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Warning fixes continued

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This commit is contained in:
Andrey Kamaev
2012-06-09 15:00:04 +00:00
parent f6b451c607
commit f2d3b9b4a1
127 changed files with 6298 additions and 6277 deletions
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166
modules/ml/src/tree.cpp
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@@ -156,7 +156,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
int vi, i, size;
char err[100];
const int *sidx = 0, *vidx = 0;
if( _update_data && data_root )
{
data = new CvDTreeTrainData( _train_data, _tflag, _responses, _var_idx,
@@ -224,7 +224,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
sample_count = sample_all;
var_count = var_all;
if( _sample_idx )
{
CV_CALL( sample_indices = cvPreprocessIndexArray( _sample_idx, sample_all ));
@@ -239,10 +239,10 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
var_count = var_idx->rows + var_idx->cols - 1;
}
is_buf_16u = false;
if ( sample_count < 65536 )
is_buf_16u = true;
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) ||
@@ -251,13 +251,13 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
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;
@@ -284,11 +284,11 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
work_var_count = var_count + (is_classifier ? 1 : 0) // for responses class_labels
+ (have_labels ? 1 : 0); // for cv_labels
buf_size = (work_var_count + 1 /*for sample_indices*/) * sample_count;
shared = _shared;
buf_count = shared ? 2 : 1;
if ( is_buf_16u )
{
CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_16UC1 ));
@@ -298,13 +298,13 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
{
CV_CALL( buf = cvCreateMat( buf_count, buf_size, 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 ));
@@ -389,12 +389,12 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
{
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++ )
{
@@ -428,7 +428,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
_idst[i] = val;
pair16u32s_ptr[i].u = udst + i;
pair16u32s_ptr[i].i = _idst + i;
}
}
else
{
idst[i] = val;
@@ -502,7 +502,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
// replace labels for missing values with -1
for( ; i < sample_count; i++ )
*int_ptr[i] = -1;
}
}
}
else if( ci < 0 ) // process ordered variable
{
@@ -536,14 +536,14 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
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);
}
@@ -564,15 +564,15 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
if( cv_n )
{
unsigned short* udst = 0;
int* idst = 0;
unsigned short* usdst = 0;
int* idst2 = 0;
if (is_buf_16u)
{
udst = (unsigned short*)(buf->data.s + (get_work_var_count()-1)*sample_count);
usdst = (unsigned short*)(buf->data.s + (get_work_var_count()-1)*sample_count);
for( i = vi = 0; i < sample_count; i++ )
{
udst[i] = (unsigned short)vi++;
usdst[i] = (unsigned short)vi++;
vi &= vi < cv_n ? -1 : 0;
}
@@ -581,15 +581,15 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
int a = (*rng)(sample_count);
int b = (*rng)(sample_count);
unsigned short unsh = (unsigned short)vi;
CV_SWAP( udst[a], udst[b], unsh );
CV_SWAP( usdst[a], usdst[b], unsh );
}
}
else
{
idst = buf->data.i + (get_work_var_count()-1)*sample_count;
idst2 = buf->data.i + (get_work_var_count()-1)*sample_count;
for( i = vi = 0; i < sample_count; i++ )
{
idst[i] = vi++;
idst2[i] = vi++;
vi &= vi < cv_n ? -1 : 0;
}
@@ -597,12 +597,12 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
{
int a = (*rng)(sample_count);
int b = (*rng)(sample_count);
CV_SWAP( idst[a], idst[b], vi );
CV_SWAP( idst2[a], idst2[b], vi );
}
}
}
if ( cat_map )
if ( cat_map )
cat_map->cols = MAX( total_c_count, 1 );
max_split_size = cvAlign(sizeof(CvDTreeSplit) +
@@ -751,7 +751,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
if (is_buf_16u)
{
unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
