1 /*M/////////////////////////////////////////////////////////////////////////////////////// 2 // 3 // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. 4 // 5 // By downloading, copying, installing or using the software you agree to this license. 6 // If you do not agree to this license, do not download, install, 7 // copy or use the software. 8 // 9 // 10 // Intel License Agreement 11 // 12 // Copyright (C) 2000, Intel Corporation, all rights reserved. 13 // Third party copyrights are property of their respective owners. 14 // 15 // Redistribution and use in source and binary forms, with or without modification, 16 // are permitted provided that the following conditions are met: 17 // 18 // * Redistribution's of source code must retain the above copyright notice, 19 // this list of conditions and the following disclaimer. 20 // 21 // * Redistribution's in binary form must reproduce the above copyright notice, 22 // this list of conditions and the following disclaimer in the documentation 23 // and/or other materials provided with the distribution. 24 // 25 // * The name of Intel Corporation may not be used to endorse or promote products 26 // derived from this software without specific prior written permission. 27 // 28 // This software is provided by the copyright holders and contributors "as is" and 29 // any express or implied warranties, including, but not limited to, the implied 30 // warranties of merchantability and fitness for a particular purpose are disclaimed. 31 // In no event shall the Intel Corporation or contributors be liable for any direct, 32 // indirect, incidental, special, exemplary, or consequential damages 33 // (including, but not limited to, procurement of substitute goods or services; 34 // loss of use, data, or profits; or business interruption) however caused 35 // and on any theory of liability, whether in contract, strict liability, 36 // or tort (including negligence or otherwise) arising in any way out of 37 // the use of this software, even if advised of the possibility of such damage. 38 // 39 //M*/ 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__ */ 355
