109 lines
2.6 KiB
C++
109 lines
2.6 KiB
C++
/*
|
|
* Copyright (C) 2018 Emeric Poupon
|
|
*
|
|
* This file is part of LMS.
|
|
*
|
|
* LMS 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 3 of the License, or
|
|
* (at your option) any later version.
|
|
*
|
|
* LMS 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 LMS. If not, see <http://www.gnu.org/licenses/>.
|
|
*/
|
|
|
|
#include "DataNormalizer.hpp"
|
|
|
|
#include <algorithm>
|
|
#include <numeric>
|
|
#include <sstream>
|
|
|
|
namespace SOM
|
|
{
|
|
|
|
template<typename T>
|
|
static
|
|
T
|
|
variance(const std::vector<T>& vec)
|
|
{
|
|
std::size_t size = vec.size();
|
|
|
|
if (size == 1)
|
|
return T{0.};
|
|
|
|
T mean = std::accumulate(vec.begin(), vec.end(), T{0.}) / size;
|
|
|
|
return std::accumulate(vec.begin(), vec.end(), T{0.},
|
|
[mean, size] (T accumulator, const T& val)
|
|
{
|
|
return accumulator + ((val - mean) * (val - mean) / (size - 1));
|
|
});
|
|
}
|
|
|
|
DataNormalizer::DataNormalizer(std::size_t inputDimCount)
|
|
: _inputDimCount(inputDimCount)
|
|
{
|
|
}
|
|
|
|
void
|
|
DataNormalizer::computeNormalizationFactors(const std::vector<InputVector>& inputVectors)
|
|
{
|
|
if (inputVectors.empty())
|
|
throw SOMException("Empty input vectors");
|
|
|
|
// For each dimension of the input, compute the min/max
|
|
_minmax.clear();
|
|
_minmax.resize(_inputDimCount);
|
|
|
|
for (std::size_t dimId = 0; dimId < _inputDimCount; ++dimId)
|
|
{
|
|
std::vector<InputVector::value_type> values;
|
|
|
|
for (const auto& inputVector: inputVectors)
|
|
{
|
|
checkSameDimensions(inputVector, _inputDimCount);
|
|
values.push_back(inputVector[dimId]);
|
|
}
|
|
|
|
auto result = std::minmax_element(values.begin(), values.end());
|
|
_minmax[dimId] = {*result.first, *result.second};
|
|
}
|
|
}
|
|
|
|
InputVector::value_type
|
|
DataNormalizer::normalizeValue(InputVector::value_type value, std::size_t dimId) const
|
|
{
|
|
// clamp
|
|
if (value > _minmax[dimId].max)
|
|
value = _minmax[dimId].max;
|
|
else if (value < _minmax[dimId].min)
|
|
value = _minmax[dimId].min;
|
|
|
|
return (value - _minmax[dimId].min) / (_minmax[dimId].max - _minmax[dimId].min);
|
|
}
|
|
|
|
void
|
|
DataNormalizer::normalizeData(InputVector& a) const
|
|
{
|
|
checkSameDimensions(a, _inputDimCount);
|
|
|
|
for (std::size_t dimId = 0; dimId < _inputDimCount; ++dimId)
|
|
{
|
|
a[dimId] = normalizeValue(a[dimId], dimId);
|
|
}
|
|
}
|
|
|
|
void
|
|
DataNormalizer::dump(std::ostream& os) const
|
|
{
|
|
for (std::size_t i = 0; i < _inputDimCount; ++i)
|
|
os << "(" << _minmax[i].min << ", " << _minmax[i].max << ")";
|
|
}
|
|
|
|
} // namespace SOM
|