Added thirdparty: boost library

thirdparty/include/boost/histogram/accumulators/mean.hpp

@@ -0,0 +1,181 @@
+// 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 <type_traits>             // for std::integral_constant, std::common_type
+
+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.
+
+    count() should be used to check whether value() and variance() are defined,
+    see documentation of value() and variance(). count() can be used to compute
+    the variance of the mean by dividing variance() by count().
+  */
+  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