minor refactoring of CvMLData interface

modules/ml/include/opencv2/ml/ml.hpp

@@ -2061,10 +2061,9 @@ CVAPI(void) cvCreateTestSet( int type, CvMat** samples,
 
 struct CV_EXPORTS CvTrainTestSplit
 {
-public:
     CvTrainTestSplit();
-    CvTrainTestSplit( int _train_sample_count, bool _mix = true);
-    CvTrainTestSplit( float _train_sample_portion, bool _mix = true);
+    CvTrainTestSplit( int train_sample_count, bool mix = true);
+    CvTrainTestSplit( float train_sample_portion, bool mix = true);
 
     union
     {
@@ -2073,14 +2072,7 @@ public:
     } train_sample_part;
     int train_sample_part_mode;
 
-    union
-    {
-        int *count;
-        float *portion;
-    } *class_part;
-    int class_part_mode;
-
-    bool mix;    
+    bool mix;
 };
 
 class CV_EXPORTS CvMLData
@@ -2094,24 +2086,24 @@ public:
     // 1 - file can not be opened or is not correct
     int read_csv( const char* filename );
 
-    const CvMat* get_values();
+    const CvMat* get_values() const;
     const CvMat* get_responses();
-    const CvMat* get_missing();
+    const CvMat* get_missing() 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();
+    int get_response_idx() const;
 
-    const CvMat* get_train_sample_idx();
-    const CvMat* get_test_sample_idx();
-    void mix_train_and_test_idx();
     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 ); // state == true to set vi-variable as predictor
 
     const CvMat* get_var_types();
-    int get_var_type( int var_idx );
+    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
@@ -2121,11 +2113,13 @@ public:
     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();
+    char get_delimiter() const;
 
     void set_miss_ch( char ch );
-    char get_miss_ch();
+    char get_miss_ch() const;
     
+    const std::map<std::string, int>& get_class_labels_map() const;
+
 protected:
     virtual void clear();
 
@@ -2151,7 +2145,7 @@ protected:
     bool mix;
    
     int total_class_count;
-    std::map<std::string, int> *class_map;
+    std::map<std::string, int> class_map;
 
     CvMat* train_sample_idx;
     CvMat* test_sample_idx;

modules/ml/src/data.cpp

@@ -48,7 +48,6 @@ CvTrainTestSplit::CvTrainTestSplit()
 {
     train_sample_part_mode = CV_COUNT;
     train_sample_part.count = -1;
-    class_part = 0;
     mix = false;
 }
 
@@ -56,7 +55,6 @@ CvTrainTestSplit::CvTrainTestSplit( int _train_sample_count, bool _mix )
 {
     train_sample_part_mode = CV_COUNT;
     train_sample_part.count = _train_sample_count;
-    class_part = 0;
     mix = _mix;
 }
     
@@ -64,7 +62,6 @@ CvTrainTestSplit::CvTrainTestSplit( float _train_sample_portion, bool _mix )
 {
     train_sample_part_mode = CV_PORTION;
     train_sample_part.portion = _train_sample_portion;
-    class_part = 0;
     mix = _mix;
 }
 
@@ -83,14 +80,12 @@ CvMLData::CvMLData()
     miss_ch = '?';
     //flt_separator = '.';
 
-    class_map = new std::map<std::string, int>();
     rng = &cv::theRNG();
 }
 
 CvMLData::~CvMLData()
 {
     clear();
-    delete class_map;
 }
 
 void CvMLData::free_train_test_idx()
@@ -102,8 +97,7 @@ void CvMLData::free_train_test_idx()
 
 void CvMLData::clear()
 {
-    if ( !class_map->empty() )
-        class_map->clear();
+    class_map.clear();
 
     cvReleaseMat( &values );
     cvReleaseMat( &missing );
@@ -244,16 +238,29 @@ int CvMLData::read_csv(const char* filename)
     return 0;
 }
 
-const CvMat* CvMLData::get_values()
+const CvMat* CvMLData::get_values() const
 {
     return values;
 }
 
