainData, int tflag,
  // const CvMat* responses, const CvMat* varIdx=0,
  // const CvMat* sampleIdx=0, const CvMat* varType=0,
  // const CvMat* missingDataMask=0,
  // CvRTParams params=CvRTParams() );
  //
  // virtual bool train( CvMLData* data, CvRTParams params=CvRTParams() );
  // virtual float predict( const CvMat* sample, const CvMat* missing = 0 ) const;
  // virtual float predict_prob( const CvMat* sample, const CvMat* missing = 0 ) const;
  //
  // CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
  // const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
  // const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
  // const cv::Mat& missingDataMask=cv::Mat(),
  // CvRTParams params=CvRTParams() );
  // CV_WRAP virtual float predict( const cv::Mat& sample, const cv::Mat& missing = cv::Mat() ) const;
  // CV_WRAP virtual float predict_prob( const cv::Mat& sample, const cv::Mat& missing = cv::Mat() ) const;
  // CV_WRAP virtual cv::Mat getVarImportance();
  //
  // CV_WRAP virtual void clear();
  //
  // virtual const CvMat* get_var_importance();
  // virtual float get_proximity( const CvMat* sample1, const CvMat* sample2,
  // const CvMat* missing1 = 0, const CvMat* missing2 = 0 ) const;
  //
  // virtual float calc_error( CvMLData* data, int type , std::vector<float>* resp = 0 ); // type in {CV_TRAIN_ERROR, CV_TEST_ERROR}
  //
  // virtual float get_train_error();
  //
  // virtual void read( CvFileStorage* fs, CvFileNode* node );
  // virtual void write( CvFileStorage* fs, const char* name ) const;
  //
  // CvMat* get_active_var_mask();
  // CvRNG* get_rng();
  //
  // int get_tree_count() const;
  // CvForestTree* get_tree(int i) const;
  //
  // protected:
  // virtual cv::String getName() const;
  //
  // virtual bool grow_forest( const CvTermCriteria term_crit );
  //
  // // array of the trees of the forest
  // CvForestTree** trees;
  // CvDTreeTrainData* data;
  // int ntrees;
  // int nclasses;
  // double oob_error;
  // CvMat* var_importance;
  // int nsamples;
  //
  // cv::RNG* rng;
  // CvMat* active_var_mask;
  // };

