Adding a clusterer on top of the network

2018-12-10 13:02:44 +01:00
parent 63abdc782c
commit de712936eb
2 changed files with 165 additions and 53 deletions
@@ -0,0 +1,149 @@
+/*
+ * 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/>.
+ */
+
+#pragma once
+
+
+#include "SOM.hpp"
+#include "DataNormalizer.hpp"
+
+/*
+ * For each InputVector, associate vector<T> values
+ */
+template<typename T>
+class Clusterer
+{
+	public:
+		using SampleType = std::pair<SOM::InputVector /* key */, T /* value*/ >;
+		Clusterer(const std::vector<SampleType>& samples, std::size_t inputDimCount, std::size_t iterationCount);
+
+		const std::vector<T>& getClusterValues(const SOM::InputVector& data) const;
+
+		void dump(std::ostream& os) const;
+
+	private:
+
+		void train(const std::vector<std::pair<SOM::InputVector, T>>& samples, std::size_t iterationCount);
+
+		std::vector<T>& getValues(SOM::Coords coords);
+		const std::vector<T>& getValues(SOM::Coords coords) const;
+
+		std::size_t _width;
+		std::size_t _height;
+		std::vector<std::vector<T>> _values; // Map of T vectors
+		SOM::DataNormalizer _dataNormalizer;
+		SOM::Network _network;
+};
+
+
+template<typename T>
+Clusterer<T>::Clusterer(const std::vector<SampleType>& samples, std::size_t inputDimCount, std::size_t iterationCount)
+:
+_width(3),
+_height(3),
+_dataNormalizer(inputDimCount),
+_network(_width, _height, inputDimCount)
+{
+	_values.resize(_width * _height);
+	train(samples, iterationCount);
+}
+
+template<typename T>
+std::vector<T>&
+Clusterer<T>::getValues(SOM::Coords coords)
+{
+	return _values[ coords.x + coords.y*_width ];
+}
+
+template<typename T>
+const std::vector<T>&
+Clusterer<T>::getValues(SOM::Coords coords) const
+{
+	return _values[ coords.x + coords.y*_width ];
+}
+
+
+template<typename T>
+void
+Clusterer<T>::train(const std::vector<std::pair<SOM::InputVector, T>>& samples, std::size_t iterationCount)
+{
+	// Train
+	{
+		std::vector<SOM::InputVector> inputVectors;
+		inputVectors.reserve(samples.size());
+
+		for (const auto& sample : samples)
+		{
+			inputVectors.push_back(sample.first);
+		}
+
+		_dataNormalizer.computeNormalizationFactors(inputVectors);
+
+		for (auto& inputVector : inputVectors)
+			_dataNormalizer.normalizeData(inputVector);
+
+		_network.train(inputVectors, iterationCount);
+	}
+
+	// Classify data
+	for (const auto& sample : samples)
+	{
+		auto inputVector = sample.first;
+		const auto& value = sample.second;
+
+		_dataNormalizer.normalizeData(inputVector);
+		auto coords = _network.classify(inputVector);
+		auto& values = getValues(coords);
+
+		values.push_back(value);
+	}
+}
+
+template<typename T>
+const std::vector<T>&
+Clusterer<T>::getClusterValues(const SOM::InputVector& inputVector) const
+{
+	auto inputVectorNormalized = inputVector;
+	_dataNormalizer.normalizeData(inputVectorNormalized);
+
+	return getValues(_network.classify(inputVectorNormalized));
+}
+
+template<typename T>
+void
+Clusterer<T>::dump(std::ostream& os) const
+{
+	os << "Internal network:" << std::endl;
+	_network.dump(os);
+	os << "Values: " << std::endl;
+	for (std::size_t x = 0; x < _width; ++x)
+	{
+		for (std::size_t y = 0; y < _height; ++y)
+		{
+			os << "[";
+			for (const auto& value : getValues({x, y}))
+				os << value << " ";
+			os << "] ";
+		}
+		os << std::endl;
+	}
+
+}
+
+