/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // Intel License Agreement // // Copyright (C) 2000, Intel Corporation, all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of Intel Corporation may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #ifndef __ML_INTERNAL_H__ #define __ML_INTERNAL_H__ #if _MSC_VER >= 1200 #pragma warning( disable: 4514 4710 4711 4710 ) #endif #include "ml.h" #include "cxmisc.h" #include <assert.h> #include <float.h> #include <limits.h> #include <math.h> #include <stdlib.h> #include <stdio.h> #include <string.h> #include <time.h> #ifndef FALSE #define FALSE 0 #endif #ifndef TRUE #define TRUE 1 #endif #define ML_IMPL CV_IMPL #define CV_MAT_ELEM_FLAG( mat, type, comp, vect, tflag ) \ (( tflag == CV_ROW_SAMPLE ) \ ? (CV_MAT_ELEM( mat, type, comp, vect )) \ : (CV_MAT_ELEM( mat, type, vect, comp ))) /* Convert matrix to vector */ #define ICV_MAT2VEC( mat, vdata, vstep, num ) \ if( MIN( (mat).rows, (mat).cols ) != 1 ) \ CV_ERROR( CV_StsBadArg, "" ); \ (vdata) = ((mat).data.ptr); \ if( (mat).rows == 1 ) \ { \ (vstep) = CV_ELEM_SIZE( (mat).type ); \ (num) = (mat).cols; \ } \ else \ { \ (vstep) = (mat).step; \ (num) = (mat).rows; \ } /* get raw data */ #define ICV_RAWDATA( mat, flags, rdata, sstep, cstep, m, n ) \ (rdata) = (mat).data.ptr; \ if( CV_IS_ROW_SAMPLE( flags ) ) \ { \ (sstep) = (mat).step; \ (cstep) = CV_ELEM_SIZE( (mat).type ); \ (m) = (mat).rows; \ (n) = (mat).cols; \ } \ else \ { \ (cstep) = (mat).step; \ (sstep) = CV_ELEM_SIZE( (mat).type ); \ (n) = (mat).rows; \ (m) = (mat).cols; \ } #define ICV_IS_MAT_OF_TYPE( mat, mat_type) \ (CV_IS_MAT( mat ) && CV_MAT_TYPE( mat->type ) == (mat_type) && \ (mat)->cols > 0 && (mat)->rows > 0) /* uchar* data; int sstep, cstep; - trainData->data uchar* classes; int clstep; int ncl;- trainClasses uchar* tmask; int tmstep; int ntm; - typeMask uchar* missed;int msstep, mcstep; -missedMeasurements... int mm, mn; == m,n == size,dim uchar* sidx;int sistep; - sampleIdx uchar* cidx;int cistep; - compIdx int k, l; == n,m == dim,size (length of cidx, sidx) int m, n; == size,dim */ #define ICV_DECLARE_TRAIN_ARGS() \ uchar* data; \ int sstep, cstep; \ uchar* classes; \ int clstep; \ int ncl; \ uchar* tmask; \ int tmstep; \ int ntm; \ uchar* missed; \ int msstep, mcstep; \ int mm, mn; \ uchar* sidx; \ int sistep; \ uchar* cidx; \ int cistep; \ int k, l; \ int m, n; \ \ data = classes = tmask = missed = sidx = cidx = NULL; \ sstep = cstep = clstep = ncl = tmstep = ntm = msstep = mcstep = mm = mn = 0; \ sistep = cistep = k = l = m = n = 0; #define ICV_TRAIN_DATA_REQUIRED( param, flags ) \ if( !ICV_IS_MAT_OF_TYPE( (param), CV_32FC1 ) ) \ { \ CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \ } \ else \ { \ ICV_RAWDATA( *(param), (flags), data, sstep, cstep, m, n ); \ k = n; \ l = m; \ } #define ICV_TRAIN_CLASSES_REQUIRED( param ) \ if( !ICV_IS_MAT_OF_TYPE( (param), CV_32FC1 ) ) \ { \ CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \ } \ else \ { \ ICV_MAT2VEC( *(param), classes, clstep, ncl ); \ if( m != ncl ) \ { \ CV_ERROR( CV_StsBadArg, "Unmatched sizes" ); \ } \ } #define ICV_ARG_NULL( param ) \ if( (param) != NULL ) \ { \ CV_ERROR( CV_StsBadArg, #param " parameter must be NULL" ); \ } #define ICV_MISSED_MEASUREMENTS_OPTIONAL( param, flags ) \ if( param ) \ { \ if( !ICV_IS_MAT_OF_TYPE( param, CV_8UC1 ) ) \ { \ CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \ } \ else \ { \ ICV_RAWDATA( *(param), (flags), missed, msstep, mcstep, mm, mn ); \ if( mm != m || mn != n ) \ { \ CV_ERROR( CV_StsBadArg, "Unmatched sizes" ); \ } \ } \ } #define ICV_COMP_IDX_OPTIONAL( param ) \ if( param ) \ { \ if( !ICV_IS_MAT_OF_TYPE( param, CV_32SC1 ) ) \ { \ CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \ } \ else \ { \ ICV_MAT2VEC( *(param), cidx, cistep, k ); \ if( k > n ) \ CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \ } \ } #define ICV_SAMPLE_IDX_OPTIONAL( param ) \ if( param ) \ { \ if( !ICV_IS_MAT_OF_TYPE( param, CV_32SC1 ) ) \ { \ CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \ } \ else \ { \ ICV_MAT2VEC( *sampleIdx, sidx, sistep, l ); \ if( l > m ) \ CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \ } \ } /****************************************************************************************/ #define ICV_CONVERT_FLOAT_ARRAY_TO_MATRICE( array, matrice ) \ { \ CvMat a, b; \ int dims = (matrice)->cols; \ int nsamples = (matrice)->rows; \ int type = CV_MAT_TYPE((matrice)->type); \ int i, offset = dims; \ \ CV_ASSERT( type == CV_32FC1 || type == CV_64FC1 ); \ offset *= ((type == CV_32FC1) ? sizeof(float) : sizeof(double));\ \ b = cvMat( 1, dims, CV_32FC1 ); \ cvGetRow( matrice, &a, 0 ); \ for( i = 0; i < nsamples; i++, a.data.ptr += offset ) \ { \ b.data.fl = (float*)array[i]; \ CV_CALL( cvConvert( &b, &a ) ); \ } \ } /****************************************************************************************\ * Auxiliary functions declarations * \****************************************************************************************/ /* Generates a set of classes centers in quantity <num_of_clusters> that are generated as uniform random vectors in parallelepiped, where <data> is concentrated. Vectors in <data> should have horizontal orientation. If <centers> != NULL, the function doesn't allocate any memory and stores generated centers in <centers>, returns <centers>. If <centers> == NULL, the function allocates memory and creates the matrice. Centers are supposed to be oriented horizontally. */ CvMat* icvGenerateRandomClusterCenters( int seed, const CvMat* data, int num_of_clusters, CvMat* centers CV_DEFAULT(0)); /* Fills the <labels> using <probs> by choosing the maximal probability. Outliers are fixed by <oulier_tresh> and have cluster label (-1). Function also controls that there weren't "empty" clusters by filling empty clusters with the maximal probability vector. If probs_sums != NULL, filles it with the sums of probabilities for each sample (it is useful for normalizing probabilities' matrice of FCM) */ void icvFindClusterLabels( const CvMat* probs, float outlier_thresh, float r, const CvMat* labels ); typedef struct CvSparseVecElem32f { int idx; float val; } CvSparseVecElem32f; /* Prepare training data and related parameters */ #define CV_TRAIN_STATMODEL_DEFRAGMENT_TRAIN_DATA 1 #define CV_TRAIN_STATMODEL_SAMPLES_AS_ROWS 2 #define CV_TRAIN_STATMODEL_SAMPLES_AS_COLUMNS 4 #define CV_TRAIN_STATMODEL_CATEGORICAL_RESPONSE 8 #define CV_TRAIN_STATMODEL_ORDERED_RESPONSE 16 #define CV_TRAIN_STATMODEL_RESPONSES_ON_OUTPUT 32 #define CV_TRAIN_STATMODEL_ALWAYS_COPY_TRAIN_DATA 64 #define CV_TRAIN_STATMODEL_SPARSE_AS_SPARSE 128 int cvPrepareTrainData( const char* /*funcname*/, const CvMat* train_data, int tflag, const CvMat* responses, int response_type, const CvMat* var_idx, const CvMat* sample_idx, bool always_copy_data, const float*** out_train_samples, int* _sample_count, int* _var_count, int* _var_all, CvMat** out_responses, CvMat** out_response_map, CvMat** out_var_idx, CvMat** out_sample_idx=0 ); void cvSortSamplesByClasses( const float** samples, const CvMat* classes, int* class_ranges, const uchar** mask CV_DEFAULT(0) ); void cvCombineResponseMaps (CvMat* _responses, const CvMat* old_response_map, CvMat* new_response_map, CvMat** out_response_map); void cvPreparePredictData( const CvArr* sample, int dims_all, const CvMat* comp_idx, int class_count, const CvMat* prob, float** row_sample, int as_sparse CV_DEFAULT(0) ); /* copies clustering [or batch "predict"] results (labels and/or centers and/or probs) back to the output arrays */ void cvWritebackLabels( const CvMat* labels, CvMat* dst_labels, const CvMat* centers, CvMat* dst_centers, const CvMat* probs, CvMat* dst_probs, const CvMat* sample_idx, int samples_all, const CvMat* comp_idx, int dims_all ); #define cvWritebackResponses cvWritebackLabels #define XML_FIELD_NAME "_name" CvFileNode* icvFileNodeGetChild(CvFileNode* father, const char* name); CvFileNode* icvFileNodeGetChildArrayElem(CvFileNode* father, const char* name,int index); CvFileNode* icvFileNodeGetNext(CvFileNode* n, const char* name); void cvCheckTrainData( const CvMat* train_data, int tflag, const CvMat* missing_mask, int* var_all, int* sample_all ); CvMat* cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, bool check_for_duplicates=false ); CvMat* cvPreprocessVarType( const CvMat* type_mask, const CvMat* var_idx, int var_all, int* response_type ); CvMat* cvPreprocessOrderedResponses( const CvMat* responses, const CvMat* sample_idx, int sample_all ); CvMat* cvPreprocessCategoricalResponses( const CvMat* responses, const CvMat* sample_idx, int sample_all, CvMat** out_response_map, CvMat** class_counts=0 ); const float** cvGetTrainSamples( const CvMat* train_data, int tflag, const CvMat* var_idx, const CvMat* sample_idx, int* _var_count, int* _sample_count, bool always_copy_data=false ); #endif /* __ML_H__ */