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35 /*! \libinternal \file
36  * \brief
37  * Declares an exponential moving average class.
38  *
39  * \author Christian Blau <blau@kth.se>
40  * \inlibraryapi
41  * \ingroup module_math
42  */
43 #ifndef GMX_MATH_EXPONENTIALMOVINGAVERAGE_H
44 #define GMX_MATH_EXPONENTIALMOVINGAVERAGE_H
45 
46 #include "gromacs/utility/keyvaluetreebuilder.h"
47 #include "gromacs/utility/real.h"
48 
49 namespace gmx
50 {
51 
52 /*! \libinternal \brief Store the state of exponential moving averages.
53  */
54 struct ExponentialMovingAverageState
55 {
56     //! The weighted sum
57     real weightedSum_ = 0;
58 
59     //! The weighted count, used for bias correction
60     real weightedCount_ = 0;
61 
62     //! Remember if adding the latest data point increased the average
63     bool increasing_ = false;
64 };
65 
66 //! Convert the exponential moving average state as key-value-tree object
67 void exponentialMovingAverageStateAsKeyValueTree(KeyValueTreeObjectBuilder            builder,
68                                                  const ExponentialMovingAverageState& state);
69 
70 //! Sets the expoential moving average state from a key-value-tree object
71 ExponentialMovingAverageState exponentialMovingAverageStateFromKeyValueTree(const KeyValueTreeObject& object);
72 
73 /*! \libinternal
74  * \brief Evaluate the exponential moving average with bias correction.
75  *
76  * The exponential moving average at the 0th data point \f$Y_0\f$ is
77  * \f$ S_0 = Y_0 \f$ and at the n-th data point \f$Y_n\f$ with n>0 it is
78  * \f$ S_n = \alpha Y_n + (1-\alpha) S_{n-1} \f$, where the smoothing factor
79  * \f$\alpha=1/t\f$ is determined via a time constant \f$t\f$.
80  *
81  * To avoid large impact of the first data point in a "burn-in" phase, the
82  * weight of points are unbiased by substituting for \f$S_{n-1}\f$ above,
83  * \f$\hat{S}_{n-1} = S_{n-1} / (1-\alpha^{n})\f$.
84  */
85 class ExponentialMovingAverage
86 {
87 public:
88     /*! \brief Construct by setting the time constant and state.
89      * Allows reinitiating with data from memory.
90      * \param[in] timeConstant time in number of data points
91      * \param[in] state of the exponential moving average
92      * \throws InconsistentInputError if timeConstant < 1
93      */
94     ExponentialMovingAverage(real timeConstant, const ExponentialMovingAverageState& state = {});
95 
96     //! Update the moving average with a data point
97     void updateWithDataPoint(real dataPoint);
98 
99     //! The exponential weighted average with bias correction
100     real biasCorrectedAverage() const;
101 
102     //! Returns true if last added data point increased the average
103     bool increasing() const;
104 
105     //! Return the current state of the exponential moving average
106     const ExponentialMovingAverageState& state() const;
107 
108     //! The inverse time constant for the exponential moving average
109     real inverseTimeConstant() const;
110 
111 private:
112     //! The current state of the exponential moving average
113     ExponentialMovingAverageState state_;
114 
115     //! The inverse time constant for the exponential moving average
116     real inverseTimeConstant_;
117 };
118 
119 } // namespace gmx
120 
121 #endif
122