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// Copyright (c) 2015 INRIA Sophia-Antipolis (France).
// All rights reserved.
//
// This file is part of CGAL (www.cgal.org).
// You can redistribute it and/or modify it under the terms of the GNU
// General Public License as published by the Free Software Foundation,
// either version 3 of the License, or (at your option) any later version.
//
// Licensees holding a valid commercial license may use this file in
// accordance with the commercial license agreement provided with the software.
//
// This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE
// WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.
//
// $URL$
// $Id$
//
//
// Author(s) : Sven Oesau, Yannick Verdie, Clément Jamin, Pierre Alliez
//
#ifndef CGAL_EFFICIENT_RANSAC_H
#define CGAL_EFFICIENT_RANSAC_H
#include <CGAL/Shape_detection_3/Octree.h>
#include <CGAL/Shape_detection_3/Shape_base.h>
#include <CGAL/Shape_detection_3/Cone.h>
#include <CGAL/Shape_detection_3/Cylinder.h>
#include <CGAL/Shape_detection_3/Plane.h>
#include <CGAL/Shape_detection_3/Sphere.h>
#include <CGAL/Shape_detection_3/Torus.h>
//for octree ------------------------------
#include <boost/iterator/filter_iterator.hpp>
#include <CGAL/bounding_box.h> //----------
#include <vector>
#include <random>
#define _USE_MATH_DEFINES
#include <cmath>
#include <fstream>
#include <sstream>
//boost --------------
#include <boost/iterator/counting_iterator.hpp>
#include <boost/range/iterator_range.hpp>
//---------------------
/*!
\file Efficient_RANSAC.h
*/
namespace CGAL {
namespace Shape_detection_3 {
/*!
\ingroup PkgPointSetShapeDetection3
\brief Traits class for definition of types. The method requires Point_3
with Vector_3 containing normal information. To avoid copying of
potentially large input data, the detection will be performed on the input
data directly and no internal copy will be created. For this reason the
input data has to be provided in form of a random access iterator given
by the `Input_iterator` template parameter. Point and normal property maps have to
be provided to extract from the input the points and the normals respectively.
\tparam Gt Geometric traits class. It must provide `Gt::FT`, `Gt::Point_3` and `Gt::Vector_3`.
`Gt::FT` must be a floating point number type like `double` or `float`.
\tparam InputIt is a model of RandomAccessIterator
\tparam Ppmap is a model of `ReadablePropertyMap`
`key_type = InputIt` and `value_type = Gt::Point_3`.
\tparam Npmap is a model of `ReadablePropertyMap`
`key_type = InputIt` and `value_type = Gt::Vector_3`.
*/
template <class Gt,
class InputIt,
class Ppmap,
class Npmap>
struct Efficient_ransac_traits {
typedef Gt Geom_traits;
///< Geometric traits.
typedef InputIt Input_iterator;
///< Random access iterator used to get the input points and normals.
typedef Ppmap Point_pmap;
///< property map: `InputIt` -> `Point_3` (the position of an input point).
typedef Npmap Normal_pmap;
///< property map: `InputIt` -> `Vector_3` (the normal of an input point).
};
/*!
\ingroup PkgPointSetShapeDetection3
\brief A shape detection algorithm using a RANSAC method.
Given a point set in 3D space with unoriented normals, sampled on surfaces,
this classes enables to detect subset of connected points lying on the surface of primitive shapes.
Each input point is assigned to either none or at most one detected primitive
shape. The implementation follows the algorithm published by Schnabel et al.
in 2007 \cgalCite{Schnabel07}.
\tparam ERTraits Efficient_RANSAC_traits
*/
template <class ERTraits>
class Efficient_ransac {
public:
/// \cond SKIP_IN_MANUAL
struct Filter_unassigned_points {
Filter_unassigned_points() : m_shape_index() {}
Filter_unassigned_points(const std::vector<int> &shapeIndex)
: m_shape_index(shapeIndex) {}
bool operator()(std::size_t x) {
if (x < m_shape_index.size())
return m_shape_index[x] == -1;
else return true; // to prevent infinite incrementing
}
std::vector<int> m_shape_index;
};
typedef boost::filter_iterator<Filter_unassigned_points,
boost::counting_iterator<std::size_t> > Point_index_iterator;
///< iterator for indices of points.
