1944 lines
71 KiB
C++
1944 lines
71 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
|
//
|
|
// By downloading, copying, installing or using the software you agree to this license.
|
|
// If you do not agree to this license, do not download, install,
|
|
// copy or use the software.
|
|
//
|
|
//
|
|
// Intel License Agreement
|
|
//
|
|
// Copyright (C) 2000, Intel Corporation, all rights reserved.
|
|
// Third party copyrights are property of their respective owners.
|
|
//
|
|
// Redistribution and use in source and binary forms, with or without modification,
|
|
// are permitted provided that the following conditions are met:
|
|
//
|
|
// * Redistribution's of source code must retain the above copyright notice,
|
|
// this list of conditions and the following disclaimer.
|
|
//
|
|
// * Redistribution's in binary form must reproduce the above copyright notice,
|
|
// this list of conditions and the following disclaimer in the documentation
|
|
// and/or other materials provided with the distribution.
|
|
//
|
|
// * The name of Intel Corporation may not be used to endorse or promote products
|
|
// derived from this software without specific prior written permission.
|
|
//
|
|
// This software is provided by the copyright holders and contributors "as is" and
|
|
// any express or implied warranties, including, but not limited to, the implied
|
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
|
// indirect, incidental, special, exemplary, or consequential damages
|
|
// (including, but not limited to, procurement of substitute goods or services;
|
|
// loss of use, data, or profits; or business interruption) however caused
|
|
// and on any theory of liability, whether in contract, strict liability,
|
|
// or tort (including negligence or otherwise) arising in any way out of
|
|
// the use of this software, even if advised of the possibility of such damage.
|
|
//
|
|
//M*/
|
|
|
|
#ifndef __OPENCV_ML_HPP__
|
|
#define __OPENCV_ML_HPP__
|
|
|
|
// disable deprecation warning which appears in VisualStudio 8.0
|
|
#if _MSC_VER >= 1400
|
|
#pragma warning( disable : 4996 )
|
|
#endif
|
|
|
|
#ifndef SKIP_INCLUDES
|
|
|
|
#include "opencv2/core/core.hpp"
|
|
#include <limits.h>
|
|
|
|
#if defined WIN32 || defined _WIN32
|
|
#include <windows.h>
|
|
#endif
|
|
|
|
#else // SKIP_INCLUDES
|
|
|
|
#if defined WIN32 || defined _WIN32
|
|
#define CV_CDECL __cdecl
|
|
#define CV_STDCALL __stdcall
|
|
#else
|
|
#define CV_CDECL
|
|
#define CV_STDCALL
|
|
#endif
|
|
|
|
#ifndef CV_EXTERN_C
|
|
#ifdef __cplusplus
|
|
#define CV_EXTERN_C extern "C"
|
|
#define CV_DEFAULT(val) = val
|
|
#else
|
|
#define CV_EXTERN_C
|
|
#define CV_DEFAULT(val)
|
|
#endif
|
|
#endif
|
|
|
|
#ifndef CV_EXTERN_C_FUNCPTR
|
|
#ifdef __cplusplus
|
|
#define CV_EXTERN_C_FUNCPTR(x) extern "C" { typedef x; }
|
|
#else
|
|
#define CV_EXTERN_C_FUNCPTR(x) typedef x
|
|
#endif
|
|
#endif
|
|
|
|
#ifndef CV_INLINE
|
|
#if defined __cplusplus
|
|
#define CV_INLINE inline
|
|
#elif (defined WIN32 || defined _WIN32) && !defined __GNUC__
|
|
#define CV_INLINE __inline
|
|
#else
|
|
#define CV_INLINE static
|
|
#endif
|
|
#endif /* CV_INLINE */
|
|
|
|
#if (defined WIN32 || defined _WIN32) && defined CVAPI_EXPORTS
|
|
#define CV_EXPORTS __declspec(dllexport)
|
|
#else
|
|
#define CV_EXPORTS
|
|
#endif
|
|
|
|
#ifndef CVAPI
|
|
#define CVAPI(rettype) CV_EXTERN_C CV_EXPORTS rettype CV_CDECL
|
|
#endif
|
|
|
|
#endif // SKIP_INCLUDES
|
|
|
|
|
|
#ifdef __cplusplus
|
|
|
|
// Apple defines a check() macro somewhere in the debug headers
|
|
// that interferes with a method definiton in this header
|
|
#undef check
|
|
|
|
/****************************************************************************************\
|
|
* Main struct definitions *
|
|
\****************************************************************************************/
|
|
|
|
/* log(2*PI) */
|
|
#define CV_LOG2PI (1.8378770664093454835606594728112)
|
|
|
|
/* columns of <trainData> matrix are training samples */
|
|
#define CV_COL_SAMPLE 0
|
|
|
|
/* rows of <trainData> matrix are training samples */
|
|
#define CV_ROW_SAMPLE 1
|
|
|
|
#define CV_IS_ROW_SAMPLE(flags) ((flags) & CV_ROW_SAMPLE)
|
|
|
|
struct CvVectors
|
|
{
|
|
int type;
|
|
int dims, count;
|
|
CvVectors* next;
|
|
union
|
|
{
|
|
uchar** ptr;
|
|
float** fl;
|
|
double** db;
|
|
} data;
|
|
};
|
|
|
|
#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
|
|
|
|
/* Variable type */
|
|
#define CV_VAR_NUMERICAL 0
|
|
#define CV_VAR_ORDERED 0
|
|
#define CV_VAR_CATEGORICAL 1
|
|
|
|
#define CV_TYPE_NAME_ML_SVM "opencv-ml-svm"
|
|
#define CV_TYPE_NAME_ML_KNN "opencv-ml-knn"
|
|
#define CV_TYPE_NAME_ML_NBAYES "opencv-ml-bayesian"
|
|
#define CV_TYPE_NAME_ML_EM "opencv-ml-em"
|
|
#define CV_TYPE_NAME_ML_BOOSTING "opencv-ml-boost-tree"
|
|
#define CV_TYPE_NAME_ML_TREE "opencv-ml-tree"
|
|
#define CV_TYPE_NAME_ML_ANN_MLP "opencv-ml-ann-mlp"
|
|
#define CV_TYPE_NAME_ML_CNN "opencv-ml-cnn"
|
|
#define CV_TYPE_NAME_ML_RTREES "opencv-ml-random-trees"
|
|
|
|
#define CV_TRAIN_ERROR 0
|
|
#define CV_TEST_ERROR 1
|
|
|
|
class CV_EXPORTS CvStatModel
|
|
{
|
|
public:
|
|
CvStatModel();
|
|
virtual ~CvStatModel();
|
|
|
|
virtual void clear();
|
|
|
|