-const CvMat* CvMLData::get_missing()
+const CvMat* CvMLData::get_missing() const
 {
+    CV_FUNCNAME( "CvMLData::get_missing" );
+    __BEGIN__;
+
+    if ( !values )
+        CV_ERROR( CV_StsInternal, "data is empty" );
+
+    __END__;
+
     return missing;
 }
 
+const std::map<std::string, int>& CvMLData::get_class_labels_map() const
+{
+    return class_map;
+}
+
 void CvMLData::str_to_flt_elem( const char* token, float& flt_elem, int& type)
 {
     
@@ -270,12 +277,12 @@ void CvMLData::str_to_flt_elem( const char* token, float& flt_elem, int& type)
     {
         if ( (*stopstring != 0) && (*stopstring != '\n') && (strcmp(stopstring, "\r\n") != 0) ) // class label
         {
-            int idx = (*class_map)[token];
+            int idx = class_map[token];
             if ( idx == 0)
             {
                 total_class_count++;
                 idx = total_class_count;
-                (*class_map)[token] = idx;
+                class_map[token] = idx;
             }
             flt_elem = (float)idx;
             type = CV_VAR_CATEGORICAL;
@@ -296,7 +303,7 @@ void CvMLData::set_delimiter(char ch)
     __END__;
 }
 
-char CvMLData::get_delimiter()
+char CvMLData::get_delimiter() const
 {
     return delimiter;
 }
@@ -314,7 +321,7 @@ void CvMLData::set_miss_ch(char ch)
     __END__;
 }
 
-char CvMLData::get_miss_ch()
+char CvMLData::get_miss_ch() const
 {
     return miss_ch;
 }
@@ -339,8 +346,14 @@ void CvMLData::set_response_idx( int idx )
     __END__;    
 }
 
-int CvMLData::get_response_idx()
+int CvMLData::get_response_idx() const
 {
+    CV_FUNCNAME( "CvMLData::get_response_idx" );
+    __BEGIN__;
+
+    if ( !values )
+        CV_ERROR( CV_StsInternal, "data is empty" );
+     __END__;
     return response_idx;
 }
 
@@ -536,7 +549,7 @@ const CvMat* CvMLData::get_var_types()
     return var_types_out;
 }
 
-int CvMLData::get_var_type( int var_idx )
+int CvMLData::get_var_type( int var_idx ) const
 {
     return var_types->data.ptr[var_idx];
 }
@@ -572,9 +585,6 @@ void CvMLData::set_train_test_split( const CvTrainTestSplit * spl)
 
     int sample_count = 0;
 
-    if ( spl->class_part )
-        CV_ERROR( CV_StsBadArg, "this division type is not supported yet" );
-    
     if ( !values )
         CV_ERROR( CV_StsInternal, "data is empty" );
 
@@ -627,19 +637,41 @@ void CvMLData::set_train_test_split( const CvTrainTestSplit * spl)
     __END__;
 }
 
-const CvMat* CvMLData::get_train_sample_idx()
+const CvMat* CvMLData::get_train_sample_idx() const
 {
+    CV_FUNCNAME( "CvMLData::get_train_sample_idx" );
+    __BEGIN__;
+
+    if ( !values )
+        CV_ERROR( CV_StsInternal, "data is empty" );
+    __END__;
+
     return train_sample_idx;
 }
 
-const CvMat* CvMLData::get_test_sample_idx()
+const CvMat* CvMLData::get_test_sample_idx() const
 {
+    CV_FUNCNAME( "CvMLData::get_test_sample_idx" );
+    __BEGIN__;
+
+    if ( !values )
+        CV_ERROR( CV_StsInternal, "data is empty" );
+    __END__;
+
     return test_sample_idx;
 }
 
 void CvMLData::mix_train_and_test_idx()
 {
-    if ( !values || !sample_idx) return;
+    CV_FUNCNAME( "CvMLData::mix_train_and_test_idx" );
+    __BEGIN__;
+
+    if ( !values )
+        CV_ERROR( CV_StsInternal, "data is empty" );
+    __END__;
+
+    if ( !sample_idx)
+        return;
 
     if ( train_sample_count > 0 && train_sample_count < values->rows )
     {