/// \endcond
/// \name Types
/// @{
typedef typename ERTraits::Input_iterator Input_iterator;
///< random access iterator for input data.
typedef typename ERTraits::Geom_traits::FT FT; ///< number type.
typedef typename ERTraits::Geom_traits::Point_3 Point; ///< point type.
typedef typename ERTraits::Geom_traits::Vector_3 Vector; ///< vector type.
typedef typename ERTraits::Point_pmap Point_pmap;
///< property map to access the location of an input point.
typedef typename ERTraits::Normal_pmap Normal_pmap;
///< property map to access the unoriented normal of an input point
typedef Shape_base<ERTraits> Shape; ///< shape type.
#ifdef DOXYGEN_RUNNING
typedef unspecified_type Shape_range;
#else
typedef typename
boost::iterator_range<typename std::vector<Shape *>::const_iterator>
Shape_range;
#endif
///< Range of extracted shapes with typ `Shape *`. Model of the `ConstRange` concept.
#ifdef DOXYGEN_RUNNING
typedef unspecified_type Point_index_range; ///< Range of indices of points of type `std::size_t` into the provided Input_iterator. Model of the `boost::BidirectionalRange` concept.
#else
typedef typename boost::iterator_range<Point_index_iterator>
Point_index_range;
#endif
/// @}
/// \name Parameters
/// @{
/*!
Parameters for the shape detection algorithm.
*/
struct Parameters {
Parameters() : probability(0.01), min_points(SIZE_MAX), normal_threshold(0.9), epsilon(-1), cluster_epsilon(-1) {}
FT probability; ///< Probability to control search endurance. %Default value 0.05.
std::size_t min_points; ///< Minimum number of points of a shape. %Default value 1% of total number of input points.
FT epsilon; ///< Maximum tolerance Euclidian distance from a point and a shape. %Default value 1% of bounding box diagonal.
FT normal_threshold; ///< Maximum tolerance normal deviation from a point's normal to the normal on shape at projected point. %Default value 0.9 (around 25 degrees).
FT cluster_epsilon; ///< Maximum distance between points to be considered connected. %Default value 1% of bounding box diagonal.
};
/// @}
private:
typedef internal::Octree<internal::DirectPointAccessor<ERTraits> >
Direct_octree;
typedef internal::Octree<internal::IndexedPointAccessor<ERTraits> >
Indexed_octree;
//--------------------------------------------typedef
// Creates a function pointer for instancing shape instances.
template <class ShapeT>
static Shape *factory() {
return new ShapeT;
}
public:
/// \name Initialization
/// @{
/*!
Constructs an empty shape detection engine.
*/
Efficient_ransac() : m_rng(std::random_device()()), m_num_subsets(0),
m_num_available_points(0), m_global_octree(NULL), m_direct_octrees(NULL),
m_valid_iterators(false) {
}
/*!
Releases all memory allocated by this instances including shapes.
*/
~Efficient_ransac() {
clear();
}
/*!
Sets the input data by providing an iterator range modeling the concept
`boost:RandomAccessRange`. The range of input points need to stay valid
until the detection has been performed and no longer access to the
results is required. The data in the input range is reordered during
`detect()` and `build_octrees()`. `clear()` is first called by this function.
*/
template <class RandomAccessInputRange>
void set_input_data(
///< Range of input data providing 'Input_iterator' for random access. Model of the 'boost:RandomAccessRange'.
RandomAccessInputRange &input_range,
///< past-the-end random access iterator over the input points.
Point_pmap point_pmap = Point_pmap(),
///< property map to access the position of an input point.
Normal_pmap normal_pmap = Normal_pmap()
///< property map to access the normal of an input point.
) {
clear();
if (m_extracted_shapes.size()) {
for (std::size_t i = 0;i<m_extracted_shapes.size();i++)
delete m_extracted_shapes[i];
}
m_point_pmap = point_pmap;
m_normal_pmap = normal_pmap;
m_inputIterator_first = input_range.begin();
m_inputIterator_beyond = input_range.end();
m_num_available_points = m_inputIterator_beyond - m_inputIterator_first;
m_valid_iterators = true;
}
/*!
Registers in the detection engine the shape type `ShapeType` that must inherits from `Shape_base`.
