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/*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*/
#include "precomp.hpp"
#include <ctype.h>
using namespace cv;
static const float ord_nan = FLT_MAX*0.5f;
static const int min_block_size = 1 << 16;
static const int block_size_delta = 1 << 10;
CvDTreeTrainData::CvDTreeTrainData()
{
var_idx = var_type = cat_count = cat_ofs = cat_map =
priors = priors_mult = counts = direction = split_buf = responses_copy = 0;
buf = 0;
tree_storage = temp_storage = 0;
clear();
}
CvDTreeTrainData::CvDTreeTrainData( const CvMat* _train_data, int _tflag,
const CvMat* _responses, const CvMat* _var_idx,
const CvMat* _sample_idx, const CvMat* _var_type,
const CvMat* _missing_mask, const CvDTreeParams& _params,
bool _shared, bool _add_labels )
{
var_idx = var_type = cat_count = cat_ofs = cat_map =
priors = priors_mult = counts = direction = split_buf = responses_copy = 0;
buf = 0;
tree_storage = temp_storage = 0;
set_data( _train_data, _tflag, _responses, _var_idx, _sample_idx,
_var_type, _missing_mask, _params, _shared, _add_labels );
}
CvDTreeTrainData::~CvDTreeTrainData()
{
clear();
}
bool CvDTreeTrainData::set_params( const CvDTreeParams& _params )
{
bool ok = false;
CV_FUNCNAME( "CvDTreeTrainData::set_params" );
__BEGIN__;
// set parameters
params = _params;
if( params.max_categories < 2 )
CV_ERROR( CV_StsOutOfRange, "params.max_categories should be >= 2" );
params.max_categories = MIN( params.max_categories, 15 );
if( params.max_depth < 0 )
CV_ERROR( CV_StsOutOfRange, "params.max_depth should be >= 0" );
params.max_depth = MIN( params.max_depth, 25 );
params.min_sample_count = MAX(params.min_sample_count,1);
if( params.cv_folds < 0 )
CV_ERROR( CV_StsOutOfRange,
"params.cv_folds should be =0 (the tree is not pruned) "
"or n>0 (tree is pruned using n-fold cross-validation)" );
if( params.cv_folds == 1 )
params.cv_folds = 0;
if( params.regression_accuracy < 0 )
CV_ERROR( CV_StsOutOfRange, "params.regression_accuracy should be >= 0" );
ok = true;
__END__;
return ok;
}
#define CV_CMP_NUM_PTR(a,b) (*(a) < *(b))
static CV_IMPLEMENT_QSORT_EX( icvSortIntPtr, int*, CV_CMP_NUM_PTR, int )
static CV_IMPLEMENT_QSORT_EX( icvSortDblPtr, double*, CV_CMP_NUM_PTR, int )
#define CV_CMP_NUM_IDX(i,j) (aux[i] < aux[j])
static CV_IMPLEMENT_QSORT_EX( icvSortIntAux, int, CV_CMP_NUM_IDX, const float* )
static CV_IMPLEMENT_QSORT_EX( icvSortUShAux, unsigned short, CV_CMP_NUM_IDX, const float* )
#define CV_CMP_PAIRS(a,b) (*((a).i) < *((b).i))
static CV_IMPLEMENT_QSORT_EX( icvSortPairs, CvPair16u32s, CV_CMP_PAIRS, int )
void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
const CvMat* _responses, const CvMat* _var_idx, const CvMat* _sample_idx,
const CvMat* _var_type, const CvMat* _missing_mask, const CvDTreeParams& _params,
bool _shared, bool _add_labels, bool _update_data )
{
CvMat* sample_indices = 0;
CvMat* var_type0 = 0;
CvMat* tmp_map = 0;
int** int_ptr = 0;
CvPair16u32s* pair16u32s_ptr = 0;
CvDTreeTrainData* data = 0;
float *_fdst = 0;
int *_idst = 0;
unsigned short* udst = 0;
int* idst = 0;
CV_FUNCNAME( "CvDTreeTrainData::set_data" );
__BEGIN__;
int sample_all = 0, r_type, cv_n;
int total_c_count = 0;
int tree_block_size, temp_block_size, max_split_size, nv_size, cv_size = 0;
int ds_step, dv_step, ms_step = 0, mv_step = 0; // {data|mask}{sample|var}_step
int vi, i, size;
char err[100];
const int *sidx = 0, *vidx = 0;
uint64 effective_buf_size = 0;
int effective_buf_height = 0, effective_buf_width = 0;
