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  40
  41 #ifndef __ML_INTERNAL_H__
  42 #define __ML_INTERNAL_H__
  43
  44 #if _MSC_VER >= 1200
  45 #pragma warning( disable: 4514 4710 4711 4710 )
  46 #endif
  47
  48 #include "ml.h"
  49 #include "cxmisc.h"
  50
  51 #include <assert.h>
  52 #include <float.h>
  53 #include <limits.h>
  54 #include <math.h>
  55 #include <stdlib.h>
  56 #include <stdio.h>
  57 #include <string.h>
  58 #include <time.h>
  59
  60 #ifndef FALSE
  61 #define FALSE 0
  62 #endif
  63 #ifndef TRUE
  64 #define TRUE 1
  65 #endif
  66
  67 #define ML_IMPL CV_IMPL
  68
  69 #define CV_MAT_ELEM_FLAG( mat, type, comp, vect, tflag )    \
  70     (( tflag == CV_ROW_SAMPLE )                             \
  71     ? (CV_MAT_ELEM( mat, type, comp, vect ))                \
  72     : (CV_MAT_ELEM( mat, type, vect, comp )))
  73
  74 /* Convert matrix to vector */
  75 #define ICV_MAT2VEC( mat, vdata, vstep, num )      \
  76     if( MIN( (mat).rows, (mat).cols ) != 1 )       \
  77         CV_ERROR( CV_StsBadArg, "" );              \
  78     (vdata) = ((mat).data.ptr);                    \
  79     if( (mat).rows == 1 )                          \
  80     {                                              \
  81         (vstep) = CV_ELEM_SIZE( (mat).type );      \
  82         (num) = (mat).cols;                        \
  83     }                                              \
  84     else                                           \
  85     {                                              \
  86         (vstep) = (mat).step;                      \
  87         (num) = (mat).rows;                        \
  88     }
  89
  90 /* get raw data */
  91 #define ICV_RAWDATA( mat, flags, rdata, sstep, cstep, m, n )         \
  92     (rdata) = (mat).data.ptr;                                        \
  93     if( CV_IS_ROW_SAMPLE( flags ) )                                  \
  94     {                                                                \
  95         (sstep) = (mat).step;                                        \
  96         (cstep) = CV_ELEM_SIZE( (mat).type );                        \
  97         (m) = (mat).rows;                                            \
  98         (n) = (mat).cols;                                            \
  99     }                                                                \
 100     else                                                             \
 101     {                                                                \
 102         (cstep) = (mat).step;                                        \
 103         (sstep) = CV_ELEM_SIZE( (mat).type );                        \
 104         (n) = (mat).rows;                                            \
 105         (m) = (mat).cols;                                            \
 106     }
 107
 108 #define ICV_IS_MAT_OF_TYPE( mat, mat_type) \
 109     (CV_IS_MAT( mat ) && CV_MAT_TYPE( mat->type ) == (mat_type) &&   \
 110     (mat)->cols > 0 && (mat)->rows > 0)
 111
 112 /*
 113     uchar* data; int sstep, cstep;      - trainData->data
 114     uchar* classes; int clstep; int ncl;- trainClasses
 115     uchar* tmask; int tmstep; int ntm;  - typeMask
 116     uchar* missed;int msstep, mcstep;   -missedMeasurements...
 117     int mm, mn;                         == m,n == size,dim
 118     uchar* sidx;int sistep;             - sampleIdx
 119     uchar* cidx;int cistep;             - compIdx
 120     int k, l;                           == n,m == dim,size (length of cidx, sidx)
 121     int m, n;                           == size,dim
 122 */
 123 #define ICV_DECLARE_TRAIN_ARGS()                                                    \
 124     uchar* data;                                                                    \
 125     int sstep, cstep;                                                               \
 126     uchar* classes;                                                                 \
 127     int clstep;                                                                     \
 128     int ncl;                                                                        \
 129     uchar* tmask;                                                                   \
 130     int tmstep;                                                                     \
 131     int ntm;                                                                        \
 132     uchar* missed;                                                                  \
 133     int msstep, mcstep;                                                             \
 134     int mm, mn;                                                                     \
 135     uchar* sidx;                                                                    \
 136     int sistep;                                                                     \
 137     uchar* cidx;                                                                    \
 138     int cistep;                                                                     \
 139     int k, l;                                                                       \
 140     int m, n;                                                                       \
 141                                                                                     \
 142     data = classes = tmask = missed = sidx = cidx = NULL;                           \
 143     sstep = cstep = clstep = ncl = tmstep = ntm = msstep = mcstep = mm = mn = 0;    \
 144     sistep = cistep = k = l = m = n = 0;
 145
 146 #define ICV_TRAIN_DATA_REQUIRED( param, flags )                                     \
 147     if( !ICV_IS_MAT_OF_TYPE( (param), CV_32FC1 ) )                                  \
 148     {                                                                               \
 149         CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" );                   \
 150     }                                                                               \
 151     else                                                                            \
 152     {                                                                               \
 153         ICV_RAWDATA( *(param), (flags), data, sstep, cstep, m, n );                 \
 154         k = n;                                                                      \
 155         l = m;                                                                      \
 156     }
 157
 158 #define ICV_TRAIN_CLASSES_REQUIRED( param )                                         \
 159     if( !ICV_IS_MAT_OF_TYPE( (param), CV_32FC1 ) )                                  \
 160     {                                                                               \
 161         CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" );                   \
 162     }                                                                               \
