Java で実装された独自のトレーニング戦略と増分学習テクノロジー

public class SelfTraining { private int k; private List> subsets; private Model model; public void train(List data, Model model, int k) { this.k = k; this.subsets = splitData(data, k); this.model = model; double bestAccuracy = 0; Model bestModel = null; for (int i = 0; i < k; i++) { List trainData = new ArrayList<>(); List testData = subsets.get(i); for (int j = 0; j < k; j++) { if (j != i) { trainData.addAll(subsets.get(j)); } } model.train(trainData); double accuracy = model.test(testData); if (accuracy > bestAccuracy) { bestAccuracy = accuracy; bestModel = model.clone(); } } this.model = bestModel; } private List> splitData(List data, int k) { List> subsets = new ArrayList<>(); int subsetSize = data.size() / k; for (int i = 0; i < k; i++) { List subset = new ArrayList<>(); for (int j = 0; j < subsetSize; j++) { int index = i * subsetSize + j; subset.add(data.get(index)); } subsets.add(subset); } return subsets; } }

public class IncrementalLearning { private Model model; public void train(List newData) { List allData = loadOldData(); allData.addAll(newData); model.train(allData); saveModel(model); } private List loadOldData() { // load old training data from disk or database return Collections.emptyList(); } private void saveModel(Model model) { // save model to disk or database } private Model loadModel() { // load model from disk or database return new Model(); } public void update() { List newData = loadNewData(); this.model = loadModel(); train(newData); backupModel(this.model); } private List loadNewData() { // load new data from disk or network return Collections.emptyList(); } private void backupModel(Model model) { // backup model to disk or database } }