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Warning fixes continued

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This commit is contained in:
Andrey Kamaev
2012-06-09 15:00:04 +00:00
parent f6b451c607
commit f2d3b9b4a1
127 changed files with 6298 additions and 6277 deletions
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232
modules/ml/include/opencv2/ml/ml.hpp
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@@ -170,12 +170,7 @@ struct CV_EXPORTS_W_MAP CvParamGrid
min_val = max_val = step = 0;
}
CvParamGrid( double min_val, double max_val, double log_step )
{
this->min_val = min_val;
this->max_val = max_val;
step = log_step;
}
CvParamGrid( double min_val, double max_val, double log_step );
//CvParamGrid( int param_id );
bool check() const;
@@ -184,6 +179,13 @@ struct CV_EXPORTS_W_MAP CvParamGrid
CV_PROP_RW double step;
};
inline CvParamGrid::CvParamGrid( double _min_val, double _max_val, double _log_step )
{
min_val = _min_val;
max_val = _max_val;
step = _log_step;
}
class CV_EXPORTS_W CvNormalBayesClassifier : public CvStatModel
{
public:
@@ -192,10 +194,10 @@ public:
CvNormalBayesClassifier( const CvMat* trainData, const CvMat* responses,
const CvMat* varIdx=0, const CvMat* sampleIdx=0 );
virtual bool train( const CvMat* trainData, const CvMat* responses,
const CvMat* varIdx = 0, const CvMat* sampleIdx=0, bool update=false );
virtual float predict( const CvMat* samples, CV_OUT CvMat* results=0 ) const;
CV_WRAP virtual void clear();
@@ -207,7 +209,7 @@ public:
bool update=false );
CV_WRAP virtual float predict( const cv::Mat& samples, CV_OUT cv::Mat* results=0 ) const;
#endif
virtual void write( CvFileStorage* storage, const char* name ) const;
virtual void read( CvFileStorage* storage, CvFileNode* node );
@@ -243,31 +245,31 @@ public:
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;
#ifndef SWIG
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 );
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;
#endif
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;
@@ -473,7 +475,7 @@ public:
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,
@@ -487,16 +489,16 @@ public:
virtual float predict( const CvMat* sample, bool returnDFVal=false ) const;
virtual float predict( const CvMat* samples, CvMat* results ) const;
#ifndef SWIG
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,
@@ -509,7 +511,7 @@ public:
bool balanced=false);
CV_WRAP virtual float predict( const cv::Mat& sample, bool returnDFVal=false ) const;
#endif
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; };
@@ -564,14 +566,14 @@ public:
// Default parameters
enum {DEFAULT_NCLUSTERS=5, DEFAULT_MAX_ITERS=100};
// The initial step
enum {START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0};
CV_WRAP EM(int nclusters=EM::DEFAULT_NCLUSTERS, int covMatType=EM::COV_MAT_DIAGONAL,
const TermCriteria& termCrit=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,
EM::DEFAULT_MAX_ITERS, FLT_EPSILON));
virtual ~EM();
CV_WRAP virtual void clear();
@@ -579,7 +581,7 @@ public:
OutputArray logLikelihoods=noArray(),
OutputArray labels=noArray(),
OutputArray probs=noArray());
CV_WRAP virtual bool trainE(InputArray samples,
InputArray means0,
InputArray covs0=noArray(),
@@ -587,13 +589,13 @@ public:
OutputArray logLikelihoods=noArray(),
OutputArray labels=noArray(),
OutputArray probs=noArray());
CV_WRAP virtual bool trainM(InputArray samples,
InputArray probs0,
OutputArray logLikelihoods=noArray(),
OutputArray labels=noArray(),
OutputArray probs=noArray());
CV_WRAP Vec2d predict(InputArray sample,
OutputArray probs=noArray()) const;
@@ -603,7 +605,7 @@ public:
virtual void read(const FileNode& fn);
protected:
virtual void setTrainData(int startStep, const Mat& samples,
const Mat* probs0,
const Mat* means0,
@@ -802,7 +804,7 @@ struct CV_EXPORTS CvDTreeTrainData
int buf_count, buf_size;
bool shared;
int is_buf_16u;
CvMat* cat_count;
CvMat* cat_ofs;
CvMat* cat_map;
@@ -871,12 +873,12 @@ public:
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();
#endif
virtual const CvMat* get_var_importance();
CV_WRAP virtual void clear();