if( _update_data && data_root )
{
data = new CvDTreeTrainData( _train_data, _tflag, _responses, _var_idx,
_sample_idx, _var_type, _missing_mask, _params, _shared, _add_labels );
// compare new and old train data
if( !(data->var_count == var_count &&
cvNorm( data->var_type, var_type, CV_C ) < FLT_EPSILON &&
cvNorm( data->cat_count, cat_count, CV_C ) < FLT_EPSILON &&
cvNorm( data->cat_map, cat_map, CV_C ) < FLT_EPSILON) )
CV_ERROR( CV_StsBadArg,
"The new training data must have the same types and the input and output variables "
"and the same categories for categorical variables" );
cvReleaseMat( &priors );
cvReleaseMat( &priors_mult );
cvReleaseMat( &buf );
cvReleaseMat( &direction );
cvReleaseMat( &split_buf );
cvReleaseMemStorage( &temp_storage );
priors = data->priors; data->priors = 0;
priors_mult = data->priors_mult; data->priors_mult = 0;
buf = data->buf; data->buf = 0;
buf_count = data->buf_count; buf_size = data->buf_size;
sample_count = data->sample_count;
direction = data->direction; data->direction = 0;
split_buf = data->split_buf; data->split_buf = 0;
temp_storage = data->temp_storage; data->temp_storage = 0;
nv_heap = data->nv_heap; cv_heap = data->cv_heap;
data_root = new_node( 0, sample_count, 0, 0 );
EXIT;
}
clear();
var_all = 0;
rng = &cv::theRNG();
CV_CALL( set_params( _params ));
// check parameter types and sizes
CV_CALL( cvCheckTrainData( _train_data, _tflag, _missing_mask, &var_all, &sample_all ));
train_data = _train_data;
responses = _responses;
if( _tflag == CV_ROW_SAMPLE )
{
ds_step = _train_data->step/CV_ELEM_SIZE(_train_data->type);
dv_step = 1;
if( _missing_mask )
ms_step = _missing_mask->step, mv_step = 1;
}
else
{
dv_step = _train_data->step/CV_ELEM_SIZE(_train_data->type);
ds_step = 1;
if( _missing_mask )
mv_step = _missing_mask->step, ms_step = 1;
}
tflag = _tflag;
sample_count = sample_all;
var_count = var_all;
if( _sample_idx )
{
CV_CALL( sample_indices = cvPreprocessIndexArray( _sample_idx, sample_all ));
sidx = sample_indices->data.i;
sample_count = sample_indices->rows + sample_indices->cols - 1;
}
if( _var_idx )
{
CV_CALL( var_idx = cvPreprocessIndexArray( _var_idx, var_all ));
vidx = var_idx->data.i;
var_count = var_idx->rows + var_idx->cols - 1;
}
is_buf_16u = false;
if ( sample_count < 65536 )
is_buf_16u = true;
if( !CV_IS_MAT(_responses) ||
(CV_MAT_TYPE(_responses->type) != CV_32SC1 &&
CV_MAT_TYPE(_responses->type) != CV_32FC1) ||
(_responses->rows != 1 && _responses->cols != 1) ||
_responses->rows + _responses->cols - 1 != sample_all )
CV_ERROR( CV_StsBadArg, "The array of _responses must be an integer or "
"floating-point vector containing as many elements as "
"the total number of samples in the training data matrix" );
r_type = CV_VAR_CATEGORICAL;
if( _var_type )
CV_CALL( var_type0 = cvPreprocessVarType( _var_type, var_idx, var_count, &r_type ));
CV_CALL( var_type = cvCreateMat( 1, var_count+2, CV_32SC1 ));
cat_var_count = 0;
ord_var_count = -1;
is_classifier = r_type == CV_VAR_CATEGORICAL;
// step 0. calc the number of categorical vars
for( vi = 0; vi < var_count; vi++ )
{
char vt = var_type0 ? var_type0->data.ptr[vi] : CV_VAR_ORDERED;
var_type->data.i[vi] = vt == CV_VAR_CATEGORICAL ? cat_var_count++ : ord_var_count--;
}
ord_var_count = ~ord_var_count;
cv_n = params.cv_folds;
// set the two last elements of var_type array to be able
// to locate responses and cross-validation labels using
// the corresponding get_* functions.
