Cockpit-cpp/EgoServer/thirdparty/Boost/include/boost/histogram/accumulators/mean.hpp

// Copyright 2015-2018 Hans Dembinski
//
// Distributed under the Boost Software License, version 1.0.
// (See accompanying file LICENSE_1_0.txt
// or copy at http://www.boost.org/LICENSE_1_0.txt)

#ifndef BOOST_HISTOGRAM_ACCUMULATORS_MEAN_HPP
#define BOOST_HISTOGRAM_ACCUMULATORS_MEAN_HPP

#include <boost/core/nvp.hpp>
#include <boost/histogram/detail/square.hpp>
#include <boost/histogram/fwd.hpp> // for mean<>
#include <boost/throw_exception.hpp>
#include <cassert>
#include <stdexcept>
#include <type_traits>

namespace boost {
namespace histogram {
namespace accumulators {

/** Calculates mean and variance of sample.

  Uses Welfords's incremental algorithm to improve the numerical
  stability of mean and variance computation.
*/
template <class ValueType>
class mean {
public:
  using value_type = ValueType;
  using const_reference = const value_type&;

  mean() = default;

  /// Allow implicit conversion from mean<T>.
  template <class T>
  mean(const mean<T>& o) noexcept
      : sum_{o.sum_}, mean_{o.mean_}, sum_of_deltas_squared_{o.sum_of_deltas_squared_} {}

  /// Initialize to external count, mean, and variance.
  mean(const_reference n, const_reference mean, const_reference variance) noexcept
      : sum_(n), mean_(mean), sum_of_deltas_squared_(variance * (n - 1)) {}

  /// Insert sample x.
  void operator()(const_reference x) noexcept {
    sum_ += static_cast<value_type>(1);
    const auto delta = x - mean_;
    mean_ += delta / sum_;
    sum_of_deltas_squared_ += delta * (x - mean_);
  }

  /// Insert sample x with weight w.
  void operator()(const weight_type<value_type>& w, const_reference x) noexcept {
    sum_ += w.value;
    const auto delta = x - mean_;
    mean_ += w.value * delta / sum_;
    sum_of_deltas_squared_ += w.value * delta * (x - mean_);
  }

  /// Add another mean accumulator.
  mean& operator+=(const mean& rhs) noexcept {
    if (rhs.sum_ == 0) return *this;

    /*
      sum_of_deltas_squared
        = sum_i (x_i - mu)^2
        = sum_i (x_i - mu)^2 + sum_k (x_k - mu)^2
        = sum_i (x_i - mu1 + (mu1 - mu))^2 + sum_k (x_k - mu2 + (mu2 - mu))^2

      first part:
      sum_i (x_i - mu1 + (mu1 - mu))^2
        = sum_i (x_i - mu1)^2 + n1 (mu1 - mu))^2 + 2 (mu1 - mu) sum_i (x_i - mu1)
        = sum_i (x_i - mu1)^2 + n1 (mu1 - mu))^2
      since sum_i (x_i - mu1) = n1 mu1 - n1 mu1 = 0

      Putting it together:
      sum_of_deltas_squared
        = sum_of_deltas_squared_1 + n1 (mu1 - mu))^2
        + sum_of_deltas_squared_2 + n2 (mu2 - mu))^2
    */

    const auto n1 = sum_;
    const auto mu1 = mean_;
    const auto n2 = rhs.sum_;
    const auto mu2 = rhs.mean_;

    sum_ += rhs.sum_;
    mean_ = (n1 * mu1 + n2 * mu2) / sum_;
    sum_of_deltas_squared_ += rhs.sum_of_deltas_squared_;
    sum_of_deltas_squared_ += n1 * detail::square(mean_ - mu1);
    sum_of_deltas_squared_ += n2 * detail::square(mean_ - mu2);

    return *this;
  }

  /** Scale by value.

   This acts as if all samples were scaled by the value.
  */
  mean& operator*=(const_reference s) noexcept {
    mean_ *= s;
    sum_of_deltas_squared_ *= s * s;
    return *this;
  }

  bool operator==(const mean& rhs) const noexcept {
    return sum_ == rhs.sum_ && mean_ == rhs.mean_ &&
           sum_of_deltas_squared_ == rhs.sum_of_deltas_squared_;
  }

  bool operator!=(const mean& rhs) const noexcept { return !operator==(rhs); }

  /// Return how many samples were accumulated.
  const_reference count() const noexcept { return sum_; }

  /** Return mean value of accumulated samples.

    The result is undefined, if `count() < 1`.
  */
  const_reference value() const noexcept { return mean_; }

  /** Return variance of accumulated samples.

    The result is undefined, if `count() < 2`.
  */
  value_type variance() const noexcept { return sum_of_deltas_squared_ / (sum_ - 1); }

  template <class Archive>
  void serialize(Archive& ar, unsigned version) {
    if (version == 0) {
      // read only
      std::size_t sum;
      ar& make_nvp("sum", sum);
      sum_ = static_cast<value_type>(sum);
    } else {
      ar& make_nvp("sum", sum_);
    }
    ar& make_nvp("mean", mean_);
    ar& make_nvp("sum_of_deltas_squared", sum_of_deltas_squared_);
  }

private:
  value_type sum_{};
  value_type mean_{};
  value_type sum_of_deltas_squared_{};
};

} // namespace accumulators
} // namespace histogram
} // namespace boost

#ifndef BOOST_HISTOGRAM_DOXYGEN_INVOKED

namespace boost {
namespace serialization {

template <class T>
struct version;

// version 1 for boost::histogram::accumulators::mean<T>
template <class T>
struct version<boost::histogram::accumulators::mean<T>> : std::integral_constant<int, 1> {
};

} // namespace serialization
} // namespace boost

namespace std {
template <class T, class U>
/// Specialization for boost::histogram::accumulators::mean.
struct common_type<boost::histogram::accumulators::mean<T>,
                   boost::histogram::accumulators::mean<U>> {
  using type = boost::histogram::accumulators::mean<common_type_t<T, U>>;
};
} // namespace std

#endif

#endif