603 lines
16 KiB
C++
603 lines
16 KiB
C++
/*
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* Copyright (C) 2018 Emeric Poupon
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*
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* This file is part of LMS.
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*
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* LMS is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* LMS is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with LMS. If not, see <http://www.gnu.org/licenses/>.
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*/
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#include "SimilarityFeaturesSearcher.hpp"
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#include <random>
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#include <boost/property_tree/ptree.hpp>
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#include <boost/property_tree/xml_parser.hpp>
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#include "database/Artist.hpp"
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#include "database/SimilaritySettings.hpp"
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#include "database/Release.hpp"
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#include "database/Track.hpp"
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#include "database/TrackFeatures.hpp"
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#include "som/DataNormalizer.hpp"
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#include "utils/Config.hpp"
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#include "utils/Logger.hpp"
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#include "utils/Utils.hpp"
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namespace Similarity {
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static
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boost::filesystem::path getCacheDirectory()
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{
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return Config::instance().getPath("working-dir") / "cache" / "features";
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}
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static boost::filesystem::path getCacheNetworkFilePath()
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{
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return getCacheDirectory() / "network";
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};
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static boost::filesystem::path getCacheTrackPositionsFilePath()
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{
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return getCacheDirectory() / "track_positions";
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}
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static
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bool
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networkToCacheFile(const SOM::Network& network, boost::filesystem::path path)
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{
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try
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{
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boost::property_tree::ptree root;
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root.put("width", network.getWidth());
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root.put("height", network.getHeight());
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root.put("dim_count", network.getInputDimCount());
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for (auto weight : network.getDataWeights())
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root.add("weights.weight", weight);
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for (SOM::Coordinate x = 0; x < network.getWidth(); ++x)
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{
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for (SOM::Coordinate y = 0; y < network.getWidth(); ++y)
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{
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const auto& refVector = network.getRefVector({x, y});
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boost::property_tree::ptree node;
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for (auto value : refVector)
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node.add("values.value", value);
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node.put("coord_x", x);
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node.put("coord_y", y);
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root.add_child("ref_vectors.ref_vector", node);
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}
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}
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boost::property_tree::write_xml(path.string(), root);
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LMS_LOG(SIMILARITY, DEBUG) << "Created network cache";
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return true;
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}
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catch (boost::property_tree::ptree_error& error)
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{
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LMS_LOG(SIMILARITY, ERROR) << "Cannot create network cache: " << error.what();
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return false;
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}
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}
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static
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boost::optional<SOM::Network>
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createNetworkFromCacheFile(boost::filesystem::path path)
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{
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try
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{
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boost::property_tree::ptree root;
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boost::property_tree::read_xml(path.string(), root);
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auto width = root.get<double>("width");
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auto height = root.get<double>("height");
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auto dimCount = root.get<std::size_t>("dim_count");
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SOM::Network res(width, height, dimCount);
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SOM::InputVector weights;
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for (const auto& val : root.get_child("weights"))
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weights.push_back(val.second.get_value<double>());
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res.setDataWeights(weights);
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for (const auto& node : root.get_child("ref_vectors"))
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{
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auto x = node.second.get<SOM::Coordinate>("coord_x");
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auto y = node.second.get<SOM::Coordinate>("coord_y");
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std::vector<double> values;
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for (const auto& val : node.second.get_child("values"))
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values.push_back(val.second.get_value<double>());
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res.setRefVector({x, y}, values);
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}
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LMS_LOG(SIMILARITY, DEBUG) << "Successfully read network from cache";
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return res;
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}
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catch (boost::property_tree::ptree_error& error)
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{
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LMS_LOG(SIMILARITY, ERROR) << "Cannot read network cache: " << error.what();
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return boost::none;
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}
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}
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static
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bool
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objectPositionToCacheFile(const std::map<Database::IdType, std::set<SOM::Position>>& objectsPosition, boost::filesystem::path path)
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{
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try
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{
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boost::property_tree::ptree root;
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for (const auto& objectPosition : objectsPosition)
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{
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boost::property_tree::ptree node;
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node.put("id", objectPosition.first);
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for (const auto& position : objectPosition.second)
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{
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boost::property_tree::ptree positionNode;
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positionNode.put("x", position.x);
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positionNode.put("y", position.y);