virtual void save( const char* filename, const char* name=0 ) const;
|
|
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 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 )
|
|
{
|
|
min_val = _min_val;
|
|
max_val = _max_val;
|
|
step = log_step;
|
|
}
|
|
//CvParamGrid( int param_id );
|
|
bool check() const;
|
|
|
|
double min_val;
|
|
double max_val;
|
|
double step;
|
|
};
|
|
|
|
class CV_EXPORTS CvNormalBayesClassifier : public CvStatModel
|
|
{
|
|
public:
|
|
CvNormalBayesClassifier();
|
|
virtual ~CvNormalBayesClassifier();
|
|
|
|
CvNormalBayesClassifier( const CvMat* _train_data, const CvMat* _responses,
|
|
const CvMat* _var_idx=0, const CvMat* _sample_idx=0 );
|
|
|
|
virtual bool train( const CvMat* _train_data, const CvMat* _responses,
|
|
const CvMat* _var_idx = 0, const CvMat* _sample_idx=0, bool update=false );
|
|
|
|
virtual float predict( const CvMat* _samples, CvMat* results=0 ) const;
|
|
virtual void clear();
|
|
|
|
#ifndef SWIG
|
|
CvNormalBayesClassifier( const cv::Mat& _train_data, const cv::Mat& _responses,
|
|
const cv::Mat& _var_idx=cv::Mat(), const cv::Mat& _sample_idx=cv::Mat() );
|
|
virtual bool train( const cv::Mat& _train_data, const cv::Mat& _responses,
|
|
const cv::Mat& _var_idx = cv::Mat(), const cv::Mat& _sample_idx=cv::Mat(),
|
|
bool update=false );
|
|
virtual float predict( const cv::Mat& _samples, cv::Mat* results=0 ) const;
|
|
#endif
|
|
|
|
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 *
|
|
\****************************************************************************************/
|
|
|
|
// k Nearest Neighbors
|
|
class CV_EXPORTS CvKNearest : public CvStatModel
|
|
{
|
|
public:
|
|
|
|
CvKNearest();
|
|
virtual ~CvKNearest();
|
|
|
|
CvKNearest( const CvMat* _train_data, const CvMat* _responses,
|
|
const CvMat* _sample_idx=0, bool _is_regression=false, int max_k=32 );
|
|
|
|
virtual bool train( const CvMat* _train_data, const CvMat* _responses,
|
|
const CvMat* _sample_idx=0, bool is_regression=false,
|
|
int _max_k=32, bool _update_base=false );
|
|
|
|
virtual float find_nearest( const CvMat* _samples, int k, CvMat* results=0,
|
|
const float** neighbors=0, CvMat* neighbor_responses=0, CvMat* dist=0 ) const;
|
|
|
|
#ifndef SWIG
|
|
CvKNearest( const cv::Mat& _train_data, const cv::Mat& _responses,
|
|
const cv::Mat& _sample_idx=cv::Mat(), bool _is_regression=false, int max_k=32 );
|
|
|
|
virtual bool train( const cv::Mat& _train_data, const cv::Mat& _responses,
|
|
const cv::Mat& _sample_idx=cv::Mat(), bool is_regression=false,
|
|
int _max_k=32, bool _update_base=false );
|
|
|
|
virtual float find_nearest( const cv::Mat& _samples, int k, cv::Mat* results=0,
|
|
const float** neighbors=0,
|
|
cv::Mat* neighbor_responses=0,
|
|
cv::Mat* dist=0 ) const;
|
|
#endif
|
|
|
|
virtual void clear();
|
|
int get_max_k() const;
|
|
int get_var_count() const;
|
|
int get_sample_count() const;
|
|
bool is_regression() const;
|
|
|
|
protected:
|
|
|
|
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;
|
|
|
|
|
|
int max_k, var_count;
|
|
int total;
|
|
bool regression;
|
|
CvVectors* samples;
|
|
};
|
|
|
|
/****************************************************************************************\
|
|
* Support Vector Machines *
|
|
\****************************************************************************************/
|
|
|
|
// SVM training parameters
|
|
struct CV_EXPORTS 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 );
|
|
|
|
int svm_type;
|
|
int kernel_type;
|
|
double degree; // for poly
|
|
double gamma; // for poly/rbf/sigmoid
|
|
double coef0; // for poly/sigmoid
|
|
|
|
double C; // for CV_SVM_C_SVC, CV_SVM_EPS_SVR and CV_SVM_NU_SVR
|
|
double nu; // for CV_SVM_NU_SVC, CV_SVM_ONE_CLASS, and CV_SVM_NU_SVR
|
|
double p; // for CV_SVM_EPS_SVR
|
|
CvMat* class_weights; // for CV_SVM_C_SVC
|
|
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_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 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 };
|
|
|
|
// SVM params type
|
|
enum { C=0, GAMMA=1, P=2, NU=3, COEF=4, DEGREE=5 };
|
|
|
|
CvSVM();
|
|
virtual ~CvSVM();
|
|
|
|
CvSVM( const CvMat* _train_data, const CvMat* _responses,
|
|
const CvMat* _var_idx=0, const CvMat* _sample_idx=0,
|
|
CvSVMParams _params=CvSVMParams() );
|
|
|
|
virtual bool train( const CvMat* _train_data, const CvMat* _responses,
|
|
const CvMat* _var_idx=0, const CvMat* _sample_idx=0,
|
|
CvSVMParams _params=CvSVMParams() );
|
|
|
|
virtual bool train_auto( const CvMat* _train_data, const CvMat* _responses,
|
|
const CvMat* _var_idx, const CvMat* _sample_idx, CvSVMParams _params,
|
|
int k_fold = 10,
|
|
CvParamGrid C_grid = get_default_grid(CvSVM::C),
|
|
CvParamGrid gamma_grid = get_default_grid(CvSVM::GAMMA),
|
|
CvParamGrid p_grid = get_default_grid(CvSVM::P),
|
|
CvParamGrid nu_grid = get_default_grid(CvSVM::NU),
|
|
CvParamGrid coef_grid = get_default_grid(CvSVM::COEF),
|
|
CvParamGrid degree_grid = get_default_grid(CvSVM::DEGREE) );
|
|
|
|
virtual float predict( const CvMat* _sample, bool returnDFVal=false ) const;
|
|
|
|
#ifndef SWIG
|
|
CvSVM( const cv::Mat& _train_data, const cv::Mat& _responses,
|
|
const cv::Mat& _var_idx=cv::Mat(), const cv::Mat& _sample_idx=cv::Mat(),
|
|
