+++ /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
-// For Open Source Computer Vision Library
-//
-// 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*/
-
-#ifdef HAVE_CONFIG_H
- #include <cvconfig.h>
-#endif
-
-#ifdef HAVE_MALLOC_H
- #include <malloc.h>
-#endif
-
-#include <stdio.h>
-#include <memory.h>
-#include <float.h>
-#include <math.h>
-
-#include <time.h>
-#include <limits.h>
-
-#include <_cvcommon.h>
-#include <cvclassifier.h>
-
-#ifdef _OPENMP
-#include <omp.h>
-#endif /* _OPENMP */
-
-#define CV_BOOST_IMPL
-
-typedef struct CvValArray
-{
- uchar* data;
- size_t step;
-} CvValArray;
-
-#define CMP_VALUES( idx1, idx2 ) \
- ( *( (float*) (aux->data + ((int) (idx1)) * aux->step ) ) < \
- *( (float*) (aux->data + ((int) (idx2)) * aux->step ) ) )
-
-CV_IMPLEMENT_QSORT_EX( icvSortIndexedValArray_16s, short, CMP_VALUES, CvValArray* )
-
-CV_IMPLEMENT_QSORT_EX( icvSortIndexedValArray_32s, int, CMP_VALUES, CvValArray* )
-
-CV_IMPLEMENT_QSORT_EX( icvSortIndexedValArray_32f, float, CMP_VALUES, CvValArray* )
-
-CV_BOOST_IMPL
-void cvGetSortedIndices( CvMat* val, CvMat* idx, int sortcols )
-{
- int idxtype = 0;
- uchar* data = NULL;
- size_t istep = 0;
- size_t jstep = 0;
-
- int i = 0;
- int j = 0;
-
- CvValArray va;
-
- assert( idx != NULL );
- assert( val != NULL );
-
- idxtype = CV_MAT_TYPE( idx->type );
- assert( idxtype == CV_16SC1 || idxtype == CV_32SC1 || idxtype == CV_32FC1 );
- assert( CV_MAT_TYPE( val->type ) == CV_32FC1 );
- if( sortcols )
- {
- assert( idx->rows == val->cols );
- assert( idx->cols == val->rows );
- istep = CV_ELEM_SIZE( val->type );
- jstep = val->step;
- }
- else
- {
- assert( idx->rows == val->rows );
- assert( idx->cols == val->cols );
- istep = val->step;
- jstep = CV_ELEM_SIZE( val->type );
- }
-
- va.data = val->data.ptr;
- va.step = jstep;
- switch( idxtype )
- {
- case CV_16SC1:
- for( i = 0; i < idx->rows; i++ )
- {
- for( j = 0; j < idx->cols; j++ )
- {
- CV_MAT_ELEM( *idx, short, i, j ) = (short) j;
- }
- icvSortIndexedValArray_16s( (short*) (idx->data.ptr + i * idx->step),
- idx->cols, &va );
- va.data += istep;
- }
- break;
-
- case CV_32SC1:
- for( i = 0; i < idx->rows; i++ )
- {
- for( j = 0; j < idx->cols; j++ )
- {
- CV_MAT_ELEM( *idx, int, i, j ) = j;
- }
- icvSortIndexedValArray_32s( (int*) (idx->data.ptr + i * idx->step),
- idx->cols, &va );
- va.data += istep;
- }
- break;
-
- case CV_32FC1:
- for( i = 0; i < idx->rows; i++ )
- {
- for( j = 0; j < idx->cols; j++ )
- {
- CV_MAT_ELEM( *idx, float, i, j ) = (float) j;
- }
- icvSortIndexedValArray_32f( (float*) (idx->data.ptr + i * idx->step),
- idx->cols, &va );
- va.data += istep;
- }
- break;
-
- default:
- assert( 0 );
- break;
- }
-}
-
-CV_BOOST_IMPL
-void cvReleaseStumpClassifier( CvClassifier** classifier )
-{
- cvFree( classifier );
- *classifier = 0;
-}
-
-CV_BOOST_IMPL
-float cvEvalStumpClassifier( CvClassifier* classifier, CvMat* sample )
-{
- assert( classifier != NULL );
- assert( sample != NULL );
- assert( CV_MAT_TYPE( sample->type ) == CV_32FC1 );
-
- if( (CV_MAT_ELEM( (*sample), float, 0,
- ((CvStumpClassifier*) classifier)->compidx )) <
- ((CvStumpClassifier*) classifier)->threshold )
- {
- return ((CvStumpClassifier*) classifier)->left;
- }
- else
- {
- return ((CvStumpClassifier*) classifier)->right;
- }
-
- return 0.0F;
-}
-
-#define ICV_DEF_FIND_STUMP_THRESHOLD( suffix, type, error ) \
-CV_BOOST_IMPL int icvFindStumpThreshold_##suffix( \
- uchar* data, size_t datastep, \
- uchar* wdata, size_t wstep, \
- uchar* ydata, size_t ystep, \
- uchar* idxdata, size_t idxstep, int num, \
- float* lerror, \
- float* rerror, \
- float* threshold, float* left, float* right, \
- float* sumw, float* sumwy, float* sumwyy ) \
-{ \
- int found = 0; \
- float wyl = 0.0F; \
- float wl = 0.0F; \
- float wyyl = 0.0F; \
- float wyr = 0.0F; \
- float wr = 0.0F; \
- \
- float curleft = 0.0F; \
- float curright = 0.0F; \
- float* prevval = NULL; \
- float* curval = NULL; \
- float curlerror = 0.0F; \
- float currerror = 0.0F; \
- float wposl; \
- float wposr; \
- \
- int i = 0; \
- int idx = 0; \
- \
- wposl = wposr = 0.0F; \
- if( *sumw == FLT_MAX ) \
- { \
- /* calculate sums */ \
- float *y = NULL; \
- float *w = NULL; \
- float wy = 0.0F; \
- \
- *sumw = 0.0F; \
- *sumwy = 0.0F; \
- *sumwyy = 0.0F; \
- for( i = 0; i < num; i++ ) \
- { \
- idx = (int) ( *((type*) (idxdata + i*idxstep)) ); \
- w = (float*) (wdata + idx * wstep); \
- *sumw += *w; \
- y = (float*) (ydata + idx * ystep); \
- wy = (*w) * (*y); \
- *sumwy += wy; \
- *sumwyy += wy * (*y); \
- } \
- } \
- \
- for( i = 0; i < num; i++ ) \
- { \
- idx = (int) ( *((type*) (idxdata + i*idxstep)) ); \
- curval = (float*) (data + idx * datastep); \
- /* for debug purpose */ \
- if( i > 0 ) assert( (*prevval) <= (*curval) ); \
- \
- wyr = *sumwy - wyl; \
- wr = *sumw - wl; \
- \
- if( wl > 0.0 ) curleft = wyl / wl; \
- else curleft = 0.0F; \
- \
- if( wr > 0.0 ) curright = wyr / wr; \
- else curright = 0.0F; \
- \
- error \
- \
- if( curlerror + currerror < (*lerror) + (*rerror) ) \
- { \
- (*lerror) = curlerror; \
- (*rerror) = currerror; \
- *threshold = *curval; \
- if( i > 0 ) { \
- *threshold = 0.5F * (*threshold + *prevval); \
- } \
- *left = curleft; \
- *right = curright; \
- found = 1; \
- } \
- \
- do \
- { \
- wl += *((float*) (wdata + idx * wstep)); \
- wyl += (*((float*) (wdata + idx * wstep))) \
- * (*((float*) (ydata + idx * ystep))); \
- wyyl += *((float*) (wdata + idx * wstep)) \
- * (*((float*) (ydata + idx * ystep))) \
- * (*((float*) (ydata + idx * ystep))); \
- } \
- while( (++i) < num && \
- ( *((float*) (data + (idx = \
- (int) ( *((type*) (idxdata + i*idxstep))) ) * datastep)) \
- == *curval ) ); \
- --i; \
- prevval = curval; \
- } /* for each value */ \
- \
- return found; \
-}
-
-/* misclassification error
- * err = MIN( wpos, wneg );
- */
-#define ICV_DEF_FIND_STUMP_THRESHOLD_MISC( suffix, type ) \
- ICV_DEF_FIND_STUMP_THRESHOLD( misc_##suffix, type, \
- wposl = 0.5F * ( wl + wyl ); \
- wposr = 0.5F * ( wr + wyr ); \
- curleft = 0.5F * ( 1.0F + curleft ); \
- curright = 0.5F * ( 1.0F + curright ); \
- curlerror = MIN( wposl, wl - wposl ); \
- currerror = MIN( wposr, wr - wposr ); \
- )
-
-/* gini error
- * err = 2 * wpos * wneg /(wpos + wneg)
- */
-#define ICV_DEF_FIND_STUMP_THRESHOLD_GINI( suffix, type ) \
- ICV_DEF_FIND_STUMP_THRESHOLD( gini_##suffix, type, \
- wposl = 0.5F * ( wl + wyl ); \
- wposr = 0.5F * ( wr + wyr ); \
- curleft = 0.5F * ( 1.0F + curleft ); \
- curright = 0.5F * ( 1.0F + curright ); \
- curlerror = 2.0F * wposl * ( 1.0F - curleft ); \
- currerror = 2.0F * wposr * ( 1.0F - curright ); \
- )
-
-#define CV_ENTROPY_THRESHOLD FLT_MIN
-
-/* entropy error
- * err = - wpos * log(wpos / (wpos + wneg)) - wneg * log(wneg / (wpos + wneg))
- */
-#define ICV_DEF_FIND_STUMP_THRESHOLD_ENTROPY( suffix, type ) \
- ICV_DEF_FIND_STUMP_THRESHOLD( entropy_##suffix, type, \
- wposl = 0.5F * ( wl + wyl ); \
- wposr = 0.5F * ( wr + wyr ); \
- curleft = 0.5F * ( 1.0F + curleft ); \
- curright = 0.5F * ( 1.0F + curright ); \
- curlerror = currerror = 0.0F; \
- if( curleft > CV_ENTROPY_THRESHOLD ) \
- curlerror -= wposl * logf( curleft ); \
- if( curleft < 1.0F - CV_ENTROPY_THRESHOLD ) \
- curlerror -= (wl - wposl) * logf( 1.0F - curleft ); \
- \
- if( curright > CV_ENTROPY_THRESHOLD ) \
- currerror -= wposr * logf( curright ); \
- if( curright < 1.0F - CV_ENTROPY_THRESHOLD ) \
- currerror -= (wr - wposr) * logf( 1.0F - curright ); \
- )
-
-/* least sum of squares error */
-#define ICV_DEF_FIND_STUMP_THRESHOLD_SQ( suffix, type ) \
- ICV_DEF_FIND_STUMP_THRESHOLD( sq_##suffix, type, \
- /* calculate error (sum of squares) */ \
- /* err = sum( w * (y - left(rigt)Val)^2 ) */ \
- curlerror = wyyl + curleft * curleft * wl - 2.0F * curleft * wyl; \
- currerror = (*sumwyy) - wyyl + curright * curright * wr - 2.0F * curright * wyr; \
- )
-
-ICV_DEF_FIND_STUMP_THRESHOLD_MISC( 16s, short )
-
-ICV_DEF_FIND_STUMP_THRESHOLD_MISC( 32s, int )
-
-ICV_DEF_FIND_STUMP_THRESHOLD_MISC( 32f, float )
-
-
-ICV_DEF_FIND_STUMP_THRESHOLD_GINI( 16s, short )
-
-ICV_DEF_FIND_STUMP_THRESHOLD_GINI( 32s, int )
-
-ICV_DEF_FIND_STUMP_THRESHOLD_GINI( 32f, float )
-
-
-ICV_DEF_FIND_STUMP_THRESHOLD_ENTROPY( 16s, short )
-
-ICV_DEF_FIND_STUMP_THRESHOLD_ENTROPY( 32s, int )
-
-ICV_DEF_FIND_STUMP_THRESHOLD_ENTROPY( 32f, float )
-
-
-ICV_DEF_FIND_STUMP_THRESHOLD_SQ( 16s, short )
-
-ICV_DEF_FIND_STUMP_THRESHOLD_SQ( 32s, int )
-
-ICV_DEF_FIND_STUMP_THRESHOLD_SQ( 32f, float )
-
-typedef int (*CvFindThresholdFunc)( uchar* data, size_t datastep,
- uchar* wdata, size_t wstep,
- uchar* ydata, size_t ystep,
- uchar* idxdata, size_t idxstep, int num,
- float* lerror,
- float* rerror,
- float* threshold, float* left, float* right,
- float* sumw, float* sumwy, float* sumwyy );
-
-CvFindThresholdFunc findStumpThreshold_16s[4] = {
- icvFindStumpThreshold_misc_16s,
- icvFindStumpThreshold_gini_16s,
- icvFindStumpThreshold_entropy_16s,
- icvFindStumpThreshold_sq_16s
- };
-
-CvFindThresholdFunc findStumpThreshold_32s[4] = {
- icvFindStumpThreshold_misc_32s,
- icvFindStumpThreshold_gini_32s,
- icvFindStumpThreshold_entropy_32s,
- icvFindStumpThreshold_sq_32s
- };
-
-CvFindThresholdFunc findStumpThreshold_32f[4] = {
- icvFindStumpThreshold_misc_32f,
- icvFindStumpThreshold_gini_32f,
- icvFindStumpThreshold_entropy_32f,
- icvFindStumpThreshold_sq_32f
- };
-
-CV_BOOST_IMPL
-CvClassifier* cvCreateStumpClassifier( CvMat* trainData,
- int flags,
- CvMat* trainClasses,
- CvMat* typeMask,
- CvMat* missedMeasurementsMask,
- CvMat* compIdx,
- CvMat* sampleIdx,
- CvMat* weights,
- CvClassifierTrainParams* trainParams
- )
-{
- CvStumpClassifier* stump = NULL;
- int m = 0; /* number of samples */
- int n = 0; /* number of components */
- uchar* data = NULL;
- int cstep = 0;
- int sstep = 0;
- uchar* ydata = NULL;
- int ystep = 0;
- uchar* idxdata = NULL;
- int idxstep = 0;
- int l = 0; /* number of indices */
- uchar* wdata = NULL;
- int wstep = 0;
-
- int* idx = NULL;
- int i = 0;
-
- float sumw = FLT_MAX;
- float sumw1 = FLT_MAX;
- float sumw0 = FLT_MAX;
- float sumwy = FLT_MAX;
- float sumwyy = FLT_MAX;
-
- assert( trainData != NULL );
- assert( CV_MAT_TYPE( trainData->type ) == CV_32FC1 );
- assert( trainClasses != NULL );
- assert( CV_MAT_TYPE( trainClasses->type ) == CV_32FC1 );
- assert( missedMeasurementsMask == NULL );
- assert( compIdx == NULL );
- assert( weights != NULL );
- assert( CV_MAT_TYPE( weights->type ) == CV_32FC1 );
- assert( trainParams != NULL );
-
- data = trainData->data.ptr;
- if( CV_IS_ROW_SAMPLE( flags ) )
- {
- cstep = CV_ELEM_SIZE( trainData->type );
- sstep = trainData->step;
- m = trainData->rows;
- n = trainData->cols;
- }
- else
- {
- sstep = CV_ELEM_SIZE( trainData->type );
- cstep = trainData->step;
- m = trainData->cols;
- n = trainData->rows;
- }
-
- ydata = trainClasses->data.ptr;
- if( trainClasses->rows == 1 )
- {
- assert( trainClasses->cols == m );
- ystep = CV_ELEM_SIZE( trainClasses->type );
- }
- else
- {
- assert( trainClasses->rows == m );
- ystep = trainClasses->step;
- }
-
- wdata = weights->data.ptr;
- if( weights->rows == 1 )
- {
- assert( weights->cols == m );
- wstep = CV_ELEM_SIZE( weights->type );
- }
- else
- {
- assert( weights->rows == m );
- wstep = weights->step;
- }
-
- l = m;
- if( sampleIdx != NULL )
- {
- assert( CV_MAT_TYPE( sampleIdx->type ) == CV_32FC1 );
-
- idxdata = sampleIdx->data.ptr;
- if( sampleIdx->rows == 1 )
- {