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node.add_child("position.position", positionNode);
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}
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root.add_child("objects.object", node);
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}
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boost::property_tree::write_xml(path.string(), root);
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return true;
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}
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catch (boost::property_tree::ptree_error& error)
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{
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LMS_LOG(SIMILARITY, ERROR) << "Cannot cache object position: " << error.what();
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return false;
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}
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}
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static
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boost::optional<std::map<Database::IdType, std::set<SOM::Position>>>
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createObjectPositionsFromCacheFile(boost::filesystem::path path)
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{
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try
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{
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boost::property_tree::ptree root;
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boost::property_tree::read_xml(path.string(), root);
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std::map<Database::IdType, std::set<SOM::Position>> res;
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for (const auto& object : root.get_child("objects"))
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{
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auto id = object.second.get<Database::IdType>("id");
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for (const auto& position : object.second.get_child("position"))
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{
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auto x = position.second.get<SOM::Coordinate>("x");
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auto y = position.second.get<SOM::Coordinate>("y");
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res[id].insert({x, y});
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}
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}
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LMS_LOG(SIMILARITY, DEBUG) << "Successfully read object position from cache";
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return res;
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}
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catch (boost::property_tree::ptree_error& error)
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{
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LMS_LOG(SIMILARITY, ERROR) << "Cannot create object position from cache file: " << error.what();
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return boost::none;
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}
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}
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bool
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FeaturesSearcher::init(Wt::Dbo::Session& session, bool& stopRequested)
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{
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Wt::Dbo::Transaction transaction(session);
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auto settings = Database::SimilaritySettings::get(session);
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struct FeatureInfo
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{
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std::size_t nbDimensions;
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double weight;
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};
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std::map<std::string, FeatureInfo> featuresInfo;
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std::size_t nbDimensions = 0;
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for (auto feature : settings->getFeatures())
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{
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featuresInfo[feature->getName()] = { feature->getNbDimensions(), feature->getWeight() };
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nbDimensions += feature->getNbDimensions();
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}
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LMS_LOG(SIMILARITY, DEBUG) << "Getting Tracks with features...";
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auto tracks = Database::Track::getAllWithFeatures(session);
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LMS_LOG(SIMILARITY, DEBUG) << "Getting Tracks with features DONE";
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std::vector<SOM::InputVector> samples;
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std::vector<Database::IdType> tracksIds;
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LMS_LOG(SIMILARITY, DEBUG) << "Extracting features...";
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for (auto track : tracks)
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{
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if (stopRequested)
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return false;
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SOM::InputVector sample;
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std::map<std::string, std::vector<double>> features;
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for (const auto& featureInfo : featuresInfo)
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features[featureInfo.first] = {};
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if (!track->getTrackFeatures()->getFeatures(features))
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continue;
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// Check dimensions for each feature
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bool ok = true;
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for (const auto& feature : features)
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{
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auto it = featuresInfo.find(feature.first);
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if (it == featuresInfo.end() || it->second.nbDimensions != feature.second.size())
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{
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LMS_LOG(SIMILARITY, WARNING) << "Dimension mismatch for feature '" << feature.first << "'. Expected " << it->second.nbDimensions << ", got " << feature.second.size();
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ok = false;
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break;
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}
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sample.insert( sample.end(), feature.second.begin(), feature.second.end() );
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}
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if (!ok)
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continue;
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samples.emplace_back(std::move(sample));
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tracksIds.emplace_back(track.id());
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}
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LMS_LOG(SIMILARITY, DEBUG) << "Extracting features DONE";
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transaction.commit();
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if (tracksIds.empty())
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{
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LMS_LOG(SIMILARITY, INFO) << "Nothing to classify!";
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return false;
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}
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LMS_LOG(SIMILARITY, DEBUG) << "Normalizing data...";
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SOM::DataNormalizer dataNormalizer(nbDimensions);
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dataNormalizer.computeNormalizationFactors(samples);
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for (auto& sample : samples)
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dataNormalizer.normalizeData(sample);
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std::size_t size = std::sqrt(samples.size()/2);
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LMS_LOG(SIMILARITY, INFO) << "Found " << samples.size() << " tracks, constructing a " << size << "*" << size << " network";
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std::vector<double> weights;
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for (const auto& featureInfo : featuresInfo)
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{
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for (std::size_t i = 0; i < featureInfo.second.nbDimensions; ++i)
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weights.push_back(1. / featureInfo.second.nbDimensions * featureInfo.second.weight);
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}
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SOM::Network network(size, size, nbDimensions);
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network.setDataWeights(weights);
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auto progressIndicator{[](const auto& iter)
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{