  (* ***************************************************************************************
    *                           Extremely randomized trees Classifier                        *
    *************************************************************************************** *)
  // struct CV_EXPORTS CvERTreeTrainData : public CvDTreeTrainData
  // {
  // virtual void set_data( const CvMat* trainData, int tflag,
  // const CvMat* responses, const CvMat* varIdx=0,
  // const CvMat* sampleIdx=0, const CvMat* varType=0,
  // const CvMat* missingDataMask=0,
  // const CvDTreeParams& params=CvDTreeParams(),
  // bool _shared=false, bool _add_labels=false,
  // bool _update_data=false );
  // virtual void get_ord_var_data( CvDTreeNode* n, int vi, float* ord_values_buf, int* missing_buf,
  // const float** ord_values, const int** missing, int* sample_buf = 0 );
  // virtual const int* get_sample_indices( CvDTreeNode* n, int* indices_buf );
  // virtual const int* get_cv_labels( CvDTreeNode* n, int* labels_buf );
  // virtual const int* get_cat_var_data( CvDTreeNode* n, int vi, int* cat_values_buf );
  // virtual void get_vectors( const CvMat* _subsample_idx, float* values, uchar* missing,
  // float* responses, bool get_class_idx=false );
  // virtual CvDTreeNode* subsample_data( const CvMat* _subsample_idx );
  // const CvMat* missing_mask;
  // };
  //
  // class CV_EXPORTS CvForestERTree : public CvForestTree
  // {
  // protected:
  // virtual double calc_node_dir( CvDTreeNode* node );
  // virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi,
  // float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
  // virtual CvDTreeSplit* find_split_cat_class( CvDTreeNode* n, int vi,
  // float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
  // virtual CvDTreeSplit* find_split_ord_reg( CvDTreeNode* n, int vi,
  // float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
  // virtual CvDTreeSplit* find_split_cat_reg( CvDTreeNode* n, int vi,
  // float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
  // virtual void split_node_data( CvDTreeNode* n );
  // };
  //
  // class CV_EXPORTS_W CvERTrees : public CvRTrees
  // {
  // public:
  // CV_WRAP CvERTrees();
  // virtual ~CvERTrees();
  // virtual bool train( const CvMat* trainData, int tflag,
  // const CvMat* responses, const CvMat* varIdx=0,
  // const CvMat* sampleIdx=0, const CvMat* varType=0,
  // const CvMat* missingDataMask=0,
  // CvRTParams params=CvRTParams());
  // CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
  // const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
  // const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
  // const cv::Mat& missingDataMask=cv::Mat(),
  // CvRTParams params=CvRTParams());
  // virtual bool train( CvMLData* data, CvRTParams params=CvRTParams() );
  // protected:
  // virtual cv::String getName() const;
  // virtual bool grow_forest( const CvTermCriteria term_crit );
  // };
  //
  //
  (* ***************************************************************************************
    *                                   Boosted tree classifier                              *
    *************************************************************************************** *)
  //
  // struct CV_EXPORTS_W_MAP CvBoostParams : public CvDTreeParams
  // {
  // CV_PROP_RW int boost_type;
  // CV_PROP_RW int weak_count;
  // CV_PROP_RW int split_criteria;
  // CV_PROP_RW double weight_trim_rate;
  //
  // CvBoostParams();
  // CvBoostParams( int boost_type, int weak_count, double weight_trim_rate,
  // int max_depth, bool use_surrogates, const float* priors );
  // };
  //
  //
  // class CvBoost;
  //
  // class CV_EXPORTS CvBoostTree: public CvDTree
  // {
  // public:
  // CvBoostTree();
  // virtual ~CvBoostTree();
  //
  // virtual bool train( CvDTreeTrainData* trainData,
  // const CvMat* subsample_idx, CvBoost* ensemble );
  //
  // virtual void scale( double s );
  // virtual void read( CvFileStorage* fs, CvFileNode* node,
  // CvBoost* ensemble, CvDTreeTrainData* _data );
  // virtual void clear();
  //
  // (* dummy methods to avoid warnings: BEGIN *)
  // virtual bool train( const CvMat* trainData, int tflag,
  // const CvMat* responses, const CvMat* varIdx=0,
  // const CvMat* sampleIdx=0, const CvMat* varType=0,
  // const CvMat* missingDataMask=0,
  // CvDTreeParams params=CvDTreeParams() );
  // virtual bool train( CvDTreeTrainData* trainData, const CvMat* _subsample_idx );
  //
