Breakable training, added cache
This commit is contained in:
emeric
@@ -20,6 +20,8 @@
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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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@@ -27,14 +29,191 @@
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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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FeaturesSearcher::FeaturesSearcher(Wt::Dbo::Session& session)
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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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@@ -64,6 +243,9 @@ FeaturesSearcher::FeaturesSearcher(Wt::Dbo::Session& session)
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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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@@ -101,24 +283,19 @@ FeaturesSearcher::FeaturesSearcher(Wt::Dbo::Session& session)
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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;
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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 normalizer(nbDimensions);
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SOM::DataNormalizer dataNormalizer(nbDimensions);
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normalizer.computeNormalizationFactors(samples);
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dataNormalizer.computeNormalizationFactors(samples);
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for (auto& sample : samples)
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normalizer.normalizeData(sample);
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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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_network = std::make_unique<SOM::Network>(size, size, nbDimensions);
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_artistsMap = SOM::Matrix<std::set<Database::IdType>>(size, size);
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_releasesMap = SOM::Matrix<std::set<Database::IdType>>(size, size);
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_tracksMap = SOM::Matrix<std::set<Database::IdType>>(size, size);
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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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@@ -126,113 +303,230 @@ FeaturesSearcher::FeaturesSearcher(Wt::Dbo::Session& session)
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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, 20);
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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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LMS_LOG(SIMILARITY, DEBUG) << "Classifying tracks...";
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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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auto coords = _network->getClosestRefVectorCoords(sample);
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_trackCoords[trackId].insert(coords);
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_tracksMap[coords].insert(trackId);
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auto track = Database::Track::getById(session, trackId);
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if (track->getRelease())
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{
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_releaseCoords[track->getRelease().id()].insert(coords);
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_releasesMap[coords].insert(track->getRelease().id());
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}
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if (track->getArtist())
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{
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_artistCoords[track->getArtist().id()].insert(coords);
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_artistsMap[coords].insert(track->getArtist().id());
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}
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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, _trackCoords, maxCount);
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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, _releaseCoords, maxCount);
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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, _artistCoords, maxCount);
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return getSimilarObjects({artistId}, _artistsMap, _artistPosition, maxCount);
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}
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#if 0
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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: " << _trackIdsCoords.size() << std::endl;
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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 (std::size_t y = 0; y < _network.getHeight(); ++y)
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for (SOM::Coordinate y = 0; y < _network.getHeight(); ++y)
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{
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for (std::size_t x = 0; x < _network.getWidth(); ++x)