 163     else                                                                            \
 164     {                                                                               \
 165         ICV_MAT2VEC( *(param), classes, clstep, ncl );                              \
 166         if( m != ncl )                                                              \
 167         {                                                                           \
 168             CV_ERROR( CV_StsBadArg, "Unmatched sizes" );                            \
 169         }                                                                           \
 170     }
 171
 172 #define ICV_ARG_NULL( param )                                                       \
 173     if( (param) != NULL )                                                           \
 174     {                                                                               \
 175         CV_ERROR( CV_StsBadArg, #param " parameter must be NULL" );                 \
 176     }
 177
 178 #define ICV_MISSED_MEASUREMENTS_OPTIONAL( param, flags )                            \
 179     if( param )                                                                     \
 180     {                                                                               \
 181         if( !ICV_IS_MAT_OF_TYPE( param, CV_8UC1 ) )                                 \
 182         {                                                                           \
 183             CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" );               \
 184         }                                                                           \
 185         else                                                                        \
 186         {                                                                           \
 187             ICV_RAWDATA( *(param), (flags), missed, msstep, mcstep, mm, mn );       \
 188             if( mm != m || mn != n )                                                \
 189             {                                                                       \
 190                 CV_ERROR( CV_StsBadArg, "Unmatched sizes" );                        \
 191             }                                                                       \
 192         }                                                                           \
 193     }
 194
 195 #define ICV_COMP_IDX_OPTIONAL( param )                                              \
 196     if( param )                                                                     \
 197     {                                                                               \
 198         if( !ICV_IS_MAT_OF_TYPE( param, CV_32SC1 ) )                                \
 199         {                                                                           \
 200             CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" );               \
 201         }                                                                           \
 202         else                                                                        \
 203         {                                                                           \
 204             ICV_MAT2VEC( *(param), cidx, cistep, k );                               \
 205             if( k > n )                                                             \
 206                 CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" );           \
 207         }                                                                           \
 208     }
 209
 210 #define ICV_SAMPLE_IDX_OPTIONAL( param )                                            \
 211     if( param )                                                                     \
 212     {                                                                               \
 213         if( !ICV_IS_MAT_OF_TYPE( param, CV_32SC1 ) )                                \
 214         {                                                                           \
 215             CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" );               \
 216         }                                                                           \
 217         else                                                                        \
 218         {                                                                           \
 219             ICV_MAT2VEC( *sampleIdx, sidx, sistep, l );                             \
 220             if( l > m )                                                             \
 221                 CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" );           \
 222         }                                                                           \
 223     }
 224
 225 /****************************************************************************************/
 226 #define ICV_CONVERT_FLOAT_ARRAY_TO_MATRICE( array, matrice )        \
 227 {                                                                   \
 228     CvMat a, b;                                                     \
 229     int dims = (matrice)->cols;                                     \
 230     int nsamples = (matrice)->rows;                                 \
 231     int type = CV_MAT_TYPE((matrice)->type);                        \
 232     int i, offset = dims;                                           \
 233                                                                     \
 234     CV_ASSERT( type == CV_32FC1 || type == CV_64FC1 );              \
 235     offset *= ((type == CV_32FC1) ? sizeof(float) : sizeof(double));\
 236                                                                     \
 237     b = cvMat( 1, dims, CV_32FC1 );                                 \
 238     cvGetRow( matrice, &a, 0 );                                     \
 239     for( i = 0; i < nsamples; i++, a.data.ptr += offset )           \
 240     {                                                               \
 241         b.data.fl = (float*)array[i];                               \
 242         CV_CALL( cvConvert( &b, &a ) );                             \
 243     }                                                               \
 244 }
 245
 246 /****************************************************************************************\
 247 *                       Auxiliary functions declarations                                 *
 248 \****************************************************************************************/
 249
 250 /* Generates a set of classes centers in quantity <num_of_clusters> that are generated as
 251    uniform random vectors in parallelepiped, where <data> is concentrated. Vectors in
 252    <data> should have horizontal orientation. If <centers> != NULL, the function doesn't
 253    allocate any memory and stores generated centers in <centers>, returns <centers>.
