+++ /dev/null
-/*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__ */
projects / opencv / blobdiff