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opencv/apps/traincascade/old_ml.hpp

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made everything compile and even run somehow
2014-08-03 05:41:09 +08:00
/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
// 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.
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// This software is provided by the copyright holders and contributors "as is" and
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// 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
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//M*/
#ifndef __OPENCV_ML_HPP__
#define __OPENCV_ML_HPP__
#ifdef __cplusplus
# include "opencv2/core.hpp"
#endif
#include "opencv2/core/core_c.h"
#include <limits.h>
#ifdef __cplusplus
#include <map>
#include <iostream>
// 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_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_TYPE_NAME_ML_ERTREES "opencv-ml-extremely-randomized-trees"
#define CV_TYPE_NAME_ML_GBT "opencv-ml-gradient-boosting-trees"
#define CV_TRAIN_ERROR 0
#define CV_TEST_ERROR 1
class 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 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 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, CV_OUT CvMat* results_prob=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, CV_OUT cv::Mat* results_prob=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 *
\****************************************************************************************/
// k Nearest Neighbors
class 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 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 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 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 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, bool returnDFVal=false ) 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();
virtual const CvSVMDecisionFunc* get_decision_function() const { return decision_func; }
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;
private:
CvSVM(const CvSVM&);
CvSVM& operator = (const CvSVM&);
};
/****************************************************************************************\
* 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 CvDTreeParams
{
CV_PROP_RW int max_categories;
CV_PROP_RW int max_depth;
CV_PROP_RW int min_sample_count;
CV_PROP_RW int cv_folds;
CV_PROP_RW bool use_surrogates;
CV_PROP_RW bool use_1se_rule;
CV_PROP_RW bool truncate_pruned_tree;
CV_PROP_RW float regression_accuracy;
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 );
};
struct 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 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;
CvMat train_data_hdr, responses_hdr;
cv::Mat train_data_mat, responses_mat;
public:
int pruned_tree_idx;
};
/****************************************************************************************\
* Random Trees Classifier *
\****************************************************************************************/
class CvRTrees;
class 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 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 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;
CvMat train_data_hdr, responses_hdr;
cv::Mat train_data_mat, responses_mat;
int ntrees;
int nclasses;
double oob_error;
CvMat* var_importance;
int nsamples;
cv::RNG* rng;
CvMat* active_var_mask;
};
/****************************************************************************************\
* Extremely randomized trees Classifier *
\****************************************************************************************/
struct 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 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 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 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 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 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;
CvMat train_data_hdr, responses_hdr;
cv::Mat train_data_mat, responses_mat;
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 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 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 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 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 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 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<> void DefaultDeleter<CvDTreeSplit>::operator ()(CvDTreeSplit* obj) const;
}
#endif // __cplusplus
#endif // __OPENCV_ML_HPP__
/* End of file. */
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