WelfordMean.hpp
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/****************************************************************************
*
* Copyright (c) 2021 PX4 Development Team. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in
* the documentation and/or other materials provided with the
* distribution.
* 3. Neither the name PX4 nor the names of its contributors may be
* used to endorse or promote products derived from this software
* without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
* OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED
* AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*
****************************************************************************/
/**
* @file WelfordMean.hpp
*
* Welford's online algorithm for computing mean and variance.
*/
#pragma once
namespace math
{
template<typename T>
class WelfordMean
{
public:
// For a new value, compute the new count, new mean, the new M2.
void update(const T &new_value)
{
if (_count == 0) {
_mean = new_value;
}
_count++;
// mean accumulates the mean of the entire dataset
const T delta{new_value - _mean};
_mean += delta / _count;
// M2 aggregates the squared distance from the mean
// count aggregates the number of samples seen so far
_M2 += delta.emult(new_value - _mean);
}
bool valid() const { return _count > 2; }
unsigned count() const { return _count; }
void reset()
{
_count = 0;
_mean = {};
_M2 = {};
}
// Retrieve the mean, variance and sample variance
T mean() const { return _mean; }
T variance() const { return _M2 / _count; }
T sample_variance() const { return _M2 / (_count - 1); }
private:
T _mean{};
T _M2{};
unsigned _count{0};
};
} // namespace math