var_type->data.i[var_count] = cat_var_count;
var_type->data.i[var_count+1] = cat_var_count+1;
// in case of single ordered predictor we need dummy cv_labels
// for safe split_node_data() operation
have_labels = cv_n > 0 || (ord_var_count == 1 && cat_var_count == 0) || _add_labels;
work_var_count = var_count + (is_classifier ? 1 : 0) // for responses class_labels
+ (have_labels ? 1 : 0); // for cv_labels
shared = _shared;
buf_count = shared ? 2 : 1;
buf_size = -1; // the member buf_size is obsolete
effective_buf_size = (uint64)(work_var_count + 1)*(uint64)sample_count * buf_count; // this is the total size of "CvMat buf" to be allocated
effective_buf_width = sample_count;
effective_buf_height = work_var_count+1;
if (effective_buf_width >= effective_buf_height)
effective_buf_height *= buf_count;
else
effective_buf_width *= buf_count;
if ((uint64)effective_buf_width * (uint64)effective_buf_height != effective_buf_size)
{
CV_Error(CV_StsBadArg, "The memory buffer cannot be allocated since its size exceeds integer fields limit");
}
if ( is_buf_16u )
{
CV_CALL( buf = cvCreateMat( effective_buf_height, effective_buf_width, CV_16UC1 ));
CV_CALL( pair16u32s_ptr = (CvPair16u32s*)cvAlloc( sample_count*sizeof(pair16u32s_ptr[0]) ));
}
else
{
CV_CALL( buf = cvCreateMat( effective_buf_height, effective_buf_width, CV_32SC1 ));
CV_CALL( int_ptr = (int**)cvAlloc( sample_count*sizeof(int_ptr[0]) ));
}
size = is_classifier ? (cat_var_count+1) : cat_var_count;
size = !size ? 1 : size;
CV_CALL( cat_count = cvCreateMat( 1, size, CV_32SC1 ));
CV_CALL( cat_ofs = cvCreateMat( 1, size, CV_32SC1 ));
size = is_classifier ? (cat_var_count + 1)*params.max_categories : cat_var_count*params.max_categories;
size = !size ? 1 : size;
CV_CALL( cat_map = cvCreateMat( 1, size, CV_32SC1 ));
// now calculate the maximum size of split,
// create memory storage that will keep nodes and splits of the decision tree
// allocate root node and the buffer for the whole training data
max_split_size = cvAlign(sizeof(CvDTreeSplit) +
(MAX(0,sample_count - 33)/32)*sizeof(int),sizeof(void*));
tree_block_size = MAX((int)sizeof(CvDTreeNode)*8, max_split_size);
tree_block_size = MAX(tree_block_size + block_size_delta, min_block_size);
CV_CALL( tree_storage = cvCreateMemStorage( tree_block_size ));
CV_CALL( node_heap = cvCreateSet( 0, sizeof(*node_heap), sizeof(CvDTreeNode), tree_storage ));
nv_size = var_count*sizeof(int);
nv_size = cvAlign(MAX( nv_size, (int)sizeof(CvSetElem) ), sizeof(void*));
temp_block_size = nv_size;
if( cv_n )
{
if( sample_count < cv_n*MAX(params.min_sample_count,10) )
CV_ERROR( CV_StsOutOfRange,
"The many folds in cross-validation for such a small dataset" );
cv_size = cvAlign( cv_n*(sizeof(int) + sizeof(double)*2), sizeof(double) );
temp_block_size = MAX(temp_block_size, cv_size);
}
