/usr/include/sopt/sampling.h is in libsopt-dev 2.0.0-2.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | #ifndef SOPT_SAMPLING_H
#define SOPT_SAMPLING_H
#include "sopt/config.h"
#include "sopt/config.h"
#include <initializer_list>
#include <memory>
#include <random>
#include <Eigen/Core>
#include "sopt/linear_transform.h"
#include "sopt/types.h"
namespace sopt {
//! \brief An operator that samples a set of measurements.
//! \details Picks some elements from a vector
class Sampling {
public:
//! Constructs from a vector
Sampling(t_uint size, std::vector<t_uint> const &indices) : indices(indices), size(size) {}
//! Constructs from the size and the number of samples to pick
template <class RNG> Sampling(t_uint size, t_uint samples, RNG &&rng);
//! Constructs from the size and the number of samples to pick
Sampling(t_uint size, t_uint samples)
: Sampling(size, samples, std::mt19937_64(std::random_device()())) {}
// Performs sampling
template <class T0, class T1>
void operator()(Eigen::DenseBase<T0> &out, Eigen::DenseBase<T1> const &x) const;
// Performs sampling
template <class T0, class T1>
void operator()(Eigen::DenseBase<T0> &&out, Eigen::DenseBase<T1> const &x) const {
operator()(out, x);
}
// Performs adjunct of sampling
template <class T0, class T1>
void adjoint(Eigen::DenseBase<T0> &out, Eigen::DenseBase<T1> const &x) const;
// Performs adjunct sampling
template <class T0, class T1>
void adjoint(Eigen::DenseBase<T0> &&out, Eigen::DenseBase<T1> const &x) const {
adjoint(out, x);
}
//! Size of the vector returned by the adjoint operation
t_uint cols() const { return size; }
//! Number of measurements
t_uint rows() const { return indices.size(); }
protected:
//! Set of indices to pick
std::vector<t_uint> indices;
//! Original vector size
t_uint size;
};
template <class T0, class T1>
void Sampling::operator()(Eigen::DenseBase<T0> &out, Eigen::DenseBase<T1> const &x) const {
out.resize(indices.size());
for(decltype(indices.size()) i(0); i < indices.size(); ++i) {
assert(indices[i] < static_cast<t_uint>(x.size()));
out[i] = x[indices[i]];
}
}
template <class T0, class T1>
void Sampling::adjoint(Eigen::DenseBase<T0> &out, Eigen::DenseBase<T1> const &x) const {
assert(static_cast<t_uint>(x.size()) == indices.size());
out.resize(out.size());
out.fill(0);
for(decltype(indices.size()) i(0); i < indices.size(); ++i) {
assert(indices[i] < static_cast<t_uint>(out.size()));
out[indices[i]] = x[i];
}
}
//! Returns linear transform version of this object.
template <class T> LinearTransform<Vector<T>> linear_transform(Sampling const &sampling) {
return linear_transform<Vector<T>>(
[sampling](Vector<T> &out, Vector<T> const &x) { sampling(out, x); },
{{0, 1, static_cast<t_int>(sampling.rows())}},
[sampling](Vector<T> &out, Vector<T> const &x) { sampling.adjoint(out, x); },
{{0, 1, static_cast<t_int>(sampling.cols())}});
}
template <class RNG>
Sampling::Sampling(t_uint size, t_uint samples, RNG &&rng) : indices(size), size(size) {
std::iota(indices.begin(), indices.end(), 0);
std::shuffle(indices.begin(), indices.end(), rng);
indices.resize(samples);
}
} /* sopt */
#endif
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