EIC code displayed by LXR master/​include/​root/​Math/​Util.h	Source navigation Diff markup Identifier search General search
	Version: master test Revert   Change

File indexing completed on 2025-09-16 09:08:14

 // @(#)root/mathcore:$Id$
 // Author: L. Moneta Tue Nov 14 15:44:38 2006
 
 /**********************************************************************
  *                                                                    *
  * Copyright (c) 2006  LCG ROOT Math Team, CERN/PH-SFT                *
  *                                                                    *
  *                                                                    *
  **********************************************************************/
 
 // Utility functions for all ROOT Math classes
 
 #ifndef ROOT_Math_Util
 #define ROOT_Math_Util
 
 #include <chrono>
 #include <cmath>
 #include <functional>
 #include <limits>
 #include <numeric>
 #include <sstream>
 #include <string>
 
 
 // This can be protected against by defining ROOT_Math_VecTypes
 // This is only used for the R__HAS_VECCORE define
 // and a single VecCore function in EvalLog
 #ifndef ROOT_Math_VecTypes
 #include "Types.h"
 #endif
 
 
 // for defining unused variables in the interfaces
 //  and have still them in the documentation
 #define MATH_UNUSED(var)   (void)var
 
 
 namespace ROOT {
 
 namespace Math {
 
 /**
    namespace defining Utility functions needed by mathcore
 */
 namespace Util {
 
 class TimingScope {
 
 public:
    TimingScope(std::function<void(std::string const&)> printer, std::string const &message);
 
    ~TimingScope();
 
 private:
    std::chrono::steady_clock::time_point fBegin;
    std::function<void(std::string const&)> fPrinter;
    const std::string fMessage;
 };
 
    /**
       Utility function for conversion to strings
    */
    template <class T>
    std::string ToString(const T &val)
    {
       std::ostringstream buf;
       buf << val;
 
       std::string ret = buf.str();
       return ret;
    }
 
    /// safe evaluation of log(x) with a protections against negative or zero argument to the log
    /// smooth linear extrapolation below function values smaller than  epsilon
    /// (better than a simple cut-off)
 
    template<class T>
    inline T EvalLog(T x) {
       static const T epsilon = T(2.0 * std::numeric_limits<double>::min());
 #ifdef R__HAS_VECCORE
       T logval = vecCore::Blend<T>(x <= epsilon, x / epsilon + std::log(epsilon) - T(1.0), std::log(x));
 #else
       T logval = x <= epsilon ? x / epsilon + std::log(epsilon) - T(1.0) : std::log(x);
 #endif
       return logval;
    }
 
    } // end namespace Util
 
    /// \class KahanSum
    /// The Kahan summation is a compensated summation algorithm, which significantly reduces numerical errors
    /// when adding a sequence of finite-precision floating point numbers.
    /// This is done by keeping a separate running compensation (a variable to accumulate small errors).
    ///
    /// ### Auto-vectorisable accumulation
    /// This class can internally use multiple accumulators (template parameter `N`).
    /// When filled from a collection that supports index access from a *contiguous* block of memory,
    /// compilers such as gcc, clang and icc can auto-vectorise the accumulation. This happens by cycling
    /// through the internal accumulators based on the value of "`index % N`", so `N` accumulators can be filled from a block
    /// of `N` numbers in a single instruction.
    ///
    /// The usage of multiple accumulators might slightly increase the precision in comparison to the single-accumulator version
    /// with `N = 1`.
    /// This depends on the order and magnitude of the numbers being accumulated. Therefore, in rare cases, the accumulation
    /// result can change *in dependence of N*, even when the data are identical.
    /// The magnitude of such differences is well below the precision of the floating point type, and will therefore mostly show
    /// in the compensation sum(see Carry()). Increasing the number of accumulators therefore only makes sense to
    /// speed up the accumulation, but not to increase precision.
    ///
    /// \param T The type of the values to be accumulated.
    /// \param N Number of accumulators. Defaults to 1. Ideal values are the widths of a vector register on the relevant architecture.
    /// Depending on the instruction set, good values are:
    /// - AVX2-float: 8
    /// - AVX2-double: 4
    /// - AVX512-float: 16
    /// - AVX512-double: 8
    ///
    /// ### Examples
    ///
    /// ~~~{.cpp}
    /// std::vector<double> numbers(1000);
    /// for (std::size_t i=0; i<1000; ++i) {
    ///    numbers[i] = rand();
    /// }
    ///
    /// ROOT::Math::KahanSum<double, 4> k;
    /// k.Add(numbers.begin(), numbers.end());
    /// // or
    /// k.Add(numbers);
    /// ~~~
    /// ~~~{.cpp}
    /// double offset = 10.;
    /// auto result = ROOT::Math::KahanSum<double, 4>::Accumulate(numbers.begin(), numbers.end(), offset);
    /// ~~~
    template<typename T = double, unsigned int N = 1>
    class KahanSum {
      public:
        /// Initialise the sum.
        /// \param[in] initialValue Initialise with this value. Defaults to 0.
        explicit KahanSum(T initialValue = T{}) {
          fSum[0] = initialValue;
          std::fill(std::begin(fSum)+1, std::end(fSum), 0.);
          std::fill(std::begin(fCarry), std::end(fCarry), 0.);
        }
 