temp_block_size = MAX( temp_block_size + block_size_delta, min_block_size );
CV_CALL( temp_storage = cvCreateMemStorage( temp_block_size ));
CV_CALL( nv_heap = cvCreateSet( 0, sizeof(*nv_heap), nv_size, temp_storage ));
if( cv_size )
CV_CALL( cv_heap = cvCreateSet( 0, sizeof(*cv_heap), cv_size, temp_storage ));
CV_CALL( data_root = new_node( 0, sample_count, 0, 0 ));
max_c_count = 1;
_fdst = 0;
_idst = 0;
if (ord_var_count)
_fdst = (float*)cvAlloc(sample_count*sizeof(_fdst[0]));
if (is_buf_16u && (cat_var_count || is_classifier))
_idst = (int*)cvAlloc(sample_count*sizeof(_idst[0]));
// transform the training data to convenient representation
for( vi = 0; vi <= var_count; vi++ )
{
int ci;
const uchar* mask = 0;
int64 m_step = 0, step;
const int* idata = 0;
const float* fdata = 0;
int num_valid = 0;
if( vi < var_count ) // analyze i-th input variable
{
int vi0 = vidx ? vidx[vi] : vi;
ci = get_var_type(vi);
step = ds_step; m_step = ms_step;
if( CV_MAT_TYPE(_train_data->type) == CV_32SC1 )
idata = _train_data->data.i + vi0*dv_step;
else
fdata = _train_data->data.fl + vi0*dv_step;
if( _missing_mask )
mask = _missing_mask->data.ptr + vi0*mv_step;
}
else // analyze _responses
{
ci = cat_var_count;
step = CV_IS_MAT_CONT(_responses->type) ?
1 : _responses->step / CV_ELEM_SIZE(_responses->type);
if( CV_MAT_TYPE(_responses->type) == CV_32SC1 )
idata = _responses->data.i;
else
fdata = _responses->data.fl;
}
if( (vi < var_count && ci>=0) ||
(vi == var_count && is_classifier) ) // process categorical variable or response
{
int c_count, prev_label;
int* c_map;
if (is_buf_16u)
udst = (unsigned short*)(buf->data.s + vi*sample_count);
else
idst = buf->data.i + vi*sample_count;
// copy data
for( i = 0; i < sample_count; i++ )
{
int val = INT_MAX, si = sidx ? sidx[i] : i;
if( !mask || !mask[(size_t)si*m_step] )
{
if( idata )
val = idata[(size_t)si*step];
else
{
float t = fdata[(size_t)si*step];
val = cvRound(t);
if( fabs(t - val) > FLT_EPSILON )
{
sprintf( err, "%d-th value of %d-th (categorical) "
"variable is not an integer", i, vi );
CV_ERROR( CV_StsBadArg, err );
}
}
if( val == INT_MAX )
{
sprintf( err, "%d-th value of %d-th (categorical) "
"variable is too large", i, vi );
CV_ERROR( CV_StsBadArg, err );
}
num_valid++;
}
if (is_buf_16u)
{
_idst[i] = val;
pair16u32s_ptr[i].u = udst + i;
pair16u32s_ptr[i].i = _idst + i;
}
else
{
idst[i] = val;
int_ptr[i] = idst + i;
}
}
c_count = num_valid > 0;
if (is_buf_16u)
{
icvSortPairs( pair16u32s_ptr, sample_count, 0 );
// count the categories
for( i = 1; i < num_valid; i++ )
if (*pair16u32s_ptr[i].i != *pair16u32s_ptr[i-1].i)
c_count ++ ;
}
else
{
icvSortIntPtr( int_ptr, sample_count, 0 );
// count the categories
for( i = 1; i < num_valid; i++ )
c_count += *int_ptr[i] != *int_ptr[i-1];
}
if( vi > 0 )
max_c_count = MAX( max_c_count, c_count );
cat_count->data.i[ci] = c_count;