CvSVMParams _params=CvSVMParams() );
|
|
|
|
virtual bool train( const cv::Mat& _train_data, const cv::Mat& _responses,
|
|
const cv::Mat& _var_idx=cv::Mat(), const cv::Mat& _sample_idx=cv::Mat(),
|
|
CvSVMParams _params=CvSVMParams() );
|
|
|
|
virtual bool train_auto( const cv::Mat& _train_data, const cv::Mat& _responses,
|
|
const cv::Mat& _var_idx, const cv::Mat& _sample_idx, CvSVMParams _params,
|
|
int k_fold = 10,
|
|
CvParamGrid C_grid = get_default_grid(CvSVM::C),
|
|
CvParamGrid gamma_grid = get_default_grid(CvSVM::GAMMA),
|
|
CvParamGrid p_grid = get_default_grid(CvSVM::P),
|
|
CvParamGrid nu_grid = get_default_grid(CvSVM::NU),
|
|
CvParamGrid coef_grid = get_default_grid(CvSVM::COEF),
|
|
CvParamGrid degree_grid = get_default_grid(CvSVM::DEGREE) );
|
|
virtual float predict( const cv::Mat& _sample, bool returnDFVal=false ) const;
|
|
#endif
|
|
|
|
virtual int get_support_vector_count() const;
|
|
virtual const float* get_support_vector(int i) const;
|
|
virtual CvSVMParams get_params() const { return params; };
|
|
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 );
|
|
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 );
|
|
|
|
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 *
|
|
\****************************************************************************************/
|
|
|
|
struct CV_EXPORTS CvEMParams
|
|
{
|
|
CvEMParams() : nclusters(10), cov_mat_type(1/*CvEM::COV_MAT_DIAGONAL*/),
|
|
start_step(0/*CvEM::START_AUTO_STEP*/), probs(0), weights(0), means(0), covs(0)
|
|
{
|
|
term_crit=cvTermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, FLT_EPSILON );
|
|
}
|
|
|
|
CvEMParams( int _nclusters, int _cov_mat_type=1/*CvEM::COV_MAT_DIAGONAL*/,
|
|
int _start_step=0/*CvEM::START_AUTO_STEP*/,
|
|
CvTermCriteria _term_crit=cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, FLT_EPSILON),
|
|
const CvMat* _probs=0, const CvMat* _weights=0, const CvMat* _means=0, const CvMat** _covs=0 ) :
|
|
nclusters(_nclusters), cov_mat_type(_cov_mat_type), start_step(_start_step),
|
|
probs(_probs), weights(_weights), means(_means), covs(_covs), term_crit(_term_crit)
|
|
{}
|
|
|
|
int nclusters;
|
|
int cov_mat_type;
|
|
int start_step;
|
|
const CvMat* probs;
|
|
const CvMat* weights;
|
|
const CvMat* means;
|
|
const CvMat** covs;
|
|
CvTermCriteria term_crit;
|
|
};
|
|
|
|
|
|
class CV_EXPORTS CvEM : public CvStatModel
|
|
{
|
|
public:
|
|
// Type of covariation matrices
|
|
enum { COV_MAT_SPHERICAL=0, COV_MAT_DIAGONAL=1, COV_MAT_GENERIC=2 };
|
|
|
|
// The initial step
|
|
enum { START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0 };
|
|
|
|
CvEM();
|
|
CvEM( const CvMat* samples, const CvMat* sample_idx=0,
|
|
CvEMParams params=CvEMParams(), CvMat* labels=0 );
|
|
//CvEM (CvEMParams params, CvMat * means, CvMat ** covs, CvMat * weights, CvMat * probs, CvMat * log_weight_div_det, CvMat * inv_eigen_values, CvMat** cov_rotate_mats);
|
|
|
|
virtual ~CvEM();
|
|
|
|
virtual bool train( const CvMat* samples, const CvMat* sample_idx=0,
|
|
CvEMParams params=CvEMParams(), CvMat* labels=0 );
|
|
|
|
virtual float predict( const CvMat* sample, CvMat* probs ) const;
|
|
|
|
#ifndef SWIG
|
|
CvEM( const cv::Mat& samples, const cv::Mat& sample_idx=cv::Mat(),
|
|
CvEMParams params=CvEMParams(), cv::Mat* labels=0 );
|
|
|
|
virtual bool train( const cv::Mat& samples, const cv::Mat& sample_idx=cv::Mat(),
|
|
CvEMParams params=CvEMParams(), cv::Mat* labels=0 );
|
|
|
|
virtual float predict( const cv::Mat& sample, cv::Mat* probs ) const;
|
|
#endif
|
|
|
|
virtual void clear();
|
|
|
|
int get_nclusters() const;
|
|
const CvMat* get_means() const;
|
|
const CvMat** get_covs() const;
|
|
const CvMat* get_weights() const;
|
|
const CvMat* get_probs() const;
|
|
|
|
inline double get_log_likelihood () const { return log_likelihood; };
|
|
|
|
// inline const CvMat * get_log_weight_div_det () const { return log_weight_div_det; };
|
|
// inline const CvMat * get_inv_eigen_values () const { return inv_eigen_values; };
|
|
// inline const CvMat ** get_cov_rotate_mats () const { return cov_rotate_mats; };
|
|
|
|
protected:
|
|
|
|
virtual void set_params( const CvEMParams& params,
|
|
const CvVectors& train_data );
|
|
virtual void init_em( const CvVectors& train_data );
|
|
virtual double run_em( const CvVectors& train_data );
|
|
virtual void init_auto( const CvVectors& samples );
|
|
virtual void kmeans( const CvVectors& train_data, int nclusters,
|
|
CvMat* labels, CvTermCriteria criteria,
|
|
const CvMat* means );
|
|
CvEMParams params;
|
|
double log_likelihood;
|
|
|
|
CvMat* means;
|
|
CvMat** covs;
|
|
CvMat* weights;
|
|
CvMat* probs;
|
|
|
|
CvMat* log_weight_div_det;
|
|
CvMat* inv_eigen_values;
|
|
CvMat** cov_rotate_mats;
|
|
};
|
|
|
|
/****************************************************************************************\
|
|
* 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)
|
|
|
|
struct CvDTreeSplit
|
|
{
|
|
int var_idx;
|
|
int condensed_idx;
|
|
int inversed;
|
|
float quality;
|
|
CvDTreeSplit* next;
|
|
union
|
|
{
|
|
int subset[2];
|
|
struct
|
|
{
|
|
float c;
|
|
int split_point;
|
|
}
|
|
ord;
|
|
};
|
|
};
|
|
|
|
|
|
struct CvDTreeNode
|
|
{
|
|
int class_idx;
|
|
int Tn;
|
|
double value;
|
|
|
|
CvDTreeNode* parent;