  // virtual void read( CvFileStorage* fs, CvFileNode* node );
  // virtual void read( CvFileStorage* fs, CvFileNode* node,
  // CvDTreeTrainData* data );
  // (* dummy methods to avoid warnings: END *)
  //
  // protected:
  //
  // virtual void try_split_node( CvDTreeNode* n );
  // virtual CvDTreeSplit* find_surrogate_split_ord( CvDTreeNode* n, int vi, uchar* ext_buf = 0 );
  // virtual CvDTreeSplit* find_surrogate_split_cat( CvDTreeNode* n, int vi, uchar* ext_buf = 0 );
  // virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi,
  // float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
  // virtual CvDTreeSplit* find_split_cat_class( CvDTreeNode* n, int vi,
  // float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
  // virtual CvDTreeSplit* find_split_ord_reg( CvDTreeNode* n, int vi,
  // float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
  // virtual CvDTreeSplit* find_split_cat_reg( CvDTreeNode* n, int vi,
  // float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
  // virtual void calc_node_value( CvDTreeNode* n );
  // virtual double calc_node_dir( CvDTreeNode* n );
  //
  // CvBoost* ensemble;
  // };
  //
  //
  // class CV_EXPORTS_W CvBoost : public CvStatModel
  // {
  // public:
  // // Boosting type
  // enum { DISCRETE=0, REAL=1, LOGIT=2, GENTLE=3 };
  //
  // // Splitting criteria
  // enum { DEFAULT=0, GINI=1, MISCLASS=3, SQERR=4 };
  //
  // CV_WRAP CvBoost();
  // virtual ~CvBoost();
  //
  // CvBoost( const CvMat* trainData, int tflag,
  // const CvMat* responses, const CvMat* varIdx=0,
  // const CvMat* sampleIdx=0, const CvMat* varType=0,
  // const CvMat* missingDataMask=0,
  // CvBoostParams params=CvBoostParams() );
  //
  // virtual bool train( const CvMat* trainData, int tflag,
  // const CvMat* responses, const CvMat* varIdx=0,
  // const CvMat* sampleIdx=0, const CvMat* varType=0,
  // const CvMat* missingDataMask=0,
  // CvBoostParams params=CvBoostParams(),
  // bool update=false );
  //
  // virtual bool train( CvMLData* data,
  // CvBoostParams params=CvBoostParams(),
  // bool update=false );
  //
  // virtual float predict( const CvMat* sample, const CvMat* missing=0,
  // CvMat* weak_responses=0, CvSlice slice=CV_WHOLE_SEQ,
  // bool raw_mode=false, bool return_sum=false ) const;
  //
  // CV_WRAP CvBoost( const cv::Mat& trainData, int tflag,
  // const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
  // const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
  // const cv::Mat& missingDataMask=cv::Mat(),
  // CvBoostParams params=CvBoostParams() );
  //
  // CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
  // const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
  // const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
  // const cv::Mat& missingDataMask=cv::Mat(),
  // CvBoostParams params=CvBoostParams(),
  // bool update=false );
  //
  // CV_WRAP virtual float predict( const cv::Mat& sample, const cv::Mat& missing=cv::Mat(),
  // const cv::Range& slice=cv::Range::all(), bool rawMode=false,
  // bool returnSum=false ) const;
  //
  // virtual float calc_error( CvMLData* _data, int type , std::vector<float> *resp = 0 ); // type in {CV_TRAIN_ERROR, CV_TEST_ERROR}
  //
  // CV_WRAP virtual void prune( CvSlice slice );
  //
  // CV_WRAP virtual void clear();
  //
  // virtual void write( CvFileStorage* storage, const char* name ) const;
  // virtual void read( CvFileStorage* storage, CvFileNode* node );
  // virtual const CvMat* get_active_vars(bool absolute_idx=true);
  //
  // CvSeq* get_weak_predictors();
  //
  // CvMat* get_weights();
  // CvMat* get_subtree_weights();
  // CvMat* get_weak_response();
  // const CvBoostParams& get_params() const;
  // const CvDTreeTrainData* get_data() const;
  //
  // protected:
  //
  // virtual bool set_params( const CvBoostParams& params );
  // virtual void update_weights( CvBoostTree* tree );
  // virtual void trim_weights();
  // virtual void write_params( CvFileStorage* fs ) const;
  // virtual void read_params( CvFileStorage* fs, CvFileNode* node );
  //
  // virtual void initialize_weights(double (&p)[2]);
  //
  // CvDTreeTrainData* data;
  // CvBoostParams params;
  // CvSeq* weak;
  //
  // CvMat* active_vars;
  // CvMat* active_vars_abs;
  // bool have_active_cat_vars;
  //
  // CvMat* orig_response;
  // CvMat* sum_response;
  // CvMat* weak_eval;
  // CvMat* subsample_mask;
  // CvMat* weights;
  // CvMat* subtree_weights;
  // bool have_subsample;
  // };