- l = sampleIdx->cols;
- idxstep = CV_ELEM_SIZE( sampleIdx->type );
- }
- else
- {
- l = sampleIdx->rows;
- idxstep = sampleIdx->step;
- }
- assert( l <= m );
- }
-
- idx = (int*) cvAlloc( l * sizeof( int ) );
- stump = (CvStumpClassifier*) cvAlloc( sizeof( CvStumpClassifier) );
-
- /* START */
- memset( (void*) stump, 0, sizeof( CvStumpClassifier ) );
-
- stump->eval = cvEvalStumpClassifier;
- stump->tune = NULL;
- stump->save = NULL;
- stump->release = cvReleaseStumpClassifier;
-
- stump->lerror = FLT_MAX;
- stump->rerror = FLT_MAX;
- stump->left = 0.0F;
- stump->right = 0.0F;
-
- /* copy indices */
- if( sampleIdx != NULL )
- {
- for( i = 0; i < l; i++ )
- {
- idx[i] = (int) *((float*) (idxdata + i*idxstep));
- }
- }
- else
- {
- for( i = 0; i < l; i++ )
- {
- idx[i] = i;
- }
- }
-
- for( i = 0; i < n; i++ )
- {
- CvValArray va;
-
- va.data = data + i * ((size_t) cstep);
- va.step = sstep;
- icvSortIndexedValArray_32s( idx, l, &va );
- if( findStumpThreshold_32s[(int) ((CvStumpTrainParams*) trainParams)->error]
- ( data + i * ((size_t) cstep), sstep,
- wdata, wstep, ydata, ystep, (uchar*) idx, sizeof( int ), l,
- &(stump->lerror), &(stump->rerror),
- &(stump->threshold), &(stump->left), &(stump->right),
- &sumw, &sumwy, &sumwyy ) )
- {
- stump->compidx = i;
- }
- } /* for each component */
-
- /* END */
-
- cvFree( &idx );
-
- if( ((CvStumpTrainParams*) trainParams)->type == CV_CLASSIFICATION_CLASS )
- {
- stump->left = 2.0F * (stump->left >= 0.5F) - 1.0F;
- stump->right = 2.0F * (stump->right >= 0.5F) - 1.0F;
- }
-
- return (CvClassifier*) stump;
-}
-
-/*
- * cvCreateMTStumpClassifier
- *
- * Multithreaded stump classifier constructor
- * Includes huge train data support through callback function
- */
-CV_BOOST_IMPL
-CvClassifier* cvCreateMTStumpClassifier( CvMat* trainData,
- int flags,
- CvMat* trainClasses,
- CvMat* typeMask,
- CvMat* missedMeasurementsMask,
- CvMat* compIdx,
- CvMat* sampleIdx,
- CvMat* weights,
- CvClassifierTrainParams* trainParams )
-{
- CvStumpClassifier* stump = NULL;
- int m = 0; /* number of samples */
- int n = 0; /* number of components */
- uchar* data = NULL;
- size_t cstep = 0;
- size_t sstep = 0;
- int datan = 0; /* num components */
- uchar* ydata = NULL;
- size_t ystep = 0;
- uchar* idxdata = NULL;
- size_t idxstep = 0;
- int l = 0; /* number of indices */
- uchar* wdata = NULL;
- size_t wstep = 0;
-
- uchar* sorteddata = NULL;
- int sortedtype = 0;
- size_t sortedcstep = 0; /* component step */
- size_t sortedsstep = 0; /* sample step */
- int sortedn = 0; /* num components */
- int sortedm = 0; /* num samples */
-
- char* filter = NULL;
- int i = 0;
-
- int compidx = 0;
- int stumperror;
- int portion;
-
- /* private variables */
- CvMat mat;
- CvValArray va;
- float lerror;
- float rerror;
- float left;
- float right;
- float threshold;
- int optcompidx;
-
- float sumw;
- float sumwy;
- float sumwyy;
-
- int t_compidx;
- int t_n;
-
- int ti;
- int tj;
- int tk;
-
- uchar* t_data;
- size_t t_cstep;
- size_t t_sstep;
-
- size_t matcstep;
- size_t matsstep;
-
- int* t_idx;
- /* end private variables */
-
- assert( trainParams != NULL );
- assert( trainClasses != NULL );
- assert( CV_MAT_TYPE( trainClasses->type ) == CV_32FC1 );
- assert( missedMeasurementsMask == NULL );
- assert( compIdx == NULL );
-
- stumperror = (int) ((CvMTStumpTrainParams*) trainParams)->error;
-
- ydata = trainClasses->data.ptr;
- if( trainClasses->rows == 1 )
- {
- m = trainClasses->cols;
- ystep = CV_ELEM_SIZE( trainClasses->type );
- }
- else
- {
- m = trainClasses->rows;
- ystep = trainClasses->step;
- }
-
- wdata = weights->data.ptr;
- if( weights->rows == 1 )
- {
- assert( weights->cols == m );
- wstep = CV_ELEM_SIZE( weights->type );
- }
- else
- {
- assert( weights->rows == m );
- wstep = weights->step;
- }
-
- if( ((CvMTStumpTrainParams*) trainParams)->sortedIdx != NULL )
- {
- sortedtype =
- CV_MAT_TYPE( ((CvMTStumpTrainParams*) trainParams)->sortedIdx->type );
- assert( sortedtype == CV_16SC1 || sortedtype == CV_32SC1
- || sortedtype == CV_32FC1 );
- sorteddata = ((CvMTStumpTrainParams*) trainParams)->sortedIdx->data.ptr;
- sortedsstep = CV_ELEM_SIZE( sortedtype );
- sortedcstep = ((CvMTStumpTrainParams*) trainParams)->sortedIdx->step;
- sortedn = ((CvMTStumpTrainParams*) trainParams)->sortedIdx->rows;
- sortedm = ((CvMTStumpTrainParams*) trainParams)->sortedIdx->cols;
- }
-
- if( trainData == NULL )
- {
- assert( ((CvMTStumpTrainParams*) trainParams)->getTrainData != NULL );
- n = ((CvMTStumpTrainParams*) trainParams)->numcomp;
- assert( n > 0 );
- }
- else
- {
- assert( CV_MAT_TYPE( trainData->type ) == CV_32FC1 );
- data = trainData->data.ptr;
- if( CV_IS_ROW_SAMPLE( flags ) )
- {
- cstep = CV_ELEM_SIZE( trainData->type );
- sstep = trainData->step;
- assert( m == trainData->rows );
- datan = n = trainData->cols;
- }
- else
- {
- sstep = CV_ELEM_SIZE( trainData->type );
- cstep = trainData->step;
- assert( m == trainData->cols );
- datan = n = trainData->rows;
- }
- if( ((CvMTStumpTrainParams*) trainParams)->getTrainData != NULL )
- {
- n = ((CvMTStumpTrainParams*) trainParams)->numcomp;
- }
- }
- assert( datan <= n );
-
- if( sampleIdx != NULL )
- {
- assert( CV_MAT_TYPE( sampleIdx->type ) == CV_32FC1 );
- idxdata = sampleIdx->data.ptr;
- idxstep = ( sampleIdx->rows == 1 )
- ? CV_ELEM_SIZE( sampleIdx->type ) : sampleIdx->step;
- l = ( sampleIdx->rows == 1 ) ? sampleIdx->cols : sampleIdx->rows;
-
- if( sorteddata != NULL )
- {
- filter = (char*) cvAlloc( sizeof( char ) * m );
- memset( (void*) filter, 0, sizeof( char ) * m );
- for( i = 0; i < l; i++ )
- {
- filter[(int) *((float*) (idxdata + i * idxstep))] = (char) 1;
- }
- }
- }
- else
- {
- l = m;
- }
-
- stump = (CvStumpClassifier*) cvAlloc( sizeof( CvStumpClassifier) );
-
- /* START */
- memset( (void*) stump, 0, sizeof( CvStumpClassifier ) );
-
- portion = ((CvMTStumpTrainParams*)trainParams)->portion;
-
- if( portion < 1 )
- {
- /* auto portion */
- portion = n;
- #ifdef _OPENMP
- portion /= omp_get_max_threads();
- #endif /* _OPENMP */
- }
-
- stump->eval = cvEvalStumpClassifier;
- stump->tune = NULL;
- stump->save = NULL;
- stump->release = cvReleaseStumpClassifier;
-
- stump->lerror = FLT_MAX;
- stump->rerror = FLT_MAX;
- stump->left = 0.0F;
- stump->right = 0.0F;
-
- compidx = 0;
- #ifdef _OPENMP
- #pragma omp parallel private(mat, va, lerror, rerror, left, right, threshold, \
- optcompidx, sumw, sumwy, sumwyy, t_compidx, t_n, \
- ti, tj, tk, t_data, t_cstep, t_sstep, matcstep, \
- matsstep, t_idx)
- #endif /* _OPENMP */
- {
- lerror = FLT_MAX;
- rerror = FLT_MAX;
- left = 0.0F;
- right = 0.0F;
- threshold = 0.0F;
- optcompidx = 0;
-
- sumw = FLT_MAX;
- sumwy = FLT_MAX;
- sumwyy = FLT_MAX;
-
- t_compidx = 0;
- t_n = 0;
-
- ti = 0;
- tj = 0;
- tk = 0;
-
- t_data = NULL;
- t_cstep = 0;
- t_sstep = 0;
-
- matcstep = 0;
- matsstep = 0;
-
- t_idx = NULL;
-
- mat.data.ptr = NULL;
-
- if( datan < n )
- {
- /* prepare matrix for callback */
- if( CV_IS_ROW_SAMPLE( flags ) )
- {
- mat = cvMat( m, portion, CV_32FC1, 0 );
- matcstep = CV_ELEM_SIZE( mat.type );
- matsstep = mat.step;
- }
- else
- {
- mat = cvMat( portion, m, CV_32FC1, 0 );
- matcstep = mat.step;
- matsstep = CV_ELEM_SIZE( mat.type );
- }
- mat.data.ptr = (uchar*) cvAlloc( sizeof( float ) * mat.rows * mat.cols );
- }
-
- if( filter != NULL || sortedn < n )
- {
- t_idx = (int*) cvAlloc( sizeof( int ) * m );
- if( sortedn == 0 || filter == NULL )
- {
- if( idxdata != NULL )
- {
- for( ti = 0; ti < l; ti++ )
- {
- t_idx[ti] = (int) *((float*) (idxdata + ti * idxstep));
- }
- }
- else
- {
- for( ti = 0; ti < l; ti++ )
- {
- t_idx[ti] = ti;
- }
- }
- }
- }
-
- #ifdef _OPENMP
- #pragma omp critical(c_compidx)
- #endif /* _OPENMP */
- {
- t_compidx = compidx;
- compidx += portion;
- }
- while( t_compidx < n )
- {
- t_n = portion;
- if( t_compidx < datan )
- {
- t_n = ( t_n < (datan - t_compidx) ) ? t_n : (datan - t_compidx);
- t_data = data;
- t_cstep = cstep;
- t_sstep = sstep;
- }
- else
- {
- t_n = ( t_n < (n - t_compidx) ) ? t_n : (n - t_compidx);
- t_cstep = matcstep;
- t_sstep = matsstep;
- t_data = mat.data.ptr - t_compidx * ((size_t) t_cstep );
-
- /* calculate components */
- ((CvMTStumpTrainParams*)trainParams)->getTrainData( &mat,
- sampleIdx, compIdx, t_compidx, t_n,
- ((CvMTStumpTrainParams*)trainParams)->userdata );
- }
-
- if( sorteddata != NULL )
- {
- if( filter != NULL )
- {
- /* have sorted indices and filter */
- switch( sortedtype )
- {
- case CV_16SC1:
- for( ti = t_compidx; ti < MIN( sortedn, t_compidx + t_n ); ti++ )
- {
- tk = 0;
- for( tj = 0; tj < sortedm; tj++ )
- {
- int curidx = (int) ( *((short*) (sorteddata
- + ti * sortedcstep + tj * sortedsstep)) );
- if( filter[curidx] != 0 )
- {
- t_idx[tk++] = curidx;
- }
- }
- if( findStumpThreshold_32s[stumperror](
- t_data + ti * t_cstep, t_sstep,
- wdata, wstep, ydata, ystep,
- (uchar*) t_idx, sizeof( int ), tk,
- &lerror, &rerror,
- &threshold, &left, &right,
- &sumw, &sumwy, &sumwyy ) )
- {
- optcompidx = ti;
- }
- }
- break;
- case CV_32SC1:
- for( ti = t_compidx; ti < MIN( sortedn, t_compidx + t_n ); ti++ )
- {
- tk = 0;
- for( tj = 0; tj < sortedm; tj++ )
- {
- int curidx = (int) ( *((int*) (sorteddata
- + ti * sortedcstep + tj * sortedsstep)) );
- if( filter[curidx] != 0 )
- {
- t_idx[tk++] = curidx;
- }
- }
- if( findStumpThreshold_32s[stumperror](
- t_data + ti * t_cstep, t_sstep,
- wdata, wstep, ydata, ystep,
- (uchar*) t_idx, sizeof( int ), tk,
- &lerror, &rerror,
- &threshold, &left, &right,
- &sumw, &sumwy, &sumwyy ) )
- {
- optcompidx = ti;
- }
- }
- break;
- case CV_32FC1:
- for( ti = t_compidx; ti < MIN( sortedn, t_compidx + t_n ); ti++ )
- {
- tk = 0;
- for( tj = 0; tj < sortedm; tj++ )
- {
- int curidx = (int) ( *((float*) (sorteddata
- + ti * sortedcstep + tj * sortedsstep)) );
- if( filter[curidx] != 0 )
- {
- t_idx[tk++] = curidx;
- }
- }
- if( findStumpThreshold_32s[stumperror](
- t_data + ti * t_cstep, t_sstep,
- wdata, wstep, ydata, ystep,
- (uchar*) t_idx, sizeof( int ), tk,
- &lerror, &rerror,
- &threshold, &left, &right,
- &sumw, &sumwy, &sumwyy ) )
- {
- optcompidx = ti;
- }
- }
- break;
- default:
- assert( 0 );
- break;
- }
- }
- else
- {
- /* have sorted indices */
- switch( sortedtype )
- {
- case CV_16SC1:
- for( ti = t_compidx; ti < MIN( sortedn, t_compidx + t_n ); ti++ )
- {
- if( findStumpThreshold_16s[stumperror](
- t_data + ti * t_cstep, t_sstep,
- wdata, wstep, ydata, ystep,
- sorteddata + ti * sortedcstep, sortedsstep, sortedm,
- &lerror, &rerror,
- &threshold, &left, &right,
- &sumw, &sumwy, &sumwyy ) )
- {
- optcompidx = ti;
- }
- }
- break;
- case CV_32SC1:
- for( ti = t_compidx; ti < MIN( sortedn, t_compidx + t_n ); ti++ )
- {
- if( findStumpThreshold_32s[stumperror](
- t_data + ti * t_cstep, t_sstep,
- wdata, wstep, ydata, ystep,
- sorteddata + ti * sortedcstep, sortedsstep, sortedm,
- &lerror, &rerror,
- &threshold, &left, &right,
- &sumw, &sumwy, &sumwyy ) )
- {
- optcompidx = ti;
- }
- }
- break;
- case CV_32FC1:
- for( ti = t_compidx; ti < MIN( sortedn, t_compidx + t_n ); ti++ )
- {
- if( findStumpThreshold_32f[stumperror](
- t_data + ti * t_cstep, t_sstep,
- wdata, wstep, ydata, ystep,
- sorteddata + ti * sortedcstep, sortedsstep, sortedm,
- &lerror, &rerror,
- &threshold, &left, &right,
- &sumw, &sumwy, &sumwyy ) )
- {
- optcompidx = ti;
- }
- }
- break;
- default:
- assert( 0 );
- break;
- }
- }
- }
-
- ti = MAX( t_compidx, MIN( sortedn, t_compidx + t_n ) );
