data = 0;
weak = 0;
default_model_name = "my_boost_tree";
+
active_vars = active_vars_abs = orig_response = sum_response = weak_eval =
subsample_mask = weights = subtree_weights = 0;
have_active_cat_vars = have_subsample = false;
weak = 0;
data = 0;
default_model_name = "my_boost_tree";
- orig_response = sum_response = weak_eval = subsample_mask = weights = 0;
+
+ active_vars = active_vars_abs = orig_response = sum_response = weak_eval =
+ subsample_mask = weights = subtree_weights = 0;
train( _train_data, _tflag, _responses, _var_idx, _sample_idx,
_var_type, _missing_mask, _params );
weak = 0;
data = 0;
default_model_name = "my_boost_tree";
- orig_response = sum_response = weak_eval = subsample_mask = weights = 0;
+ active_vars = active_vars_abs = orig_response = sum_response = weak_eval =
+ subsample_mask = weights = subtree_weights = 0;
train( _train_data, _tflag, _responses, _var_idx, _sample_idx,
_var_type, _missing_mask, _params );
CV_Assert( DataType<ValueType>::type == validDescriptors.type() );
int dimension = validDescriptors.cols;
- DistanceType curMaxDist = std::numeric_limits<DistanceType>::min();
+ DistanceType curMaxDist = -std::numeric_limits<DistanceType>::max();
for( int y = 0; y < validDescriptors.rows; y++ )
{
DistanceType dist = distance( validDescriptors.ptr<ValueType>(y), calcDescriptors.ptr<ValueType>(y), dimension );
Mat curDisp; disp.copyTo( curDisp );
if( !unknDispMask.empty() )
- curDisp.setTo( Scalar(numeric_limits<float>::min()), unknDispMask );
+ curDisp.setTo( Scalar(-numeric_limits<float>::max()), unknDispMask );
Mat maxNeighbDisp; dilate( curDisp, maxNeighbDisp, Mat(3, 3, CV_8UC1, Scalar(1)) );
if( !unknDispMask.empty() )
curDisp.setTo( Scalar(numeric_limits<float>::max()), unknDispMask );