1169     sv_total = 0;  in clear()
1191     return sv_total;  in get_support_vector_count()
1197     return sv && (unsigned)i < (unsigned)sv_total ? sv[i] : 0;  in get_support_vector()
1344         sv_total = df->sv_count = sv_count;  in do_train()
1489         sv_total = k;  in do_train()
1490         CV_CALL( sv = (float**)cvMemStorageAlloc( storage, sv_total*sizeof(sv[0])));  in do_train()
1512                     assert( (unsigned)df->sv_index[k] < (unsigned)sv_total );  in do_train()
1902     buf_sz = sv_total*sizeof(buffer[0]) + (class_count+1)*sizeof(int);  in predict()
1929         int* vote = (int*)(buffer + sv_total);  in predict()
1933         kernel->calc( sv_total, var_count, (const float**)sv, row_sample, buffer );  in predict()
2073     cvWriteInt( fs, "sv_total", sv_total );  in write()
2075     for( i = 0; i < sv_total; i++ )  in write()
2105             CV_ASSERT( sv_count == sv_total );  in write()
2219     sv_total = cvReadIntByName( fs, svm_node, "sv_total", -1 );  in read()
2224     if( sv_total <= 0 || var_all <= 0 || var_count <= 0 || var_count > var_all || class_count < 0 )  in read()
2243     block_size = MAX( block_size, sv_total*(int)sizeof(CvSVMKernelRow));  in read()
2244     block_size = MAX( block_size, sv_total*2*(int)sizeof(double));  in read()
2248                                 sv_total*sizeof(sv[0]) ));  in read()
2253     for( i = 0; i < sv_total; i++ )  in read()
2333     int i, sv_total, df_count;
2356     sv_total = dst->sv_total = src->sv_total;
2359                                     sv_total*sizeof(dst->sv[0]) ));
2363     for( i = 0; i < sv_total; i++ )