- for( ; ti < t_compidx + t_n; ti++ )
- {
- va.data = t_data + ti * t_cstep;
- va.step = t_sstep;
- icvSortIndexedValArray_32s( t_idx, l, &va );
- if( findStumpThreshold_32s[stumperror](
- t_data + ti * t_cstep, t_sstep,
- wdata, wstep, ydata, ystep,
- (uchar*)t_idx, sizeof( int ), l,
- &lerror, &rerror,
- &threshold, &left, &right,
- &sumw, &sumwy, &sumwyy ) )
- {
- optcompidx = ti;
- }
- }
- #ifdef _OPENMP
- #pragma omp critical(c_compidx)
- #endif /* _OPENMP */
- {
- t_compidx = compidx;
- compidx += portion;
- }
- } /* while have training data */
-
- /* get the best classifier */
- #ifdef _OPENMP
- #pragma omp critical(c_beststump)
- #endif /* _OPENMP */
- {
- if( lerror + rerror < stump->lerror + stump->rerror )
- {
- stump->lerror = lerror;
- stump->rerror = rerror;
- stump->compidx = optcompidx;
- stump->threshold = threshold;
- stump->left = left;
- stump->right = right;
- }
- }
-
- /* free allocated memory */
- if( mat.data.ptr != NULL )
- {
- cvFree( &(mat.data.ptr) );
- }
- if( t_idx != NULL )
- {
- cvFree( &t_idx );
- }
- } /* end of parallel region */
-
- /* END */
-
- /* free allocated memory */
- if( filter != NULL )
- {
- cvFree( &filter );
- }
-
- if( ((CvMTStumpTrainParams*) trainParams)->type == CV_CLASSIFICATION_CLASS )
- {
- stump->left = 2.0F * (stump->left >= 0.5F) - 1.0F;
- stump->right = 2.0F * (stump->right >= 0.5F) - 1.0F;
- }
-
- return (CvClassifier*) stump;
-}
-
-CV_BOOST_IMPL
-float cvEvalCARTClassifier( CvClassifier* classifier, CvMat* sample )
-{
- CV_FUNCNAME( "cvEvalCARTClassifier" );
-
- int idx;
-
- __BEGIN__;
-
-
- CV_ASSERT( classifier != NULL );
- CV_ASSERT( sample != NULL );
- CV_ASSERT( CV_MAT_TYPE( sample->type ) == CV_32FC1 );
- CV_ASSERT( sample->rows == 1 || sample->cols == 1 );
-
- idx = 0;
- if( sample->rows == 1 )
- {
- do
- {
- if( (CV_MAT_ELEM( (*sample), float, 0,
- ((CvCARTClassifier*) classifier)->compidx[idx] )) <
- ((CvCARTClassifier*) classifier)->threshold[idx] )
- {
- idx = ((CvCARTClassifier*) classifier)->left[idx];
- }
- else
- {
- idx = ((CvCARTClassifier*) classifier)->right[idx];
- }
- } while( idx > 0 );
- }
- else
- {
- do
- {
- if( (CV_MAT_ELEM( (*sample), float,
- ((CvCARTClassifier*) classifier)->compidx[idx], 0 )) <
- ((CvCARTClassifier*) classifier)->threshold[idx] )
- {
- idx = ((CvCARTClassifier*) classifier)->left[idx];
- }
- else
- {
- idx = ((CvCARTClassifier*) classifier)->right[idx];
- }
- } while( idx > 0 );
- }
-
- __END__;
-
- return ((CvCARTClassifier*) classifier)->val[-idx];
-}
-
-CV_BOOST_IMPL
-float cvEvalCARTClassifierIdx( CvClassifier* classifier, CvMat* sample )
-{
- CV_FUNCNAME( "cvEvalCARTClassifierIdx" );
-
- int idx;
-
- __BEGIN__;
-
-
- CV_ASSERT( classifier != NULL );
- CV_ASSERT( sample != NULL );
- CV_ASSERT( CV_MAT_TYPE( sample->type ) == CV_32FC1 );
- CV_ASSERT( sample->rows == 1 || sample->cols == 1 );
-
- idx = 0;
- if( sample->rows == 1 )
- {
- do
- {
- if( (CV_MAT_ELEM( (*sample), float, 0,
- ((CvCARTClassifier*) classifier)->compidx[idx] )) <
- ((CvCARTClassifier*) classifier)->threshold[idx] )
- {
- idx = ((CvCARTClassifier*) classifier)->left[idx];
- }
- else
- {
- idx = ((CvCARTClassifier*) classifier)->right[idx];
- }
- } while( idx > 0 );
- }
- else
- {
- do
- {
- if( (CV_MAT_ELEM( (*sample), float,
- ((CvCARTClassifier*) classifier)->compidx[idx], 0 )) <
- ((CvCARTClassifier*) classifier)->threshold[idx] )
- {
- idx = ((CvCARTClassifier*) classifier)->left[idx];
- }
- else
- {
- idx = ((CvCARTClassifier*) classifier)->right[idx];
- }
- } while( idx > 0 );
- }
-
- __END__;
-
- return (float) (-idx);
-}
-
-CV_BOOST_IMPL
-void cvReleaseCARTClassifier( CvClassifier** classifier )
-{
- cvFree( classifier );
- *classifier = NULL;
-}
-
-void CV_CDECL icvDefaultSplitIdx_R( int compidx, float threshold,
- CvMat* idx, CvMat** left, CvMat** right,
- void* userdata )
-{
- CvMat* trainData = (CvMat*) userdata;
- int i = 0;
-
- *left = cvCreateMat( 1, trainData->rows, CV_32FC1 );
- *right = cvCreateMat( 1, trainData->rows, CV_32FC1 );
- (*left)->cols = (*right)->cols = 0;
- if( idx == NULL )
- {
- for( i = 0; i < trainData->rows; i++ )
- {
- if( CV_MAT_ELEM( *trainData, float, i, compidx ) < threshold )
- {
- (*left)->data.fl[(*left)->cols++] = (float) i;
- }
- else
- {
- (*right)->data.fl[(*right)->cols++] = (float) i;
- }
- }
- }
- else
- {
- uchar* idxdata;
- int idxnum;
- int idxstep;
- int index;
-
- idxdata = idx->data.ptr;
- idxnum = (idx->rows == 1) ? idx->cols : idx->rows;
- idxstep = (idx->rows == 1) ? CV_ELEM_SIZE( idx->type ) : idx->step;
- for( i = 0; i < idxnum; i++ )
- {
- index = (int) *((float*) (idxdata + i * idxstep));
- if( CV_MAT_ELEM( *trainData, float, index, compidx ) < threshold )
- {
- (*left)->data.fl[(*left)->cols++] = (float) index;
- }
- else
- {
- (*right)->data.fl[(*right)->cols++] = (float) index;
- }
- }
- }
-}
-
-void CV_CDECL icvDefaultSplitIdx_C( int compidx, float threshold,
- CvMat* idx, CvMat** left, CvMat** right,
- void* userdata )
-{
- CvMat* trainData = (CvMat*) userdata;
- int i = 0;
-
- *left = cvCreateMat( 1, trainData->cols, CV_32FC1 );
- *right = cvCreateMat( 1, trainData->cols, CV_32FC1 );
- (*left)->cols = (*right)->cols = 0;
- if( idx == NULL )
- {
- for( i = 0; i < trainData->cols; i++ )
- {
- if( CV_MAT_ELEM( *trainData, float, compidx, i ) < threshold )
- {
- (*left)->data.fl[(*left)->cols++] = (float) i;
- }
- else
- {
- (*right)->data.fl[(*right)->cols++] = (float) i;
- }
- }
- }
- else
- {
- uchar* idxdata;
- int idxnum;
- int idxstep;
- int index;
-
- idxdata = idx->data.ptr;
- idxnum = (idx->rows == 1) ? idx->cols : idx->rows;
- idxstep = (idx->rows == 1) ? CV_ELEM_SIZE( idx->type ) : idx->step;
- for( i = 0; i < idxnum; i++ )
- {
- index = (int) *((float*) (idxdata + i * idxstep));
- if( CV_MAT_ELEM( *trainData, float, compidx, index ) < threshold )
- {
- (*left)->data.fl[(*left)->cols++] = (float) index;
- }
- else
- {
- (*right)->data.fl[(*right)->cols++] = (float) index;
- }
- }
- }
-}
-
-/* internal structure used in CART creation */
-typedef struct CvCARTNode
-{
- CvMat* sampleIdx;
- CvStumpClassifier* stump;
- int parent;
- int leftflag;
- float errdrop;
-} CvCARTNode;
-
-CV_BOOST_IMPL
-CvClassifier* cvCreateCARTClassifier( CvMat* trainData,
- int flags,
- CvMat* trainClasses,
- CvMat* typeMask,
- CvMat* missedMeasurementsMask,
- CvMat* compIdx,
- CvMat* sampleIdx,
- CvMat* weights,
- CvClassifierTrainParams* trainParams )
-{
- CvCARTClassifier* cart = NULL;
- size_t datasize = 0;
- int count = 0;
- int i = 0;
- int j = 0;
-
- CvCARTNode* intnode = NULL;
- CvCARTNode* list = NULL;
- int listcount = 0;
- CvMat* lidx = NULL;
- CvMat* ridx = NULL;
-
- float maxerrdrop = 0.0F;
- int idx = 0;
-
- void (*splitIdxCallback)( int compidx, float threshold,
- CvMat* idx, CvMat** left, CvMat** right,
- void* userdata );
- void* userdata;
-
- count = ((CvCARTTrainParams*) trainParams)->count;
-
- assert( count > 0 );
-
- datasize = sizeof( *cart ) + (sizeof( float ) + 3 * sizeof( int )) * count +
- sizeof( float ) * (count + 1);
-
- cart = (CvCARTClassifier*) cvAlloc( datasize );
- memset( cart, 0, datasize );
-
- cart->count = count;
-
- cart->eval = cvEvalCARTClassifier;
- cart->save = NULL;
- cart->release = cvReleaseCARTClassifier;
-
- cart->compidx = (int*) (cart + 1);
- cart->threshold = (float*) (cart->compidx + count);
- cart->left = (int*) (cart->threshold + count);
- cart->right = (int*) (cart->left + count);
- cart->val = (float*) (cart->right + count);
-
- datasize = sizeof( CvCARTNode ) * (count + count);
- intnode = (CvCARTNode*) cvAlloc( datasize );
- memset( intnode, 0, datasize );
- list = (CvCARTNode*) (intnode + count);
-
- splitIdxCallback = ((CvCARTTrainParams*) trainParams)->splitIdx;
- userdata = ((CvCARTTrainParams*) trainParams)->userdata;
- if( splitIdxCallback == NULL )
- {
- splitIdxCallback = ( CV_IS_ROW_SAMPLE( flags ) )
- ? icvDefaultSplitIdx_R : icvDefaultSplitIdx_C;
- userdata = trainData;
- }
-
- /* create root of the tree */
- intnode[0].sampleIdx = sampleIdx;
- intnode[0].stump = (CvStumpClassifier*)
- ((CvCARTTrainParams*) trainParams)->stumpConstructor( trainData, flags,
- trainClasses, typeMask, missedMeasurementsMask, compIdx, sampleIdx, weights,
- ((CvCARTTrainParams*) trainParams)->stumpTrainParams );
- cart->left[0] = cart->right[0] = 0;
-
- /* build tree */
- listcount = 0;
- for( i = 1; i < count; i++ )
- {
- /* split last added node */
- splitIdxCallback( intnode[i-1].stump->compidx, intnode[i-1].stump->threshold,
- intnode[i-1].sampleIdx, &lidx, &ridx, userdata );
-
- if( intnode[i-1].stump->lerror != 0.0F )
- {
- list[listcount].sampleIdx = lidx;
- list[listcount].stump = (CvStumpClassifier*)
- ((CvCARTTrainParams*) trainParams)->stumpConstructor( trainData, flags,
- trainClasses, typeMask, missedMeasurementsMask, compIdx,
- list[listcount].sampleIdx,
- weights, ((CvCARTTrainParams*) trainParams)->stumpTrainParams );
- list[listcount].errdrop = intnode[i-1].stump->lerror
- - (list[listcount].stump->lerror + list[listcount].stump->rerror);
- list[listcount].leftflag = 1;
- list[listcount].parent = i-1;
- listcount++;
- }
- else
- {
- cvReleaseMat( &lidx );
- }
- if( intnode[i-1].stump->rerror != 0.0F )
- {
- list[listcount].sampleIdx = ridx;
- list[listcount].stump = (CvStumpClassifier*)
- ((CvCARTTrainParams*) trainParams)->stumpConstructor( trainData, flags,
- trainClasses, typeMask, missedMeasurementsMask, compIdx,
- list[listcount].sampleIdx,
- weights, ((CvCARTTrainParams*) trainParams)->stumpTrainParams );
- list[listcount].errdrop = intnode[i-1].stump->rerror
- - (list[listcount].stump->lerror + list[listcount].stump->rerror);
- list[listcount].leftflag = 0;
- list[listcount].parent = i-1;
- listcount++;
- }
- else
- {
- cvReleaseMat( &ridx );
- }
-
- if( listcount == 0 ) break;
-
- /* find the best node to be added to the tree */
- idx = 0;
- maxerrdrop = list[idx].errdrop;
- for( j = 1; j < listcount; j++ )
- {
- if( list[j].errdrop > maxerrdrop )
- {
- idx = j;
- maxerrdrop = list[j].errdrop;
- }
- }
- intnode[i] = list[idx];
- if( list[idx].leftflag )
- {
- cart->left[list[idx].parent] = i;
- }
- else
- {
- cart->right[list[idx].parent] = i;
- }
- if( idx != (listcount - 1) )
- {
- list[idx] = list[listcount - 1];
- }
- listcount--;
- }
-
- /* fill <cart> fields */
- j = 0;
- cart->count = 0;
- for( i = 0; i < count && (intnode[i].stump != NULL); i++ )
- {
- cart->count++;
- cart->compidx[i] = intnode[i].stump->compidx;
- cart->threshold[i] = intnode[i].stump->threshold;
-
- /* leaves */
- if( cart->left[i] <= 0 )
- {
- cart->left[i] = -j;
- cart->val[j] = intnode[i].stump->left;
- j++;
- }
- if( cart->right[i] <= 0 )
- {
- cart->right[i] = -j;
- cart->val[j] = intnode[i].stump->right;
- j++;
- }
- }
-
- /* CLEAN UP */
- for( i = 0; i < count && (intnode[i].stump != NULL); i++ )
- {
- intnode[i].stump->release( (CvClassifier**) &(intnode[i].stump) );
- if( i != 0 )
- {
- cvReleaseMat( &(intnode[i].sampleIdx) );
- }
- }
- for( i = 0; i < listcount; i++ )
- {
- list[i].stump->release( (CvClassifier**) &(list[i].stump) );
- cvReleaseMat( &(list[i].sampleIdx) );
- }
-
- cvFree( &intnode );
-
- return (CvClassifier*) cart;
-}
-
-/****************************************************************************************\
-* Boosting *
-\****************************************************************************************/
-
-typedef struct CvBoostTrainer
-{
- CvBoostType type;