|
|
CvDTreeNode* left;
|
|
CvDTreeNode* right;
|
|
|
|
CvDTreeSplit* split;
|
|
|
|
int sample_count;
|
|
int depth;
|
|
int* num_valid;
|
|
int offset;
|
|
int buf_idx;
|
|
double maxlr;
|
|
|
|
// global pruning data
|
|
int complexity;
|
|
double alpha;
|
|
double node_risk, tree_risk, tree_error;
|
|
|
|
// cross-validation pruning data
|
|
int* cv_Tn;
|
|
double* cv_node_risk;
|
|
double* cv_node_error;
|
|
|
|
int get_num_valid(int vi) { return num_valid ? num_valid[vi] : sample_count; }
|
|
void set_num_valid(int vi, int n) { if( num_valid ) num_valid[vi] = n; }
|
|
};
|
|
|
|
|
|
struct CV_EXPORTS CvDTreeParams
|
|
{
|
|
int max_categories;
|
|
int max_depth;
|
|
int min_sample_count;
|
|
int cv_folds;
|
|
bool use_surrogates;
|
|
bool use_1se_rule;
|
|
bool truncate_pruned_tree;
|
|
float regression_accuracy;
|
|
const float* priors;
|
|
|
|
CvDTreeParams() : max_categories(10), max_depth(INT_MAX), min_sample_count(10),
|
|
cv_folds(10), use_surrogates(true), use_1se_rule(true),
|
|
truncate_pruned_tree(true), regression_accuracy(0.01f), priors(0)
|
|
{}
|
|
|
|
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 ) :
|
|
max_categories(_max_categories), max_depth(_max_depth),
|
|
min_sample_count(_min_sample_count), cv_folds (_cv_folds),
|
|
use_surrogates(_use_surrogates), use_1se_rule(_use_1se_rule),
|
|
truncate_pruned_tree(_truncate_pruned_tree),
|
|
regression_accuracy(_regression_accuracy),
|
|
priors(_priors)
|
|
{}
|
|
};
|
|
|
|
|
|
struct CV_EXPORTS CvDTreeTrainData
|
|
{
|
|
CvDTreeTrainData();
|
|
CvDTreeTrainData( const CvMat* _train_data, int _tflag,
|
|
const CvMat* _responses, const CvMat* _var_idx=0,
|
|
const CvMat* _sample_idx=0, const CvMat* _var_type=0,
|
|
const CvMat* _missing_mask=0,
|
|
const CvDTreeParams& _params=CvDTreeParams(),
|
|
bool _shared=false, bool _add_labels=false );
|
|
virtual ~CvDTreeTrainData();
|
|
|
|
virtual void set_data( const CvMat* _train_data, int _tflag,
|
|
const CvMat* _responses, const CvMat* _var_idx=0,
|
|
const CvMat* _sample_idx=0, const CvMat* _var_type=0,
|
|
const CvMat* _missing_mask=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;
|
|
bool shared;
|
|
int is_buf_16u;
|
|
|
|
CvMat* cat_count;
|
|
CvMat* cat_ofs;
|
|
CvMat* cat_map;
|
|
|
|
CvMat* counts;
|
|
CvMat* buf;
|
|
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;
|
|
|
|
CvRNG rng;
|
|
};
|
|
|
|
class CvDTree;
|
|
class CvForestTree;
|
|
|
|
namespace cv
|
|
{
|
|
struct DTreeBestSplitFinder;
|
|
struct ForestTreeBestSplitFinder;
|
|
}
|
|
|
|
class CV_EXPORTS CvDTree : public CvStatModel
|
|
{
|
|
public:
|
|
CvDTree();
|
|
virtual ~CvDTree();
|
|
|
|
virtual bool train( const CvMat* _train_data, int _tflag,
|
|
const CvMat* _responses, const CvMat* _var_idx=0,
|
|
const CvMat* _sample_idx=0, const CvMat* _var_type=0,
|
|
const CvMat* _missing_mask=0,
|
|
CvDTreeParams params=CvDTreeParams() );
|
|
|
|
virtual bool train( CvMLData* _data, CvDTreeParams _params=CvDTreeParams() );
|
|
|
|
virtual float calc_error( CvMLData* _data, int type , std::vector<float> *resp = 0 ); // type in {CV_TRAIN_ERROR, CV_TEST_ERROR}
|
|
|
|
virtual bool train( CvDTreeTrainData* _train_data, const CvMat* _subsample_idx );
|
|
|
|
virtual CvDTreeNode* predict( const CvMat* _sample, const CvMat* _missing_data_mask=0,
|
|
bool preprocessed_input=false ) const;
|
|
|
|
#ifndef SWIG
|
|
virtual bool train( const cv::Mat& _train_data, int _tflag,
|
|
const cv::Mat& _responses, const cv::Mat& _var_idx=cv::Mat(),
|
|
const cv::Mat& _sample_idx=cv::Mat(), const cv::Mat& _var_type=cv::Mat(),
|
|
const cv::Mat& _missing_mask=cv::Mat(),
|
|
CvDTreeParams params=CvDTreeParams() );
|
|
|
|
virtual CvDTreeNode* predict( const cv::Mat& _sample, const cv::Mat& _missing_data_mask=cv::Mat(),
|
|
bool preprocessed_input=false ) const;
|
|
#endif
|
|
|
|
virtual const CvMat* get_var_importance();
|
|
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* _train_data, 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* _train_data, int _tflag,
|
|
const CvMat* _responses, const CvMat* _var_idx=0,
|
|
const CvMat* _sample_idx=0, const CvMat* _var_type=0,
|
|
const CvMat* _missing_mask=0,
|
|
CvDTreeParams params=CvDTreeParams() );
|
|
|
|
virtual bool train( CvDTreeTrainData* _train_data, 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 CvRTParams : public CvDTreeParams
|
|
{
|
|
//Parameters for the forest
|
|
bool calc_var_importance; // true <=> RF processes variable importance
|
|
int nactive_vars;
|
|
CvTermCriteria term_crit;
|
|
|
|
CvRTParams() : CvDTreeParams( 5, 10, 0, false, 10, 0, false, false, 0 ),
|
|
calc_var_importance(false), nactive_vars(0)
|
|
{
|
|
term_crit = cvTermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 50, 0.1 );
|
|
}
|
|
|
|
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 ) :
|
|
CvDTreeParams( _max_depth, _min_sample_count, _regression_accuracy,
|
|
_use_surrogates, _max_categories, 0,
|
|
false, false, _priors ),
|
|
calc_var_importance(_calc_var_importance),
|
|
nactive_vars(_nactive_vars)
|
|
{
|
|
term_crit = cvTermCriteria(termcrit_type,
|
|
max_num_of_trees_in_the_forest, forest_accuracy);
|
|
}
|
|
};
|
|
|
|
|
|
class CV_EXPORTS CvRTrees : public CvStatModel
|
|
{
|
|
public:
|
|