cat_ofs->data.i[ci] = total_c_count;
// resize cat_map, if need
if( cat_map->cols < total_c_count + c_count )
{
tmp_map = cat_map;
CV_CALL( cat_map = cvCreateMat( 1,
MAX(cat_map->cols*3/2,total_c_count+c_count), CV_32SC1 ));
for( i = 0; i < total_c_count; i++ )
cat_map->data.i[i] = tmp_map->data.i[i];
cvReleaseMat( &tmp_map );
}
c_map = cat_map->data.i + total_c_count;
total_c_count += c_count;
c_count = -1;
if (is_buf_16u)
{
// compact the class indices and build the map
prev_label = ~*pair16u32s_ptr[0].i;
for( i = 0; i < num_valid; i++ )
{
int cur_label = *pair16u32s_ptr[i].i;
if( cur_label != prev_label )
c_map[++c_count] = prev_label = cur_label;
*pair16u32s_ptr[i].u = (unsigned short)c_count;
}
// replace labels for missing values with -1
for( ; i < sample_count; i++ )
*pair16u32s_ptr[i].u = 65535;
}
else
{
// compact the class indices and build the map
prev_label = ~*int_ptr[0];
for( i = 0; i < num_valid; i++ )
{
int cur_label = *int_ptr[i];
if( cur_label != prev_label )
c_map[++c_count] = prev_label = cur_label;
*int_ptr[i] = c_count;
}
// replace labels for missing values with -1
for( ; i < sample_count; i++ )
*int_ptr[i] = -1;
}
}
else if( ci < 0 ) // process ordered variable
{
if (is_buf_16u)
udst = (unsigned short*)(buf->data.s + vi*sample_count);
else
idst = buf->data.i + vi*sample_count;
for( i = 0; i < sample_count; i++ )
{
float val = ord_nan;
int si = sidx ? sidx[i] : i;
if( !mask || !mask[(size_t)si*m_step] )
{
if( idata )
val = (float)idata[(size_t)si*step];
else
val = fdata[(size_t)si*step];
if( fabs(val) >= ord_nan )
{
sprintf( err, "%d-th value of %d-th (ordered) "
"variable (=%g) is too large", i, vi, val );
CV_ERROR( CV_StsBadArg, err );
}
num_valid++;
}
if (is_buf_16u)
udst[i] = (unsigned short)i; // TODO: memory corruption may be here
else
idst[i] = i;
_fdst[i] = val;
}
if (is_buf_16u)
icvSortUShAux( udst, sample_count, _fdst);
else
icvSortIntAux( idst, sample_count, _fdst );
}
if( vi < var_count )
data_root->set_num_valid(vi, num_valid);
}
// set sample labels
if (is_buf_16u)
udst = (unsigned short*)(buf->data.s + work_var_count*sample_count);
else
idst = buf->data.i + work_var_count*sample_count;
for (i = 0; i < sample_count; i++)
{
if (udst)
udst[i] = sidx ? (unsigned short)sidx[i] : (unsigned short)i;
else
idst[i] = sidx ? sidx[i] : i;
}
if( cv_n )
{
unsigned short* usdst = 0;
int* idst2 = 0;
if (is_buf_16u)
{
usdst = (unsigned short*)(buf->data.s + (get_work_var_count()-1)*sample_count);
for( i = vi = 0; i < sample_count; i++ )
{
usdst[i] = (unsigned short)vi++;
vi &= vi < cv_n ? -1 : 0;
}
for( i = 0; i < sample_count; i++ )
{
int a = (*rng)(sample_count);
int b = (*rng)(sample_count);
unsigned short unsh = (unsigned short)vi;
CV_SWAP( usdst[a], usdst[b], unsh );
}
}
else
{
idst2 = buf->data.i + (get_work_var_count()-1)*sample_count;