*/
template <class ShapeType>
void add_shape_factory() {
m_shape_factories.push_back(factory<ShapeType>);
}
/*!
Constructs internal data structures required for the shape detection.
These structures only depend on the input data, i.e. the points and
normal vectors.
*/
bool build_octrees() {
if (m_num_available_points == 0)
return false;
// Generation of subsets
m_num_subsets = (std::max<std::size_t>)((std::size_t)
std::floor(std::log(double(m_num_available_points))/std::log(2.))-9, 2);
// SUBSET GENERATION ->
// approach with increasing subset sizes -> replace with octree later on
Input_iterator last = m_inputIterator_beyond - 1;
std::size_t remainingPoints = m_num_available_points;
m_available_octree_sizes.resize(m_num_subsets);
m_direct_octrees = new Direct_octree *[m_num_subsets];
for (int s = m_num_subsets - 1;s >= 0;--s) {
std::size_t subsetSize = remainingPoints;
std::vector<std::size_t> indices(subsetSize);
if (s) {
subsetSize >>= 1;
for (std::size_t i = 0;i<subsetSize;i++) {
std::size_t index = m_rng() % 2;
index = index + (i<<1);
index = (index >= remainingPoints) ? remainingPoints - 1 : index;
indices[i] = index;
}
// move points to the end of the point vector
for (int i = subsetSize - 1;i >= 0;i--) {
typename std::iterator_traits<Input_iterator>::value_type
tmp = (*last);
*last = m_inputIterator_first[indices[std::size_t(i)]];
m_inputIterator_first[indices[std::size_t(i)]] = tmp;
last--;
}
m_direct_octrees[s] = new Direct_octree(last + 1,
last + subsetSize + 1,
remainingPoints - subsetSize);
}
else
m_direct_octrees[0] = new Direct_octree(m_inputIterator_first,
m_inputIterator_first + (subsetSize),
0);
m_available_octree_sizes[s] = subsetSize;
m_direct_octrees[s]->createTree();
remainingPoints -= subsetSize;
}
m_global_octree = new Indexed_octree(m_inputIterator_first, m_inputIterator_beyond);
m_global_octree->createTree();
return true;
}
/// @}
/// \name Memory Management
/// @{
/*!
Removes all shape types registered for detection.
*/
void clear_shape_factories() {
m_shape_factories.clear();
}
/*!
Frees memory allocated for the internal search structures but keep the detected shapes.
It invalidates the range retrieved using `unassigned_points()`.
*/
void clear_octrees() {
// If there is no data yet, there are no data structures.
if (!m_valid_iterators)
return;
if (m_global_octree) {
delete m_global_octree;
m_global_octree = NULL;
}
if (m_direct_octrees) {
for (std::size_t i = 0;i<m_num_subsets;i++)
delete m_direct_octrees[i];
delete [] m_direct_octrees;
m_direct_octrees = NULL;
}
m_num_subsets = 0;
}
/*!
Calls `clear_shape_factories()`, `clear_octrees()` and removes all detected shapes.
All internal structures are cleaned, including any formerly detected shapes.
Thus iterators and ranges retrieved through `shapes()` and `unassigned_points()` are invalidated.
*/
void clear() {
clear_shape_factories();
// If there is no data yet, there are no data structures.
if (!m_valid_iterators)
return;
std::vector<int>().swap(m_shape_index);
for (std::size_t i = 0;i<m_extracted_shapes.size();i++) {
delete m_extracted_shapes[i];
}
m_extracted_shapes.clear();
m_num_available_points = m_inputIterator_beyond - m_inputIterator_first;
clear_octrees();
}
/// @}
/// \name Detection
/// @{
/*!
Performs the shape detection. Shape types considered during the detection
are those registered using `add_shape_factory()`.
\return `true` if shape types have been registered and
input data has been set. Otherwise, `false` is returned.
*/
bool detect(
const Parameters &options = Parameters()
///< Parameters for shape detection.