CvRTrees();
|
|
virtual ~CvRTrees();
|
|
virtual bool train( const CvMat* _train_data, int _tflag,
|
|
const CvMat* _responses, const CvMat* _var_idx=0,
|
|
const CvMat* _sample_idx=0, const CvMat* _var_type=0,
|
|
const CvMat* _missing_mask=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;
|
|
|
|
#ifndef SWIG
|
|
virtual bool train( const cv::Mat& _train_data, int _tflag,
|
|
const cv::Mat& _responses, const cv::Mat& _var_idx=cv::Mat(),
|
|
const cv::Mat& _sample_idx=cv::Mat(), const cv::Mat& _var_type=cv::Mat(),
|
|
const cv::Mat& _missing_mask=cv::Mat(),
|
|
CvRTParams params=CvRTParams() );
|
|
virtual float predict( const cv::Mat& sample, const cv::Mat& missing = cv::Mat() ) const;
|
|
virtual float predict_prob( const cv::Mat& sample, const cv::Mat& missing = cv::Mat() ) const;
|
|
#endif
|
|
|
|
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 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;
|
|
|
|
CvRNG rng;
|
|
CvMat* active_var_mask;
|
|
};
|
|
|
|
/****************************************************************************************\
|
|
* Extremely randomized trees Classifier *
|
|
\****************************************************************************************/
|
|
struct CV_EXPORTS CvERTreeTrainData : public CvDTreeTrainData
|
|
{
|
|
virtual void set_data( const CvMat* _train_data, int _tflag,
|
|
const CvMat* _responses, const CvMat* _var_idx=0,
|
|
const CvMat* _sample_idx=0, const CvMat* _var_type=0,
|
|
const CvMat* _missing_mask=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 CvERTrees : public CvRTrees
|
|
{
|
|
public:
|
|
CvERTrees();
|
|
virtual ~CvERTrees();
|
|
virtual bool train( const CvMat* _train_data, int _tflag,
|
|
const CvMat* _responses, const CvMat* _var_idx=0,
|
|
const CvMat* _sample_idx=0, const CvMat* _var_type=0,
|
|
const CvMat* _missing_mask=0,
|
|
CvRTParams params=CvRTParams());
|
|
#ifndef SWIG
|
|
virtual bool train( const cv::Mat& _train_data, int _tflag,
|
|
const cv::Mat& _responses, const cv::Mat& _var_idx=cv::Mat(),
|
|
const cv::Mat& _sample_idx=cv::Mat(), const cv::Mat& _var_type=cv::Mat(),
|
|
const cv::Mat& _missing_mask=cv::Mat(),
|
|
CvRTParams params=CvRTParams());
|
|
#endif
|
|
virtual bool train( CvMLData* data, CvRTParams params=CvRTParams() );
|
|
protected:
|
|
virtual bool grow_forest( const CvTermCriteria term_crit );
|
|
};
|
|
|
|
|
|
/****************************************************************************************\
|
|
* Boosted tree classifier *
|
|
\****************************************************************************************/
|
|
|
|
struct CV_EXPORTS CvBoostParams : public CvDTreeParams
|
|
{
|
|
int boost_type;
|
|
int weak_count;
|
|
int split_criteria;
|
|
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* _train_data,
|
|
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* _train_data, int _tflag,
|
|
const CvMat* _responses, const CvMat* _var_idx=0,
|
|
const CvMat* _sample_idx=0, const CvMat* _var_type=0,
|
|
const CvMat* _missing_mask=0,
|
|
CvDTreeParams params=CvDTreeParams() );
|
|
virtual bool train( CvDTreeTrainData* _train_data, 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 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 };
|
|
|
|
CvBoost();
|
|
virtual ~CvBoost();
|
|
|
|
CvBoost( const CvMat* _train_data, int _tflag,
|
|
const CvMat* _responses, const CvMat* _var_idx=0,
|
|
const CvMat* _sample_idx=0, const CvMat* _var_type=0,
|
|
const CvMat* _missing_mask=0,
|
|
CvBoostParams params=CvBoostParams() );
|
|
|
|
virtual bool train( const CvMat* _train_data, int _tflag,
|
|
const CvMat* _responses, const CvMat* _var_idx=0,
|
|
const CvMat* _sample_idx=0, const CvMat* _var_type=0,
|
|
const CvMat* _missing_mask=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;
|
|
|
|
#ifndef SWIG
|
|
CvBoost( const cv::Mat& _train_data, int _tflag,
|
|
const cv::Mat& _responses, const cv::Mat& _var_idx=cv::Mat(),
|
|
const cv::Mat& _sample_idx=cv::Mat(), const cv::Mat& _var_type=cv::Mat(),
|
|
const cv::Mat& _missing_mask=cv::Mat(),
|
|
CvBoostParams params=CvBoostParams() );
|
|
|
|
virtual bool train( const cv::Mat& _train_data, int _tflag,
|
|
const cv::Mat& _responses, const cv::Mat& _var_idx=cv::Mat(),
|
|
const cv::Mat& _sample_idx=cv::Mat(), const cv::Mat& _var_type=cv::Mat(),
|
|
const cv::Mat& _missing_mask=cv::Mat(),
|
|
CvBoostParams params=CvBoostParams(),
|
|
bool update=false );
|
|
|
|
virtual float predict( const cv::Mat& _sample, const cv::Mat& _missing=cv::Mat(),
|
|
cv::Mat* weak_responses=0, CvSlice slice=CV_WHOLE_SEQ,
|
|
bool raw_mode=false, bool return_sum=false ) const;
|
|
#endif
|
|
|
|
virtual float calc_error( CvMLData* _data, int type , std::vector<float> *resp = 0 ); // type in {CV_TRAIN_ERROR, CV_TEST_ERROR}
|
|
|
|
virtual void prune( CvSlice slice );
|
|
|
|
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 );
|
|
|
|
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;
|
|
};
|
|
|
|
|
|
/****************************************************************************************\
|
|
* Artificial Neural Networks (ANN) *
|
|
\****************************************************************************************/
|
|
|
|
/////////////////////////////////// Multi-Layer Perceptrons //////////////////////////////