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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 << "{";
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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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os << track->getArtist()->getName() << " - ";
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if (track->getRelease())
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os << track->getRelease()->getName();
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os << "} ";
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os << std::endl;
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}
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os << "; ";
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}
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os << std::endl;
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}
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}
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#endif
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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::Coords>
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getMatchingRefVectorsCoords(const std::set<Database::IdType>& ids, const std::map<Database::IdType, std::set<SOM::Coords>>& objectCoords)
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std::set<SOM::Position>
|
||||
getMatchingRefVectorsPosition(const std::set<Database::IdType>& ids, const std::map<Database::IdType, std::set<SOM::Position>>& objectPosition)
|
||||
{
|
||||
std::set<SOM::Coords> res;
|
||||
std::set<SOM::Position> res;
|
||||
|
||||
if (ids.empty())
|
||||
return res;
|
||||
|
||||
for (auto id : ids)
|
||||
{
|
||||
auto it = objectCoords.find(id);
|
||||
if (it == objectCoords.end())
|
||||
auto it = objectPosition.find(id);
|
||||
if (it == objectPosition.end())
|
||||
continue;
|
||||
|
||||
for (const auto& coords : it->second)
|
||||
res.insert(coords);
|
||||
for (const auto& position : it->second)
|
||||
res.insert(position);
|
||||
}
|
||||
|
||||
return res;
|
||||
@@ -240,13 +534,13 @@ getMatchingRefVectorsCoords(const std::set<Database::IdType>& ids, const std::ma
|
||||
|
||||
static
|
||||
std::set<Database::IdType>
|
||||
getObjectsIds(const std::set<SOM::Coords>& coordsSet, const SOM::Matrix<std::set<Database::IdType>>& objectsMap )
|
||||
getObjectsIds(const std::set<SOM::Position>& positionSet, const SOM::Matrix<std::set<Database::IdType>>& objectsMap )
|
||||
{
|
||||
std::set<Database::IdType> res;
|
||||
|
||||
for (const auto& coords : coordsSet)
|
||||
for (const auto& position : positionSet)
|
||||
{
|
||||
for (auto id : objectsMap.get(coords))
|
||||
for (auto id : objectsMap.get(position))
|
||||
res.insert(id);
|
||||
}
|
||||
|
||||
@@ -256,7 +550,7 @@ getObjectsIds(const std::set<SOM::Coords>& coordsSet, const SOM::Matrix<std::set
|
||||
std::vector<Database::IdType>
|
||||
FeaturesSearcher::getSimilarObjects(const std::set<Database::IdType>& ids,
|
||||
const SOM::Matrix<std::set<Database::IdType>>& objectsMap,
|
||||
const std::map<Database::IdType, std::set<SOM::Coords>>& objectCoords,
|
||||
const std::map<Database::IdType, std::set<SOM::Position>>& objectPosition,
|
||||
std::size_t maxCount) const
|
||||
{
|
||||
std::vector<Database::IdType> res;
|
||||
@@ -264,18 +558,21 @@ FeaturesSearcher::getSimilarObjects(const std::set<Database::IdType>& ids,
|
||||
auto now = std::chrono::system_clock::now();
|
||||
std::mt19937 randGenerator(std::chrono::duration_cast<std::chrono::milliseconds>(now.time_since_epoch()).count());
|
||||
|
||||
std::set<SOM::Coords> searchedRefVectorsCoords = getMatchingRefVectorsCoords(ids, objectCoords);
|
||||
if (searchedRefVectorsCoords.empty())
|
||||
std::set<SOM::Position> searchedRefVectorsPosition = getMatchingRefVectorsPosition(ids, objectPosition);
|
||||
if (searchedRefVectorsPosition.empty())
|
||||
return res;
|
||||
|
||||
while (1)
|
||||
{
|
||||
std::set<Database::IdType> closestObjectIds = getObjectsIds(searchedRefVectorsCoords, objectsMap);
|
||||
std::set<Database::IdType> closestObjectIds = getObjectsIds(searchedRefVectorsPosition, objectsMap);
|
||||
|
||||
// Remove objects that are already in input
|
||||
// Remove objects that are already in input or already reported
|
||||
for (auto id : ids)
|
||||
closestObjectIds.erase(id);
|
||||
|
||||
for (auto id : res)
|
||||
closestObjectIds.erase(id);
|
||||
|
||||
{
|
||||
std::vector<Database::IdType> objectIdsToAdd(closestObjectIds.begin(), closestObjectIds.end());
|
||||
|
||||
@@ -290,11 +587,11 @@ FeaturesSearcher::getSimilarObjects(const std::set<Database::IdType>& ids,
|
||||
break;
|
||||
|
||||
// If there is not enough objects, try again with closest neighbour until there is too much distance
|
||||
auto closestRefVectorCoords = _network->getClosestRefVectorCoords(searchedRefVectorsCoords, _networkRefVectorsDistanceMedian * 0.75);
|
||||
if (!closestRefVectorCoords)
|
||||
auto closestRefVectorPosition = _network.getClosestRefVectorPosition(searchedRefVectorsPosition, _networkRefVectorsDistanceMedian * 0.75);
|
||||
if (!closestRefVectorPosition)
|
||||
break;
|
||||
|
||||
searchedRefVectorsCoords.insert(*closestRefVectorCoords);
|
||||
searchedRefVectorsPosition.insert(*closestRefVectorPosition);
|
||||
}
|
||||
|
||||
return res;
|
||||
|
||||
Reference in New Issue
Block a user