for( i = vi = 0; i < sample_count; i++ )
{
idst2[i] = vi++;
vi &= vi < cv_n ? -1 : 0;
}
for( i = 0; i < sample_count; i++ )
{
int a = (*rng)(sample_count);
int b = (*rng)(sample_count);
CV_SWAP( idst2[a], idst2[b], vi );
}
}
}
if ( cat_map )
cat_map->cols = MAX( total_c_count, 1 );
max_split_size = cvAlign(sizeof(CvDTreeSplit) +
(MAX(0,max_c_count - 33)/32)*sizeof(int),sizeof(void*));
CV_CALL( split_heap = cvCreateSet( 0, sizeof(*split_heap), max_split_size, tree_storage ));
have_priors = is_classifier && params.priors;
if( is_classifier )
{
int m = get_num_classes();
double sum = 0;
CV_CALL( priors = cvCreateMat( 1, m, CV_64F ));
for( i = 0; i < m; i++ )
{
double val = have_priors ? params.priors[i] : 1.;
if( val <= 0 )
CV_ERROR( CV_StsOutOfRange, "Every class weight should be positive" );
priors->data.db[i] = val;
sum += val;
}
// normalize weights
if( have_priors )
cvScale( priors, priors, 1./sum );
CV_CALL( priors_mult = cvCloneMat( priors ));
CV_CALL( counts = cvCreateMat( 1, m, CV_32SC1 ));
}
CV_CALL( direction = cvCreateMat( 1, sample_count, CV_8UC1 ));
CV_CALL( split_buf = cvCreateMat( 1, sample_count, CV_32SC1 ));
__END__;
if( data )
delete data;
if (_fdst)
cvFree( &_fdst );
if (_idst)
cvFree( &_idst );
cvFree( &int_ptr );
cvFree( &pair16u32s_ptr);
cvReleaseMat( &var_type0 );
cvReleaseMat( &sample_indices );
cvReleaseMat( &tmp_map );
}
void CvDTreeTrainData::do_responses_copy()
{
responses_copy = cvCreateMat( responses->rows, responses->cols, responses->type );
cvCopy( responses, responses_copy);
responses = responses_copy;
}
CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
{
CvDTreeNode* root = 0;
CvMat* isubsample_idx = 0;
CvMat* subsample_co = 0;
bool isMakeRootCopy = true;
CV_FUNCNAME( "CvDTreeTrainData::subsample_data" );
__BEGIN__;
if( !data_root )
CV_ERROR( CV_StsError, "No training data has been set" );
if( _subsample_idx )
{
CV_CALL( isubsample_idx = cvPreprocessIndexArray( _subsample_idx, sample_count ));
if( isubsample_idx->cols + isubsample_idx->rows - 1 == sample_count )
{
const int* sidx = isubsample_idx->data.i;
for( int i = 0; i < sample_count; i++ )
{
if( sidx[i] != i )
{
isMakeRootCopy = false;
break;
}
}
}
else
isMakeRootCopy = false;
}
if( isMakeRootCopy )
{
// make a copy of the root node
CvDTreeNode temp;
int i;
root = new_node( 0, 1, 0, 0 );
temp = *root;
*root = *data_root;
root->num_valid = temp.num_valid;
if( root->num_valid )
{
for( i = 0; i < var_count; i++ )
root->num_valid[i] = data_root->num_valid[i];
}
root->cv_Tn = temp.cv_Tn;
root->cv_node_risk = temp.cv_node_risk;
root->cv_node_error = temp.cv_node_error;
}
else
{
int* sidx = isubsample_idx->data.i;
// co - array of count/offset pairs (to handle duplicated values in _subsample_idx)
int* co, cur_ofs = 0;
int vi, i;
int workVarCount = get_work_var_count();