vi*sample_count + root->offset);
for( i = 0; i < count; i++ )
{
@@ -762,7 +762,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
}
else
{
int* idst = buf->data.i + root->buf_idx*buf->cols +
int* idst = buf->data.i + root->buf_idx*buf->cols +
vi*sample_count + root->offset;
for( i = 0; i < count; i++ )
{
@@ -788,7 +788,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
if (is_buf_16u)
{
unsigned short* udst_idx = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
unsigned short* udst_idx = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
vi*sample_count + data_root->offset);
for( i = 0; i < num_valid; i++ )
{
@@ -812,7 +812,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
}
else
{
int* idst_idx = buf->data.i + root->buf_idx*buf->cols +
int* idst_idx = buf->data.i + root->buf_idx*buf->cols +
vi*sample_count + root->offset;
for( i = 0; i < num_valid; i++ )
{
@@ -840,14 +840,14 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
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*buf->cols +
unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
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*buf->cols +
int* sample_idx_dst = buf->data.i + root->buf_idx*buf->cols +
workVarCount*sample_count + root->offset;
for (i = 0; i < count; i++)
sample_idx_dst[i] = sample_idx_src[sidx[i]];
@@ -865,7 +865,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx,
float* values, uchar* missing,
float* responses, bool get_class_idx )
float* _responses, bool get_class_idx )
{
CvMat* subsample_idx = 0;
CvMat* subsample_co = 0;
@@ -962,7 +962,7 @@ void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx,
}
// copy responses
if( responses )
if( _responses )
{
if( is_classifier )
{
@@ -972,7 +972,7 @@ void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx,
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;
_responses[i] = (float)val;
}
}
else
@@ -983,7 +983,7 @@ void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx,
for( i = 0; i < count; i++ )
{
int idx = sidx ? sidx[i] : i;
responses[i] = _values[idx];
_responses[i] = _values[idx];
}
}
}
@@ -1122,7 +1122,7 @@ void CvDTreeTrainData::clear()
cvReleaseMat( &cat_map );
cvReleaseMat( &priors );
cvReleaseMat( &priors_mult );
node_heap = split_heap = 0;
sample_count = var_all = var_count = max_c_count = ord_var_count = cat_var_count = 0;
@@ -1130,7 +1130,7 @@ void CvDTreeTrainData::clear()
buf_count = buf_size = 0;
shared = false;
data_root = 0;
rng = &cv::theRNG();
@@ -1152,7 +1152,7 @@ void CvDTreeTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord_valu
const float** ord_values, const int** sorted_indices, int* sample_indices_buf )
{
int vidx = var_idx ? var_idx->data.i[vi] : vi;
int node_sample_count = n->sample_count;
int node_sample_count = n->sample_count;
int td_step = train_data->step/CV_ELEM_SIZE(train_data->type);
const int* sample_indices = get_sample_indices(n, sample_indices_buf);
@@ -1161,16 +1161,16 @@ void CvDTreeTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord_valu
*sorted_indices = buf->data.i + n->buf_idx*buf->cols +
vi*sample_count + n->offset;
else {
const unsigned short* short_indices = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols +
const unsigned short* short_indices = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols +
vi*sample_count + n->offset );
for( int i = 0; i < node_sample_count; i++ )
sorted_indices_buf[i] = short_indices[i];
*sorted_indices = sorted_indices_buf;
}
if( tflag == CV_ROW_SAMPLE )
{
for( int i = 0; i < node_sample_count &&
for( int i = 0; i < node_sample_count &&
((((*sorted_indices)[i] >= 0) && !is_buf_16u) || (((*sorted_indices)[i] != 65535) && is_buf_16u)); i++ )
{
int idx = (*sorted_indices)[i];
@@ -1179,14 +1179,14 @@ void CvDTreeTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord_valu
}
}
else
for( int i = 0; i < node_sample_count &&
for( int i = 0; i < node_sample_count &&