- int count; /* (idx) ? number_of_indices : number_of_samples */
- int* idx;
- float* F;
-} CvBoostTrainer;
-
-/*
- * cvBoostStartTraining, cvBoostNextWeakClassifier, cvBoostEndTraining
- *
- * These functions perform training of 2-class boosting classifier
- * using ANY appropriate weak classifier
- */
-
-CV_BOOST_IMPL
-CvBoostTrainer* icvBoostStartTraining( CvMat* trainClasses,
- CvMat* weakTrainVals,
- CvMat* weights,
- CvMat* sampleIdx,
- CvBoostType type )
-{
- uchar* ydata;
- int ystep;
- int m;
- uchar* traindata;
- int trainstep;
- int trainnum;
- int i;
- int idx;
-
- size_t datasize;
- CvBoostTrainer* ptr;
-
- int idxnum;
- int idxstep;
- uchar* idxdata;
-
- assert( trainClasses != NULL );
- assert( CV_MAT_TYPE( trainClasses->type ) == CV_32FC1 );
- assert( weakTrainVals != NULL );
- assert( CV_MAT_TYPE( weakTrainVals->type ) == CV_32FC1 );
-
- CV_MAT2VEC( *trainClasses, ydata, ystep, m );
- CV_MAT2VEC( *weakTrainVals, traindata, trainstep, trainnum );
-
- assert( m == trainnum );
-
- idxnum = 0;
- idxstep = 0;
- idxdata = NULL;
- if( sampleIdx )
- {
- CV_MAT2VEC( *sampleIdx, idxdata, idxstep, idxnum );
- }
-
- datasize = sizeof( *ptr ) + sizeof( *ptr->idx ) * idxnum;
- ptr = (CvBoostTrainer*) cvAlloc( datasize );
- memset( ptr, 0, datasize );
- ptr->F = NULL;
- ptr->idx = NULL;
-
- ptr->count = m;
- ptr->type = type;
-
- if( idxnum > 0 )
- {
- CvScalar s;
-
- ptr->idx = (int*) (ptr + 1);
- ptr->count = idxnum;
- for( i = 0; i < ptr->count; i++ )
- {
- cvRawDataToScalar( idxdata + i*idxstep, CV_MAT_TYPE( sampleIdx->type ), &s );
- ptr->idx[i] = (int) s.val[0];
- }
- }
- for( i = 0; i < ptr->count; i++ )
- {
- idx = (ptr->idx) ? ptr->idx[i] : i;
-
- *((float*) (traindata + idx * trainstep)) =
- 2.0F * (*((float*) (ydata + idx * ystep))) - 1.0F;
- }
-
- return ptr;
-}
-
-/*
- *
- * Discrete AdaBoost functions
- *
- */
-CV_BOOST_IMPL
-float icvBoostNextWeakClassifierDAB( CvMat* weakEvalVals,
- CvMat* trainClasses,
- CvMat* weakTrainVals,
- CvMat* weights,
- CvBoostTrainer* trainer )
-{
- uchar* evaldata;
- int evalstep;
- int m;
- uchar* ydata;
- int ystep;
- int ynum;
- uchar* wdata;
- int wstep;
- int wnum;
-
- float sumw;
- float err;
- int i;
- int idx;
-
- assert( weakEvalVals != NULL );
- assert( CV_MAT_TYPE( weakEvalVals->type ) == CV_32FC1 );
- assert( trainClasses != NULL );
- assert( CV_MAT_TYPE( trainClasses->type ) == CV_32FC1 );
- assert( weights != NULL );
- assert( CV_MAT_TYPE( weights ->type ) == CV_32FC1 );
-
- CV_MAT2VEC( *weakEvalVals, evaldata, evalstep, m );
- CV_MAT2VEC( *trainClasses, ydata, ystep, ynum );
- CV_MAT2VEC( *weights, wdata, wstep, wnum );
-
- assert( m == ynum );
- assert( m == wnum );
-
- sumw = 0.0F;
- err = 0.0F;
- for( i = 0; i < trainer->count; i++ )
- {
- idx = (trainer->idx) ? trainer->idx[i] : i;
-
- sumw += *((float*) (wdata + idx*wstep));
- err += (*((float*) (wdata + idx*wstep))) *
- ( (*((float*) (evaldata + idx*evalstep))) !=
- 2.0F * (*((float*) (ydata + idx*ystep))) - 1.0F );
- }
- err /= sumw;
- err = -cvLogRatio( err );
-
- for( i = 0; i < trainer->count; i++ )
- {
- idx = (trainer->idx) ? trainer->idx[i] : i;
-
- *((float*) (wdata + idx*wstep)) *= expf( err *
- ((*((float*) (evaldata + idx*evalstep))) !=
- 2.0F * (*((float*) (ydata + idx*ystep))) - 1.0F) );
- sumw += *((float*) (wdata + idx*wstep));
- }
- for( i = 0; i < trainer->count; i++ )
- {
- idx = (trainer->idx) ? trainer->idx[i] : i;
-
- *((float*) (wdata + idx * wstep)) /= sumw;
- }
-
- return err;
-}
-
-/*
- *
- * Real AdaBoost functions
- *
- */
-CV_BOOST_IMPL
-float icvBoostNextWeakClassifierRAB( CvMat* weakEvalVals,
- CvMat* trainClasses,
- CvMat* weakTrainVals,
- CvMat* weights,
- CvBoostTrainer* trainer )
-{
- uchar* evaldata;
- int evalstep;
- int m;
- uchar* ydata;
- int ystep;
- int ynum;
- uchar* wdata;
- int wstep;
- int wnum;
-
- float sumw;
- int i, idx;
-
- assert( weakEvalVals != NULL );
- assert( CV_MAT_TYPE( weakEvalVals->type ) == CV_32FC1 );
- assert( trainClasses != NULL );
- assert( CV_MAT_TYPE( trainClasses->type ) == CV_32FC1 );
- assert( weights != NULL );
- assert( CV_MAT_TYPE( weights ->type ) == CV_32FC1 );
-
- CV_MAT2VEC( *weakEvalVals, evaldata, evalstep, m );
- CV_MAT2VEC( *trainClasses, ydata, ystep, ynum );
- CV_MAT2VEC( *weights, wdata, wstep, wnum );
-
- assert( m == ynum );
- assert( m == wnum );
-
-
- sumw = 0.0F;
- for( i = 0; i < trainer->count; i++ )
- {
- idx = (trainer->idx) ? trainer->idx[i] : i;
-
- *((float*) (wdata + idx*wstep)) *= expf( (-(*((float*) (ydata + idx*ystep))) + 0.5F)
- * cvLogRatio( *((float*) (evaldata + idx*evalstep)) ) );
- sumw += *((float*) (wdata + idx*wstep));
- }
- for( i = 0; i < trainer->count; i++ )
- {
- idx = (trainer->idx) ? trainer->idx[i] : i;
-
- *((float*) (wdata + idx*wstep)) /= sumw;
- }
-
- return 1.0F;
-}
-
-/*
- *
- * LogitBoost functions
- *
- */
-#define CV_LB_PROB_THRESH 0.01F
-#define CV_LB_WEIGHT_THRESHOLD 0.0001F
-
-CV_BOOST_IMPL
-void icvResponsesAndWeightsLB( int num, uchar* wdata, int wstep,
- uchar* ydata, int ystep,
- uchar* fdata, int fstep,
- uchar* traindata, int trainstep,
- int* indices )
-{
- int i, idx;
- float p;
-
- for( i = 0; i < num; i++ )
- {
- idx = (indices) ? indices[i] : i;
-
- p = 1.0F / (1.0F + expf( -(*((float*) (fdata + idx*fstep)))) );
- *((float*) (wdata + idx*wstep)) = MAX( p * (1.0F - p), CV_LB_WEIGHT_THRESHOLD );
- if( *((float*) (ydata + idx*ystep)) == 1.0F )
- {
- *((float*) (traindata + idx*trainstep)) =
- 1.0F / (MAX( p, CV_LB_PROB_THRESH ));
- }
- else
- {
- *((float*) (traindata + idx*trainstep)) =
- -1.0F / (MAX( 1.0F - p, CV_LB_PROB_THRESH ));
- }
- }
-}
-
-CV_BOOST_IMPL
-CvBoostTrainer* icvBoostStartTrainingLB( CvMat* trainClasses,
- CvMat* weakTrainVals,
- CvMat* weights,
- CvMat* sampleIdx,
- CvBoostType type )
-{
- size_t datasize;
- CvBoostTrainer* ptr;
-
- uchar* ydata;
- int ystep;
- int m;
- uchar* traindata;
- int trainstep;
- int trainnum;
- uchar* wdata;
- int wstep;
- int wnum;
- int i;
-
- int idxnum;
- int idxstep;
- uchar* idxdata;
-
- assert( trainClasses != NULL );
- assert( CV_MAT_TYPE( trainClasses->type ) == CV_32FC1 );
- assert( weakTrainVals != NULL );
- assert( CV_MAT_TYPE( weakTrainVals->type ) == CV_32FC1 );
- assert( weights != NULL );
- assert( CV_MAT_TYPE( weights->type ) == CV_32FC1 );
-
- CV_MAT2VEC( *trainClasses, ydata, ystep, m );
- CV_MAT2VEC( *weakTrainVals, traindata, trainstep, trainnum );
- CV_MAT2VEC( *weights, wdata, wstep, wnum );
-
- assert( m == trainnum );
- assert( m == wnum );
-
-
- idxnum = 0;
- idxstep = 0;
- idxdata = NULL;
- if( sampleIdx )
- {
- CV_MAT2VEC( *sampleIdx, idxdata, idxstep, idxnum );
- }
-
- datasize = sizeof( *ptr ) + sizeof( *ptr->F ) * m + sizeof( *ptr->idx ) * idxnum;
- ptr = (CvBoostTrainer*) cvAlloc( datasize );
- memset( ptr, 0, datasize );
- ptr->F = (float*) (ptr + 1);
- ptr->idx = NULL;
-
- ptr->count = m;
- ptr->type = type;
-
- if( idxnum > 0 )
- {
- CvScalar s;
-
- ptr->idx = (int*) (ptr->F + m);
- ptr->count = idxnum;
- for( i = 0; i < ptr->count; i++ )
- {
- cvRawDataToScalar( idxdata + i*idxstep, CV_MAT_TYPE( sampleIdx->type ), &s );
- ptr->idx[i] = (int) s.val[0];
- }
- }
-
- for( i = 0; i < m; i++ )
- {
- ptr->F[i] = 0.0F;
- }
-
- icvResponsesAndWeightsLB( ptr->count, wdata, wstep, ydata, ystep,
- (uchar*) ptr->F, sizeof( *ptr->F ),
- traindata, trainstep, ptr->idx );
-
- return ptr;
-}
-
-CV_BOOST_IMPL
-float icvBoostNextWeakClassifierLB( CvMat* weakEvalVals,
- CvMat* trainClasses,
- CvMat* weakTrainVals,
- CvMat* weights,
- CvBoostTrainer* trainer )
-{
- uchar* evaldata;
- int evalstep;
- int m;
- uchar* ydata;
- int ystep;
- int ynum;
- uchar* traindata;
- int trainstep;
- int trainnum;
- uchar* wdata;
- int wstep;
- int wnum;
- int i, idx;
-
- assert( weakEvalVals != NULL );
- assert( CV_MAT_TYPE( weakEvalVals->type ) == CV_32FC1 );
- assert( trainClasses != NULL );
- assert( CV_MAT_TYPE( trainClasses->type ) == CV_32FC1 );
- assert( weakTrainVals != NULL );
- assert( CV_MAT_TYPE( weakTrainVals->type ) == CV_32FC1 );
- assert( weights != NULL );
- assert( CV_MAT_TYPE( weights ->type ) == CV_32FC1 );
-
- CV_MAT2VEC( *weakEvalVals, evaldata, evalstep, m );
- CV_MAT2VEC( *trainClasses, ydata, ystep, ynum );
- CV_MAT2VEC( *weakTrainVals, traindata, trainstep, trainnum );
- CV_MAT2VEC( *weights, wdata, wstep, wnum );
-
- assert( m == ynum );
- assert( m == wnum );
- assert( m == trainnum );
- //assert( m == trainer->count );
-
- for( i = 0; i < trainer->count; i++ )
- {
- idx = (trainer->idx) ? trainer->idx[i] : i;
-
- trainer->F[idx] += *((float*) (evaldata + idx * evalstep));
- }
-
- icvResponsesAndWeightsLB( trainer->count, wdata, wstep, ydata, ystep,
- (uchar*) trainer->F, sizeof( *trainer->F ),
- traindata, trainstep, trainer->idx );
-
- return 1.0F;
-}
-
-/*
- *
- * Gentle AdaBoost
- *
- */
-CV_BOOST_IMPL
-float icvBoostNextWeakClassifierGAB( CvMat* weakEvalVals,
- CvMat* trainClasses,
- CvMat* weakTrainVals,
- CvMat* weights,
- CvBoostTrainer* trainer )
-{
- uchar* evaldata;
- int evalstep;
- int m;
- uchar* ydata;
- int ystep;
- int ynum;
- uchar* wdata;
- int wstep;
- int wnum;
-
- int i, idx;
- float sumw;
-
- assert( weakEvalVals != NULL );
- assert( CV_MAT_TYPE( weakEvalVals->type ) == CV_32FC1 );
- assert( trainClasses != NULL );
- assert( CV_MAT_TYPE( trainClasses->type ) == CV_32FC1 );
- assert( weights != NULL );
- assert( CV_MAT_TYPE( weights->type ) == CV_32FC1 );
-
- CV_MAT2VEC( *weakEvalVals, evaldata, evalstep, m );
- CV_MAT2VEC( *trainClasses, ydata, ystep, ynum );
- CV_MAT2VEC( *weights, wdata, wstep, wnum );
-
- assert( m == ynum );
- assert( m == wnum );
-
- sumw = 0.0F;
- for( i = 0; i < trainer->count; i++ )
- {
- idx = (trainer->idx) ? trainer->idx[i] : i;
-
- *((float*) (wdata + idx*wstep)) *=
- expf( -(*((float*) (evaldata + idx*evalstep)))
- * ( 2.0F * (*((float*) (ydata + idx*ystep))) - 1.0F ) );
- sumw += *((float*) (wdata + idx*wstep));
- }
-
- for( i = 0; i < trainer->count; i++ )
- {
- idx = (trainer->idx) ? trainer->idx[i] : i;
-
- *((float*) (wdata + idx*wstep)) /= sumw;
- }
-
- return 1.0F;
-}
-
-typedef CvBoostTrainer* (*CvBoostStartTraining)( CvMat* trainClasses,
- CvMat* weakTrainVals,
- CvMat* weights,
- CvMat* sampleIdx,
- CvBoostType type );
-
-typedef float (*CvBoostNextWeakClassifier)( CvMat* weakEvalVals,
- CvMat* trainClasses,
- CvMat* weakTrainVals,
- CvMat* weights,
- CvBoostTrainer* data );
-
-CvBoostStartTraining startTraining[4] = {
- icvBoostStartTraining,
- icvBoostStartTraining,
- icvBoostStartTrainingLB,
- icvBoostStartTraining
- };
-
-CvBoostNextWeakClassifier nextWeakClassifier[4] = {
- icvBoostNextWeakClassifierDAB,
- icvBoostNextWeakClassifierRAB,
- icvBoostNextWeakClassifierLB,
- icvBoostNextWeakClassifierGAB
- };
-
-/*
- *
- * Dispatchers
- *
- */
-CV_BOOST_IMPL
-CvBoostTrainer* cvBoostStartTraining( CvMat* trainClasses,
- CvMat* weakTrainVals,
- CvMat* weights,
- CvMat* sampleIdx,
- CvBoostType type )
-{
- return startTraining[type]( trainClasses, weakTrainVals, weights, sampleIdx, type );
-}
-
-CV_BOOST_IMPL
-void cvBoostEndTraining( CvBoostTrainer** trainer )
-{
- cvFree( trainer );
- *trainer = NULL;
-}
-
-CV_BOOST_IMPL
-float cvBoostNextWeakClassifier( CvMat* weakEvalVals,
- CvMat* trainClasses,
- CvMat* weakTrainVals,
- CvMat* weights,
- CvBoostTrainer* trainer )
-{
- return nextWeakClassifier[trainer->type]( weakEvalVals, trainClasses,
- weakTrainVals, weights, trainer );
-}
-
