| /*M/////////////////////////////////////////////////////////////////////////////////////// |
| // |
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| // |
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| // 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 |
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| // 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__ */ |