int count = isubsample_idx->rows + isubsample_idx->cols - 1;
root = new_node( 0, count, 1, 0 );
CV_CALL( subsample_co = cvCreateMat( 1, sample_count*2, CV_32SC1 ));
cvZero( subsample_co );
co = subsample_co->data.i;
for( i = 0; i < count; i++ )
co[sidx[i]*2]++;
for( i = 0; i < sample_count; i++ )
{
if( co[i*2] )
{
co[i*2+1] = cur_ofs;
cur_ofs += co[i*2];
}
else
co[i*2+1] = -1;
}
cv::AutoBuffer<uchar> inn_buf(sample_count*(2*sizeof(int) + sizeof(float)));
for( vi = 0; vi < workVarCount; vi++ )
{
int ci = get_var_type(vi);
if( ci >= 0 || vi >= var_count )
{
int num_valid = 0;
const int* src = CvDTreeTrainData::get_cat_var_data( data_root, vi, (int*)(uchar*)inn_buf );
if (is_buf_16u)
{
unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
vi*sample_count + root->offset);
for( i = 0; i < count; i++ )
{
int val = src[sidx[i]];
udst[i] = (unsigned short)val;
num_valid += val >= 0;
}
}
else
{
int* idst = buf->data.i + root->buf_idx*get_length_subbuf() +
vi*sample_count + root->offset;
for( i = 0; i < count; i++ )
{
int val = src[sidx[i]];
idst[i] = val;
num_valid += val >= 0;
}
}
if( vi < var_count )
root->set_num_valid(vi, num_valid);
}
else
{
int *src_idx_buf = (int*)(uchar*)inn_buf;
float *src_val_buf = (float*)(src_idx_buf + sample_count);
int* sample_indices_buf = (int*)(src_val_buf + sample_count);
const int* src_idx = 0;
const float* src_val = 0;
get_ord_var_data( data_root, vi, src_val_buf, src_idx_buf, &src_val, &src_idx, sample_indices_buf );
int j = 0, idx, count_i;
int num_valid = data_root->get_num_valid(vi);
if (is_buf_16u)
{
unsigned short* udst_idx = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
vi*sample_count + data_root->offset);
for( i = 0; i < num_valid; i++ )
{
idx = src_idx[i];
count_i = co[idx*2];
if( count_i )
for( cur_ofs = co[idx*2+1]; count_i > 0; count_i--, j++, cur_ofs++ )
udst_idx[j] = (unsigned short)cur_ofs;
}
root->set_num_valid(vi, j);
for( ; i < sample_count; i++ )
{
idx = src_idx[i];
count_i = co[idx*2];
if( count_i )
for( cur_ofs = co[idx*2+1]; count_i > 0; count_i--, j++, cur_ofs++ )
udst_idx[j] = (unsigned short)cur_ofs;
}
}
else
{
int* idst_idx = buf->data.i + root->buf_idx*get_length_subbuf() +
vi*sample_count + root->offset;
for( i = 0; i < num_valid; i++ )
{
idx = src_idx[i];
count_i = co[idx*2];
if( count_i )
for( cur_ofs = co[idx*2+1]; count_i > 0; count_i--, j++, cur_ofs++ )
idst_idx[j] = cur_ofs;
}
root->set_num_valid(vi, j);
for( ; i < sample_count; i++ )
{
idx = src_idx[i];
count_i = co[idx*2];
if( count_i )
for( cur_ofs = co[idx*2+1]; count_i > 0; count_i--, j++, cur_ofs++ )
idst_idx[j] = cur_ofs;
}
}
}
}
// sample indices subsampling
const int* sample_idx_src = get_sample_indices(data_root, (int*)(uchar*)inn_buf);
if (is_buf_16u)
{
unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
workVarCount*sample_count + root->offset);