-/****************************************************************************************\
-* Boosted tree models *
-\****************************************************************************************/
-
-typedef struct CvBtTrainer
-{
- /* {{ external */
- CvMat* trainData;
- int flags;
-
- CvMat* trainClasses;
- int m;
- uchar* ydata;
- int ystep;
-
- CvMat* sampleIdx;
- int numsamples;
-
- float param[2];
- CvBoostType type;
- int numclasses;
- /* }} external */
-
- CvMTStumpTrainParams stumpParams;
- CvCARTTrainParams cartParams;
-
- float* f; /* F_(m-1) */
- CvMat* y; /* yhat */
- CvMat* weights;
- CvBoostTrainer* boosttrainer;
-} CvBtTrainer;
-
-/*
- * cvBtStart, cvBtNext, cvBtEnd
- *
- * These functions perform iterative training of
- * 2-class (CV_DABCLASS - CV_GABCLASS, CV_L2CLASS), K-class (CV_LKCLASS) classifier
- * or fit regression model (CV_LSREG, CV_LADREG, CV_MREG)
- * using decision tree as a weak classifier.
- */
-
-typedef void (*CvZeroApproxFunc)( float* approx, CvBtTrainer* trainer );
-
-/* Mean zero approximation */
-void icvZeroApproxMean( float* approx, CvBtTrainer* trainer )
-{
- int i;
- int idx;
-
- approx[0] = 0.0F;
- for( i = 0; i < trainer->numsamples; i++ )
- {
- idx = icvGetIdxAt( trainer->sampleIdx, i );
- approx[0] += *((float*) (trainer->ydata + idx * trainer->ystep));
- }
- approx[0] /= (float) trainer->numsamples;
-}
-
-/*
- * Median zero approximation
- */
-void icvZeroApproxMed( float* approx, CvBtTrainer* trainer )
-{
- int i;
- int idx;
-
- for( i = 0; i < trainer->numsamples; i++ )
- {
- idx = icvGetIdxAt( trainer->sampleIdx, i );
- trainer->f[i] = *((float*) (trainer->ydata + idx * trainer->ystep));
- }
-
- icvSort_32f( trainer->f, trainer->numsamples, 0 );
- approx[0] = trainer->f[trainer->numsamples / 2];
-}
-
-/*
- * 0.5 * log( mean(y) / (1 - mean(y)) ) where y in {0, 1}
- */
-void icvZeroApproxLog( float* approx, CvBtTrainer* trainer )
-{
- float y_mean;
-
- icvZeroApproxMean( &y_mean, trainer );
- approx[0] = 0.5F * cvLogRatio( y_mean );
-}
-
-/*
- * 0 zero approximation
- */
-void icvZeroApprox0( float* approx, CvBtTrainer* trainer )
-{
- int i;
-
- for( i = 0; i < trainer->numclasses; i++ )
- {
- approx[i] = 0.0F;
- }
-}
-
-static CvZeroApproxFunc icvZeroApproxFunc[] =
-{
- icvZeroApprox0, /* CV_DABCLASS */
- icvZeroApprox0, /* CV_RABCLASS */
- icvZeroApprox0, /* CV_LBCLASS */
- icvZeroApprox0, /* CV_GABCLASS */
- icvZeroApproxLog, /* CV_L2CLASS */
- icvZeroApprox0, /* CV_LKCLASS */
- icvZeroApproxMean, /* CV_LSREG */
- icvZeroApproxMed, /* CV_LADREG */
- icvZeroApproxMed, /* CV_MREG */
-};
-
-CV_BOOST_IMPL
-void cvBtNext( CvCARTClassifier** trees, CvBtTrainer* trainer );
-
-CV_BOOST_IMPL
-CvBtTrainer* cvBtStart( CvCARTClassifier** trees,
- CvMat* trainData,
- int flags,
- CvMat* trainClasses,
- CvMat* sampleIdx,
- int numsplits,
- CvBoostType type,
- int numclasses,
- float* param )
-{
- CvBtTrainer* ptr;
-
- CV_FUNCNAME( "cvBtStart" );
-
- __BEGIN__;
-
- size_t data_size;
- float* zero_approx;
- int m;
- int i, j;
-
- if( trees == NULL )
- {
- CV_ERROR( CV_StsNullPtr, "Invalid trees parameter" );
- }
-
- if( type < CV_DABCLASS || type > CV_MREG )
- {
- CV_ERROR( CV_StsUnsupportedFormat, "Unsupported type parameter" );
- }
- if( type == CV_LKCLASS )
- {
- CV_ASSERT( numclasses >= 2 );
- }
- else
- {
- numclasses = 1;
- }
-
- m = MAX( trainClasses->rows, trainClasses->cols );
- ptr = NULL;
- data_size = sizeof( *ptr );
- if( type > CV_GABCLASS )
- {
- data_size += m * numclasses * sizeof( *(ptr->f) );
- }
- CV_CALL( ptr = (CvBtTrainer*) cvAlloc( data_size ) );
- memset( ptr, 0, data_size );
- ptr->f = (float*) (ptr + 1);
-
- ptr->trainData = trainData;
- ptr->flags = flags;
- ptr->trainClasses = trainClasses;
- CV_MAT2VEC( *trainClasses, ptr->ydata, ptr->ystep, ptr->m );
-
- memset( &(ptr->cartParams), 0, sizeof( ptr->cartParams ) );
- memset( &(ptr->stumpParams), 0, sizeof( ptr->stumpParams ) );
-
- switch( type )
- {
- case CV_DABCLASS:
- ptr->stumpParams.error = CV_MISCLASSIFICATION;
- ptr->stumpParams.type = CV_CLASSIFICATION_CLASS;
- break;
- case CV_RABCLASS:
- ptr->stumpParams.error = CV_GINI;
- ptr->stumpParams.type = CV_CLASSIFICATION;
- break;
- default:
- ptr->stumpParams.error = CV_SQUARE;
- ptr->stumpParams.type = CV_REGRESSION;
- }
- ptr->cartParams.count = numsplits;
- ptr->cartParams.stumpTrainParams = (CvClassifierTrainParams*) &(ptr->stumpParams);
- ptr->cartParams.stumpConstructor = cvCreateMTStumpClassifier;
-
- ptr->param[0] = param[0];
- ptr->param[1] = param[1];
- ptr->type = type;
- ptr->numclasses = numclasses;
-
- CV_CALL( ptr->y = cvCreateMat( 1, m, CV_32FC1 ) );
- ptr->sampleIdx = sampleIdx;
- ptr->numsamples = ( sampleIdx == NULL ) ? ptr->m
- : MAX( sampleIdx->rows, sampleIdx->cols );
-
- ptr->weights = cvCreateMat( 1, m, CV_32FC1 );
- cvSet( ptr->weights, cvScalar( 1.0 ) );
-
- if( type <= CV_GABCLASS )
- {
- ptr->boosttrainer = cvBoostStartTraining( ptr->trainClasses, ptr->y,
- ptr->weights, NULL, type );
-
- CV_CALL( cvBtNext( trees, ptr ) );
- }
- else
- {
- data_size = sizeof( *zero_approx ) * numclasses;
- CV_CALL( zero_approx = (float*) cvAlloc( data_size ) );
- icvZeroApproxFunc[type]( zero_approx, ptr );
- for( i = 0; i < m; i++ )
- {
- for( j = 0; j < numclasses; j++ )
- {
- ptr->f[i * numclasses + j] = zero_approx[j];
- }
- }
-
- CV_CALL( cvBtNext( trees, ptr ) );
-
- for( i = 0; i < numclasses; i++ )
- {
- for( j = 0; j <= trees[i]->count; j++ )
- {
- trees[i]->val[j] += zero_approx[i];
- }
- }
- CV_CALL( cvFree( &zero_approx ) );
- }
-
- __END__;
-
- return ptr;
-}
-
-void icvBtNext_LSREG( CvCARTClassifier** trees, CvBtTrainer* trainer )
-{
- int i;
-
- /* yhat_i = y_i - F_(m-1)(x_i) */
- for( i = 0; i < trainer->m; i++ )
- {
- trainer->y->data.fl[i] =
- *((float*) (trainer->ydata + i * trainer->ystep)) - trainer->f[i];
- }
-
- trees[0] = (CvCARTClassifier*) cvCreateCARTClassifier( trainer->trainData,
- trainer->flags,
- trainer->y, NULL, NULL, NULL, trainer->sampleIdx, trainer->weights,
- (CvClassifierTrainParams*) &trainer->cartParams );
-}
-
-
-void icvBtNext_LADREG( CvCARTClassifier** trees, CvBtTrainer* trainer )
-{
- CvCARTClassifier* ptr;
- int i, j;
- CvMat sample;
- int sample_step;
- uchar* sample_data;
- int index;
-
- int data_size;
- int* idx;
- float* resp;
- int respnum;
- float val;
-
- data_size = trainer->m * sizeof( *idx );
- idx = (int*) cvAlloc( data_size );
- data_size = trainer->m * sizeof( *resp );
- resp = (float*) cvAlloc( data_size );
-
- /* yhat_i = sign(y_i - F_(m-1)(x_i)) */
- for( i = 0; i < trainer->numsamples; i++ )
- {
- index = icvGetIdxAt( trainer->sampleIdx, i );
- trainer->y->data.fl[index] = (float)
- CV_SIGN( *((float*) (trainer->ydata + index * trainer->ystep))
- - trainer->f[index] );
- }
-
- ptr = (CvCARTClassifier*) cvCreateCARTClassifier( trainer->trainData, trainer->flags,
- trainer->y, NULL, NULL, NULL, trainer->sampleIdx, trainer->weights,
- (CvClassifierTrainParams*) &trainer->cartParams );
-
- CV_GET_SAMPLE( *trainer->trainData, trainer->flags, 0, sample );
- CV_GET_SAMPLE_STEP( *trainer->trainData, trainer->flags, sample_step );
- sample_data = sample.data.ptr;
- for( i = 0; i < trainer->numsamples; i++ )
- {
- index = icvGetIdxAt( trainer->sampleIdx, i );
- sample.data.ptr = sample_data + index * sample_step;
- idx[index] = (int) cvEvalCARTClassifierIdx( (CvClassifier*) ptr, &sample );
- }
- for( j = 0; j <= ptr->count; j++ )
- {
- respnum = 0;
- for( i = 0; i < trainer->numsamples; i++ )
- {
- index = icvGetIdxAt( trainer->sampleIdx, i );
- if( idx[index] == j )
- {
- resp[respnum++] = *((float*) (trainer->ydata + index * trainer->ystep))
- - trainer->f[index];
- }
- }
- if( respnum > 0 )
- {
- icvSort_32f( resp, respnum, 0 );
- val = resp[respnum / 2];
- }
- else
- {
- val = 0.0F;
- }
- ptr->val[j] = val;
- }
-
- cvFree( &idx );
- cvFree( &resp );
-
- trees[0] = ptr;
-}
-
-
-void icvBtNext_MREG( CvCARTClassifier** trees, CvBtTrainer* trainer )
-{
- CvCARTClassifier* ptr;
- int i, j;
- CvMat sample;
- int sample_step;
- uchar* sample_data;
-
- int data_size;
- int* idx;
- float* resid;
- float* resp;
- int respnum;
- float rhat;
- float val;
- float delta;
- int index;
-
- data_size = trainer->m * sizeof( *idx );
- idx = (int*) cvAlloc( data_size );
- data_size = trainer->m * sizeof( *resp );
- resp = (float*) cvAlloc( data_size );
- data_size = trainer->m * sizeof( *resid );
- resid = (float*) cvAlloc( data_size );
-
- /* resid_i = (y_i - F_(m-1)(x_i)) */
- for( i = 0; i < trainer->numsamples; i++ )
- {
- index = icvGetIdxAt( trainer->sampleIdx, i );
- resid[index] = *((float*) (trainer->ydata + index * trainer->ystep))
- - trainer->f[index];
- /* for delta */
- resp[i] = (float) fabs( resid[index] );
- }
-
- /* delta = quantile_alpha{abs(resid_i)} */
- icvSort_32f( resp, trainer->numsamples, 0 );
- delta = resp[(int)(trainer->param[1] * (trainer->numsamples - 1))];
-
- /* yhat_i */
- for( i = 0; i < trainer->numsamples; i++ )
- {
- index = icvGetIdxAt( trainer->sampleIdx, i );
- trainer->y->data.fl[index] = MIN( delta, ((float) fabs( resid[index] )) ) *
- CV_SIGN( resid[index] );
- }
-
- ptr = (CvCARTClassifier*) cvCreateCARTClassifier( trainer->trainData, trainer->flags,
- trainer->y, NULL, NULL, NULL, trainer->sampleIdx, trainer->weights,
- (CvClassifierTrainParams*) &trainer->cartParams );
-
- CV_GET_SAMPLE( *trainer->trainData, trainer->flags, 0, sample );
- CV_GET_SAMPLE_STEP( *trainer->trainData, trainer->flags, sample_step );
- sample_data = sample.data.ptr;
- for( i = 0; i < trainer->numsamples; i++ )
- {
- index = icvGetIdxAt( trainer->sampleIdx, i );
- sample.data.ptr = sample_data + index * sample_step;
- idx[index] = (int) cvEvalCARTClassifierIdx( (CvClassifier*) ptr, &sample );
- }
- for( j = 0; j <= ptr->count; j++ )
- {
- respnum = 0;
-
- for( i = 0; i < trainer->numsamples; i++ )
- {
- index = icvGetIdxAt( trainer->sampleIdx, i );
- if( idx[index] == j )
- {
- resp[respnum++] = *((float*) (trainer->ydata + index * trainer->ystep))
- - trainer->f[index];
- }
- }
- if( respnum > 0 )
- {
- /* rhat = median(y_i - F_(m-1)(x_i)) */
- icvSort_32f( resp, respnum, 0 );
- rhat = resp[respnum / 2];
-
- /* val = sum{sign(r_i - rhat_i) * min(delta, abs(r_i - rhat_i)}
- * r_i = y_i - F_(m-1)(x_i)
- */
- val = 0.0F;
- for( i = 0; i < respnum; i++ )
- {
- val += CV_SIGN( resp[i] - rhat )
- * MIN( delta, (float) fabs( resp[i] - rhat ) );
- }
-
- val = rhat + val / (float) respnum;
- }
- else
- {
- val = 0.0F;
- }
-
- ptr->val[j] = val;
-
- }
-
- cvFree( &resid );
- cvFree( &resp );
- cvFree( &idx );
-
- trees[0] = ptr;
-}
-
-//#define CV_VAL_MAX 1e304
-
-//#define CV_LOG_VAL_MAX 700.0
-
-#define CV_VAL_MAX 1e+8
-
-#define CV_LOG_VAL_MAX 18.0
-
-void icvBtNext_L2CLASS( CvCARTClassifier** trees, CvBtTrainer* trainer )
-{
- CvCARTClassifier* ptr;
- int i, j;
- CvMat sample;
- int sample_step;
- uchar* sample_data;
-
- int data_size;
- int* idx;
- int respnum;
- float val;
- double val_f;
-
- float sum_weights;
- float* weights;
- float* sorted_weights;
- CvMat* trimmed_idx;
- CvMat* sample_idx;
- int index;
- int trimmed_num;
-
- data_size = trainer->m * sizeof( *idx );
- idx = (int*) cvAlloc( data_size );
-
- data_size = trainer->m * sizeof( *weights );
- weights = (float*) cvAlloc( data_size );
- data_size = trainer->m * sizeof( *sorted_weights );
- sorted_weights = (float*) cvAlloc( data_size );
-
- /* yhat_i = (4 * y_i - 2) / ( 1 + exp( (4 * y_i - 2) * F_(m-1)(x_i) ) ).