((((*sorted_indices)[i] >= 0) && !is_buf_16u) || (((*sorted_indices)[i] != 65535) && is_buf_16u)); i++ )
{
int idx = (*sorted_indices)[i];
idx = sample_indices[idx];
ord_values_buf[i] = *(train_data->data.fl + vidx* td_step + idx);
}
*ord_values = ord_values_buf;
}
@@ -1205,17 +1205,17 @@ const int* CvDTreeTrainData::get_sample_indices( CvDTreeNode* n, int* indices_bu
const float* CvDTreeTrainData::get_ord_responses( CvDTreeNode* n, float* values_buf, int*sample_indices_buf )
{
int sample_count = n->sample_count;
int _sample_count = n->sample_count;
int r_step = CV_IS_MAT_CONT(responses->type) ? 1 : responses->step/CV_ELEM_SIZE(responses->type);
const int* indices = get_sample_indices(n, sample_indices_buf);
for( int i = 0; i < sample_count &&
for( int i = 0; i < _sample_count &&
(((indices[i] >= 0) && !is_buf_16u) || ((indices[i] != 65535) && is_buf_16u)); i++ )
{
int idx = indices[i];
values_buf[i] = *(responses->data.fl + idx * r_step);
}
return values_buf;
}
@@ -1235,7 +1235,7 @@ const int* CvDTreeTrainData::get_cat_var_data( CvDTreeNode* n, int vi, int* cat_
cat_values = buf->data.i + n->buf_idx*buf->cols +
vi*sample_count + n->offset;
else {
const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols +
const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols +
vi*sample_count + n->offset);
for( int i = 0; i < n->sample_count; i++ )
cat_values_buf[i] = short_values[i];
@@ -1562,7 +1562,7 @@ bool CvDTree::train( const Mat& _train_data, int _tflag,
const Mat& _missing_mask, CvDTreeParams _params )
{
CvMat tdata = _train_data, responses = _responses, vidx=_var_idx,
sidx=_sample_idx, vtype=_var_type, mmask=_missing_mask;
sidx=_sample_idx, vtype=_var_type, mmask=_missing_mask;
return train(&tdata, _tflag, &responses, vidx.data.ptr ? &vidx : 0, sidx.data.ptr ? &sidx : 0,
vtype.data.ptr ? &vtype : 0, mmask.data.ptr ? &mmask : 0, _params);
}
@@ -1794,7 +1794,7 @@ double CvDTree::calc_node_dir( CvDTreeNode* node )
const float* val = 0;
const int* sorted = 0;
data->get_ord_var_data( node, vi, val_buf, sorted_buf, &val, &sorted, sample_idx_buf);
assert( 0 <= split_point && split_point < n1-1 );
if( !data->have_priors )
@@ -1848,7 +1848,7 @@ template<> CV_EXPORTS void Ptr<CvDTreeSplit>::delete_obj()
{
fastFree(obj);
}
DTreeBestSplitFinder::DTreeBestSplitFinder( CvDTree* _tree, CvDTreeNode* _node)
{
tree = _tree;
@@ -2310,7 +2310,7 @@ CvDTreeSplit* CvDTree::find_split_cat_class( CvDTreeNode* node, int vi, float in
}
CvDTreeSplit* split = 0;
if( best_subset >= 0 )
if( best_subset >= 0 )
{
split = _split ? _split : data->new_split_cat( 0, -1.0f );
split->var_idx = vi;
@@ -2933,7 +2933,7 @@ void CvDTree::complete_node_dir( CvDTreeNode* node )
{
int idx = labels[i];
if( !dir[i] && ( ((idx >= 0)&&(!data->is_buf_16u)) || ((idx != 65535)&&(data->is_buf_16u)) ))
{
int d = CV_DTREE_CAT_DIR(idx,subset);
dir[i] = (char)((d ^ inversed_mask) - inversed_mask);
@@ -3049,7 +3049,7 @@ void CvDTree::split_node_data( CvDTreeNode* node )
{
unsigned short *ldst, *rdst, *ldst0, *rdst0;
//unsigned short tl, tr;
ldst0 = ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols +
ldst0 = ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols +
vi*scount + left->offset);
rdst0 = rdst = (unsigned short*)(ldst + nl);
@@ -3095,9 +3095,9 @@ void CvDTree::split_node_data( CvDTreeNode* node )
else
{
int *ldst0, *ldst, *rdst0, *rdst;
ldst0 = ldst = buf->data.i + left->buf_idx*buf->cols +
ldst0 = ldst = buf->data.i + left->buf_idx*buf->cols +
vi*scount + left->offset;
rdst0 = rdst = buf->data.i + right->buf_idx*buf->cols +
rdst0 = rdst = buf->data.i + right->buf_idx*buf->cols +
vi*scount + right->offset;
// split sorted
@@ -3146,7 +3146,7 @@ void CvDTree::split_node_data( CvDTreeNode* node )
{
int ci = data->get_var_type(vi);
int n1 = node->get_num_valid(vi), nr1 = 0;
if( ci < 0 || (vi < data->var_count && !split_input_data) )
continue;
@@ -3158,11 +3158,11 @@ void CvDTree::split_node_data( CvDTreeNode* node )
if (data->is_buf_16u)