) {
// no shape types for detection or no points provided, exit
if (m_shape_factories.size() == 0 ||
(m_inputIterator_beyond - m_inputIterator_first) == 0)
return false;
if (m_num_subsets == 0 || m_global_octree == 0) {
if (!build_octrees())
return false;
}
// Reset data structures possibly used by former search
if (m_extracted_shapes.size()) {
for (std::size_t i = 0;i<m_extracted_shapes.size();i++)
delete m_extracted_shapes[i];
}
m_extracted_shapes.clear();
m_num_available_points = m_inputIterator_beyond - m_inputIterator_first;
for (std::size_t i = 0;i<m_num_subsets;i++) {
m_available_octree_sizes[i] = m_direct_octrees[i]->size();
}
// use bounding box diagonal as reference for default values
Bbox_3 bbox = m_global_octree->boundingBox();
FT bboxDiagonal = sqrt((bbox.xmax() - bbox.xmin()) * (bbox.xmax() - bbox.xmin()) + (bbox.ymax() - bbox.ymin()) * (bbox.ymax() - bbox.ymin()) + (bbox.zmax() - bbox.zmin()) * (bbox.zmax() - bbox.zmin()));
m_options = options;
// epsilon or cluster_epsilon have been set by the user? if not, derive from bounding box diagonal
m_options.epsilon = (m_options.epsilon < 0) ? bboxDiagonal * 0.01 : m_options.epsilon;
m_options.cluster_epsilon = (m_options.cluster_epsilon < 0) ? bboxDiagonal * 0.01 : m_options.cluster_epsilon;
// minimum number of points has been set?
m_options.min_points = (m_options.min_points >= m_num_available_points) ? (std::size_t)((FT)0.001 * m_num_available_points) : m_options.min_points;
// initializing the shape index
m_shape_index.assign(m_num_available_points, -1);
// list of all randomly drawn candidates
// with the minimum number of points
std::vector<Shape *> candidates;
// Identifying minimum number of samples
std::size_t requiredSamples = 0;
for (std::size_t i = 0;i<m_shape_factories.size();i++) {
Shape *tmp = (Shape *) m_shape_factories[i]();
requiredSamples = (std::max<std::size_t>)(requiredSamples, tmp->minimum_sample_size());
delete tmp;
}
std::size_t firstSample; // first sample for RANSAC
FT bestExp = 0;
// number of points that have been assigned to a shape
std::size_t numInvalid = 0;
std::size_t nbNewCandidates = 0;
std::size_t nbFailedCandidates = 0;
bool forceExit = false;
do { // main loop
bestExp = 0;
do { //generate candidate
//1. pick a point p1 randomly among available points
std::set<std::size_t> indices;
bool done = false;
do {
do
firstSample = m_rng() % m_num_available_points;
while (m_shape_index[firstSample] != -1);
done = m_global_octree->drawSamplesFromCellContainingPoint(
get(m_point_pmap,
*(m_inputIterator_first + firstSample)),
select_random_octree_level(),
indices,
m_shape_index,
requiredSamples);
} while (m_shape_index[firstSample] != -1 || !done);
nbNewCandidates++;
//add candidate for each type of primitives
for(typename std::vector<Shape *(*)()>::iterator it =
m_shape_factories.begin(); it != m_shape_factories.end(); it++) {
Shape *p = (Shape *) (*it)();
//compute the primitive and says if the candidate is valid
p->compute(indices,
m_inputIterator_first,
m_point_pmap,
m_normal_pmap,
m_options.epsilon,
m_options.normal_threshold);
if (p->is_valid()) {
improve_bound(p, m_num_available_points - numInvalid, 1, 500);
//evaluate the candidate
if(p->max_bound() >= m_options.min_points) {
if (bestExp < p->expected_value())
bestExp = p->expected_value();
candidates.push_back(p);
}
else {
nbFailedCandidates++;
delete p;
}
}
else {
nbFailedCandidates++;
delete p;
}
}
if (nbFailedCandidates >= 10000)
forceExit = true;
} while( !forceExit
&& stop_probability(bestExp,
m_num_available_points - numInvalid,
nbNewCandidates,
m_global_octree->maxLevel())
> m_options.probability
&& stop_probability(m_options.min_points,
m_num_available_points - numInvalid,
nbNewCandidates,
m_global_octree->maxLevel())
> m_options.probability);
// end of generate candidate
if (forceExit) {
break;
}
if (candidates.empty())
continue;
// Now get the best candidate in the current set of all candidates
// Note that the function sorts the candidates:
// the best candidate is always the last element of the vector
Shape *best_Candidate =
get_best_candidate(candidates, m_num_available_points - numInvalid);
if (!best_Candidate)
continue;
best_Candidate->m_indices.clear();
best_Candidate->m_score =
m_global_octree->score(best_Candidate,
m_shape_index,
3 * m_options.epsilon,
m_options.normal_threshold);
best_Candidate->connected_component(best_Candidate->m_indices, m_options.cluster_epsilon);
if (stop_probability(best_Candidate->expected_value(),
(m_num_available_points - numInvalid),
nbNewCandidates,
m_global_octree->maxLevel())
<= m_options.probability) {
//we keep it
if (best_Candidate->assigned_point_indices().size() >=
m_options.min_points) {
candidates.back() = NULL;
//1. add best candidate to final result.