|
|
|
|
struct CV_EXPORTS 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 };
|
|
|
|
CvTermCriteria term_crit;
|
|
int train_method;
|
|
|
|
// backpropagation parameters
|
|
double bp_dw_scale, bp_moment_scale;
|
|
|
|
// rprop parameters
|
|
double rp_dw0, rp_dw_plus, rp_dw_minus, rp_dw_min, rp_dw_max;
|
|
};
|
|
|
|
|
|
class CV_EXPORTS CvANN_MLP : public CvStatModel
|
|
{
|
|
public:
|
|
CvANN_MLP();
|
|
CvANN_MLP( const CvMat* _layer_sizes,
|
|
int _activ_func=SIGMOID_SYM,
|
|
double _f_param1=0, double _f_param2=0 );
|
|
|
|
virtual ~CvANN_MLP();
|
|
|
|
virtual void create( const CvMat* _layer_sizes,
|
|
int _activ_func=SIGMOID_SYM,
|
|
double _f_param1=0, double _f_param2=0 );
|
|
|
|
virtual int train( const CvMat* _inputs, const CvMat* _outputs,
|
|
const CvMat* _sample_weights, const CvMat* _sample_idx=0,
|
|
CvANN_MLP_TrainParams _params = CvANN_MLP_TrainParams(),
|
|
int flags=0 );
|
|
virtual float predict( const CvMat* _inputs, CvMat* _outputs ) const;
|
|
|
|
#ifndef SWIG
|
|
CvANN_MLP( const cv::Mat& _layer_sizes,
|
|
int _activ_func=SIGMOID_SYM,
|
|
double _f_param1=0, double _f_param2=0 );
|
|
|
|
virtual void create( const cv::Mat& _layer_sizes,
|
|
int _activ_func=SIGMOID_SYM,
|
|
double _f_param1=0, double _f_param2=0 );
|
|
|
|
virtual int train( const cv::Mat& _inputs, const cv::Mat& _outputs,
|
|
const cv::Mat& _sample_weights, const cv::Mat& _sample_idx=cv::Mat(),
|
|
CvANN_MLP_TrainParams _params = CvANN_MLP_TrainParams(),
|
|
int flags=0 );
|
|
|
|
virtual float predict( const cv::Mat& _inputs, cv::Mat& _outputs ) const;
|
|
#endif
|
|
|
|
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;
|
|
}
|
|
|
|
protected:
|
|
|
|
virtual bool prepare_to_train( const CvMat* _inputs, const CvMat* _outputs,
|
|
const CvMat* _sample_weights, const CvMat* _sample_idx,
|
|
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 calc_activ_func_deriv( CvMat* xf, CvMat* deriv, 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;
|
|
CvRNG rng;
|
|
};
|
|
|
|
#if 0
|
|
/****************************************************************************************\
|
|
* Convolutional Neural Network *
|
|
\****************************************************************************************/
|
|
typedef struct CvCNNLayer CvCNNLayer;
|
|
typedef struct CvCNNetwork CvCNNetwork;
|
|
|
|
#define CV_CNN_LEARN_RATE_DECREASE_HYPERBOLICALLY 1
|
|
#define CV_CNN_LEARN_RATE_DECREASE_SQRT_INV 2
|
|
#define CV_CNN_LEARN_RATE_DECREASE_LOG_INV 3
|
|
|
|
#define CV_CNN_GRAD_ESTIM_RANDOM 0
|
|
#define CV_CNN_GRAD_ESTIM_BY_WORST_IMG 1
|
|
|
|
#define ICV_CNN_LAYER 0x55550000
|
|
#define ICV_CNN_CONVOLUTION_LAYER 0x00001111
|
|
#define ICV_CNN_SUBSAMPLING_LAYER 0x00002222
|
|
#define ICV_CNN_FULLCONNECT_LAYER 0x00003333
|
|
|
|
#define ICV_IS_CNN_LAYER( layer ) \
|
|
( ((layer) != NULL) && ((((CvCNNLayer*)(layer))->flags & CV_MAGIC_MASK)\
|
|
== ICV_CNN_LAYER ))
|
|
|
|
#define ICV_IS_CNN_CONVOLUTION_LAYER( layer ) \
|
|
( (ICV_IS_CNN_LAYER( layer )) && (((CvCNNLayer*) (layer))->flags \
|
|
& ~CV_MAGIC_MASK) == ICV_CNN_CONVOLUTION_LAYER )
|
|
|
|
#define ICV_IS_CNN_SUBSAMPLING_LAYER( layer ) \
|
|
( (ICV_IS_CNN_LAYER( layer )) && (((CvCNNLayer*) (layer))->flags \
|
|
& ~CV_MAGIC_MASK) == ICV_CNN_SUBSAMPLING_LAYER )
|
|
|
|
#define ICV_IS_CNN_FULLCONNECT_LAYER( layer ) \
|
|
( (ICV_IS_CNN_LAYER( layer )) && (((CvCNNLayer*) (layer))->flags \
|
|
& ~CV_MAGIC_MASK) == ICV_CNN_FULLCONNECT_LAYER )
|
|
|
|
typedef void (CV_CDECL *CvCNNLayerForward)
|
|
( CvCNNLayer* layer, const CvMat* input, CvMat* output );
|
|
|
|
typedef void (CV_CDECL *CvCNNLayerBackward)
|
|
( CvCNNLayer* layer, int t, const CvMat* X, const CvMat* dE_dY, CvMat* dE_dX );
|
|
|
|
typedef void (CV_CDECL *CvCNNLayerRelease)
|
|
(CvCNNLayer** layer);
|
|
|
|
typedef void (CV_CDECL *CvCNNetworkAddLayer)
|
|
(CvCNNetwork* network, CvCNNLayer* layer);
|
|
|
|
typedef void (CV_CDECL *CvCNNetworkRelease)
|
|
(CvCNNetwork** network);
|
|
|
|
#define CV_CNN_LAYER_FIELDS() \
|
|
/* Indicator of the layer's type */ \
|
|
int flags; \
|
|
\
|
|
/* Number of input images */ \
|
|
int n_input_planes; \
|
|
/* Height of each input image */ \
|
|
int input_height; \
|
|
/* Width of each input image */ \
|
|
int input_width; \
|
|
\
|
|
/* Number of output images */ \
|
|
int n_output_planes; \
|
|
/* Height of each output image */ \
|
|
int output_height; \
|
|
/* Width of each output image */ \
|
|
int output_width; \
|
|
\
|
|
/* Learning rate at the first iteration */ \
|
|
float init_learn_rate; \
|
|
/* Dynamics of learning rate decreasing */ \
|
|
int learn_rate_decrease_type; \
|
|
/* Trainable weights of the layer (including bias) */ \
|
|
/* i-th row is a set of weights of the i-th output plane */ \
|
|
CvMat* weights; \
|
|
\
|
|
CvCNNLayerForward forward; \
|
|
CvCNNLayerBackward backward; \
|
|
CvCNNLayerRelease release; \
|
|
/* Pointers to the previous and next layers in the network */ \
|
|
CvCNNLayer* prev_layer; \
|
|
CvCNNLayer* next_layer
|
|
|
|
typedef struct CvCNNLayer
|
|
{
|
|
CV_CNN_LAYER_FIELDS();
|
|
}CvCNNLayer;
|
|
|
|
typedef struct CvCNNConvolutionLayer
|
|
{
|
|
CV_CNN_LAYER_FIELDS();
|
|
// Kernel size (height and width) for convolution.
