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// Copyright (c) 2018 GeometryFactory Sarl (France).
// All rights reserved.
//
// This file is part of CGAL (www.cgal.org).
//
// $URL: https://github.com/CGAL/cgal/blob/v6.1.1/Classification/include/CGAL/Classification/Feature/Cluster_variance_of_feature.h $
// $Id: include/CGAL/Classification/Feature/Cluster_variance_of_feature.h 08b27d3db14 $
// SPDX-License-Identifier: GPL-3.0-or-later OR LicenseRef-Commercial
//
// Author(s) : Simon Giraudot
#ifndef CGAL_CLASSIFICATION_FEATURE_CLUSTER_VARIANCE_FEATURE_H
#define CGAL_CLASSIFICATION_FEATURE_CLUSTER_VARIANCE_FEATURE_H
#include <CGAL/license/Classification.h>
#include <vector>
#include <sstream>
#include <CGAL/Classification/Feature_base.h>
namespace CGAL {
namespace Classification {
namespace Feature {
/*!
\ingroup PkgClassificationCluster
\brief %Feature that computes the variance values of an itemwise
feature over the respective items of clusters.
Its default name is "variance_" + the name of the itemwise feature.
*/
class Cluster_variance_of_feature : public CGAL::Classification::Feature_base
{
std::vector<float> m_values;
public:
/*!
\brief constructs the feature.
\tparam ClusterRange model of `ConstRange`. Its iterator type
is `RandomAccessIterator` and its value type is the key type of
`Cluster`.
\param clusters input range.
\param itemwise_feature feature that takes values on the range of
items from which `clusters` is a subset.
\param mean_feature `Cluster_mean_of_feature` computed on
`itemwise_feature`.
*/
template <typename ClusterRange>
Cluster_variance_of_feature (ClusterRange& clusters,
Feature_handle itemwise_feature,
Feature_handle mean_feature)
{
std::ostringstream oss;
oss << "variance_" << itemwise_feature->name();
this->set_name (oss.str());
m_values.reserve (clusters.size());
for (std::size_t i = 0; i < clusters.size(); ++ i)
{
double mean = double (mean_feature->value(i));
double variance = 0.;
for (std::size_t j = 0; j < clusters[i].size(); ++ j)
{
double v = double (itemwise_feature->value (clusters[i].index(j)));
variance += (v - mean) * (v - mean);
}
variance /= clusters[i].size();
m_values.push_back (float(variance));
}
}
/// \cond SKIP_IN_MANUAL
virtual float value (std::size_t cluster_index)
{
return m_values[cluster_index];
}
/// \endcond
};
} // namespace Feature
} // namespace Classification
} // namespace CGAL
#endif // CGAL_CLASSIFICATION_FEATURE_CLUSTER_VARIANCE_FEATURE_H
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