[WIP] Added Artist/ReleaseInfo

src/similarity/som/Network.hpp

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+/*
+ * 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 <vector>
+#include <ostream>
+#include <functional>
+
+#include "Matrix.hpp"
+
+#include "utils/Exception.hpp"
+
+namespace SOM
+{
+
+using InputVector = std::vector<double>;
+void checkSameDimensions(const InputVector& a, const InputVector& b);
+void checkSameDimensions(const InputVector& a, std::size_t inputDimCount);
+std::ostream& operator<<(std::ostream& os, const InputVector& a);
+
+class SOMException : public LmsException
+{
+	public:
+		SOMException(const std::string& msg) : LmsException(msg) {}
+};
+
+
+class Network
+{
+	public:
+
+		// Init a network with random values
+		Network(std::size_t width, std::size_t height, std::size_t inputDimCount);
+
+		// Init a network with serialized values
+		Network(const std::string& data);
+
+		std::size_t getWidth() const { return _refVectors.getWidth(); }
+		std::size_t getHeight() const { return _refVectors.getHeight(); }
+		std::size_t getInputDimCount() const {return _inputDimCount;}
+		// Set weight for each dimension (default is 1 for each weight)
+		void setDataWeights(const InputVector& weights);
+
+		// data must be normalized
+		void train(const std::vector<InputVector>& dataSamples, std::size_t nbIterations);
+
+		// data must be normalized
+		Coords classify(const InputVector& data) const;
+
+		// ordered from closest to farthest
+		std::vector<Coords> classify(const InputVector& data, std::size_t size) const;
+
+		void dump(std::ostream& os) const;
+
+		// For each ref vector, update formula is:
+		// i is the current iteration
+		// refVector(i+1) = refVector(i) + LearningFactor(i) * NeighborhoodFunc(i) * (MatchingRefVector - refVector)
+
+		using DistanceFunc = std::function<InputVector::value_type(const InputVector& /* a */, const InputVector& /* b */, const InputVector& /* weights */)>;
+		void setDistanceFunc(DistanceFunc distanceFunc);
+
+		struct Progress
+		{
+			std::size_t idIteration;
+			std::size_t iterationCount;
+		};
+
+		using LearningFactorFunc = std::function<InputVector::value_type(Progress)>;
+		void setLearningFactorFunc(LearningFactorFunc learningFactorFunc);
+
+		using NeighborhoodFunc = std::function<InputVector::value_type(InputVector::value_type /* norm(Coords - CoordMatchingRefVector) */, Progress)>;
+		void setNeighborhoodFunc(NeighborhoodFunc neighborhoodFunc);
+
+		std::string serializeTo() const;
+
+	private:
+
+		void serializeFrom(const std::string& data);
+
+		Coords getClosestRefVector(const InputVector& data) const;
+
+		void updateRefVectors(Coords closestRefVectorCoords, const InputVector& input, Progress progress);
+
+		std::size_t _inputDimCount;
+		InputVector _weights;	// weight for each dimension
+		Matrix<InputVector> _refVectors;
+
+		DistanceFunc _distanceFunc;
+		LearningFactorFunc _learningFactorFunc;
+		NeighborhoodFunc _neighborhoodFunc;
+};
+
+} // namespace SOM