m_extracted_shapes.push_back(best_Candidate);
//2. remove the points
//2.1 update boolean
const std::vector<std::size_t> &indices_points_best_candidate =
best_Candidate->assigned_point_indices();
for (std::size_t i = 0;i<indices_points_best_candidate.size();i++) {
m_shape_index[indices_points_best_candidate.at(i)] =
m_extracted_shapes.size() - 1;
numInvalid++;
bool exactlyOnce = true;
for (std::size_t j = 0;j<m_num_subsets;j++) {
if (m_direct_octrees[j] && m_direct_octrees[j]->m_root) {
std::size_t offset = m_direct_octrees[j]->offset();
if (offset <= indices_points_best_candidate.at(i) &&
(indices_points_best_candidate.at(i) - offset)
< m_direct_octrees[j]->size()) {
exactlyOnce = false;
m_available_octree_sizes[j]--;
}
}
}
}
//2.3 block also the points for the subtrees
nbNewCandidates--;
nbFailedCandidates = 0;
bestExp = 0;
}
std::vector<std::size_t> subsetSizes(m_num_subsets);
subsetSizes[0] = m_available_octree_sizes[0];
for (std::size_t i = 1;i<m_num_subsets;i++) {
subsetSizes[i] = subsetSizes[i-1] + m_available_octree_sizes[i];
}
//3. Remove points from candidates common with extracted primitive
//#pragma omp parallel for
bestExp = 0;
for (std::size_t i=0;i< candidates.size()-1;i++) {
if (candidates[i]) {
candidates[i]->update_points(m_shape_index);
if (candidates[i]->max_bound() < m_options.min_points) {
delete candidates[i];
candidates[i] = NULL;
}
else {
candidates[i]->compute_bound(
subsetSizes[candidates[i]->m_nb_subset_used - 1],
m_num_available_points - numInvalid);
bestExp = (candidates[i]->expected_value() > bestExp) ?
candidates[i]->expected_value() : bestExp;
}
}
}
std::size_t start = 0, end = candidates.size() - 1;
while (start < end) {
while (candidates[start] && start < end) start++;
while (!candidates[end] && start < end) end--;
if (!candidates[start] && candidates[end] && start < end) {
candidates[start] = candidates[end];
candidates[end] = NULL;
start++;
end--;
}
}
candidates.resize(end);
}
}
while((stop_probability(m_options.min_points,
m_num_available_points - numInvalid,
nbNewCandidates,
m_global_octree->maxLevel())
> m_options.probability
&& FT(m_num_available_points - numInvalid) >= m_options.min_points)
|| bestExp >= m_options.min_points);
m_num_available_points -= numInvalid;
return true;
}
/// @}
/// \name Access
/// @{
/*!
Returns a range over the detected shapes in the order of detection.
The memory allocated for the shapes is released by `clear()` or the
the destructor of the class. Depending on the chosen probability
for the detection, the shapes are ordered with decreasing size.
*/
Shape_range shapes() const {
return boost::make_iterator_range(m_extracted_shapes.begin(),
m_extracted_shapes.end());
}
/*!
Number of points not assigned to a shape.
*/
std::size_t number_of_unassigned_points() {
return m_num_available_points;
}
/*!
Provides a boost::iterator_range to indices into the input data that has
not been assigned to a shape. A boost::iterator_range does not provide
the size of the range. Instead the method number_of_unassigned_points is
provided.