  (* ***************************************************************************************
    *                                   Gradient Boosted Trees                               *
    *************************************************************************************** *)

  // / DataType: STRUCT CvGBTreesParams
  // / Parameters of GBT (Gradient Boosted trees model), including single
  // / tree settings and ensemble parameters.
  // /
  // / weak_count          - count of trees in the ensemble
  // / loss_function_type  - loss function used for ensemble training
  // / subsample_portion   - portion of whole training set used for
  // /                       every single tree training.
  // /                       subsample_portion value is in (0.0, 1.0].
  // /                       subsample_portion == 1.0 when whole dataset is
  // /                       used on each step. Count of sample used on each
  // /                       step is computed as
  // /                       int(total_samples_count * subsample_portion).
  // / shrinkage           - regularization parameter.
  // /                       Each tree prediction is multiplied on shrinkage value.


  // struct CV_EXPORTS_W_MAP CvGBTreesParams : public CvDTreeParams
  // {
  // CV_PROP_RW int weak_count;
  // CV_PROP_RW int loss_function_type;
  // CV_PROP_RW float subsample_portion;
  // CV_PROP_RW float shrinkage;
  //
  // CvGBTreesParams();
  // CvGBTreesParams( int loss_function_type, int weak_count, float shrinkage,
  // float subsample_portion, int max_depth, bool use_surrogates );
  // };

  // / DataType: CLASS CvGBTrees
  // / Gradient Boosting Trees (GBT) algorithm implementation.
  // /
  // / data             - training dataset
  // / params           - parameters of the CvGBTrees
  // / weak             - array[0..(class_count-1)] of CvSeq
  // /                    for storing tree ensembles
  // / orig_response    - original responses of the training set samples
  // / sum_response     - predicitons of the current model on the training dataset.
  // /                    this matrix is updated on every iteration.
  // / sum_response_tmp - predicitons of the model on the training set on the next
  // /                    step. On every iteration values of sum_responses_tmp are
  // /                    computed via sum_responses values. When the current
  // /                    step is complete sum_response values become equal to
  // /                    sum_responses_tmp.
  // / sampleIdx       - indices of samples used for training the ensemble.
  // /                    CvGBTrees training procedure takes a set of samples
  // /                    (train_data) and a set of responses (responses).
  // /                    Only pairs (train_data[i], responses[i]), where i is
  // /                    in sample_idx are used for training the ensemble.
  // / subsample_train  - indices of samples used for training a single decision
  // /                    tree on the current step. This indices are countered
  // /                    relatively to the sample_idx, so that pairs
  // /                    (train_data[sample_idx[i]], responses[sample_idx[i]])
  // /                    are used for training a decision tree.
  // /                    Training set is randomly splited
  // /                    in two parts (subsample_train and subsample_test)
  // /                    on every iteration accordingly to the portion parameter.
  // / subsample_test   - relative indices of samples from the training set,
  // /                    which are not used for training a tree on the current
  // /                    step.
  // / missing          - mask of the missing values in the training set. This
  // /                    matrix has the same size as train_data. 1 - missing
  // /                    value, 0 - not a missing value.
  // / class_labels     - output class labels map.
  // / rng              - random number generator. Used for spliting the
  // /                    training set.
  // / class_count      - count of output classes.
  // /                    class_count == 1 in the case of regression,
  // /                    and > 1 in the case of classification.
  // / delta            - Huber loss function parameter.
  // / base_value       - start point of the gradient descent procedure.
  // /                    model prediction is
  // /                    f(x) = f_0 + sum_{i=1..weak_count-1}(f_i(x)), where
  // /                    f_0 is the base value.



  // class CV_EXPORTS_W CvGBTrees : public CvStatModel
  // {
  // public:
  //
  (*
    / DataType: ENUM
    / Loss functions implemented in CvGBTrees.

    / SQUARED_LOSS
    / problem: regression
    / loss = (x - x')^2

    / ABSOLUTE_LOSS
    / problem: regression
    / loss = abs(x - x')

    / HUBER_LOSS
    / problem: regression
    / loss = delta*( abs(x - x') - delta/2), if abs(x - x') > delta
    /           1/2*(x - x')^2, if abs(x - x') <= delta,
    /           where delta is the alpha-quantile of pseudo responses from
    /           the training set.

    / DEVIANCE_LOSS
    / problem: classification
  *)

  // enum {SQUARED_LOSS=0, ABSOLUTE_LOSS, HUBER_LOSS=3, DEVIANCE_LOSS};

  (*
    // Default constructor. Creates a model only (without training).
    // Should be followed by one form of the train(...) function.
    //
    // API
    // CvGBTrees();

    // INPUT
    // OUTPUT
    // RESULT
  *)
  // CV_WRAP CvGBTrees();

  (*
    Full form constructor. Creates a gradient boosting model and does the
    train.