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LMS_LOG(SIMILARITY, DEBUG) << "Current pass = " << iter.idIteration << " / " << iter.iterationCount;
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}};
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auto stopper{[&]() { return stopRequested; }};
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LMS_LOG(SIMILARITY, DEBUG) << "Training network...";
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network.train(samples, 1, progressIndicator, stopper);
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LMS_LOG(SIMILARITY, DEBUG) << "Training network DONE";
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if (stopRequested)
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return false;
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LMS_LOG(SIMILARITY, DEBUG) << "Classifying tracks...";
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std::map<Database::IdType, std::set<SOM::Position>> trackPosition;
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for (std::size_t i = 0; i < samples.size(); ++i)
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{
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if (stopRequested)
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return false;
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Wt::Dbo::Transaction transaction(session);
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const auto& sample = samples[i];
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auto trackId = tracksIds[i];
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auto position = network.getClosestRefVectorPosition(sample);
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trackPosition[trackId].insert(position);
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}
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LMS_LOG(SIMILARITY, DEBUG) << "Classifying tracks DONE";
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init(session, std::move(network), std::move(trackPosition));
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saveToCache();
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return true;
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}
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bool
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FeaturesSearcher::initFromCache(Wt::Dbo::Session& session)
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{
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auto network{createNetworkFromCacheFile(getCacheNetworkFilePath())};
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if (!network)
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{
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clearCache();
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return false;
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}
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auto trackPositions{createObjectPositionsFromCacheFile(getCacheTrackPositionsFilePath())};
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if (!trackPositions)
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{
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clearCache();
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return false;
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}
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init(session, std::move(*network), std::move(*trackPositions));
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LMS_LOG(SIMILARITY, DEBUG) << "Init from cache OK";
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return true;
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}
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void
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FeaturesSearcher::invalidateCache()
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{
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boost::filesystem::remove(getCacheNetworkFilePath());
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boost::filesystem::remove(getCacheTrackPositionsFilePath());
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}
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std::vector<Database::IdType>
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FeaturesSearcher::getSimilarTracks(const std::set<Database::IdType>& tracksIds, std::size_t maxCount) const
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{
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return getSimilarObjects(tracksIds, _tracksMap, _trackPosition, maxCount);
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}
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std::vector<Database::IdType>
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FeaturesSearcher::getSimilarReleases(Database::IdType releaseId, std::size_t maxCount) const
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{
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return getSimilarObjects({releaseId}, _releasesMap, _releasePosition, maxCount);
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}
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std::vector<Database::IdType>
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FeaturesSearcher::getSimilarArtists(Database::IdType artistId, std::size_t maxCount) const
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{
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return getSimilarObjects({artistId}, _artistsMap, _artistPosition, maxCount);
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}
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void
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FeaturesSearcher::dump(Wt::Dbo::Session& session, std::ostream& os) const
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{
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os << "Number of tracks classified: " << _trackPosition.size() << std::endl;
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os << "Network size: " << _network.getWidth() << " * " << _network.getHeight() << std::endl;
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os << "Ref vectors median distance = " << _networkRefVectorsDistanceMedian << std::endl;
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Wt::Dbo::Transaction transaction(session);
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for (SOM::Coordinate y = 0; y < _network.getHeight(); ++y)
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{
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for (SOM::Coordinate x = 0; x < _network.getWidth(); ++x)
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{
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const auto& trackIds = _tracksMap[{x, y}];
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os << "{" << x << ", " << y << "}";
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if (y > 0)
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os << " - {" << x << ", " << y - 1 << "}: " << _network.getRefVectorsDistance({x, y}, {x, y - 1});
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if (x > 0)
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os << " - {" << x - 1 << ", " << y << "}: " << _network.getRefVectorsDistance({x, y}, {x - 1, y});
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if (y != _network.getHeight() - 1)
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os << " - {" << x << ", " << y + 1 << "}: " << _network.getRefVectorsDistance({x, y}, {x, y + 1});
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if (x != _network.getWidth() - 1)
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os << " - {" << x + 1 << ", " << y << "}: " << _network.getRefVectorsDistance({x, y}, {x + 1, y});
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os << std::endl;
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for (auto trackId : trackIds)
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{
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auto track = Database::Track::getById(session, trackId);
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if (!track)
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continue;
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os << "\t - " << track->getName() << " - ";
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if (track->getArtist())
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os << track->getArtist()->getName() << " - ";
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if (track->getRelease())
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os << track->getRelease()->getName();
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os << std::endl;
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}
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}
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os << std::endl;
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}
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}
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void
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FeaturesSearcher::init(Wt::Dbo::Session& session,
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SOM::Network network,
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std::map<Database::IdType, std::set<SOM::Position>> tracksPosition)
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{
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_network = std::move(network);
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_networkRefVectorsDistanceMedian = _network.computeRefVectorsDistanceMedian();
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LMS_LOG(SIMILARITY, DEBUG) << "Median distance betweend ref vectors = " << _networkRefVectorsDistanceMedian;
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auto width = _network.getWidth();