 254    If <centers> == NULL, the function allocates memory and creates the matrice. Centers
 255    are supposed to be oriented horizontally. */
 256 CvMat* icvGenerateRandomClusterCenters( int seed,
 257                                         const CvMat* data,
 258                                         int num_of_clusters,
 259                                         CvMat* centers CV_DEFAULT(0));
 260
 261 /* Fills the <labels> using <probs> by choosing the maximal probability. Outliers are
 262    fixed by <oulier_tresh> and have cluster label (-1). Function also controls that there
 263    weren't "empty" clusters by filling empty clusters with the maximal probability vector.
 264    If probs_sums != NULL, filles it with the sums of probabilities for each sample (it is
 265    useful for normalizing probabilities' matrice of FCM) */
 266 void icvFindClusterLabels( const CvMat* probs, float outlier_thresh, float r,
 267                            const CvMat* labels );
 268
 269 typedef struct CvSparseVecElem32f
 270 {
 271     int idx;
 272     float val;
 273 }
 274 CvSparseVecElem32f;
 275
 276 /* Prepare training data and related parameters */
 277 #define CV_TRAIN_STATMODEL_DEFRAGMENT_TRAIN_DATA    1
 278 #define CV_TRAIN_STATMODEL_SAMPLES_AS_ROWS          2
 279 #define CV_TRAIN_STATMODEL_SAMPLES_AS_COLUMNS       4
 280 #define CV_TRAIN_STATMODEL_CATEGORICAL_RESPONSE     8
 281 #define CV_TRAIN_STATMODEL_ORDERED_RESPONSE         16
 282 #define CV_TRAIN_STATMODEL_RESPONSES_ON_OUTPUT      32
 283 #define CV_TRAIN_STATMODEL_ALWAYS_COPY_TRAIN_DATA   64
 284 #define CV_TRAIN_STATMODEL_SPARSE_AS_SPARSE         128
 285
 286 int
 287 cvPrepareTrainData( const char* /*funcname*/,
 288                     const CvMat* train_data, int tflag,
 289                     const CvMat* responses, int response_type,
 290                     const CvMat* var_idx,
 291                     const CvMat* sample_idx,
 292                     bool always_copy_data,
 293                     const float*** out_train_samples,
 294                     int* _sample_count,
 295                     int* _var_count,
 296                     int* _var_all,
 297                     CvMat** out_responses,
 298                     CvMat** out_response_map,
 299                     CvMat** out_var_idx,
 300                     CvMat** out_sample_idx=0 );
 301
 302 void
 303 cvSortSamplesByClasses( const float** samples, const CvMat* classes,
 304                         int* class_ranges, const uchar** mask CV_DEFAULT(0) );
 305
 306 void
 307 cvCombineResponseMaps (CvMat*  _responses,
 308                  const CvMat*  old_response_map,
 309                        CvMat*  new_response_map,
 310                        CvMat** out_response_map);
 311
 312 void
 313 cvPreparePredictData( const CvArr* sample, int dims_all, const CvMat* comp_idx,
 314                       int class_count, const CvMat* prob, float** row_sample,
 315                       int as_sparse CV_DEFAULT(0) );
 316
 317 /* copies clustering [or batch "predict"] results
 318    (labels and/or centers and/or probs) back to the output arrays */
 319 void
 320 cvWritebackLabels( const CvMat* labels, CvMat* dst_labels,
 321                    const CvMat* centers, CvMat* dst_centers,
 322                    const CvMat* probs, CvMat* dst_probs,
 323                    const CvMat* sample_idx, int samples_all,
 324                    const CvMat* comp_idx, int dims_all );
 325 #define cvWritebackResponses cvWritebackLabels
 326
 327 #define XML_FIELD_NAME "_name"
 328 CvFileNode* icvFileNodeGetChild(CvFileNode* father, const char* name);
 329 CvFileNode* icvFileNodeGetChildArrayElem(CvFileNode* father, const char* name,int index);
 330 CvFileNode* icvFileNodeGetNext(CvFileNode* n, const char* name);
 331
 332
 333 void cvCheckTrainData( const CvMat* train_data, int tflag,
 334                        const CvMat* missing_mask,
 335                        int* var_all, int* sample_all );
 336
 337 CvMat* cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, bool check_for_duplicates=false );
 338
 339 CvMat* cvPreprocessVarType( const CvMat* type_mask, const CvMat* var_idx,
 340                             int var_all, int* response_type );
 341
 342 CvMat* cvPreprocessOrderedResponses( const CvMat* responses,
 343                 const CvMat* sample_idx, int sample_all );
 344
 345 CvMat* cvPreprocessCategoricalResponses( const CvMat* responses,
 346                 const CvMat* sample_idx, int sample_all,
 347                 CvMat** out_response_map, CvMat** class_counts=0 );
 348
 349 const float** cvGetTrainSamples( const CvMat* train_data, int tflag,
 350                    const CvMat* var_idx, const CvMat* sample_idx,
 351                    int* _var_count, int* _sample_count,
 352                    bool always_copy_data=false );
 353
 354 #endif /* __ML_H__ */