for (i = 0; i < count; i++)
sample_idx_dst[i] = (unsigned short)sample_idx_src[sidx[i]];
}
else
{
int* sample_idx_dst = buf->data.i + root->buf_idx*get_length_subbuf() +
workVarCount*sample_count + root->offset;
for (i = 0; i < count; i++)
sample_idx_dst[i] = sample_idx_src[sidx[i]];
}
}
__END__;
cvReleaseMat( &isubsample_idx );
cvReleaseMat( &subsample_co );
return root;
}
void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx,
float* values, uchar* missing,
float* _responses, bool get_class_idx )
{
CvMat* subsample_idx = 0;
CvMat* subsample_co = 0;
CV_FUNCNAME( "CvDTreeTrainData::get_vectors" );
__BEGIN__;
int i, vi, total = sample_count, count = total, cur_ofs = 0;
int* sidx = 0;
int* co = 0;
cv::AutoBuffer<uchar> inn_buf(sample_count*(2*sizeof(int) + sizeof(float)));
if( _subsample_idx )
{
CV_CALL( subsample_idx = cvPreprocessIndexArray( _subsample_idx, sample_count ));
sidx = subsample_idx->data.i;
CV_CALL( subsample_co = cvCreateMat( 1, sample_count*2, CV_32SC1 ));
co = subsample_co->data.i;
cvZero( subsample_co );
count = subsample_idx->cols + subsample_idx->rows - 1;
for( i = 0; i < count; i++ )
co[sidx[i]*2]++;
for( i = 0; i < total; i++ )
{
int count_i = co[i*2];
if( count_i )
{
co[i*2+1] = cur_ofs*var_count;
cur_ofs += count_i;
}
}
}
if( missing )
memset( missing, 1, count*var_count );
for( vi = 0; vi < var_count; vi++ )
{
int ci = get_var_type(vi);
if( ci >= 0 ) // categorical
{
float* dst = values + vi;
uchar* m = missing ? missing + vi : 0;
const int* src = get_cat_var_data(data_root, vi, (int*)(uchar*)inn_buf);
for( i = 0; i < count; i++, dst += var_count )
{
int idx = sidx ? sidx[i] : i;
int val = src[idx];
*dst = (float)val;
if( m )
{
*m = (!is_buf_16u && val < 0) || (is_buf_16u && (val == 65535));
m += var_count;
}
}
}
else // ordered
{
float* dst = values + vi;
uchar* m = missing ? missing + vi : 0;
int count1 = data_root->get_num_valid(vi);
float *src_val_buf = (float*)(uchar*)inn_buf;
int* src_idx_buf = (int*)(src_val_buf + sample_count);
int* sample_indices_buf = src_idx_buf + sample_count;
const float *src_val = 0;
const int* src_idx = 0;
get_ord_var_data(data_root, vi, src_val_buf, src_idx_buf, &src_val, &src_idx, sample_indices_buf);
for( i = 0; i < count1; i++ )
{
int idx = src_idx[i];
int count_i = 1;
if( co )
{
count_i = co[idx*2];
cur_ofs = co[idx*2+1];
}
else
cur_ofs = idx*var_count;
if( count_i )
{
float val = src_val[i];
for( ; count_i > 0; count_i--, cur_ofs += var_count )
{
dst[cur_ofs] = val;
if( m )
m[cur_ofs] = 0;
}
}
}
}
}
// copy responses
if( _responses )
{
if( is_classifier )
{
const int* src = get_class_labels(data_root, (int*)(uchar*)inn_buf);
for( i = 0; i < count; i++ )
{
int idx = sidx ? sidx[i] : i;
int val = get_class_idx ? src[idx] :
cat_map->data.i[cat_ofs->data.i[cat_var_count]+src[idx]];
_responses[i] = (float)val;
}
}
else