/usr/include/ableton/link/Kalman.hpp is in ableton-link-dev 1.0.0+dfsg-2.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 2 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*
* If you would like to incorporate Link into a proprietary software application,
* please contact <link-devs@ableton.com>.
*/
#pragma once
#include <array>
#include <cfloat>
#include <cmath>
#include <limits>
#if LINK_PLATFORM_WINDOWS
// Windows.h (or more specifically, minwindef.h) define the max(a, b) macro
// which conflicts with the symbol provided by std::numeric_limits.
#ifdef max
#undef max
#endif
#endif
namespace ableton
{
namespace link
{
template <std::size_t n>
struct Kalman
{
Kalman()
: mValue(0)
, mGain(0)
, mVVariance(1)
, mWVariance(1)
, mCoVariance(1)
, mVarianceLength(n)
, mCounter(mVarianceLength)
{
}
double getValue()
{
return mValue;
}
double calculateVVariance()
{
auto vVar = 0.;
auto meanOfDiffs = 0.;
for (size_t k = 0; k < (mVarianceLength); k++)
{
meanOfDiffs += (mMeasuredValues[k] - mFilterValues[k]);
}
meanOfDiffs /= (mVarianceLength);
for (size_t i = 0; i < (mVarianceLength); i++)
{
vVar += (pow(mMeasuredValues[i] - mFilterValues[i] - meanOfDiffs, 2.0));
}
vVar /= (mVarianceLength - 1);
return vVar;
}
double calculateWVariance()
{
auto wVar = 0.;
auto meanOfDiffs = 0.;
for (size_t k = 0; k < (mVarianceLength); k++)
{
meanOfDiffs += (mFilterValues[(mCounter - k - 1) % mVarianceLength]
- mFilterValues[(mCounter - k - 2) % mVarianceLength]);
}
meanOfDiffs /= (mVarianceLength);
for (size_t i = 0; i < (mVarianceLength); i++)
{
wVar += (pow(mFilterValues[(mCounter - i - 1) % mVarianceLength]
- mFilterValues[(mCounter - i - 2) % mVarianceLength] - meanOfDiffs,
2.0));
}
wVar /= (mVarianceLength - 1);
return wVar;
}
void iterate(const double value)
{
const std::size_t currentIndex = mCounter % mVarianceLength;
mMeasuredValues[currentIndex] = value;
if (mCounter < (mVarianceLength + mVarianceLength))
{
if (mCounter == mVarianceLength)
{
mValue = value;
}
else
{
mValue = (mValue + value) / 2;
}
}
else
{
// prediction equations
const double prevFilterValue = mFilterValues[(mCounter - 1) % mVarianceLength];
mFilterValues[currentIndex] = prevFilterValue;
mWVariance = calculateWVariance();
const double coVarianceEstimation = mCoVariance + mWVariance;
// update equations
mVVariance = calculateVVariance();
if ((coVarianceEstimation + mVVariance) != 0)
{
mGain = coVarianceEstimation / (coVarianceEstimation + mVVariance);
}
else
{
mGain = std::numeric_limits<double>::max();
}
mValue = prevFilterValue + mGain * (value - prevFilterValue);
mCoVariance = (1 - mGain) * coVarianceEstimation;
}
mFilterValues[currentIndex] = mValue;
++mCounter;
}
double mValue;
double mGain;
double mVVariance;
double mWVariance;
double mCoVariance;
size_t mVarianceLength;
size_t mCounter;
std::array<double, n> mFilterValues;
std::array<double, n> mMeasuredValues;
};
} // namespace link
} // namespace ableton
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