{
unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*buf->cols +
unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*buf->cols +
vi*scount + left->offset);
unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*buf->cols +
unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*buf->cols +
vi*scount + right->offset);
for( i = 0; i < n; i++ )
{
int d = dir[i];
@@ -3188,11 +3188,11 @@ void CvDTree::split_node_data( CvDTreeNode* node )
}
else
{
int *ldst = buf->data.i + left->buf_idx*buf->cols +
int *ldst = buf->data.i + left->buf_idx*buf->cols +
vi*scount + left->offset;
int *rdst = buf->data.i + right->buf_idx*buf->cols +
int *rdst = buf->data.i + right->buf_idx*buf->cols +
vi*scount + right->offset;
for( i = 0; i < n; i++ )
{
int d = dir[i];
@@ -3208,7 +3208,7 @@ void CvDTree::split_node_data( CvDTreeNode* node )
*ldst = idx;
ldst++;
}
}
if( vi < data->var_count )
@@ -3216,7 +3216,7 @@ void CvDTree::split_node_data( CvDTreeNode* node )
left->set_num_valid(vi, n1 - nr1);
right->set_num_valid(vi, nr1);
}
}
}
}
@@ -3230,9 +3230,9 @@ void CvDTree::split_node_data( CvDTreeNode* node )
int pos = data->get_work_var_count();
if (data->is_buf_16u)
{
unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols +
unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols +
pos*scount + left->offset);
unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols +
unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols +
pos*scount + right->offset);
for (i = 0; i < n; i++)
{
@@ -3252,9 +3252,9 @@ void CvDTree::split_node_data( CvDTreeNode* node )
}
else
{
int* ldst = buf->data.i + left->buf_idx*buf->cols +
int* ldst = buf->data.i + left->buf_idx*buf->cols +
pos*scount + left->offset;
int* rdst = buf->data.i + right->buf_idx*buf->cols +
int* rdst = buf->data.i + right->buf_idx*buf->cols +
pos*scount + right->offset;
for (i = 0; i < n; i++)
{
@@ -3272,9 +3272,9 @@ void CvDTree::split_node_data( CvDTreeNode* node )
}
}
}
// deallocate the parent node data that is not needed anymore
data->free_node_data(node);
data->free_node_data(node);
}
float CvDTree::calc_error( CvMLData* _data, int type, vector<float> *resp )
@@ -3304,9 +3304,9 @@ float CvDTree::calc_error( CvMLData* _data, int type, vector<float> *resp )
{
CvMat sample, miss;
int si = sidx ? sidx[i] : i;
cvGetRow( values, &sample, si );
if( missing )
cvGetRow( missing, &miss, si );
cvGetRow( values, &sample, si );
if( missing )
cvGetRow( missing, &miss, si );
float r = (float)predict( &sample, missing ? &miss : 0 )->value;
if( pred_resp )
pred_resp[i] = r;
@@ -3321,16 +3321,16 @@ float CvDTree::calc_error( CvMLData* _data, int type, vector<float> *resp )
{
CvMat sample, miss;
int si = sidx ? sidx[i] : i;
cvGetRow( values, &sample, si );
if( missing )
cvGetRow( missing, &miss, si );
cvGetRow( values, &sample, si );
if( missing )
cvGetRow( missing, &miss, si );
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];
err += d*d;
}
err = sample_count ? err / (float)sample_count : -FLT_MAX;
err = sample_count ? err / (float)sample_count : -FLT_MAX;
}
return err;
}
@@ -3527,7 +3527,7 @@ int CvDTree::cut_tree( int T, int fold, double min_alpha )
}
void CvDTree::free_prune_data(bool cut_tree)
void CvDTree::free_prune_data(bool _cut_tree)
{
CvDTreeNode* node = root;
@@ -3548,7 +3548,7 @@ void CvDTree::free_prune_data(bool cut_tree)
for( parent = node->parent; parent && parent->right == node;
node = parent, parent = parent->parent )
{
if( cut_tree && parent->Tn <= pruned_tree_idx )
if( _cut_tree && parent->Tn <= pruned_tree_idx )
{
data->free_node( parent->left );
data->free_node( parent->right );
@@ -3650,12 +3650,12 @@ CvDTreeNode* CvDTree::predict( const CvMat* _sample,
{
int a = c = cofs[ci];
int b = (ci+1 >= data->cat_ofs->cols) ? data->cat_map->cols : cofs[ci+1];
int ival = cvRound(val);
if( ival != val )
CV_Error( CV_StsBadArg,
"one of input categorical variable is not an integer" );
int sh = 0;
while( a < b )
{