    API
    CvGBTrees( const CvMat* trainData, int tflag,
    const CvMat* responses, const CvMat* varIdx=0,
    const CvMat* sampleIdx=0, const CvMat* varType=0,
    const CvMat* missingDataMask=0,
    CvGBTreesParams params=CvGBTreesParams() );

    / INPUT
    / trainData    - a set of input feature vectors.
    /                  size of matrix is
    /                  <count of samples> x <variables count>
    /                  or <variables count> x <count of samples>
    /                  depending on the tflag parameter.
    /                  matrix values are float.
    / tflag         - a flag showing how do samples stored in the
    /                  trainData matrix row by row (tflag=CV_ROW_SAMPLE)
    /                  or column by column (tflag=CV_COL_SAMPLE).
    / responses     - a vector of responses corresponding to the samples
    /                  in trainData.
    / varIdx       - indices of used variables. zero value means that all
    /                  variables are active.
    / sampleIdx    - indices of used samples. zero value means that all
    /                  samples from trainData are in the training set.
    / varType      - vector of <variables count> length. gives every
    /                  variable type CV_VAR_CATEGORICAL or CV_VAR_ORDERED.
    /                  varType = 0 means all variables are numerical.
    / missingDataMask  - a mask of misiing values in trainData.
    /                  missingDataMask = 0 means that there are no missing
    /                  values.
    / params         - parameters of GTB algorithm.
    / OUTPUT
    / RESULT
  *)

  // CvGBTrees( const CvMat* trainData, int tflag,
  // const CvMat* responses, const CvMat* varIdx=0,
  // const CvMat* sampleIdx=0, const CvMat* varType=0,
  // const CvMat* missingDataMask=0,
  // CvGBTreesParams params=CvGBTreesParams() );

  (*
    Destructor.
  *)
  // virtual ~CvGBTrees();
  //
  //
  (*
    // Gradient tree boosting model training
    //
    // API
    // virtual bool train( const CvMat* trainData, int tflag,
    const CvMat* responses, const CvMat* varIdx=0,
    const CvMat* sampleIdx=0, const CvMat* varType=0,
    const CvMat* missingDataMask=0,
    CvGBTreesParams params=CvGBTreesParams(),
    bool update=false );

    // INPUT
    // trainData    - a set of input feature vectors.
    //                  size of matrix is
    //                  <count of samples> x <variables count>
    //                  or <variables count> x <count of samples>
    //                  depending on the tflag parameter.
    //                  matrix values are float.
    // tflag         - a flag showing how do samples stored in the
    //                  trainData matrix row by row (tflag=CV_ROW_SAMPLE)
    //                  or column by column (tflag=CV_COL_SAMPLE).
    // responses     - a vector of responses corresponding to the samples
    //                  in trainData.
    // varIdx       - indices of used variables. zero value means that all
    //                  variables are active.
    // sampleIdx    - indices of used samples. zero value means that all
    //                  samples from trainData are in the training set.
    // varType      - vector of <variables count> length. gives every
    //                  variable type CV_VAR_CATEGORICAL or CV_VAR_ORDERED.
    //                  varType = 0 means all variables are numerical.
    // missingDataMask  - a mask of misiing values in trainData.
    //                  missingDataMask = 0 means that there are no missing
    //                  values.
    // params         - parameters of GTB algorithm.
    // update         - is not supported now. (!)
    // OUTPUT
    // RESULT
    // Error state.
  *)
  // virtual bool train( const CvMat* trainData, int tflag,
  // const CvMat* responses, const CvMat* varIdx=0,
  // const CvMat* sampleIdx=0, const CvMat* varType=0,
  // const CvMat* missingDataMask=0,
  // CvGBTreesParams params=CvGBTreesParams(),
  // bool update=false );
  //
  //
  (*
    // Gradient tree boosting model training
    //
    // API
    // virtual bool train( CvMLData* data,
    CvGBTreesParams params=CvGBTreesParams(),
    bool update=false ) {return false;};

    // INPUT
    // data          - training set.
    // params        - parameters of GTB algorithm.
    // update        - is not supported now. (!)
    // OUTPUT
    // RESULT
    // Error state.
  *)
  // virtual bool train( CvMLData* data,
  // CvGBTreesParams params=CvGBTreesParams(),
  // bool update=false );
  //
  //
  (*
    // Response value prediction
    //
    // API
    // virtual float predict_serial( const CvMat* sample, const CvMat* missing=0,
    CvMat* weak_responses=0, CvSlice slice = CV_WHOLE_SEQ,
    int k=-1 ) const;

    // INPUT
    // sample         - input sample of the same type as in the training set.
    // missing        - missing values mask. missing=0 if there are no
    //                   missing values in sample vector.
    // weak_responses  - predictions of all of the trees.
    //                   not implemented (!)
    // slice           - part of the ensemble used for prediction.
    //                   slice = CV_WHOLE_SEQ when all trees are used.
    // k               - number of ensemble used.
    //                   k is in {-1,0,1,..,<count of output classes-1>}.
    //                   in the case of classification problem
    //                   <count of output classes-1> ensembles are built.
    //                   If k = -1 ordinary prediction is the result,
    //                   otherwise function gives the prediction of the
    //                   k-th ensemble only.
    // OUTPUT
    // RESULT
    // Predicted value.
  *)
  // virtual float predict_serial( const CvMat* sample, const CvMat* missing=0,
  // CvMat* weakResponses=0, CvSlice slice = CV_WHOLE_SEQ,
  // int k=-1 ) const;
  //
  (*
    // Response value prediction.
    // Parallel version (in the case of TBB existence)
    //
    // API
    // virtual float predict( const CvMat* sample, const CvMat* missing=0,
    CvMat* weak_responses=0, CvSlice slice = CV_WHOLE_SEQ,
    int k=-1 ) const;

