From 83a25d8baf13a679d851ecb90b2e7e7456ff3c06 Mon Sep 17 00:00:00 2001 From: inter Date: Thu, 4 Sep 2025 14:08:48 +0800 Subject: [PATCH] Add File --- .../rnnJumpNerveCenter/RRNerveManager.java | 208 ++++++++++++++++++ 1 file changed, 208 insertions(+) create mode 100644 src/main/java/org/dromara/easyai/rnnJumpNerveCenter/RRNerveManager.java diff --git a/src/main/java/org/dromara/easyai/rnnJumpNerveCenter/RRNerveManager.java b/src/main/java/org/dromara/easyai/rnnJumpNerveCenter/RRNerveManager.java new file mode 100644 index 0000000..e8580ea --- /dev/null +++ b/src/main/java/org/dromara/easyai/rnnJumpNerveCenter/RRNerveManager.java @@ -0,0 +1,208 @@ +package org.dromara.easyai.rnnJumpNerveCenter; + + +import org.dromara.easyai.matrixTools.Matrix; +import org.dromara.easyai.config.SentenceConfig; +import org.dromara.easyai.entity.TypeMapping; +import org.dromara.easyai.entity.WordBack; +import org.dromara.easyai.function.Tanh; +import org.dromara.easyai.i.OutBack; +import org.dromara.easyai.naturalLanguage.word.WordEmbedding; +import org.dromara.easyai.rnnJumpNerveEntity.MyWordFeature; +import org.dromara.easyai.rnnJumpNerveEntity.SensoryNerve; + +import java.util.*; + +public class RRNerveManager { + private final WordEmbedding wordEmbedding; + private final Map mapping = new HashMap<>();//主键是真实id,值是映射识别用id + private NerveJumpManager typeNerveManager;//类别网络 + private int typeNub;//分类数量 + private int vectorDimension;//特征纵向维度 + private int maxFeatureLength;//特征最长长度 + private float studyPoint;//词向量学习学习率 + private boolean showLog;//是否输出学习数据 + private int minLength;//最小长度 + private float trustPowerTh = 0;//可信阈值 + private int rzModel;//正则模式 + private float rzParam;//正则系数 + + public RRNerveManager(WordEmbedding wordEmbedding) { + this.wordEmbedding = wordEmbedding; + } + + public void init(SentenceConfig config) throws Exception { + if (config.getTypeNub() > 0) { + this.trustPowerTh = config.getTrustPowerTh(); + this.minLength = config.getMinLength(); + this.typeNub = config.getTypeNub(); + this.vectorDimension = config.getWordVectorDimension(); + this.maxFeatureLength = config.getMaxWordLength(); + this.studyPoint = config.getWeStudyPoint(); + this.showLog = config.isShowLog(); + this.rzModel = config.getRzModel(); + this.rzParam = config.getParam(); + initNerveManager(); + } else { + throw new Exception("分类种类数量必须大于0"); + } + } + + private void initNerveManager() throws Exception { + typeNerveManager = new NerveJumpManager(vectorDimension, vectorDimension, typeNub, maxFeatureLength - 1, new Tanh(), false, + studyPoint, rzModel, rzParam); + typeNerveManager.initRnn(true, showLog, true, false, 0); + } + + private int getMappingType(int key) {//通过自增主键查找原映射 + int id = 0; + for (Map.Entry entry : mapping.entrySet()) { + if (entry.getValue() == key) { + id = entry.getKey(); + break; + } + } + return id; + } + + private int balance(Map> model) {//强行均衡 + int maxNumber = 300; + int index = 1; + for (Map.Entry> entry : model.entrySet()) {//查找最大数量 + mapping.put(entry.getKey(), index); + if (entry.getValue().size() > maxNumber) { + maxNumber = entry.getValue().size(); + } + index++; + } + for (Map.Entry> entry : model.entrySet()) { + int size = entry.getValue().size(); + if (maxNumber > size) { + int times = maxNumber / size - 1;//循环几次 + int sub = maxNumber % size;//余数 + List list = entry.getValue(); + List otherList = new ArrayList<>(list); + for (int i = 0; i < times; i++) { + list.addAll(otherList); + } + list.addAll(otherList.subList(0, sub)); + } + } + return maxNumber; + } + + private void studyNerve(long eventId, List sensoryNerves, List featureList, Matrix rnnMatrix, Map E, boolean isStudy, OutBack convBack, int[] storeys) throws Exception { + if (sensoryNerves.size() == featureList.size()) { + for (int