|
|
int K;
|
|
// connections matrix, (i,j)-th element is 1 iff there is a connection between
|
|
// i-th plane of the current layer and j-th plane of the previous layer;
|
|
// (i,j)-th element is equal to 0 otherwise
|
|
CvMat *connect_mask;
|
|
// value of the learning rate for updating weights at the first iteration
|
|
}CvCNNConvolutionLayer;
|
|
|
|
typedef struct CvCNNSubSamplingLayer
|
|
{
|
|
CV_CNN_LAYER_FIELDS();
|
|
// ratio between the heights (or widths - ratios are supposed to be equal)
|
|
// of the input and output planes
|
|
int sub_samp_scale;
|
|
// amplitude of sigmoid activation function
|
|
float a;
|
|
// scale parameter of sigmoid activation function
|
|
float s;
|
|
// exp2ssumWX = exp(2<s>*(bias+w*(x1+...+x4))), where x1,...x4 are some elements of X
|
|
// - is the vector used in computing of the activation function in backward
|
|
CvMat* exp2ssumWX;
|
|
// (x1+x2+x3+x4), where x1,...x4 are some elements of X
|
|
// - is the vector used in computing of the activation function in backward
|
|
CvMat* sumX;
|
|
}CvCNNSubSamplingLayer;
|
|
|
|
// Structure of the last layer.
|
|
typedef struct CvCNNFullConnectLayer
|
|
{
|
|
CV_CNN_LAYER_FIELDS();
|
|
// amplitude of sigmoid activation function
|
|
float a;
|
|
// scale parameter of sigmoid activation function
|
|
float s;
|
|
// exp2ssumWX = exp(2*<s>*(W*X)) - is the vector used in computing of the
|
|
// activation function and it's derivative by the formulae
|
|
// activ.func. = <a>(exp(2<s>WX)-1)/(exp(2<s>WX)+1) == <a> - 2<a>/(<exp2ssumWX> + 1)
|
|
// (activ.func.)' = 4<a><s>exp(2<s>WX)/(exp(2<s>WX)+1)^2
|
|
CvMat* exp2ssumWX;
|
|
}CvCNNFullConnectLayer;
|
|
|
|
typedef struct CvCNNetwork
|
|
{
|
|
int n_layers;
|
|
CvCNNLayer* layers;
|
|
CvCNNetworkAddLayer add_layer;
|
|
CvCNNetworkRelease release;
|
|
}CvCNNetwork;
|
|
|
|
typedef struct CvCNNStatModel
|
|
{
|
|
CV_STAT_MODEL_FIELDS();
|
|
CvCNNetwork* network;
|
|
// etalons are allocated as rows, the i-th etalon has label cls_labeles[i]
|
|
CvMat* etalons;
|
|
// classes labels
|
|
CvMat* cls_labels;
|
|
}CvCNNStatModel;
|
|
|
|
typedef struct CvCNNStatModelParams
|
|
{
|
|
CV_STAT_MODEL_PARAM_FIELDS();
|
|
// network must be created by the functions cvCreateCNNetwork and <add_layer>
|
|
CvCNNetwork* network;
|
|
CvMat* etalons;
|
|
// termination criteria
|
|
int max_iter;
|
|
int start_iter;
|
|
int grad_estim_type;
|
|
}CvCNNStatModelParams;
|
|
|
|
CVAPI(CvCNNLayer*) cvCreateCNNConvolutionLayer(
|
|
int n_input_planes, int input_height, int input_width,
|
|
int n_output_planes, int K,
|
|
float init_learn_rate, int learn_rate_decrease_type,
|
|
CvMat* connect_mask CV_DEFAULT(0), CvMat* weights CV_DEFAULT(0) );
|
|
|
|
CVAPI(CvCNNLayer*) cvCreateCNNSubSamplingLayer(
|
|
int n_input_planes, int input_height, int input_width,
|
|
int sub_samp_scale, float a, float s,
|
|
float init_learn_rate, int learn_rate_decrease_type, CvMat* weights CV_DEFAULT(0) );
|
|
|
|
CVAPI(CvCNNLayer*) cvCreateCNNFullConnectLayer(
|
|
int n_inputs, int n_outputs, float a, float s,
|
|
float init_learn_rate, int learning_type, CvMat* weights CV_DEFAULT(0) );
|
|
|
|
CVAPI(CvCNNetwork*) cvCreateCNNetwork( CvCNNLayer* first_layer );
|
|
|
|
CVAPI(CvStatModel*) cvTrainCNNClassifier(
|
|
const CvMat* train_data, int tflag,
|
|
const CvMat* responses,
|
|
const CvStatModelParams* params,
|
|
const CvMat* CV_DEFAULT(0),
|
|
const CvMat* sample_idx CV_DEFAULT(0),
|
|
const CvMat* CV_DEFAULT(0), const CvMat* CV_DEFAULT(0) );
|
|
|
|
/****************************************************************************************\
|
|
* Estimate classifiers algorithms *
|
|
\****************************************************************************************/
|
|
typedef const CvMat* (CV_CDECL *CvStatModelEstimateGetMat)
|
|
( const CvStatModel* estimateModel );
|
|
|
|
typedef int (CV_CDECL *CvStatModelEstimateNextStep)
|
|
( CvStatModel* estimateModel );
|
|
|
|
typedef void (CV_CDECL *CvStatModelEstimateCheckClassifier)
|
|
( CvStatModel* estimateModel,
|
|
const CvStatModel* model,
|
|
const CvMat* features,
|
|
int sample_t_flag,
|
|
const CvMat* responses );
|
|
|
|
typedef void (CV_CDECL *CvStatModelEstimateCheckClassifierEasy)
|
|
( CvStatModel* estimateModel,
|
|
const CvStatModel* model );
|
|
|
|
typedef float (CV_CDECL *CvStatModelEstimateGetCurrentResult)
|
|
( const CvStatModel* estimateModel,
|
|
float* correlation );