    // INPUT
    // sample         - input sample of the same type as in the training set.
    // missing        - missing values mask. missing=0 if there are no
    //                   missing values in sample vector.
    // weak_responses  - predictions of all of the trees.
    //                   not implemented (!)
    // slice           - part of the ensemble used for prediction.
    //                   slice = CV_WHOLE_SEQ when all trees are used.
    // k               - number of ensemble used.
    //                   k is in {-1,0,1,..,<count of output classes-1>}.
    //                   in the case of classification problem
    //                   <count of output classes-1> ensembles are built.
    //                   If k = -1 ordinary prediction is the result,
    //                   otherwise function gives the prediction of the
    //                   k-th ensemble only.
    // OUTPUT
    // RESULT
    // Predicted value.
  *)
  // virtual float predict( const CvMat* sample, const CvMat* missing=0,
  // CvMat* weakResponses=0, CvSlice slice = CV_WHOLE_SEQ,
  // int k=-1 ) const;
  //
  // (*
  // // Deletes all the data.
  // //
  // // API
  // // virtual void clear();
  //
  // // INPUT
  // // OUTPUT
  // // delete data, weak, orig_response, sum_response,
  // //        weak_eval, subsample_train, subsample_test,
  // //        sample_idx, missing, lass_labels
  // // delta = 0.0
  // // RESULT
  // *)
  // CV_WRAP virtual void clear();
  //
  // (*
  // // Compute error on the train/test set.
  // //
  // // API
  // // virtual float calc_error( CvMLData* _data, int type,
  // //        std::vector<float> *resp = 0 );
  // //
  // // INPUT
  // // data  - dataset
  // // type  - defines which error is to compute: train (CV_TRAIN_ERROR) or
  // //         test (CV_TEST_ERROR).
  // // OUTPUT
  // // resp  - vector of predicitons
  // // RESULT
  // // Error value.
  // *)
  // virtual float calc_error( CvMLData* _data, int type,
  // std::vector<float> *resp = 0 );
  //
  // (*
  // //
  // // Write parameters of the gtb model and data. Write learned model.
  // //
  // // API
  // // virtual void write( CvFileStorage* fs, const char* name ) const;
  // //
  // // INPUT
  // // fs     - file storage to read parameters from.
  // // name   - model name.
  // // OUTPUT
  // // RESULT
  // *)
  // virtual void write( CvFileStorage* fs, const char* name ) const;
  //
  //
  // (*
  // //
  // // Read parameters of the gtb model and data. Read learned model.
  // //
  // // API
  // // virtual void read( CvFileStorage* fs, CvFileNode* node );
  // //
  // // INPUT
  // // fs     - file storage to read parameters from.
  // // node   - file node.
  // // OUTPUT
  // // RESULT
  // *)
  // virtual void read( CvFileStorage* fs, CvFileNode* node );
  //
  //
  // // new-style C++ interface
  // CV_WRAP CvGBTrees( const cv::Mat& trainData, int tflag,
  // const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
  // const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
  // const cv::Mat& missingDataMask=cv::Mat(),
  // CvGBTreesParams params=CvGBTreesParams() );
  //
  // CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
  // const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
  // const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
  // const cv::Mat& missingDataMask=cv::Mat(),
  // CvGBTreesParams params=CvGBTreesParams(),
  // bool update=false );
  //
  // CV_WRAP virtual float predict( const cv::Mat& sample, const cv::Mat& missing=cv::Mat(),
  // const cv::Range& slice = cv::Range::all(),
  // int k=-1 ) const;
  //
  // protected:
  //
  // (*
  // // Compute the gradient vector components.
  // //
  // // API
  // // virtual void find_gradient( const int k = 0);
  //
  // // INPUT
  // // k        - used for classification problem, determining current
  // //            tree ensemble.
  // // OUTPUT
  // // changes components of data->responses
  // // which correspond to samples used for training
  // // on the current step.
  // // RESULT
  // *)
  // virtual void find_gradient( const int k = 0);
  //
  //
  // (*
  // //
  // // Change values in tree leaves according to the used loss function.
  // //
  // // API
  // // virtual void change_values(CvDTree* tree, const int k = 0);
  // //
  // // INPUT
  // // tree      - decision tree to change.
  // // k         - used for classification problem, determining current
  // //             tree ensemble.
  // // OUTPUT
  // // changes 'value' fields of the trees' leaves.
  // // changes sum_response_tmp.
  // // RESULT
  // *)
  // virtual void change_values(CvDTree* tree, const int k = 0);
  //
  //
  // (*
  // //
  // // Find optimal constant prediction value according to the used loss
  // // function.
  // // The goal is to find a constant which gives the minimal summary loss
  // // on the _Idx samples.
  // //
  // // API
  // // virtual float find_optimal_value( const CvMat* _Idx );
  // //
  // // INPUT
  // // _Idx        - indices of the samples from the training set.
  // // OUTPUT
  // // RESULT
  // // optimal constant value.
  // *)
  // virtual float find_optimal_value( const CvMat* _Idx );
  //
  //
  // (*
  // //
  // // Randomly split the whole training set in two parts according
  // // to params.portion.
  // //
  // // API
  // // virtual void do_subsample();
  // //
  // // INPUT
  // // OUTPUT
  // // subsample_train - indices of samples used for training
  // // subsample_test  - indices of samples used for test
  // // RESULT
  // *)
  // virtual void do_subsample();
  //
  //
  // (*
  // //
  // // Internal recursive function giving an array of subtree tree leaves.
  // //
  // // API
  // // void leaves_get( CvDTreeNode** leaves, int& count, CvDTreeNode* node );
  // //
  // // INPUT
  // // node         - current leaf.
  // // OUTPUT
  // // count        - count of leaves in the subtree.
  // // leaves       - array of pointers to leaves.
  // // RESULT
  // *)
  // void leaves_get( CvDTreeNode** leaves, int& count, CvDTreeNode* node );
  //
  //
  // (*
  // //
  // // Get leaves of the tree.
  // //
  // // API
  // // CvDTreeNode** GetLeaves( const CvDTree* dtree, int& len );
  // //
  // // INPUT
  // // dtree            - decision tree.
  // // OUTPUT
  // // len              - count of the leaves.
  // // RESULT
  // // CvDTreeNode**    - array of pointers to leaves.
  // *)
  // CvDTreeNode** GetLeaves( const CvDTree* dtree, int& len );
  //
  //
  // (*
  // //
  // // Is it a regression or a classification.
  // //
  // // API
  // // bool problem_type();
  // //
  // // INPUT
  // // OUTPUT
  // // RESULT
  // // false if it is a classification problem,
  // // true - if regression.
  // *)
  // virtual bool problem_type() const;
  //
  //
  // (*
  // //
  // // Write parameters of the gtb model.
  // //
  // // API
  // // virtual void write_params( CvFileStorage* fs ) const;
  // //
  // // INPUT
  // // fs           - file storage to write parameters to.
  // // OUTPUT
  // // RESULT
  // *)
  // virtual void write_params( CvFileStorage* fs ) const;
  //
  //
  // (*
  // //
  // // Read parameters of the gtb model and data.
  // //
  // // API
  // // virtual void read_params( CvFileStorage* fs );
  // //
  // // INPUT
  // // fs           - file storage to read parameters from.
  // // OUTPUT
  // // params       - parameters of the gtb model.
  // // data         - contains information about the structure
  // //                of the data set (count of variables,
  // //                their types, etc.).
  // // class_labels - output class labels map.
  // // RESULT
  // *)
  // virtual void read_params( CvFileStorage* fs, CvFileNode* fnode );
  // int get_len(const CvMat* mat) const;
  //
  //
  // CvDTreeTrainData* data;
  // CvGBTreesParams params;
  //
  // CvSeq** weak;
  // CvMat* orig_response;
  // CvMat* sum_response;
  // CvMat* sum_response_tmp;
  // CvMat* sample_idx;
  // CvMat* subsample_train;
  // CvMat* subsample_test;
  // CvMat* missing;
  // CvMat* class_labels;
  //
  // cv::RNG* rng;
  //
  // int class_count;
  // float delta;
  // float base_value;
  //
  // };