        /// Initialise with a sum value and a carry value.
        /// \param[in] initialSumValue Initialise the sum with this value.
        /// \param[in] initialCarryValue Initialise the carry with this value.
        KahanSum(T initialSumValue, T initialCarryValue) {
           fSum[0] = initialSumValue;
           fCarry[0] = initialCarryValue;
           std::fill(std::begin(fSum)+1, std::end(fSum), 0.);
           std::fill(std::begin(fCarry)+1, std::end(fCarry), 0.);
        }
 
        /// Initialise the sum with a pre-existing state.
        /// \param[in] sumBegin Begin of iterator range with values to initialise the sum with.
        /// \param[in] sumEnd End of iterator range with values to initialise the sum with.
        /// \param[in] carryBegin Begin of iterator range with values to initialise the carry with.
        /// \param[in] carryEnd End of iterator range with values to initialise the carry with.
        template<class Iterator>
        KahanSum(Iterator sumBegin, Iterator sumEnd, Iterator carryBegin, Iterator carryEnd) {
           assert(std::distance(sumBegin, sumEnd) == N);
           assert(std::distance(carryBegin, carryEnd) == N);
           std::copy(sumBegin, sumEnd, std::begin(fSum));
           std::copy(carryBegin, carryEnd, std::begin(fCarry));
        }
 
        /// Constructor to create a KahanSum from another KahanSum with a different number of accumulators
        template <unsigned int M>
        KahanSum(KahanSum<T,M> const& other) {
          fSum[0] = other.Sum();
          fCarry[0] = other.Carry();
          std::fill(std::begin(fSum)+1, std::end(fSum), 0.);
          std::fill(std::begin(fCarry)+1, std::end(fCarry), 0.);
        }
 
        /// Single-element accumulation. Will not vectorise.
        void Add(T x) {
           auto y = x - fCarry[0];
           auto t = fSum[0] + y;
           fCarry[0] = (t - fSum[0]) - y;
           fSum[0] = t;
        }
 
 
        /// Accumulate from a range denoted by iterators.
        ///
        /// This function will auto-vectorise with random-access iterators.
        /// \param[in] begin Beginning of a range. Needs to be a random access iterator for automatic
        /// vectorisation, because a contiguous block of memory needs to be read.
        /// \param[in] end   End of the range.
        template <class Iterator>
        void Add(Iterator begin, Iterator end) {
            static_assert(std::is_floating_point<
                typename std::remove_reference<decltype(*begin)>::type>::value,
                "Iterator needs to point to floating-point values.");
            const std::size_t n = std::distance(begin, end);
 
            for (std::size_t i=0; i<n; ++i) {
              AddIndexed(*(begin++), i);
            }
        }
 
 
        /// Fill from a container that supports index access.
        /// \param[in] inputs Container with index access such as std::vector or array.
        template<class Container_t>
        void Add(const Container_t& inputs) {
          static_assert(std::is_floating_point<typename Container_t::value_type>::value,
              "Container does not hold floating-point values.");
          for (std::size_t i=0; i < inputs.size(); ++i) {
            AddIndexed(inputs[i], i);
          }
        }
 
 
        /// Iterate over a range and return an instance of a KahanSum.
        ///
        /// See Add(Iterator,Iterator) for details.
        /// \param[in] begin Beginning of a range.
        /// \param[in] end   End of the range.
        /// \param[in] initialValue Optional initial value.
        template <class Iterator>
        static KahanSum<T, N> Accumulate(Iterator begin, Iterator end,
            T initialValue = T{}) {
            KahanSum<T, N> theSum(initialValue);
            theSum.Add(begin, end);
 
            return theSum;
        }
 
 
        /// Add `input` to the sum.
        ///
        /// Particularly helpful when filling from a for loop.
        /// This function can be inlined and auto-vectorised if
        /// the index parameter is used to enumerate *consecutive* fills.
        /// Use Add() or Accumulate() when no index is available.
        /// \param[in] input Value to accumulate.
        /// \param[in] index Index of the value. Determines internal accumulator that this
        /// value is added to. Make sure that consecutive fills have consecutive indices
        /// to make a loop auto-vectorisable. The actual value of the index does not matter,
        /// as long as it is consecutive.
        void AddIndexed(T input, std::size_t index) {
          const unsigned int i = index % N;
          const T y = input - fCarry[i];
          const T t = fSum[i] + y;
          fCarry[i] = (t - fSum[i]) - y;
          fSum[i] = t;
        }
 
