// disable deprecation warning which appears in VisualStudio 8.0
#if _MSC_VER >= 1400
-#pragma warning( disable : 4996 )
+#pragma warning( disable : 4996 )
#endif
#ifndef SKIP_INCLUDES
virtual ~CvStatModel();
virtual void clear();
-
+
virtual void save( const char* filename, const char* name=0 );
virtual void load( const char* filename, const char* name=0 );
-
+
virtual void write( CvFileStorage* storage, const char* name );
virtual void read( CvFileStorage* storage, CvFileNode* node );
{
min_val = max_val = step = 0;
}
-
+
CvParamGrid( double _min_val, double _max_val, double log_step )
{
min_val = _min_val;
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 );
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 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;
double _degree, double _gamma, double _coef0,
double _C, double _nu, double _p,
CvMat* _class_weights, CvTermCriteria _term_crit );
-
+
int svm_type;
int kernel_type;
double degree; // for poly
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 );
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, char* y,
+ 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, char* y,
+ 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 void clear();
virtual bool solve_generic( CvSVMSolutionInfo& si );
-
- virtual bool solve_c_svc( int count, int var_count, const float** samples, char* y,
+
+ 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, char* y,
+ 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,
double* alpha;
// -1 - lower bound, 0 - free, 1 - upper bound
- char* alpha_status;
+ schar* alpha_status;
- char* y;
+ 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;
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() );
enum { START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0 };
CvEM();
-
+
// TODO: implement non-default constructor!
// see bug 1830346 on the sourceforge bug tracker
//CvEM( const CvMat* samples, const CvMat* sample_idx=0,
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),
+ 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)
virtual void read( CvFileStorage* fs, CvFileNode* node );
virtual void write( CvFileStorage* fs, const char* name );
-
+
// 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 CvDTreeNode* get_root() const;
int get_pruned_tree_idx() const;
CvDTreeTrainData* get_data();
virtual void read_tree_nodes( CvFileStorage* fs, CvFileNode* node );
CvDTreeNode* root;
-
+
int pruned_tree_idx;
CvMat* var_importance;
/* dummy methods to avoid warnings: END */
protected:
-
+
virtual void try_split_node( CvDTreeNode* n );
virtual CvDTreeSplit* find_surrogate_split_ord( CvDTreeNode* n, int vi );
virtual CvDTreeSplit* find_surrogate_split_cat( CvDTreeNode* n, int vi );
public:
// Boosting type
enum { DISCRETE=0, REAL=1, LOGIT=2, GENTLE=3 };
-
+
// Splitting criteria
enum { DEFAULT=0, GINI=1, MISCLASS=3, SQERR=4 };
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,
virtual void read_params( CvFileStorage* fs, CvFileNode* node );
CvDTreeTrainData* data;
- CvBoostParams params;
+ CvBoostParams params;
CvSeq* weak;
-
+
CvMat* orig_response;
CvMat* sum_response;
CvMat* weak_eval;
// 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;
};
// 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 );
float a;
// scale parameter of sigmoid activation function
float s;
- // exp2ssumWX = exp(2*<s>*(W*X)) - is the vector used in computing of the
+ // 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
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(
+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) );
typedef void (CV_CDECL *CvStatModelEstimateCheckClassifier)
( CvStatModel* estimateModel,
- const CvStatModel* model,
- const CvMat* features,
+ const CvStatModel* model,
+ const CvMat* features,
int sample_t_flag,
const CvMat* responses );
CV_CROSS_VALIDATION_ESTIMATE_CLASSIFIER_FIELDS();
} CvCrossValidationModel;
-CVAPI(CvStatModel*)
+CVAPI(CvStatModel*)
cvCreateCrossValidationEstimateModel
( int samples_all,
const CvStatModelParams* estimateParams CV_DEFAULT(0),
const CvMat* sampleIdx CV_DEFAULT(0) );
-CVAPI(float)
+CVAPI(float)
cvCrossValidation( const CvMat* trueData,
int tflag,
const CvMat* trueClasses,
projects / opencv / blobdiff