- * y_i in {0, 1}
- */
- sum_weights = 0.0F;
- for( i = 0; i < trainer->numsamples; i++ )
- {
- index = icvGetIdxAt( trainer->sampleIdx, i );
- val = 4.0F * (*((float*) (trainer->ydata + index * trainer->ystep))) - 2.0F;
- val_f = val * trainer->f[index];
- val_f = ( val_f < CV_LOG_VAL_MAX ) ? exp( val_f ) : CV_LOG_VAL_MAX;
- val = (float) ( (double) val / ( 1.0 + val_f ) );
- trainer->y->data.fl[index] = val;
- val = (float) fabs( val );
- weights[index] = val * (2.0F - val);
- sorted_weights[i] = weights[index];
- sum_weights += sorted_weights[i];
- }
-
- trimmed_idx = NULL;
- sample_idx = trainer->sampleIdx;
- trimmed_num = trainer->numsamples;
- if( trainer->param[1] < 1.0F )
- {
- /* perform weight trimming */
-
- float threshold;
- int count;
-
- icvSort_32f( sorted_weights, trainer->numsamples, 0 );
-
- sum_weights *= (1.0F - trainer->param[1]);
-
- i = -1;
- do { sum_weights -= sorted_weights[++i]; }
- while( sum_weights > 0.0F && i < (trainer->numsamples - 1) );
-
- threshold = sorted_weights[i];
-
- while( i > 0 && sorted_weights[i-1] == threshold ) i--;
-
- if( i > 0 )
- {
- trimmed_num = trainer->numsamples - i;
- trimmed_idx = cvCreateMat( 1, trimmed_num, CV_32FC1 );
- count = 0;
- for( i = 0; i < trainer->numsamples; i++ )
- {
- index = icvGetIdxAt( trainer->sampleIdx, i );
- if( weights[index] >= threshold )
- {
- CV_MAT_ELEM( *trimmed_idx, float, 0, count ) = (float) index;
- count++;
- }
- }
-
- assert( count == trimmed_num );
-
- sample_idx = trimmed_idx;
-
- printf( "Used samples %%: %g\n",
- (float) trimmed_num / (float) trainer->numsamples * 100.0F );
- }
- }
-
- ptr = (CvCARTClassifier*) cvCreateCARTClassifier( trainer->trainData, trainer->flags,
- trainer->y, NULL, NULL, NULL, sample_idx, trainer->weights,
- (CvClassifierTrainParams*) &trainer->cartParams );
-
- CV_GET_SAMPLE( *trainer->trainData, trainer->flags, 0, sample );
- CV_GET_SAMPLE_STEP( *trainer->trainData, trainer->flags, sample_step );
- sample_data = sample.data.ptr;
- for( i = 0; i < trimmed_num; i++ )
- {
- index = icvGetIdxAt( sample_idx, i );
- sample.data.ptr = sample_data + index * sample_step;
- idx[index] = (int) cvEvalCARTClassifierIdx( (CvClassifier*) ptr, &sample );
- }
- for( j = 0; j <= ptr->count; j++ )
- {
- respnum = 0;
- val = 0.0F;
- sum_weights = 0.0F;
- for( i = 0; i < trimmed_num; i++ )
- {
- index = icvGetIdxAt( sample_idx, i );
- if( idx[index] == j )
- {
- val += trainer->y->data.fl[index];
- sum_weights += weights[index];
- respnum++;
- }
- }
- if( sum_weights > 0.0F )
- {
- val /= sum_weights;
- }
- else
- {
- val = 0.0F;
- }
- ptr->val[j] = val;
- }
-
- if( trimmed_idx != NULL ) cvReleaseMat( &trimmed_idx );
- cvFree( &sorted_weights );
- cvFree( &weights );
- cvFree( &idx );
-
- trees[0] = ptr;
-}
-
-void icvBtNext_LKCLASS( CvCARTClassifier** trees, CvBtTrainer* trainer )
-{
- int i, j, k, kk, num;
- CvMat sample;
- int sample_step;
- uchar* sample_data;
-
- int data_size;
- int* idx;
- int respnum;
- float val;
-
- float sum_weights;
- float* weights;
- float* sorted_weights;
- CvMat* trimmed_idx;
- CvMat* sample_idx;
- int index;
- int trimmed_num;
- double sum_exp_f;
- double exp_f;
- double f_k;
-
- data_size = trainer->m * sizeof( *idx );
- idx = (int*) cvAlloc( data_size );
- data_size = trainer->m * sizeof( *weights );
- weights = (float*) cvAlloc( data_size );
- data_size = trainer->m * sizeof( *sorted_weights );
- sorted_weights = (float*) cvAlloc( data_size );
- trimmed_idx = cvCreateMat( 1, trainer->numsamples, CV_32FC1 );
-
- for( k = 0; k < trainer->numclasses; k++ )
- {
- /* yhat_i = y_i - p_k(x_i), y_i in {0, 1} */
- /* p_k(x_i) = exp(f_k(x_i)) / (sum_exp_f(x_i)) */
- sum_weights = 0.0F;
- for( i = 0; i < trainer->numsamples; i++ )
- {
- index = icvGetIdxAt( trainer->sampleIdx, i );
- /* p_k(x_i) = 1 / (1 + sum(exp(f_kk(x_i) - f_k(x_i)))), kk != k */
- num = index * trainer->numclasses;
- f_k = (double) trainer->f[num + k];
- sum_exp_f = 1.0;
- for( kk = 0; kk < trainer->numclasses; kk++ )
- {
- if( kk == k ) continue;
- exp_f = (double) trainer->f[num + kk] - f_k;
- exp_f = (exp_f < CV_LOG_VAL_MAX) ? exp( exp_f ) : CV_VAL_MAX;
- if( exp_f == CV_VAL_MAX || exp_f >= (CV_VAL_MAX - sum_exp_f) )
- {
- sum_exp_f = CV_VAL_MAX;
- break;
- }
- sum_exp_f += exp_f;
- }
-
- val = (float) ( (*((float*) (trainer->ydata + index * trainer->ystep)))
- == (float) k );
- val -= (float) ( (sum_exp_f == CV_VAL_MAX) ? 0.0 : ( 1.0 / sum_exp_f ) );
-
- assert( val >= -1.0F );
- assert( val <= 1.0F );
-
- trainer->y->data.fl[index] = val;
- val = (float) fabs( val );
- weights[index] = val * (1.0F - val);
- sorted_weights[i] = weights[index];
- sum_weights += sorted_weights[i];
- }
-
- sample_idx = trainer->sampleIdx;
- trimmed_num = trainer->numsamples;
- if( trainer->param[1] < 1.0F )
- {
- /* perform weight trimming */
-
- float threshold;
- int count;
-
- icvSort_32f( sorted_weights, trainer->numsamples, 0 );
-
- sum_weights *= (1.0F - trainer->param[1]);
-
- i = -1;
- do { sum_weights -= sorted_weights[++i]; }
- while( sum_weights > 0.0F && i < (trainer->numsamples - 1) );
-
- threshold = sorted_weights[i];
-
- while( i > 0 && sorted_weights[i-1] == threshold ) i--;
-
- if( i > 0 )
- {
- trimmed_num = trainer->numsamples - i;
- trimmed_idx->cols = trimmed_num;
- count = 0;
- for( i = 0; i < trainer->numsamples; i++ )
- {
- index = icvGetIdxAt( trainer->sampleIdx, i );
- if( weights[index] >= threshold )
- {
- CV_MAT_ELEM( *trimmed_idx, float, 0, count ) = (float) index;
- count++;
- }
- }
-
- assert( count == trimmed_num );
-
- sample_idx = trimmed_idx;
-
- printf( "k: %d Used samples %%: %g\n", k,
- (float) trimmed_num / (float) trainer->numsamples * 100.0F );
- }
- } /* weight trimming */
-
- trees[k] = (CvCARTClassifier*) cvCreateCARTClassifier( trainer->trainData,
- trainer->flags, trainer->y, NULL, NULL, NULL, sample_idx, trainer->weights,
- (CvClassifierTrainParams*) &trainer->cartParams );
-
- CV_GET_SAMPLE( *trainer->trainData, trainer->flags, 0, sample );
- CV_GET_SAMPLE_STEP( *trainer->trainData, trainer->flags, sample_step );
- sample_data = sample.data.ptr;
- for( i = 0; i < trimmed_num; i++ )
- {
- index = icvGetIdxAt( sample_idx, i );
- sample.data.ptr = sample_data + index * sample_step;
- idx[index] = (int) cvEvalCARTClassifierIdx( (CvClassifier*) trees[k],
- &sample );
- }
- for( j = 0; j <= trees[k]->count; j++ )
- {
- respnum = 0;
- val = 0.0F;
- sum_weights = 0.0F;
- for( i = 0; i < trimmed_num; i++ )
- {
- index = icvGetIdxAt( sample_idx, i );
- if( idx[index] == j )
- {
- val += trainer->y->data.fl[index];
- sum_weights += weights[index];
- respnum++;
- }
- }
- if( sum_weights > 0.0F )
- {
- val = ((float) (trainer->numclasses - 1)) * val /
- ((float) (trainer->numclasses)) / sum_weights;
- }
- else
- {
- val = 0.0F;
- }
- trees[k]->val[j] = val;
- }
- } /* for each class */
-
- cvReleaseMat( &trimmed_idx );
- cvFree( &sorted_weights );
- cvFree( &weights );
- cvFree( &idx );
-}
-
-
-void icvBtNext_XXBCLASS( CvCARTClassifier** trees, CvBtTrainer* trainer )
-{
- float alpha;
- int i;
- CvMat* weak_eval_vals;
- CvMat* sample_idx;
- int num_samples;
- CvMat sample;
- uchar* sample_data;
- int sample_step;
-
- weak_eval_vals = cvCreateMat( 1, trainer->m, CV_32FC1 );
-
- sample_idx = cvTrimWeights( trainer->weights, trainer->sampleIdx,
- trainer->param[1] );
- num_samples = ( sample_idx == NULL )
- ? trainer->m : MAX( sample_idx->rows, sample_idx->cols );
-
- printf( "Used samples %%: %g\n",
- (float) num_samples / (float) trainer->numsamples * 100.0F );
-
- trees[0] = (CvCARTClassifier*) cvCreateCARTClassifier( trainer->trainData,
- trainer->flags, trainer->y, NULL, NULL, NULL,
- sample_idx, trainer->weights,
- (CvClassifierTrainParams*) &trainer->cartParams );
-
- /* evaluate samples */
- CV_GET_SAMPLE( *trainer->trainData, trainer->flags, 0, sample );
- CV_GET_SAMPLE_STEP( *trainer->trainData, trainer->flags, sample_step );
- sample_data = sample.data.ptr;
-
- for( i = 0; i < trainer->m; i++ )
- {
- sample.data.ptr = sample_data + i * sample_step;
- weak_eval_vals->data.fl[i] = trees[0]->eval( (CvClassifier*) trees[0], &sample );
- }
-
- alpha = cvBoostNextWeakClassifier( weak_eval_vals, trainer->trainClasses,
- trainer->y, trainer->weights, trainer->boosttrainer );
-
- /* multiply tree by alpha */
- for( i = 0; i <= trees[0]->count; i++ )
- {
- trees[0]->val[i] *= alpha;
- }
- if( trainer->type == CV_RABCLASS )
- {
- for( i = 0; i <= trees[0]->count; i++ )
- {
- trees[0]->val[i] = cvLogRatio( trees[0]->val[i] );
- }
- }
-
- if( sample_idx != NULL && sample_idx != trainer->sampleIdx )
- {
- cvReleaseMat( &sample_idx );
- }
- cvReleaseMat( &weak_eval_vals );
-}
-
-typedef void (*CvBtNextFunc)( CvCARTClassifier** trees, CvBtTrainer* trainer );
-
-static CvBtNextFunc icvBtNextFunc[] =
-{
- icvBtNext_XXBCLASS,
- icvBtNext_XXBCLASS,
- icvBtNext_XXBCLASS,
- icvBtNext_XXBCLASS,
- icvBtNext_L2CLASS,
- icvBtNext_LKCLASS,
- icvBtNext_LSREG,
- icvBtNext_LADREG,
- icvBtNext_MREG
-};
-
-CV_BOOST_IMPL
-void cvBtNext( CvCARTClassifier** trees, CvBtTrainer* trainer )
-{
-
- CV_FUNCNAME( "cvBtNext" );
-
- __BEGIN__;
-
- int i, j;
- int index;
- CvMat sample;
- int sample_step;
- uchar* sample_data;
-
- icvBtNextFunc[trainer->type]( trees, trainer );
-
- /* shrinkage */
- if( trainer->param[0] != 1.0F )
- {
- for( j = 0; j < trainer->numclasses; j++ )
- {
- for( i = 0; i <= trees[j]->count; i++ )
- {
- trees[j]->val[i] *= trainer->param[0];
- }
- }
- }
-
- if( trainer->type > CV_GABCLASS )
- {
- /* update F_(m-1) */
- CV_GET_SAMPLE( *(trainer->trainData), trainer->flags, 0, sample );
- CV_GET_SAMPLE_STEP( *(trainer->trainData), trainer->flags, sample_step );
- sample_data = sample.data.ptr;
- for( i = 0; i < trainer->numsamples; i++ )
- {
- index = icvGetIdxAt( trainer->sampleIdx, i );
- sample.data.ptr = sample_data + index * sample_step;