  (* ***************************************************************************************
    *                              Artificial Neural Networks (ANN)                          *
    *************************************************************************************** *)

  /// ////////////////////////////////// Multi-Layer Perceptrons //////////////////////////////
  //
  // struct CV_EXPORTS_W_MAP CvANN_MLP_TrainParams
  // {
  // CvANN_MLP_TrainParams();
  // CvANN_MLP_TrainParams( CvTermCriteria term_crit, int train_method,
  // double param1, double param2=0 );
  // ~CvANN_MLP_TrainParams();
  //
  // enum { BACKPROP=0, RPROP=1 };
  //
  // CV_PROP_RW CvTermCriteria term_crit;
  // CV_PROP_RW int train_method;
  //
  // // backpropagation parameters
  // CV_PROP_RW double bp_dw_scale, bp_moment_scale;
  //
  // // rprop parameters
  // CV_PROP_RW double rp_dw0, rp_dw_plus, rp_dw_minus, rp_dw_min, rp_dw_max;
  // };
  //
  //
  // class CV_EXPORTS_W CvANN_MLP : public CvStatModel
  // {
  // public:
  // CV_WRAP CvANN_MLP();
  // CvANN_MLP( const CvMat* layerSizes,
  // int activateFunc=CvANN_MLP::SIGMOID_SYM,
  // double fparam1=0, double fparam2=0 );
  //
  // virtual ~CvANN_MLP();
  //
  // virtual void create( const CvMat* layerSizes,
  // int activateFunc=CvANN_MLP::SIGMOID_SYM,
  // double fparam1=0, double fparam2=0 );
  //
  // virtual int train( const CvMat* inputs, const CvMat* outputs,
  // const CvMat* sampleWeights, const CvMat* sampleIdx=0,
  // CvANN_MLP_TrainParams params = CvANN_MLP_TrainParams(),
  // int flags=0 );
  // virtual float predict( const CvMat* inputs, CV_OUT CvMat* outputs ) const;
  //
  // CV_WRAP CvANN_MLP( const cv::Mat& layerSizes,
  // int activateFunc=CvANN_MLP::SIGMOID_SYM,
  // double fparam1=0, double fparam2=0 );
  //
  // CV_WRAP virtual void create( const cv::Mat& layerSizes,
  // int activateFunc=CvANN_MLP::SIGMOID_SYM,
  // double fparam1=0, double fparam2=0 );
  //
  // CV_WRAP virtual int train( const cv::Mat& inputs, const cv::Mat& outputs,
  // const cv::Mat& sampleWeights, const cv::Mat& sampleIdx=cv::Mat(),
  // CvANN_MLP_TrainParams params = CvANN_MLP_TrainParams(),
  // int flags=0 );
  //
  // CV_WRAP virtual float predict( const cv::Mat& inputs, CV_OUT cv::Mat& outputs ) const;
  //
  // CV_WRAP virtual void clear();
  //
  // // possible activation functions
  // enum { IDENTITY = 0, SIGMOID_SYM = 1, GAUSSIAN = 2 };
  //
  // // available training flags
  // enum { UPDATE_WEIGHTS = 1, NO_INPUT_SCALE = 2, NO_OUTPUT_SCALE = 4 };
  //
  // virtual void read( CvFileStorage* fs, CvFileNode* node );
  // virtual void write( CvFileStorage* storage, const char* name ) const;
  //
  // int get_layer_count() { return layer_sizes ? layer_sizes->cols : 0; }
  // const CvMat* get_layer_sizes() { return layer_sizes; }
  // double* get_weights(int layer)
  // {
  // return layer_sizes && weights &&
  // (unsigned)layer <= (unsigned)layer_sizes->cols ? weights[layer] : 0;
  // }
  //
  // virtual void calc_activ_func_deriv( CvMat* xf, CvMat* deriv, const double* bias ) const;
  //
  // protected:
  //
  // virtual bool prepare_to_train( const CvMat* _inputs, const CvMat* _outputs,
  // const CvMat* _sample_weights, const CvMat* sampleIdx,
  // CvVectors* _ivecs, CvVectors* _ovecs, double** _sw, int _flags );
  //
  // // sequential random backpropagation
  // virtual int train_backprop( CvVectors _ivecs, CvVectors _ovecs, const double* _sw );
  //
  // // RPROP algorithm
  // virtual int train_rprop( CvVectors _ivecs, CvVectors _ovecs, const double* _sw );
  //
  // virtual void calc_activ_func( CvMat* xf, const double* bias ) const;
  // virtual void set_activ_func( int _activ_func=SIGMOID_SYM,
  // double _f_param1=0, double _f_param2=0 );
  // virtual void init_weights();
  // virtual void scale_input( const CvMat* _src, CvMat* _dst ) const;
  // virtual void scale_output( const CvMat* _src, CvMat* _dst ) const;
  // virtual void calc_input_scale( const CvVectors* vecs, int flags );
  // virtual void calc_output_scale( const CvVectors* vecs, int flags );
  //
  // virtual void write_params( CvFileStorage* fs ) const;
  // virtual void read_params( CvFileStorage* fs, CvFileNode* node );
  //
  // CvMat* layer_sizes;
  // CvMat* wbuf;
  // CvMat* sample_weights;
  // double** weights;
  // double f_param1, f_param2;
  // double min_val, max_val, min_val1, max_val1;
  // int activ_func;
  // int max_count, max_buf_sz;
  // CvANN_MLP_TrainParams params;
  // cv::RNG* rng;
  // };
  //
  (* ***************************************************************************************\
    // *                           Auxilary functions declarations                              *
    // \*************************************************************************************** *)
  //
  (* Generates <sample> from multivariate normal distribution, where <mean> - is an
    // average row vector, <cov> - symmetric covariation matrix *)
  // CVAPI(void) cvRandMVNormal( CvMat* mean, CvMat* cov, CvMat* sample,
  // CvRNG* rng CV_DEFAULT(0) );
  //
  (* Generates sample from gaussian mixture distribution *)
  // CVAPI(void) cvRandGaussMixture( CvMat* means[],
  // CvMat* covs[],
  // float weights[],
  // int clsnum,
  // CvMat* sample,
  // CvMat* sampClasses CV_DEFAULT(0) );
  //
  // #define CV_TS_CONCENTRIC_SPHERES 0
  //
  (* creates test set *)
  // CVAPI(void) cvCreateTestSet( int type, CvMat** samples,
  // int num_samples,
  // int num_features,
  // CvMat** responses,
  // int num_classes, ... );

  (* ***************************************************************************************
    *                                      Data                                             *
    *************************************************************************************** *)

