// 1 - file can not be opened or is not correct
int read_csv(const char* filename);
- const CvMat* get_values(){ return values; };
+ const CvMat* get_values(){ return values; }
const CvMat* get_responses();
- const CvMat* get_missing(){ return missing; };
+ const CvMat* get_missing(){ return missing; }
void set_response_idx( int idx ); // old response become predictors, new response_idx = idx
// if idx < 0 there will be no response
int get_response_idx() { return response_idx; }
- const CvMat* get_train_sample_idx() { return train_sample_idx; };
- const CvMat* get_test_sample_idx() { return test_sample_idx; };
+ const CvMat* get_train_sample_idx() { return train_sample_idx; }
+ const CvMat* get_test_sample_idx() { return test_sample_idx; }
void mix_train_and_test_idx();
void set_train_test_split( const CvTrainTestSplit * spl);
void chahge_var_idx( int vi, bool state ); // state == true to set vi-variable as predictor
const CvMat* get_var_types();
- int get_var_type( int var_idx ) { return var_types->data.ptr[var_idx]; };
+ int get_var_type( int var_idx ) { return var_types->data.ptr[var_idx]; }
// following 2 methods enable to change vars type
// use these methods to assign CV_VAR_CATEGORICAL type for categorical variable
// with numerical labels; in the other cases var types are correctly determined automatically
void change_var_type( int var_idx, int type); // type in { CV_VAR_ORDERED, CV_VAR_CATEGORICAL }
void set_delimiter( char ch );
- char get_delimiter() { return delimiter; };
+ char get_delimiter() { return delimiter; }
void set_miss_ch( char ch );
- char get_miss_ch() { return miss_ch; };
+ char get_miss_ch() { return miss_ch; }
protected:
virtual void clear();