- for( j = 0; j < trainer->numclasses; j++ )
- {
- trainer->f[index * trainer->numclasses + j] +=
- trees[j]->eval( (CvClassifier*) (trees[j]), &sample );
- }
- }
- }
-
- __END__;
-}
-
-CV_BOOST_IMPL
-void cvBtEnd( CvBtTrainer** trainer )
-{
- CV_FUNCNAME( "cvBtEnd" );
-
- __BEGIN__;
-
- if( trainer == NULL || (*trainer) == NULL )
- {
- CV_ERROR( CV_StsNullPtr, "Invalid trainer parameter" );
- }
-
- if( (*trainer)->y != NULL )
- {
- CV_CALL( cvReleaseMat( &((*trainer)->y) ) );
- }
- if( (*trainer)->weights != NULL )
- {
- CV_CALL( cvReleaseMat( &((*trainer)->weights) ) );
- }
- if( (*trainer)->boosttrainer != NULL )
- {
- CV_CALL( cvBoostEndTraining( &((*trainer)->boosttrainer) ) );
- }
- CV_CALL( cvFree( trainer ) );
-
- __END__;
-}
-
-/****************************************************************************************\
-* Boosted tree model as a classifier *
-\****************************************************************************************/
-
-CV_BOOST_IMPL
-float cvEvalBtClassifier( CvClassifier* classifier, CvMat* sample )
-{
- float val;
-
- CV_FUNCNAME( "cvEvalBtClassifier" );
-
- __BEGIN__;
-
- int i;
-
- val = 0.0F;
- if( CV_IS_TUNABLE( classifier->flags ) )
- {
- CvSeqReader reader;
- CvCARTClassifier* tree;
-
- CV_CALL( cvStartReadSeq( ((CvBtClassifier*) classifier)->seq, &reader ) );
- for( i = 0; i < ((CvBtClassifier*) classifier)->numiter; i++ )
- {
- CV_READ_SEQ_ELEM( tree, reader );
- val += tree->eval( (CvClassifier*) tree, sample );
- }
- }
- else
- {
- CvCARTClassifier** ptree;
-
- ptree = ((CvBtClassifier*) classifier)->trees;
- for( i = 0; i < ((CvBtClassifier*) classifier)->numiter; i++ )
- {
- val += (*ptree)->eval( (CvClassifier*) (*ptree), sample );
- ptree++;
- }
- }
-
- __END__;
-
- return val;
-}
-
-CV_BOOST_IMPL
-float cvEvalBtClassifier2( CvClassifier* classifier, CvMat* sample )
-{
- float val;
-
- CV_FUNCNAME( "cvEvalBtClassifier2" );
-
- __BEGIN__;
-
- CV_CALL( val = cvEvalBtClassifier( classifier, sample ) );
-
- __END__;
-
- return (float) (val >= 0.0F);
-}
-
-CV_BOOST_IMPL
-float cvEvalBtClassifierK( CvClassifier* classifier, CvMat* sample )
-{
- int cls;
-
- CV_FUNCNAME( "cvEvalBtClassifierK" );
-
- __BEGIN__;
-
- int i, k;
- float max_val;
- int numclasses;
-
- float* vals;
- size_t data_size;
-
- numclasses = ((CvBtClassifier*) classifier)->numclasses;
- data_size = sizeof( *vals ) * numclasses;
- CV_CALL( vals = (float*) cvAlloc( data_size ) );
- memset( vals, 0, data_size );
-
- if( CV_IS_TUNABLE( classifier->flags ) )
- {
- CvSeqReader reader;
- CvCARTClassifier* tree;
-
- CV_CALL( cvStartReadSeq( ((CvBtClassifier*) classifier)->seq, &reader ) );
- for( i = 0; i < ((CvBtClassifier*) classifier)->numiter; i++ )
- {
- for( k = 0; k < numclasses; k++ )
- {
- CV_READ_SEQ_ELEM( tree, reader );
- vals[k] += tree->eval( (CvClassifier*) tree, sample );
- }
- }
-
- }
- else
- {
- CvCARTClassifier** ptree;
-
- ptree = ((CvBtClassifier*) classifier)->trees;
- for( i = 0; i < ((CvBtClassifier*) classifier)->numiter; i++ )
- {
- for( k = 0; k < numclasses; k++ )
- {
- vals[k] += (*ptree)->eval( (CvClassifier*) (*ptree), sample );
- ptree++;
- }
- }
- }
-
- cls = 0;
- max_val = vals[cls];
- for( k = 1; k < numclasses; k++ )
- {
- if( vals[k] > max_val )
- {
- max_val = vals[k];
- cls = k;
- }
- }
-
- CV_CALL( cvFree( &vals ) );
-
- __END__;
-
- return (float) cls;
-}
-
-typedef float (*CvEvalBtClassifier)( CvClassifier* classifier, CvMat* sample );
-
-static CvEvalBtClassifier icvEvalBtClassifier[] =
-{
- cvEvalBtClassifier2,
- cvEvalBtClassifier2,
- cvEvalBtClassifier2,
- cvEvalBtClassifier2,
- cvEvalBtClassifier2,
- cvEvalBtClassifierK,
- cvEvalBtClassifier,
- cvEvalBtClassifier,
- cvEvalBtClassifier
-};
-
-CV_BOOST_IMPL
-int cvSaveBtClassifier( CvClassifier* classifier, const char* filename )
-{
- CV_FUNCNAME( "cvSaveBtClassifier" );
-
- __BEGIN__;
-
- FILE* file;
- int i, j;
- CvSeqReader reader;
- CvCARTClassifier* tree;
-
- CV_ASSERT( classifier );
- CV_ASSERT( filename );
-
- if( !icvMkDir( filename ) || !(file = fopen( filename, "w" )) )
- {
- CV_ERROR( CV_StsError, "Unable to create file" );
- }
-
- if( CV_IS_TUNABLE( classifier->flags ) )
- {
- CV_CALL( cvStartReadSeq( ((CvBtClassifier*) classifier)->seq, &reader ) );
- }
- fprintf( file, "%d %d\n%d\n%d\n", (int) ((CvBtClassifier*) classifier)->type,
- ((CvBtClassifier*) classifier)->numclasses,
- ((CvBtClassifier*) classifier)->numfeatures,
- ((CvBtClassifier*) classifier)->numiter );
-
- for( i = 0; i < ((CvBtClassifier*) classifier)->numclasses *
- ((CvBtClassifier*) classifier)->numiter; i++ )
- {
- if( CV_IS_TUNABLE( classifier->flags ) )
- {
- CV_READ_SEQ_ELEM( tree, reader );
- }
- else
- {
- tree = ((CvBtClassifier*) classifier)->trees[i];
- }
-
- fprintf( file, "%d\n", tree->count );
- for( j = 0; j < tree->count; j++ )
- {
- fprintf( file, "%d %g %d %d\n", tree->compidx[j],
- tree->threshold[j],
- tree->left[j],
- tree->right[j] );
- }
- for( j = 0; j <= tree->count; j++ )
- {
- fprintf( file, "%g ", tree->val[j] );
- }
- fprintf( file, "\n" );
- }
-
- fclose( file );
-
- __END__;
-
- return 1;
-}
-
-
-CV_BOOST_IMPL
-void cvReleaseBtClassifier( CvClassifier** ptr )
-{
- CV_FUNCNAME( "cvReleaseBtClassifier" );
-
- __BEGIN__;
-
- int i;
-
- if( ptr == NULL || *ptr == NULL )
- {
- CV_ERROR( CV_StsNullPtr, "" );
- }
- if( CV_IS_TUNABLE( (*ptr)->flags ) )
- {
- CvSeqReader reader;
- CvCARTClassifier* tree;
-
- CV_CALL( cvStartReadSeq( ((CvBtClassifier*) *ptr)->seq, &reader ) );
- for( i = 0; i < ((CvBtClassifier*) *ptr)->numclasses *
- ((CvBtClassifier*) *ptr)->numiter; i++ )
- {
- CV_READ_SEQ_ELEM( tree, reader );
- tree->release( (CvClassifier**) (&tree) );
- }
- CV_CALL( cvReleaseMemStorage( &(((CvBtClassifier*) *ptr)->seq->storage) ) );
- }
- else
- {
- CvCARTClassifier** ptree;
-
- ptree = ((CvBtClassifier*) *ptr)->trees;
- for( i = 0; i < ((CvBtClassifier*) *ptr)->numclasses *
- ((CvBtClassifier*) *ptr)->numiter; i++ )
- {
- (*ptree)->release( (CvClassifier**) ptree );
- ptree++;
- }
- }
-
- CV_CALL( cvFree( ptr ) );
- *ptr = NULL;
-
- __END__;
-}
-
-void cvTuneBtClassifier( CvClassifier* classifier, CvMat*, int flags,
- CvMat*, CvMat* , CvMat*, CvMat*, CvMat* )
-{
- CV_FUNCNAME( "cvTuneBtClassifier" );
-
- __BEGIN__;
-
- size_t data_size;
-
- if( CV_IS_TUNABLE( flags ) )
- {
- if( !CV_IS_TUNABLE( classifier->flags ) )
- {
- CV_ERROR( CV_StsUnsupportedFormat,
- "Classifier does not support tune function" );
- }
- else
- {
- /* tune classifier */
- CvCARTClassifier** trees;
-
- printf( "Iteration %d\n", ((CvBtClassifier*) classifier)->numiter + 1 );
-
- data_size = sizeof( *trees ) * ((CvBtClassifier*) classifier)->numclasses;
- CV_CALL( trees = (CvCARTClassifier**) cvAlloc( data_size ) );
- CV_CALL( cvBtNext( trees,
- (CvBtTrainer*) ((CvBtClassifier*) classifier)->trainer ) );
- CV_CALL( cvSeqPushMulti( ((CvBtClassifier*) classifier)->seq,
- trees, ((CvBtClassifier*) classifier)->numclasses ) );
- CV_CALL( cvFree( &trees ) );
- ((CvBtClassifier*) classifier)->numiter++;
- }
- }
- else
- {
- if( CV_IS_TUNABLE( classifier->flags ) )
- {
- /* convert */
- void* ptr;
-
- assert( ((CvBtClassifier*) classifier)->seq->total ==
- ((CvBtClassifier*) classifier)->numiter *
- ((CvBtClassifier*) classifier)->numclasses );
-
- data_size = sizeof( ((CvBtClassifier*) classifier)->trees[0] ) *
- ((CvBtClassifier*) classifier)->seq->total;
- CV_CALL( ptr = cvAlloc( data_size ) );
- CV_CALL( cvCvtSeqToArray( ((CvBtClassifier*) classifier)->seq, ptr ) );
- CV_CALL( cvReleaseMemStorage(
- &(((CvBtClassifier*) classifier)->seq->storage) ) );
- ((CvBtClassifier*) classifier)->trees = (CvCARTClassifier**) ptr;
- classifier->flags &= ~CV_TUNABLE;
- CV_CALL( cvBtEnd( (CvBtTrainer**)
- &(((CvBtClassifier*) classifier)->trainer )) );
- ((CvBtClassifier*) classifier)->trainer = NULL;
- }
- }
-
- __END__;
-}
-
-CvBtClassifier* icvAllocBtClassifier( CvBoostType type, int flags, int numclasses,
- int numiter )
-{
- CvBtClassifier* ptr;
- size_t data_size;
-
- assert( numclasses >= 1 );
- assert( numiter >= 0 );
- assert( ( numclasses == 1 ) || (type == CV_LKCLASS) );
-
- data_size = sizeof( *ptr );
- ptr = (CvBtClassifier*) cvAlloc( data_size );
- memset( ptr, 0, data_size );
-
- if( CV_IS_TUNABLE( flags ) )
- {
- ptr->seq = cvCreateSeq( 0, sizeof( *(ptr->seq) ), sizeof( *(ptr->trees) ),
- cvCreateMemStorage() );
- ptr->numiter = 0;
- }
- else
- {
- data_size = numclasses * numiter * sizeof( *(ptr->trees) );
- ptr->trees = (CvCARTClassifier**) cvAlloc( data_size );
- memset( ptr->trees, 0, data_size );
-
- ptr->numiter = numiter;
- }
-
- ptr->flags = flags;
- ptr->numclasses = numclasses;
- ptr->type = type;
-
- ptr->eval = icvEvalBtClassifier[(int) type];
- ptr->tune = cvTuneBtClassifier;
- ptr->save = cvSaveBtClassifier;
- ptr->release = cvReleaseBtClassifier;
-
- return ptr;
-}
-
-CV_BOOST_IMPL
-CvClassifier* cvCreateBtClassifier( CvMat* trainData,
- int flags,
- CvMat* trainClasses,
- CvMat* typeMask,
- CvMat* missedMeasurementsMask,
- CvMat* compIdx,
- CvMat* sampleIdx,
- CvMat* weights,
- CvClassifierTrainParams* trainParams )
-{
- CvBtClassifier* ptr;
-
- CV_FUNCNAME( "cvCreateBtClassifier" );
-
- __BEGIN__;
- CvBoostType type;
- int num_classes;
- int num_iter;
- int i;
- CvCARTClassifier** trees;
- size_t data_size;
-
- CV_ASSERT( trainData != NULL );
- CV_ASSERT( trainClasses != NULL );
- CV_ASSERT( typeMask == NULL );
- CV_ASSERT( missedMeasurementsMask == NULL );
- CV_ASSERT( compIdx == NULL );
- CV_ASSERT( weights == NULL );
- CV_ASSERT( trainParams != NULL );
-
- type = ((CvBtClassifierTrainParams*) trainParams)->type;
-
- if( type >= CV_DABCLASS && type <= CV_GABCLASS && sampleIdx )
- {
- CV_ERROR( CV_StsBadArg, "Sample indices are not supported for this type" );
- }
-
- if( type == CV_LKCLASS )
- {
