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#include "correlation-calibrator.hpp"
#include "variance.hpp"
#include "compat/math.hpp"
#include "compat/meta.hpp"
#include <cmath>
#include <iterator>
#include <QDebug>
#define DEBUG_PRINT
#ifdef DEBUG_PRINT
# include <cstdio>
# include <cwchar>
using std::fwprintf;
using std::fflush;
#endif
using namespace correlation_calibrator_impl;
correlation_calibrator::correlation_calibrator()
{
}
static constexpr unsigned nbuckets[6] =
{
x_nbuckets,
y_nbuckets,
z_nbuckets,
yaw_nbuckets,
pitch_nbuckets,
roll_nbuckets,
};
static constexpr double spacing[6] =
{
translation_spacing,
translation_spacing,
translation_spacing,
yaw_spacing_in_degrees,
pitch_spacing_in_degrees,
roll_spacing_in_degrees,
};
static constexpr wchar_t const* const names[6] {
L"x", L"y", L"z",
L"yaw", L"pitch", L"roll",
};
bool correlation_calibrator::check_buckets(const vec6& data)
{
bool ret = false;
unsigned pos[6];
for (unsigned k = 0; k < 6; k++)
{
const double val = clamp(data[k], min[k], max[k]);
pos[k] = (val-min[k])/spacing[k];
if (pos[k] >= nbuckets[k])
{
eval_once(qDebug() << "idx" << k
<< "bucket" << (int)pos[k]
<< "outside bounds" << nbuckets[k]);
return false;
}
if (!buckets[k][pos[k]])
{
ret = true;
buckets[k][pos[k]] = true;
}
}
return ret;
}
void correlation_calibrator::input(const vec6& data_)
{
if (!check_buckets(data_))
return;
data.push_back(data_);
}
mat66 correlation_calibrator::get_coefficients() const
{
if (data.size() < min_samples)
{
qDebug() << "correlation-calibrator: not enough data";
mat66 ret;
for (unsigned k = 0; k < 6; k++)
ret(k, k) = 1;
return ret;
}
variance vs[6];
vec6 devs, means;
for (const vec6& x : data)
for (unsigned i = 0; i < 6; i++)
vs[i].input(x(i));
for (unsigned i = 0; i < 6; i++)
{
means(i) = vs[i].avg();
devs(i) = vs[i].stddev();
constexpr double EPS = 1e-4;
if (devs(i) < EPS)
devs(i) = EPS;
}
mat66 cs;
for (const vec6& x : data)
for (unsigned k = 0; k < 6; k++)
{
for (unsigned idx = 0; idx < 6; idx++)
{
const double zi = (x(idx) - means(idx)),
zk = (x(k) - means(k));
cs(idx, k) += zi * zk / (devs(k)*devs(k));
}
}
cs = cs * (1./(data.size() - 1));
#if defined DEBUG_PRINT
fwprintf(stderr, L"v:change-of h:due-to\n");
fwprintf(stderr, L"%10s ", L"");
for (wchar_t const* k : names)
fwprintf(stderr, L"%10s", k);
fwprintf(stderr, L"\n");
for (unsigned i = 0; i < 6; i++)
{
fwprintf(stderr, L"%10s ", names[i]);
for (unsigned k = 0; k < 6; k++)
fwprintf(stderr, L"%10.3f", cs(i, k));
fwprintf(stderr, L"\n");
}
fflush(stderr);
#endif
for (unsigned k = 0; k < 6; k++)
cs(k, k) = 1;
// derivations from
// https://www.thoughtco.com/how-to-calculate-the-correlation-coefficient-3126228
return cs;
}
unsigned correlation_calibrator::sample_count() const
{
return data.size();
}
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