mirror of
https://github.com/RobTillaart/Arduino.git
synced 2024-10-03 18:09:02 -04:00
147 lines
4.4 KiB
C++
147 lines
4.4 KiB
C++
//
|
||
// FILE: Statistic.cpp
|
||
// AUTHOR: Rob dot Tillaart at gmail dot com
|
||
// modified at 0.3 by Gil Ross at physics dot org
|
||
// VERSION: 0.4.3
|
||
// PURPOSE: Recursive statistical library for Arduino
|
||
//
|
||
// NOTE: 2011-01-07 Gill Ross
|
||
// Rob Tillaart's Statistic library uses one-pass of the data (allowing
|
||
// each value to be discarded), but expands the Sum of Squares Differences to
|
||
// difference the Sum of Squares and the Average Squared. This is susceptible
|
||
// to bit length precision errors with the float type (only 5 or 6 digits
|
||
// absolute precision) so for long runs and high ratios of
|
||
// the average value to standard deviation the estimate of the
|
||
// standard error (deviation) becomes the difference of two large
|
||
// numbers and will tend to zero.
|
||
//
|
||
// For small numbers of iterations and small Average/SE th original code is
|
||
// likely to work fine.
|
||
// It should also be recognised that for very large samples, questions
|
||
// of stability of the sample assume greater importance than the
|
||
// correctness of the asymptotic estimators.
|
||
//
|
||
// This recursive algorithm, which takes slightly more computation per
|
||
// iteration is numerically stable.
|
||
// It updates the number, mean, max, min and SumOfSquaresDiff each step to
|
||
// deliver max min average, population standard error (standard deviation) and
|
||
// unbiassed SE.
|
||
// -------------
|
||
//
|
||
// HISTORY:
|
||
// 0.1 2010-10-29 initial version
|
||
// 0.2 2010-10-29 stripped to minimal functionality
|
||
// 0.2.01 2010-10-30
|
||
// added minimim, maximum, unbiased stdev,
|
||
// changed counter to long -> int overflows @32K samples
|
||
// 0.3 2011-01-07
|
||
// branched from 0.2.01 version of Rob Tillaart's code
|
||
// 0.3.1 2012-11-10 minor edits
|
||
// 0.3.2 2012-11-10 minor edits
|
||
// changed count -> unsigned long allows for 2^32 samples
|
||
// added variance()
|
||
// 0.3.3 2015-03-07
|
||
// float -> double to support ARM (compiles)
|
||
// moved count() sum() min() max() to .h; for optimizing compiler
|
||
// 0.3.4 2017-07-31
|
||
// Refactored const in many places
|
||
// [reverted] double to float on request as float is 99.99% of the cases
|
||
// good enough and float(32 bit) is supported in HW for some processors.
|
||
// 0.3.5 2017-09-27
|
||
// Added #include <Arduino.h> to fix uint32_t bug
|
||
// 0.4.0 2020-05-13
|
||
// refactor
|
||
// Added flag to switch on the use of stdDev runtime. [idea marc.recksiedl]
|
||
// 0.4.1 2020-06-19 fix library.json
|
||
// 0.4.2 2021-01-08 add Arduino-CI + unit tests
|
||
// 0.4.3 2021-01-20 add() returns how much was actually added.
|
||
|
||
|
||
#include "Statistic.h"
|
||
|
||
|
||
Statistic::Statistic(bool useStdDev)
|
||
{
|
||
clear(useStdDev);
|
||
}
|
||
|
||
|
||
void Statistic::clear(bool useStdDev) // useStdDev default true.
|
||
{
|
||
_cnt = 0;
|
||
_sum = 0;
|
||
_min = 0;
|
||
_max = 0;
|
||
_useStdDev = useStdDev;
|
||
_ssqdif = 0.0;
|
||
// note not _ssq but sum of square differences
|
||
// which is SUM(from i = 1 to N) of f(i)-_ave_N)**2
|
||
}
|
||
|
||
|
||
// adds a new value to the data-set
|
||
float Statistic::add(const float value)
|
||
{
|
||
float previousSum = _sum;
|
||
if (_cnt == 0)
|
||
{
|
||
_min = value;
|
||
_max = value;
|
||
} else {
|
||
if (value < _min) _min = value;
|
||
else if (value > _max) _max = value;
|
||
}
|
||
_sum += value;
|
||
_cnt++;
|
||
|
||
if (_useStdDev && (_cnt > 1))
|
||
{
|
||
float _store = (_sum / _cnt - value);
|
||
_ssqdif = _ssqdif + _cnt * _store * _store / (_cnt - 1);
|
||
|
||
// ~10% faster but limits the amount of samples to 65K as _cnt*_cnt overflows
|
||
// float _store = _sum - _cnt * value;
|
||
// _ssqdif = _ssqdif + _store * _store / (_cnt*_cnt - _cnt);
|
||
//
|
||
// solution: TODO verify
|
||
// _ssqdif = _ssqdif + (_store * _store / _cnt) / (_cnt - 1);
|
||
}
|
||
return _sum - previousSum;
|
||
}
|
||
|
||
|
||
// returns the average of the data-set added sofar
|
||
float Statistic::average() const
|
||
{
|
||
if (_cnt == 0) return NAN; // prevent DIV0 error
|
||
return _sum / _cnt;
|
||
}
|
||
|
||
|
||
// Population standard deviation = s = sqrt [ S ( Xi - <20> )2 / N ]
|
||
// http://www.suite101.com/content/how-is-standard-deviation-used-a99084
|
||
float Statistic::variance() const
|
||
{
|
||
if (!_useStdDev) return NAN;
|
||
if (_cnt == 0) return NAN; // prevent DIV0 error
|
||
return _ssqdif / _cnt;
|
||
}
|
||
|
||
|
||
float Statistic::pop_stdev() const
|
||
{
|
||
if (!_useStdDev) return NAN;
|
||
if (_cnt == 0) return NAN; // prevent DIV0 error
|
||
return sqrt( _ssqdif / _cnt);
|
||
}
|
||
|
||
|
||
float Statistic::unbiased_stdev() const
|
||
{
|
||
if (!_useStdDev) return NAN;
|
||
if (_cnt < 2) return NAN; // prevent DIV0 error
|
||
return sqrt( _ssqdif / (_cnt - 1));
|
||
}
|
||
|
||
// -- END OF FILE --
|