|
|
|
|
typedef void (CV_CDECL *CvStatModelEstimateReset)
|
|
( CvStatModel* estimateModel );
|
|
|
|
//-------------------------------- Cross-validation --------------------------------------
|
|
#define CV_CROSS_VALIDATION_ESTIMATE_CLASSIFIER_PARAM_FIELDS() \
|
|
CV_STAT_MODEL_PARAM_FIELDS(); \
|
|
int k_fold; \
|
|
int is_regression; \
|
|
CvRNG* rng
|
|
|
|
typedef struct CvCrossValidationParams
|
|
{
|
|
CV_CROSS_VALIDATION_ESTIMATE_CLASSIFIER_PARAM_FIELDS();
|
|
} CvCrossValidationParams;
|
|
|
|
#define CV_CROSS_VALIDATION_ESTIMATE_CLASSIFIER_FIELDS() \
|
|
CvStatModelEstimateGetMat getTrainIdxMat; \
|
|
CvStatModelEstimateGetMat getCheckIdxMat; \
|
|
CvStatModelEstimateNextStep nextStep; \
|
|
CvStatModelEstimateCheckClassifier check; \
|
|
CvStatModelEstimateGetCurrentResult getResult; \
|
|
CvStatModelEstimateReset reset; \
|
|
int is_regression; \
|
|
int folds_all; \
|
|
int samples_all; \
|
|
int* sampleIdxAll; \
|
|
int* folds; \
|
|
int max_fold_size; \
|
|
int current_fold; \
|
|
int is_checked; \
|
|
CvMat* sampleIdxTrain; \
|
|
CvMat* sampleIdxEval; \
|
|
CvMat* predict_results; \
|
|
int correct_results; \
|
|
int all_results; \
|
|
double sq_error; \
|
|
double sum_correct; \
|
|
double sum_predict; \
|
|
double sum_cc; \
|
|
double sum_pp; \
|
|
double sum_cp
|
|
|
|
typedef struct CvCrossValidationModel
|
|
{
|
|
CV_STAT_MODEL_FIELDS();
|
|
CV_CROSS_VALIDATION_ESTIMATE_CLASSIFIER_FIELDS();
|
|
} CvCrossValidationModel;
|
|
|
|
CVAPI(CvStatModel*)
|
|
cvCreateCrossValidationEstimateModel
|
|
( int samples_all,
|
|
const CvStatModelParams* estimateParams CV_DEFAULT(0),
|
|
const CvMat* sampleIdx CV_DEFAULT(0) );
|
|
|
|
CVAPI(float)
|
|
cvCrossValidation( const CvMat* trueData,
|
|
int tflag,
|
|
const CvMat* trueClasses,
|
|
CvStatModel* (*createClassifier)( const CvMat*,
|
|
int,
|
|
const CvMat*,
|
|
const CvStatModelParams*,
|
|
const CvMat*,
|
|
const CvMat*,
|
|
const CvMat*,
|
|
const CvMat* ),
|
|
const CvStatModelParams* estimateParams CV_DEFAULT(0),
|
|
const CvStatModelParams* trainParams CV_DEFAULT(0),
|
|
const CvMat* compIdx CV_DEFAULT(0),
|
|
const CvMat* sampleIdx CV_DEFAULT(0),
|
|
CvStatModel** pCrValModel CV_DEFAULT(0),
|
|
const CvMat* typeMask CV_DEFAULT(0),
|
|
const CvMat* missedMeasurementMask CV_DEFAULT(0) );
|
|
#endif
|
|
|
|
/****************************************************************************************\
|
|
* 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, ... );
|
|
|
|
|
|
#endif
|
|
|
|
/****************************************************************************************\
|
|
* Data *
|
|
\****************************************************************************************/
|
|
|
|
#include <map>
|
|
#include <string>
|
|
#include <iostream>
|
|
|
|
#define CV_COUNT 0
|
|
#define CV_PORTION 1
|
|
|
|
struct CV_EXPORTS CvTrainTestSplit
|
|
{
|
|
public:
|
|
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;
|
|
|
|
union
|
|
{
|
|
int *count;
|
|
float *portion;
|
|
} *class_part;
|
|
int class_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(){ return values; };
|
|
|
|
const CvMat* get_responses();
|
|
|
|
const CvMat* get_missing(){ return missing; };
|
|
|
|
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() { return response_idx; }
|
|
|
|
const CvMat* get_train_sample_idx() { return train_sample_idx; };
|
|
const CvMat* get_test_sample_idx() { return test_sample_idx; };
|
|
void mix_train_and_test_idx();
|
|
void set_train_test_split( const CvTrainTestSplit * spl);
|
|
|
|
const CvMat* get_var_idx();
|
|
void chahge_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 ) { return var_types->data.ptr[var_idx]; };
|
|
// 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() { return delimiter; };
|
|
|
|
void set_miss_ch( char ch );
|
|
char get_miss_ch() { return miss_ch; };
|
|
|
|
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 response_idx;
|
|
|
|
int train_sample_count;
|
|
bool mix;
|
|
|
|
int total_class_count;
|
|
std::map<std::string, int> *class_map;
|
|
|
|
CvMat* train_sample_idx;
|
|
CvMat* test_sample_idx;
|
|
int* sample_idx; // data of train_sample_idx and test_sample_idx
|
|
|
|
CvRNG 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 CvEMParams EMParams;
|
|
typedef CvEM ExpectationMaximization;
|
|
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;
|
|
|
|
}
|
|
|
|
#endif
|
|
/* End of file. */
|