i = 0; i < sensoryNerves.size(); i++) { + sensoryNerves.get(i).postMessage(eventId, featureList.get(i), isStudy, E, convBack, rnnMatrix, storeys, 0); + } + } else { + throw new Exception("1size not equals,feature size:" + featureList.size() + "," + + "sensorySize:" + sensoryNerves.size()); + } + } + + public int getType(String sentence, long eventID) throws Exception {//进行理解 + if (sentence.length() > maxFeatureLength) { + sentence = sentence.substring(0, maxFeatureLength); + } + MyWordFeature myWordFeature = wordEmbedding.getEmbedding(sentence, eventID, false); + List featureList = myWordFeature.getFirstFeatureList(); + Matrix featureMatrix = myWordFeature.getFeatureMatrix(); + int[] storeys = new int[featureMatrix.getX()]; + for (int i = 0; i < storeys.length; i++) { + storeys[i] = i; + } + WordBack wordBack = new WordBack();//trustPowerTh + studyNerve(eventID, typeNerveManager.getSensoryNerves(), featureList, featureMatrix, null, false, wordBack, storeys); + if (wordBack.getOut() > trustPowerTh) { + return getMappingType(wordBack.getId()); + } else { + return -1; + } + } + + public void insertModel(RandomModel randomModel) throws Exception { + typeNerveManager.insertModelParameter(randomModel.getTypeModelParameter()); + List typeMappings = randomModel.getTypeMappings(); + mapping.clear(); + for (TypeMapping typeMapping : typeMappings) { + mapping.put(typeMapping.getType(), typeMapping.getMapping()); + } + } + + public RandomModel getModel() throws Exception { + RandomModel randomModel = new RandomModel(); + randomModel.setTypeModelParameter(typeNerveManager.getModelParameter()); + List typeMappings = new ArrayList<>(); + randomModel.setTypeMappings(typeMappings); + for (Map.Entry entry : mapping.entrySet()) { + TypeMapping typeMapping = new TypeMapping(); + typeMapping.setType(entry.getKey()); + typeMapping.setMapping(entry.getValue()); + typeMappings.add(typeMapping); + } + return randomModel; + } + + public RandomModel studyType(Map> model) throws Exception { + int maxNumber = balance(model);//平衡样本 + for (int i = 0; i < maxFeatureLength; i++) {//第一阶段学习 + System.out.println("1第：" + (i + 1) + "次。共:" + maxFeatureLength + "次"); + myStudy(maxNumber, model, i + 1); + } + return getModel(); + } + + private void myStudy(int maxNumber, Map> model, int time) throws Exception { + int index = 0; + Map E = new HashMap<>(); + do { + for (Map.Entry> entry : model.entrySet()) { + System.out.println("index======" + index + "," + time + "次"); + E.clear(); + List sentence = entry.getValue(); + int key = mapping.get(entry.getKey()); + E.put(key, 1f); + String word = sentence.get(index); + if (word.length() > maxFeatureLength) { + word = word.substring(0, maxFeatureLength); + } + randomTypeStudy(wordEmbedding.getEmbedding(word, 1, false), E); + } + index++; + } while (index < maxNumber); + } + + private void randomTypeStudy(MyWordFeature myWordFeature, Map E) throws Exception { + Matrix featureMatrix = myWordFeature.getFeatureMatrix(); + List firstFeatureList = myWordFeature.getFirstFeatureList(); + int len = featureMatrix.getX();//文字长度 + Random random = new Random(); + if (len > 1) {//长度大于1才可以进行训练 + int[] storeys; + if (len < minLength) { + storeys = new int[len]; + for (int i = 0; i < len; i++) { + storeys[i] = i; + } + } else { + List list = new ArrayList<>(); + for (int i = 1; i < len; i++) { + list.add(i); + } + int myLen = (int) (minLength + (float)Math.random() * (len - minLength + 1)); + storeys = new int[myLen]; + for (int i = 1; i < myLen; i++) { + int index = random.nextInt(list.size()); + storeys[i] = list.get(index); + list.remove(index); + } + Arrays.sort(storeys); + } + studyNerve(1, typeNerveManager.getSensoryNerves(), firstFeatureList, featureMatrix + , E, true, null, storeys); + } + } + +}