  // #define CV_COUNT     0
  // #define CV_PORTION   1
  //
  // struct CV_EXPORTS CvTrainTestSplit
  // {
  // CvTrainTestSplit();
  // CvTrainTestSplit( int train_sample_count, bool mix = true);
  // CvTrainTestSplit( float train_sample_portion, bool mix = true);
  //
  // union
  // {
  // int count;
  // float portion;
  // } train_sample_part;
  // int train_sample_part_mode;
  //
  // bool mix;
  // };
  //
  // class CV_EXPORTS CvMLData
  // {
  // public:
  // CvMLData();
  // virtual ~CvMLData();
  //
  // // returns:
  // // 0 - OK
  // // -1 - file can not be opened or is not correct
  // int read_csv( const char* filename );
  //
  // const CvMat* get_values() const;
  // const CvMat* get_responses();
  // const CvMat* get_missing() const;
  //
  // void set_header_lines_number( int n );
  // int get_header_lines_number() const;
  //
  // void set_response_idx( int idx ); // old response become predictors, new response_idx = idx
  // // if idx < 0 there will be no response
  // int get_response_idx() const;
  //
  // void set_train_test_split( const CvTrainTestSplit * spl );
  // const CvMat* get_train_sample_idx() const;
  // const CvMat* get_test_sample_idx() const;
  // void mix_train_and_test_idx();
  //
  // const CvMat* get_var_idx();
  // void chahge_var_idx( int vi, bool state ); // misspelled (saved for back compitability),
  // // use change_var_idx
  // void change_var_idx( int vi, bool state ); // state == true to set vi-variable as predictor
  //
  // const CvMat* get_var_types();
  // int get_var_type( int var_idx ) const;
  // // following 2 methods enable to change vars type
  // // use these methods to assign CV_VAR_CATEGORICAL type for categorical variable
  // // with numerical labels; in the other cases var types are correctly determined automatically
  // void set_var_types( const char* str );  // str examples:
  // // "ord[0-17],cat[18]", "ord[0,2,4,10-12], cat[1,3,5-9,13,14]",
  // // "cat", "ord" (all vars are categorical/ordered)
  // void change_var_type( int var_idx, int type); // type in { CV_VAR_ORDERED, CV_VAR_CATEGORICAL }
  //
  // void set_delimiter( char ch );
  // char get_delimiter() const;
  //
  // void set_miss_ch( char ch );
  // char get_miss_ch() const;
  //
  // const std::map<cv::String, int>& get_class_labels_map() const;
  //
  // protected:
  // virtual void clear();
  //
  // void str_to_flt_elem( const char* token, float& flt_elem, int& type);
  // void free_train_test_idx();
  //
  // char delimiter;
  // char miss_ch;
  // //char flt_separator;
  //
  // CvMat* values;
  // CvMat* missing;
  // CvMat* var_types;
  // CvMat* var_idx_mask;
  //
  // CvMat* response_out; // header
  // CvMat* var_idx_out; // mat
  // CvMat* var_types_out; // mat
  //
  // int header_lines_number;
  //
  // int response_idx;
  //
  // int train_sample_count;
  // bool mix;
  //
  // int total_class_count;
  // std::map<cv::String, int> class_map;
  //
  // CvMat* train_sample_idx;
  // CvMat* test_sample_idx;
  // int* sample_idx; // data of train_sample_idx and test_sample_idx
  //
  // cv::RNG* rng;
  // };
  //
  //
  // namespace cv
  // {
  //
  // typedef CvStatModel StatModel;
  // typedef CvParamGrid ParamGrid;
  // typedef CvNormalBayesClassifier NormalBayesClassifier;
  // typedef CvKNearest KNearest;
  // typedef CvSVMParams SVMParams;
  // typedef CvSVMKernel SVMKernel;
  // typedef CvSVMSolver SVMSolver;
  // typedef CvSVM SVM;
  // typedef CvDTreeParams DTreeParams;
  // typedef CvMLData TrainData;
  // typedef CvDTree DecisionTree;
  // typedef CvForestTree ForestTree;
  // typedef CvRTParams RandomTreeParams;
  // typedef CvRTrees RandomTrees;
  // typedef CvERTreeTrainData ERTreeTRainData;
  // typedef CvForestERTree ERTree;
  // typedef CvERTrees ERTrees;
  // typedef CvBoostParams BoostParams;
  // typedef CvBoostTree BoostTree;
  // typedef CvBoost Boost;
  // typedef CvANN_MLP_TrainParams ANN_MLP_TrainParams;
  // typedef CvANN_MLP NeuralNet_MLP;
  // typedef CvGBTreesParams GradientBoostingTreeParams;
  // typedef CvGBTrees GradientBoostingTrees;
  //
  // template<> CV_EXPORTS void Ptr<CvDTreeSplit>::delete_obj();
  //
  // CV_EXPORTS bool initModule_ml(void);
  // }

  // function CreateCvKNearest: TCvKNearest; stdcall; overload;
  // function CreateCvKNearest(const trainData: pCvMat; const responses: pCvMat; const sampleIdx: pCvMat = nil;
  // isRegression: bool = false; max_k: Integer = 32): TCvKNearest; stdcall; overload;
  // procedure ReleaseCvKNearest(ex: TCvKNearest); stdcall;

implementation

uses ocv.lib;

function CreateCvKNearest: TCvKNearest; stdcall; external opencv_classes_lib; overload;
function CreateCvKNearest(const trainData: pCvMat; const responses: pCvMat; const sampleIdx: pCvMat = nil;
  isRegression: bool = false; max_k: Integer = 32): TCvKNearest; stdcall; external opencv_classes_lib; overload;
procedure ReleaseCvKNearest(ex: TCvKNearest); stdcall; external opencv_classes_lib;

function CV_IS_ROW_SAMPLE(flags: Integer): Boolean;
begin
  Result := (flags and CV_ROW_SAMPLE) <> 0;
end;

{ TCvDTreeNode }

function TCvDTreeNode.get_num_valid(vi: Integer): Integer;
begin
  if num_valid = nil then
    Result := sample_count
  else
    Result := num_valid[vi];
end;

procedure TCvDTreeNode.set_num_valid(vi, n: Integer);
begin
  if num_valid <> nil then
    num_valid[vi] := n;
end;

{ TCvKNearest }

class function TCvKNearest.Create: TCvKNearest;
begin
  Result := CreateCvKNearest;
end;

class function TCvKNearest.Create(const trainData, responses, sampleIdx: pCvMat; isRegression: bool; max_k: Integer)
  : TCvKNearest;
begin
  Result := CreateCvKNearest(trainData, responses, sampleIdx, isRegression, max_k);
end;

procedure TCvKNearest.Free;
begin
  ReleaseCvKNearest(Self);
end;

end.