Reorganized sources, better accuracy for similarities based on features

src/similarity/features/som/DataNormalizer.cpp

@@ -0,0 +1,108 @@
+/*
+ * 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