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auto height = _network.getHeight();
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_artistsMap = SOM::Matrix<std::set<Database::IdType>>(width, height);
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_releasesMap = SOM::Matrix<std::set<Database::IdType>>(width, height);
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_tracksMap = SOM::Matrix<std::set<Database::IdType>>(width, height);
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Wt::Dbo::Transaction transaction(session);
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for (auto itTrackCoord : tracksPosition)
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{
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auto trackId = itTrackCoord.first;
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const auto& positionSet = itTrackCoord.second;
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auto track = Database::Track::getById(session, trackId);
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if (!track)
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continue;
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for (const auto& position : positionSet)
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{
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_tracksMap[position].insert(trackId);
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_trackPosition[trackId].insert(position);
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if (track->getRelease())
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{
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_releasePosition[track->getRelease().id()].insert(position);
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_releasesMap[position].insert(track->getRelease().id());
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}
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if (track->getArtist())
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{
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_artistPosition[track->getArtist().id()].insert(position);
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_artistsMap[position].insert(track->getArtist().id());
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}
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}
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}
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LMS_LOG(SIMILARITY, DEBUG) << "Classifying tracks DONE";
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}
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void
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FeaturesSearcher::saveToCache() const
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{
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if (!networkToCacheFile(_network, getCacheNetworkFilePath())
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|| !objectPositionToCacheFile(_trackPosition, getCacheTrackPositionsFilePath()))
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{
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LMS_LOG(SIMILARITY, ERROR) << "Failed cache data";
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clearCache();
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}
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}
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void
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FeaturesSearcher::clearCache() const
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{
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for (boost::filesystem::directory_iterator itEnd, it(getCacheDirectory()); it != itEnd; ++it)
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boost::filesystem::remove_all(it->path());
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}
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static
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std::set<SOM::Position>
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getMatchingRefVectorsPosition(const std::set<Database::IdType>& ids, const std::map<Database::IdType, std::set<SOM::Position>>& objectPosition)
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{
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std::set<SOM::Position> res;
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if (ids.empty())
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return res;
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for (auto id : ids)
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{
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auto it = objectPosition.find(id);
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if (it == objectPosition.end())
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continue;
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for (const auto& position : it->second)
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res.insert(position);
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}
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return res;
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}
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static
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std::set<Database::IdType>
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getObjectsIds(const std::set<SOM::Position>& positionSet, const SOM::Matrix<std::set<Database::IdType>>& objectsMap )
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|
{
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std::set<Database::IdType> res;
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|
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for (const auto& position : positionSet)
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{
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for (auto id : objectsMap.get(position))
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|
res.insert(id);
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}
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|
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return res;
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}
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|
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|
std::vector<Database::IdType>
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|
FeaturesSearcher::getSimilarObjects(const std::set<Database::IdType>& ids,
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const SOM::Matrix<std::set<Database::IdType>>& objectsMap,
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const std::map<Database::IdType, std::set<SOM::Position>>& objectPosition,
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|
std::size_t maxCount) const
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|
{
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|
std::vector<Database::IdType> res;
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|
|
|
auto now = std::chrono::system_clock::now();
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|
std::mt19937 randGenerator(std::chrono::duration_cast<std::chrono::milliseconds>(now.time_since_epoch()).count());
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|
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std::set<SOM::Position> searchedRefVectorsPosition = getMatchingRefVectorsPosition(ids, objectPosition);
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|
if (searchedRefVectorsPosition.empty())
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return res;
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|
|
|
while (1)
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|
{
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|
std::set<Database::IdType> closestObjectIds = getObjectsIds(searchedRefVectorsPosition, objectsMap);
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|
|
|
// Remove objects that are already in input or already reported
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|
for (auto id : ids)
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|
closestObjectIds.erase(id);
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|
|
|
for (auto id : res)
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|
closestObjectIds.erase(id);
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|
|
|
{
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|
std::vector<Database::IdType> objectIdsToAdd(closestObjectIds.begin(), closestObjectIds.end());
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|
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|
std::shuffle(objectIdsToAdd.begin(), objectIdsToAdd.end(), randGenerator);
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|
std::copy(objectIdsToAdd.begin(), objectIdsToAdd.end(), std::back_inserter(res));
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|
}
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|
|
|
if (res.size() > maxCount)
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|
res.resize(maxCount);
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|
|
|
if (res.size() == maxCount)
|
|
break;
|
|
|
|
// If there is not enough objects, try again with closest neighbour until there is too much distance
|
|
auto closestRefVectorPosition = _network.getClosestRefVectorPosition(searchedRefVectorsPosition, _networkRefVectorsDistanceMedian * 0.75);
|
|
if (!closestRefVectorPosition)
|
|
break;
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|
|
|
searchedRefVectorsPosition.insert(*closestRefVectorPosition);
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|
}
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|
|
|
return res;
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|
}
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|
|
|
|
|
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} // ns Similarity
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