@@ -900,13 +902,13 @@ protected:
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,
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,
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,
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 );
@@ -1003,7 +1005,7 @@ public:
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;
@@ -1018,16 +1020,16 @@ public:
CV_WRAP virtual float predict_prob( const cv::Mat& sample, const cv::Mat& missing = cv::Mat() ) const;
CV_WRAP virtual cv::Mat getVarImportance();
#endif
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 float get_train_error();
virtual void read( CvFileStorage* fs, CvFileNode* node );
virtual void write( CvFileStorage* fs, const char* name ) const;
@@ -1083,13 +1085,13 @@ class CV_EXPORTS CvForestERTree : public CvForestTree
{
protected:
virtual double calc_node_dir( CvDTreeNode* node );
virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi,
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,
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,
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 );
};
@@ -1169,13 +1171,13 @@ 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,
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,
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,
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 );
@@ -1201,14 +1203,14 @@ public:
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 );
@@ -1223,19 +1225,19 @@ public:
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;
#endif
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 );
@@ -1315,7 +1317,7 @@ struct CV_EXPORTS_W_MAP CvGBTreesParams : public CvDTreeParams
// 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
@@ -1347,7 +1349,7 @@ struct CV_EXPORTS_W_MAP CvGBTreesParams : public CvDTreeParams
// 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.
// class_labels - output class labels map.
// rng - random number generator. Used for spliting the
// training set.
// class_count - count of output classes.
@@ -1368,15 +1370,15 @@ 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
@@ -1386,18 +1388,18 @@ public:
//
// 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
@@ -1415,7 +1417,7 @@ public:
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
@@ -1448,13 +1450,13 @@ public:
const CvMat* missingDataMask=0,
CvGBTreesParams params=CvGBTreesParams() );
/*
// Destructor.
*/
virtual ~CvGBTrees();
/*
// Gradient tree boosting model training
//
@@ -1465,7 +1467,7 @@ public:
const CvMat* missingDataMask=0,
CvGBTreesParams params=CvGBTreesParams(),
bool update=false );
// INPUT
// trainData - a set of input feature vectors.
// size of matrix is
@@ -1500,8 +1502,8 @@ public:
const CvMat* missingDataMask=0,
CvGBTreesParams params=CvGBTreesParams(),
bool update=false );
/*
// Gradient tree boosting model training
//
@@ -1509,7 +1511,7 @@ public:
// virtual bool train( CvMLData* data,
CvGBTreesParams params=CvGBTreesParams(),
bool update=false ) {return false;};
// INPUT
// data - training set.
// params - parameters of GTB algorithm.
@@ -1522,7 +1524,7 @@ public:
CvGBTreesParams params=CvGBTreesParams(),
bool update=false );
/*
// Response value prediction
//
@@ -1530,7 +1532,7 @@ public:
// 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
@@ -1541,7 +1543,7 @@ public:
// 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
// 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
@@ -1553,7 +1555,7 @@ public:
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)
@@ -1562,7 +1564,7 @@ public:
// 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
@@ -1573,7 +1575,7 @@ public:
// 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
// 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
@@ -1581,7 +1583,7 @@ public:
// 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;
@@ -1591,7 +1593,7 @@ public:
//
// API
// virtual void clear();
// INPUT
// OUTPUT
// delete data, weak, orig_response, sum_response,
@@ -1622,7 +1624,7 @@ public:
std::vector<float> *resp = 0 );
/*
//
//
// Write parameters of the gtb model and data. Write learned model.