        /// \return Compensated sum.
        T Sum() const {
          return std::accumulate(std::begin(fSum), std::end(fSum), 0.);
        }
 
        /// \return Compensated sum.
        T Result() const {
          return Sum();
        }
 
        /// \return The sum used for compensation.
        T Carry() const {
          return std::accumulate(std::begin(fCarry), std::end(fCarry), 0.);
        }
 
        /// Add `arg` into accumulator. Does not vectorise.
        KahanSum<T, N>& operator+=(T arg) {
          Add(arg);
          return *this;
        }
 
        /// Add other KahanSum into accumulator. Does not vectorise.
        ///
        /// Based on KahanIncrement from:
        /// Y. Tian, S. Tatikonda and B. Reinwald, "Scalable and Numerically Stable Descriptive Statistics in SystemML," 2012 IEEE 28th International Conference on Data Engineering, 2012, pp. 1351-1359, doi: 10.1109/ICDE.2012.12.
        /// Note that while Tian et al. add the carry in the first step, we subtract
        /// the carry, in accordance with the Add(Indexed) implementation(s) above.
        /// This is purely an implementation choice that has no impact on performance.
        ///
        /// \note Take care when using += (and -=) to add other KahanSums into a zero-initialized
        ///       KahanSum. The operator behaves correctly in this case, but the result may be slightly
        ///       off if you expect 0 + x to yield exactly x (where 0 is the zero-initialized KahanSum
        ///       and x another KahanSum). In particular, x's carry term may get lost. This doesn't
        ///       just happen with zero-initialized KahanSums; see the SubtractWithABitTooSmallCarry
        ///       test case in the testKahan unittest for other examples. This behavior is internally
        ///       consistent: the carry also gets lost if you switch the operands and it also happens with
        ///       other KahanSum operators.
        template<typename U, unsigned int M>
        KahanSum<T, N>& operator+=(const KahanSum<U, M>& other) {
          U corrected_arg_sum = other.Sum() - (fCarry[0] + other.Carry());
          U sum = fSum[0] + corrected_arg_sum;
          U correction = (sum - fSum[0]) - corrected_arg_sum;
          fSum[0] = sum;
          fCarry[0] = correction;
          return *this;
        }
 
        /// Subtract other KahanSum. Does not vectorise.
        ///
        /// Based on KahanIncrement from: Tian et al., 2012 (see operator+= documentation).
        template<typename U, unsigned int M>
        KahanSum<T, N>& operator-=(KahanSum<U, M> const& other) {
          U corrected_arg_sum = -other.Sum() - (fCarry[0] - other.Carry());
          U sum = fSum[0] + corrected_arg_sum;
          U correction = (sum - fSum[0]) - corrected_arg_sum;
          fSum[0] = sum;
          fCarry[0] = correction;
          return *this;
        }
 
        KahanSum<T, N> operator-()
        {
           return {-this->fSum[0], -this->fCarry[0]};
        }
 
        template<typename U, unsigned int M>
        bool operator ==(KahanSum<U, M> const& other) const {
           return (this->Sum() == other.Sum()) && (this->Carry() == other.Carry());
        }
 
        template<typename U, unsigned int M>
        bool operator !=(KahanSum<U, M> const& other) const {
           return !(*this == other);
        }
 
      private:
        T fSum[N];
        T fCarry[N];
    };
 
    /// Add two non-vectorized KahanSums.
    template<typename T, unsigned int N, typename U, unsigned int M>
    KahanSum<T, N> operator+(const KahanSum<T, N>& left, const KahanSum<U, M>& right) {
       KahanSum<T, N> sum(left);
       sum += right;
       return sum;
    }
 
    /// Subtract two non-vectorized KahanSums.
    template<typename T, unsigned int N, typename U, unsigned int M>
    KahanSum<T, N> operator-(const KahanSum<T, N>& left, const KahanSum<U, M>& right) {
       KahanSum<T, N> sum(left);
       sum -= right;
       return sum;
    }
 
    } // end namespace Math
 
 } // end namespace ROOT
 
 
 #endif /* ROOT_Math_Util */

This page was automatically generated by the 2.3.7 LXR engine. The LXR team