generic-library/opencv
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Changed types of some variables from int64 back to int.

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Also corrected some indexes to be size_t.
This commit is contained in:
LeonidBeynenson 2013-02-01 14:26:02 +04:00
parent 6de422701a
commit 013d54c230
2 changed files with 28 additions and 28 deletions
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26
modules/ml/src/ertrees.cpp
View File

@ -75,13 +75,13 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
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;
int64 ds_step, dv_step, ms_step = 0, mv_step = 0; // {data|mask}{sample|var}_step
int64 vi, i, size;
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 = -1;
int effective_buf_height = -1, effective_buf_width = -1;
uint64 effective_buf_size = 0;
int effective_buf_height = 0, effective_buf_width = 0;
if ( _params.use_surrogates )
CV_ERROR(CV_StsBadArg, "CvERTrees do not support surrogate splits");
@ -312,17 +312,17 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
for( i = 0; i < sample_count; i++ )
{
int val = INT_MAX, si = sidx ? sidx[i] : i;
if( !mask || !mask[si*m_step] )
if( !mask || !mask[(size_t)si*m_step] )
{
if( idata )
val = idata[si*step];
val = idata[(size_t)si*step];
else
{
float t = fdata[si*step];
float t = fdata[(size_t)si*step];
val = cvRound(t);
if( val != t )
{
sprintf( err, "%ld-th value of %ld-th (categorical) "
sprintf( err, "%d-th value of %d-th (categorical) "
"variable is not an integer", i, vi );
CV_ERROR( CV_StsBadArg, err );
}
@ -330,7 +330,7 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
if( val == INT_MAX )
{
sprintf( err, "%ld-th value of %ld-th (categorical) "
sprintf( err, "%d-th value of %d-th (categorical) "
"variable is too large", i, vi );
CV_ERROR( CV_StsBadArg, err );
}
@ -424,16 +424,16 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
{
float val = ord_nan;
int si = sidx ? sidx[i] : i;
if( !mask || !mask[si*m_step] )
if( !mask || !mask[(size_t)si*m_step] )
{
if( idata )
val = (float)idata[si*step];
val = (float)idata[(size_t)si*step];
else
val = fdata[si*step];
val = fdata[(size_t)si*step];
if( fabs(val) >= ord_nan )
{
sprintf( err, "%ld-th value of %ld-th (ordered) "
sprintf( err, "%d-th value of %d-th (ordered) "
"variable (=%g) is too large", i, vi, val );
CV_ERROR( CV_StsBadArg, err );
}

30
modules/ml/src/tree.cpp
View File

@ -154,8 +154,8 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
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;
int64 ds_step, dv_step, ms_step = 0, mv_step = 0; // {data|mask}{sample|var}_step
int64 vi, i, size;
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;
@ -421,17 +421,17 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
for( i = 0; i < sample_count; i++ )
{
int val = INT_MAX, si = sidx ? sidx[i] : i;
if( !mask || !mask[si*m_step] )
if( !mask || !mask[(size_t)si*m_step] )
{
if( idata )
val = idata[si*step];
val = idata[(size_t)si*step];
else
{
float t = fdata[si*step];
float t = fdata[(size_t)si*step];
val = cvRound(t);
if( fabs(t - val) > FLT_EPSILON )
{
sprintf( err, "%ld-th value of %ld-th (categorical) "
sprintf( err, "%d-th value of %d-th (categorical) "
"variable is not an integer", i, vi );
CV_ERROR( CV_StsBadArg, err );
}
@ -439,7 +439,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
if( val == INT_MAX )
{
sprintf( err, "%ld-th value of %ld-th (categorical) "
sprintf( err, "%d-th value of %d-th (categorical) "
"variable is too large", i, vi );
CV_ERROR( CV_StsBadArg, err );
}
@ -537,16 +537,16 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
{
float val = ord_nan;
int si = sidx ? sidx[i] : i;
if( !mask || !mask[si*m_step] )
if( !mask || !mask[(size_t)si*m_step] )
{
if( idata )
val = (float)idata[si*step];
val = (float)idata[(size_t)si*step];
else
val = fdata[si*step];
val = fdata[(size_t)si*step];
if( fabs(val) >= ord_nan )
{
sprintf( err, "%ld-th value of %ld-th (ordered) "
sprintf( err, "%d-th value of %d-th (ordered) "
"variable (=%g) is too large", i, vi, val );
CV_ERROR( CV_StsBadArg, err );
}
@ -3333,7 +3333,7 @@ float CvDTree::calc_error( CvMLData* _data, int type, vector<float> *resp )
float r = (float)predict( &sample, missing ? &miss : 0 )->value;
if( pred_resp )
pred_resp[i] = r;
int d = fabs((double)r - response->data.fl[si*r_step]) <= FLT_EPSILON ? 0 : 1;
int d = fabs((double)r - response->data.fl[(size_t)si*r_step]) <= FLT_EPSILON ? 0 : 1;
err += d;
}
err = sample_count ? err / (float)sample_count * 100 : -FLT_MAX;
@ -3350,7 +3350,7 @@ float CvDTree::calc_error( CvMLData* _data, int type, vector<float> *resp )
float r = (float)predict( &sample, missing ? &miss : 0 )->value;
if( pred_resp )
pred_resp[i] = r;
float d = r - response->data.fl[si*r_step];
float d = r - response->data.fl[(size_t)si*r_step];
err += d*d;
}
err = sample_count ? err / (float)sample_count : -FLT_MAX;
@ -3656,8 +3656,8 @@ CvDTreeNode* CvDTree::predict( const CvMat* _sample,
int vi = split->var_idx;
int ci = vtype[vi];
i = vidx ? vidx[vi] : vi;
float val = sample[i*step];
if( m && m[i*mstep] )
float val = sample[(size_t)i*step];
if( m && m[(size_t)i*mstep] )
continue;
if( ci < 0 ) // ordered
dir = val <= split->ord.c ? -1 : 1;