//
// API
@@ -1638,7 +1640,7 @@ public:
/*
//
//
// Read parameters of the gtb model and data. Read learned model.
//
// API
@@ -1652,14 +1654,14 @@ public:
*/
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(),
@@ -1670,7 +1672,7 @@ public:
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:
/*
@@ -1678,7 +1680,7 @@ protected:
//
// API
// virtual void find_gradient( const int k = 0);
// INPUT
// k - used for classification problem, determining current
// tree ensemble.
@@ -1690,9 +1692,9 @@ protected:
*/
virtual void find_gradient( const int k = 0);
/*
//
//
// Change values in tree leaves according to the used loss function.
//
// API
@@ -1711,7 +1713,7 @@ protected:
/*
//
//
// Find optimal constant prediction value according to the used loss
// function.
// The goal is to find a constant which gives the minimal summary loss
@@ -1728,9 +1730,9 @@ protected:
*/
virtual float find_optimal_value( const CvMat* _Idx );
/*
//
//
// Randomly split the whole training set in two parts according
// to params.portion.
//
@@ -1747,7 +1749,7 @@ protected:
/*
//
//
// Internal recursive function giving an array of subtree tree leaves.
//
// API
@@ -1761,10 +1763,10 @@ protected:
// RESULT
*/
void leaves_get( CvDTreeNode** leaves, int& count, CvDTreeNode* node );
/*
//
//
// Get leaves of the tree.
//
// API
@@ -1779,9 +1781,9 @@ protected:
*/
CvDTreeNode** GetLeaves( const CvDTree* dtree, int& len );
/*
//
//
// Is it a regression or a classification.
//
// API
@@ -1797,7 +1799,7 @@ protected:
/*
//
//
// Write parameters of the gtb model.
//
// API
@@ -1812,7 +1814,7 @@ protected:
/*
//
//
// Read parameters of the gtb model and data.
//
// API
@@ -1829,9 +1831,9 @@ protected:
// RESULT
*/
virtual void read_params( CvFileStorage* fs, CvFileNode* fnode );
int get_len(const CvMat* mat) const;
int get_len(const CvMat* mat) const;
CvDTreeTrainData* data;
CvGBTreesParams params;
@@ -1894,30 +1896,30 @@ public:
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;
#ifndef SWIG
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 );
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 );
int flags=0 );
CV_WRAP virtual float predict( const cv::Mat& inputs, CV_OUT cv::Mat& outputs ) const;
#endif
CV_WRAP virtual void clear();
// possible activation functions
@@ -2031,7 +2033,7 @@ public:
virtual ~CvMLData();
// returns:
// 0 - OK
// 0 - OK
// -1 - file can not be opened or is not correct
int read_csv( const char* filename );
@@ -2039,8 +2041,8 @@ public:
const CvMat* get_responses();
const CvMat* get_missing() const;
void set_header_lines_number( int n );
int get_header_lines_number() 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
@@ -2050,7 +2052,7 @@ public:
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
@@ -2064,14 +2066,14 @@ public:
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 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<std::string, int>& get_class_labels_map() const;
protected:
@@ -2079,7 +2081,7 @@ protected:
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;
@@ -2093,13 +2095,13 @@ protected:
CvMat* var_idx_out; // mat
CvMat* var_types_out; // mat
int header_lines_number;
int header_lines_number;
int response_idx;
int train_sample_count;
bool mix;
int total_class_count;
std::map<std::string, int> class_map;
@@ -2113,7 +2115,7 @@ protected:
namespace cv
{
typedef CvStatModel StatModel;
typedef CvParamGrid ParamGrid;
typedef CvNormalBayesClassifier NormalBayesClassifier;
@@ -2142,7 +2144,7 @@ typedef CvGBTrees GradientBoostingTrees;
template<> CV_EXPORTS void Ptr<CvDTreeSplit>::delete_obj();
CV_EXPORTS bool initModule_ml(void);
}
#endif // __cplusplus