- double min_val;
- double max_val;
-
- cvMinMaxLoc( trainClasses, &min_val, &max_val );
- num_classes = (int) (max_val + 1.0);
-
- CV_ASSERT( num_classes >= 2 );
- }
- else
- {
- num_classes = 1;
- }
- num_iter = ((CvBtClassifierTrainParams*) trainParams)->numiter;
-
- CV_ASSERT( num_iter > 0 );
-
- ptr = icvAllocBtClassifier( type, CV_TUNABLE | flags, num_classes, num_iter );
- ptr->numfeatures = (CV_IS_ROW_SAMPLE( flags )) ? trainData->cols : trainData->rows;
-
- i = 0;
-
- printf( "Iteration %d\n", 1 );
-
- data_size = sizeof( *trees ) * ptr->numclasses;
- CV_CALL( trees = (CvCARTClassifier**) cvAlloc( data_size ) );
-
- CV_CALL( ptr->trainer = cvBtStart( trees, trainData, flags, trainClasses, sampleIdx,
- ((CvBtClassifierTrainParams*) trainParams)->numsplits, type, num_classes,
- &(((CvBtClassifierTrainParams*) trainParams)->param[0]) ) );
-
- CV_CALL( cvSeqPushMulti( ptr->seq, trees, ptr->numclasses ) );
- CV_CALL( cvFree( &trees ) );
- ptr->numiter++;
-
- for( i = 1; i < num_iter; i++ )
- {
- ptr->tune( (CvClassifier*) ptr, NULL, CV_TUNABLE, NULL, NULL, NULL, NULL, NULL );
- }
- if( !CV_IS_TUNABLE( flags ) )
- {
- /* convert */
- ptr->tune( (CvClassifier*) ptr, NULL, 0, NULL, NULL, NULL, NULL, NULL );
- }
-
- __END__;
-
- return (CvClassifier*) ptr;
-}
-
-CV_BOOST_IMPL
-CvClassifier* cvCreateBtClassifierFromFile( const char* filename )
-{
- CvBtClassifier* ptr;
-
- CV_FUNCNAME( "cvCreateBtClassifierFromFile" );
-
- __BEGIN__;
-
- FILE* file;
- int i, j;
- int data_size;
- int num_classifiers;
- int num_features;
- int num_classes;
- int type;
-
- CV_ASSERT( filename != NULL );
-
- ptr = NULL;
- file = fopen( filename, "r" );
- if( !file )
- {
- CV_ERROR( CV_StsError, "Unable to open file" );
- }
-
- fscanf( file, "%d %d %d %d", &type, &num_classes, &num_features, &num_classifiers );
-
- CV_ASSERT( type >= (int) CV_DABCLASS && type <= (int) CV_MREG );
- CV_ASSERT( num_features > 0 );
- CV_ASSERT( num_classifiers > 0 );
-
- if( (CvBoostType) type != CV_LKCLASS )
- {
- num_classes = 1;
- }
- ptr = icvAllocBtClassifier( (CvBoostType) type, 0, num_classes, num_classifiers );
- ptr->numfeatures = num_features;
-
- for( i = 0; i < num_classes * num_classifiers; i++ )
- {
- int count;
- CvCARTClassifier* tree;
-
- fscanf( file, "%d", &count );
-
- data_size = sizeof( *tree )
- + count * ( sizeof( *(tree->compidx) ) + sizeof( *(tree->threshold) ) +
- sizeof( *(tree->right) ) + sizeof( *(tree->left) ) )
- + (count + 1) * ( sizeof( *(tree->val) ) );
- CV_CALL( tree = (CvCARTClassifier*) cvAlloc( data_size ) );
- memset( tree, 0, data_size );
- tree->eval = cvEvalCARTClassifier;
- tree->tune = NULL;
- tree->save = NULL;
- tree->release = cvReleaseCARTClassifier;
- tree->compidx = (int*) ( tree + 1 );
- tree->threshold = (float*) ( tree->compidx + count );
- tree->left = (int*) ( tree->threshold + count );
- tree->right = (int*) ( tree->left + count );
- tree->val = (float*) ( tree->right + count );
-
- tree->count = count;
- for( j = 0; j < tree->count; j++ )
- {
- fscanf( file, "%d %g %d %d", &(tree->compidx[j]),
- &(tree->threshold[j]),
- &(tree->left[j]),
- &(tree->right[j]) );
- }
- for( j = 0; j <= tree->count; j++ )
- {
- fscanf( file, "%g", &(tree->val[j]) );
- }
- ptr->trees[i] = tree;
- }
-
- fclose( file );
-
- __END__;
-
- return (CvClassifier*) ptr;
-}
-
-/****************************************************************************************\
-* Utility functions *
-\****************************************************************************************/
-
-CV_BOOST_IMPL
-CvMat* cvTrimWeights( CvMat* weights, CvMat* idx, float factor )
-{
- CvMat* ptr;
-
- CV_FUNCNAME( "cvTrimWeights" );
- __BEGIN__;
- int i, index, num;
- float sum_weights;
- uchar* wdata;
- size_t wstep;
- int wnum;
- float threshold;
- int count;
- float* sorted_weights;
-
- CV_ASSERT( CV_MAT_TYPE( weights->type ) == CV_32FC1 );
-
- ptr = idx;
- sorted_weights = NULL;
-
- if( factor > 0.0F && factor < 1.0F )
- {
- size_t data_size;
-
- CV_MAT2VEC( *weights, wdata, wstep, wnum );
- num = ( idx == NULL ) ? wnum : MAX( idx->rows, idx->cols );
-
- data_size = num * sizeof( *sorted_weights );
- sorted_weights = (float*) cvAlloc( data_size );
- memset( sorted_weights, 0, data_size );
-
- sum_weights = 0.0F;
- for( i = 0; i < num; i++ )
- {
- index = icvGetIdxAt( idx, i );
- sorted_weights[i] = *((float*) (wdata + index * wstep));
- sum_weights += sorted_weights[i];
- }
-
- icvSort_32f( sorted_weights, num, 0 );
-
- sum_weights *= (1.0F - factor);
-
- i = -1;
- do { sum_weights -= sorted_weights[++i]; }
- while( sum_weights > 0.0F && i < (num - 1) );
-
- threshold = sorted_weights[i];
-
- while( i > 0 && sorted_weights[i-1] == threshold ) i--;
-
- if( i > 0 || ( idx != NULL && CV_MAT_TYPE( idx->type ) != CV_32FC1 ) )
- {
- CV_CALL( ptr = cvCreateMat( 1, num - i, CV_32FC1 ) );
- count = 0;
- for( i = 0; i < num; i++ )
- {
- index = icvGetIdxAt( idx, i );
- if( *((float*) (wdata + index * wstep)) >= threshold )
- {
- CV_MAT_ELEM( *ptr, float, 0, count ) = (float) index;
- count++;
- }
- }
-
- assert( count == ptr->cols );
- }
- cvFree( &sorted_weights );
- }
-
- __END__;
-
- return ptr;
-}
-
-
-CV_BOOST_IMPL
-void cvReadTrainData( const char* filename, int flags,
- CvMat** trainData,
- CvMat** trainClasses )
-{
-
- CV_FUNCNAME( "cvReadTrainData" );
-
- __BEGIN__;
-
- FILE* file;
- int m, n;
- int i, j;
- float val;
-
- if( filename == NULL )
- {
- CV_ERROR( CV_StsNullPtr, "filename must be specified" );
- }
- if( trainData == NULL )
- {
- CV_ERROR( CV_StsNullPtr, "trainData must be not NULL" );
- }
- if( trainClasses == NULL )
- {
- CV_ERROR( CV_StsNullPtr, "trainClasses must be not NULL" );
- }
-
- *trainData = NULL;
- *trainClasses = NULL;
- file = fopen( filename, "r" );
- if( !file )
- {
- CV_ERROR( CV_StsError, "Unable to open file" );
- }
-
- fscanf( file, "%d %d", &m, &n );
-
- if( CV_IS_ROW_SAMPLE( flags ) )
- {
- CV_CALL( *trainData = cvCreateMat( m, n, CV_32FC1 ) );
- }
- else
- {
- CV_CALL( *trainData = cvCreateMat( n, m, CV_32FC1 ) );
- }
-
- CV_CALL( *trainClasses = cvCreateMat( 1, m, CV_32FC1 ) );
-
- for( i = 0; i < m; i++ )
- {
- for( j = 0; j < n; j++ )
- {
- fscanf( file, "%f", &val );
- if( CV_IS_ROW_SAMPLE( flags ) )
- {
- CV_MAT_ELEM( **trainData, float, i, j ) = val;
- }
- else
- {
- CV_MAT_ELEM( **trainData, float, j, i ) = val;
- }
- }
- fscanf( file, "%f", &val );
- CV_MAT_ELEM( **trainClasses, float, 0, i ) = val;
- }
-
- fclose( file );
-
- __END__;
-
-}
-
-CV_BOOST_IMPL
-void cvWriteTrainData( const char* filename, int flags,
- CvMat* trainData, CvMat* trainClasses, CvMat* sampleIdx )
-{
- CV_FUNCNAME( "cvWriteTrainData" );
-
- __BEGIN__;
-
- FILE* file;
- int m, n;
- int i, j;
- int clsrow;
- int count;
- int idx;
- CvScalar sc;
-
- if( filename == NULL )
- {
- CV_ERROR( CV_StsNullPtr, "filename must be specified" );
- }
- if( trainData == NULL || CV_MAT_TYPE( trainData->type ) != CV_32FC1 )
- {
- CV_ERROR( CV_StsUnsupportedFormat, "Invalid trainData" );
- }
- if( CV_IS_ROW_SAMPLE( flags ) )
- {
- m = trainData->rows;
- n = trainData->cols;
- }
- else
- {
- n = trainData->rows;
- m = trainData->cols;
- }
- if( trainClasses == NULL || CV_MAT_TYPE( trainClasses->type ) != CV_32FC1 ||
- MIN( trainClasses->rows, trainClasses->cols ) != 1 )
- {
- CV_ERROR( CV_StsUnsupportedFormat, "Invalid trainClasses" );
- }
- clsrow = (trainClasses->rows == 1);
- if( m != ( (clsrow) ? trainClasses->cols : trainClasses->rows ) )
- {
- CV_ERROR( CV_StsUnmatchedSizes, "Incorrect trainData and trainClasses sizes" );
- }
-
- if( sampleIdx != NULL )
- {
- count = (sampleIdx->rows == 1) ? sampleIdx->cols : sampleIdx->rows;
- }
- else
- {
- count = m;
- }
-
-
- file = fopen( filename, "w" );
- if( !file )
- {
- CV_ERROR( CV_StsError, "Unable to create file" );
- }
-
- fprintf( file, "%d %d\n", count, n );
-
- for( i = 0; i < count; i++ )
- {
- if( sampleIdx )
- {
- if( sampleIdx->rows == 1 )
- {
- sc = cvGet2D( sampleIdx, 0, i );
- }
- else
- {
- sc = cvGet2D( sampleIdx, i, 0 );
- }
- idx = (int) sc.val[0];
- }
- else
- {
- idx = i;
- }
- for( j = 0; j < n; j++ )
- {
- fprintf( file, "%g ", ( (CV_IS_ROW_SAMPLE( flags ))
- ? CV_MAT_ELEM( *trainData, float, idx, j )
- : CV_MAT_ELEM( *trainData, float, j, idx ) ) );
- }
- fprintf( file, "%g\n", ( (clsrow)
- ? CV_MAT_ELEM( *trainClasses, float, 0, idx )
- : CV_MAT_ELEM( *trainClasses, float, idx, 0 ) ) );
- }
-
- fclose( file );
-
- __END__;
-}
-
-
-#define ICV_RAND_SHUFFLE( suffix, type ) \
-void icvRandShuffle_##suffix( uchar* data, size_t step, int num ) \
-{ \
- CvRandState state; \
- time_t seed; \
- type tmp; \
- int i; \
- float rn; \
- \
- time( &seed ); \
- \
- cvRandInit( &state, (double) 0, (double) 0, (int)seed ); \
- for( i = 0; i < (num-1); i++ ) \
- { \
- rn = ((float) cvRandNext( &state )) / (1.0F + UINT_MAX); \
- CV_SWAP( *((type*)(data + i * step)), \
- *((type*)(data + ( i + (int)( rn * (num - i ) ) )* step)), \
- tmp ); \
- } \
-}
-
-ICV_RAND_SHUFFLE( 8U, uchar )
-
-ICV_RAND_SHUFFLE( 16S, short )
-
-ICV_RAND_SHUFFLE( 32S, int )
-
-ICV_RAND_SHUFFLE( 32F, float )
-
-CV_BOOST_IMPL
-void cvRandShuffleVec( CvMat* mat )
-{
- CV_FUNCNAME( "cvRandShuffle" );
-
- __BEGIN__;
-
- uchar* data;
- size_t step;
- int num;
-
- if( (mat == NULL) || !CV_IS_MAT( mat ) || MIN( mat->rows, mat->cols ) != 1 )
- {
- CV_ERROR( CV_StsUnsupportedFormat, "" );
- }
-
- CV_MAT2VEC( *mat, data, step, num );
- switch( CV_MAT_TYPE( mat->type ) )
- {
- case CV_8UC1:
- icvRandShuffle_8U( data, step, num);
- break;
- case CV_16SC1:
- icvRandShuffle_16S( data, step, num);
- break;
- case CV_32SC1:
- icvRandShuffle_32S( data, step, num);
- break;
- case CV_32FC1:
- icvRandShuffle_32F( data, step, num);
- break;
- default:
- CV_ERROR( CV_StsUnsupportedFormat, "" );
- }
-
- __END__;
-}
-
-/* End of file. */
projects / opencv / blobdiff