*/
Point_index_range unassigned_points() {
Filter_unassigned_points fup(m_shape_index);
Point_index_iterator p1 =
boost::make_filter_iterator<Filter_unassigned_points>(
fup,
boost::counting_iterator<std::size_t>(0),
boost::counting_iterator<std::size_t>(m_shape_index.size()));
return boost::make_iterator_range(p1, Point_index_iterator(p1.end()));
}
/// @}
private:
int select_random_octree_level() {
return m_rng() % (m_global_octree->maxLevel() + 1);
}
Shape* get_best_candidate(std::vector<Shape* >& candidates,
const int _SizeP) {
if (candidates.size() == 1)
return candidates.back();
int index_worse_candidate = 0;
bool improved = true;
while (index_worse_candidate < (int)candidates.size() - 1 && improved) {
improved = false;
typename Shape::Compare_by_max_bound comp;
std::sort(candidates.begin() + index_worse_candidate,
candidates.end(),
comp);
//refine the best one
improve_bound(candidates.back(),
_SizeP, m_num_subsets,
m_options.min_points);
int position_stop;
//Take all those intersecting the best one, check for equal ones
for (position_stop = candidates.size() - 1;
position_stop > index_worse_candidate;
position_stop--) {
if (candidates.back()->min_bound() >
candidates.at(position_stop)->max_bound())
break;//the intervals do not overlaps anymore
if (candidates.at(position_stop)->max_bound()
<= m_options.min_points)
break; //the following candidate doesnt have enough points!
//if we reach this point, there is an overlap
// between best one and position_stop
//so request refining bound on position_stop
improved |= improve_bound(candidates.at(position_stop),
_SizeP,
m_num_subsets,
m_options.min_points);
//test again after refined
if (candidates.back()->min_bound() >
candidates.at(position_stop)->max_bound())
break;//the intervals do not overlaps anymore
}
index_worse_candidate = position_stop;
}
return candidates.back();
}
bool improve_bound(Shape *candidate,
const int _SizeP,
std::size_t max_subset,
std::size_t min_points) {
if (candidate->m_nb_subset_used >= max_subset)
return false;
if (candidate->m_nb_subset_used >= m_num_subsets)
return false;
candidate->m_nb_subset_used =
(candidate->m_nb_subset_used >= m_num_subsets) ?
m_num_subsets - 1 : candidate->m_nb_subset_used;
//what it does is add another subset and recompute lower and upper bound
//the next subset to include is provided by m_nb_subset_used
std::size_t numPointsEvaluated = 0;
for (std::size_t i=0;i<candidate->m_nb_subset_used;i++)
numPointsEvaluated += m_available_octree_sizes[i];
// need score of new subset as well as sum of
// the score of the previous considered subset
std::size_t newScore = 0;
std::size_t newSampledPoints = 0;
do {
newScore = m_direct_octrees[candidate->m_nb_subset_used]->score(
candidate,
m_shape_index,
m_options.epsilon,
m_options.normal_threshold);
candidate->m_score += newScore;
numPointsEvaluated +=
m_available_octree_sizes[candidate->m_nb_subset_used];
newSampledPoints +=
m_available_octree_sizes[candidate->m_nb_subset_used];
candidate->m_nb_subset_used++;
} while (newSampledPoints < min_points &&
candidate->m_nb_subset_used < m_num_subsets);
candidate->m_score = candidate->m_indices.size();
candidate->compute_bound(numPointsEvaluated, _SizeP);
return true;
}
inline FT stop_probability(FT _sizeC, FT _np, FT _dC, FT _l) const {
return (std::min<FT>)(std::pow(1.f - _sizeC / (_np * _l * 3), _dC), 1.);
}
private:
Parameters m_options;
std::mt19937 m_rng;
Direct_octree **m_direct_octrees;
Indexed_octree *m_global_octree;
std::vector<int> m_available_octree_sizes;
std::size_t m_num_subsets;
// maps index into points to assigned extracted primitive
std::vector<int> m_shape_index;
std::size_t m_num_available_points;
//give the index of the subset of point i
std::vector<int> m_index_subsets;
std::vector<Shape *> m_extracted_shapes;
std::vector<Shape *(*)()> m_shape_factories;
// iterators of input data
bool m_valid_iterators;
Input_iterator m_inputIterator_first, m_inputIterator_beyond;
Point_pmap m_point_pmap;
Normal_pmap m_normal_pmap;
std::vector<FT> m_level_weighting; // sum must be 1
};
}
}
#endif
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