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package org.dromara.easyai.rnnJumpNerveCenter;
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import org.dromara.easyai.matrixTools.Matrix;
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import org.dromara.easyai.config.SentenceConfig;
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import org.dromara.easyai.entity.CreatorModel;
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import org.dromara.easyai.entity.SemanticsBack;
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import org.dromara.easyai.entity.TalkBody;
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import org.dromara.easyai.function.Tanh;
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import org.dromara.easyai.naturalLanguage.word.WordEmbedding;
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import org.dromara.easyai.rnnJumpNerveEntity.MyWordFeature;
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import org.dromara.easyai.rnnJumpNerveEntity.NerveCenter;
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import org.dromara.easyai.rnnJumpNerveEntity.SensoryNerve;
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import java.util.*;
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@Deprecated()
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public class CustomManager {//
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private final WordEmbedding wordEmbedding;
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private NerveJumpManager semanticsManager;//网络
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private final int vectorDimension;//特征纵向维度
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private final int maxFeatureLength;//特征最长长度
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private final float studyPoint;//词向量学习学习率
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private final int minLength;//最小长度
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private final int answerMaxLength;//回答最长长度
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private int times;
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private final float param;
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private final float powerTh;
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private final int rzModel;
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public CustomManager(WordEmbedding wordEmbedding, SentenceConfig config) {
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this.minLength = config.getMinLength();
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this.wordEmbedding = wordEmbedding;
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this.vectorDimension = config.getQaWordVectorDimension();
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this.maxFeatureLength = config.getMaxWordLength();
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this.studyPoint = config.getWeStudyPoint();
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this.answerMaxLength = config.getMaxAnswerLength();
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this.powerTh = config.getSentenceTrustPowerTh();
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this.times = config.getTimes();
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this.param = config.getParam();
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this.rzModel = config.getRzModel();
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if (this.times < 1) {
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this.times = 1;
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}
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}
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public void init() throws Exception {
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semanticsManager = new NerveJumpManager(vectorDimension, vectorDimension, wordEmbedding.getWordList().size(), maxFeatureLength + answerMaxLength - 1, new Tanh(), false,
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studyPoint, rzModel, param);
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semanticsManager.setPowerTh(powerTh);
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semanticsManager.initRnn(true, true, true, true, maxFeatureLength);
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List<NerveCenter> nerveCenterList = semanticsManager.getNerveCenterList();
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for (NerveCenter nerveCenter : nerveCenterList) {
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nerveCenter.setWordEmbedding(wordEmbedding);
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}
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}
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public void insertModel(CreatorModel creatorModel) throws Exception {
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semanticsManager.insertModelParameter(creatorModel.getSemanticsModel());
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}
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public String getAnswer(String question, long eventID) throws Exception {
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SemanticsBack semanticsBack = new SemanticsBack();
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if (question.length() > maxFeatureLength) {
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question = question.substring(0, maxFeatureLength);
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}
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MyWordFeature myWordFeature = wordEmbedding.getEmbedding(question, eventID, false);
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List<Float> featureList = myWordFeature.getFirstFeatureList();
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Matrix featureMatrix = myWordFeature.getFeatureMatrix();
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featureMatrix = insertZero(featureMatrix, featureMatrix.getX());
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int[] storeys = getStoreys2(question.length(), maxFeatureLength);
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studySemanticsNerve(eventID, featureList, false, null, semanticsBack, featureMatrix, storeys, question.length());
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return semanticsBack.getWord();
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}
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public CreatorModel study(List<TalkBody> talkBodies) throws Exception {
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Random random = new Random();
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CreatorModel creatorModel = new CreatorModel();
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int maxTime = maxFeatureLength * answerMaxLength * times;
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int wordSize = talkBodies.size() * maxTime;
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int index = 0;
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for (int i = 0; i < maxTime; i++) {//第一阶段学习
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System.out.println("生成模型学习完成次数：" + (i + 1) + "总共次数:" + maxTime);
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for (TalkBody talkBody : talkBodies) {
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index++;
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String question = talkBody.getQuestion();
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String answer = talkBody.getAnswer();
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if (question.length() > maxFeatureLength) {
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question = question.substring(0, maxFeatureLength);
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}
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if (answer.length() > answerMaxLength) {
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answer = answer.substring(0, answerMaxLength);
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}
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String word = question + answer;
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MyWordFeature myWordFeature = wordEmbedding.getEmbedding(word, 1, false);
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semanticsStudy(myWordFeature, question, answer, 1, random);
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float point = (float) index / (float) (wordSize) * 100;
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String result = String.format("%.6f", point);
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System.out.println("训练进度：" + result + "%");
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}
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}
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creatorModel.setSemanticsModel(semanticsManager.getModelParameter());
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return creatorModel;
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}
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private int[] getStoreys2(int len, int finishIndex) {
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int[] storeys = new int[len + 1];
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for (int i = 0; i < storeys.length; i++) {
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if (i < storeys.length - 1) {
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storeys[i] = i;
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} else {
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storeys[i] = finishIndex;
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}
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}
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return storeys;
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}
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private int[] getStoreys(int len, Random random, int startIndex) {
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int[] storeys;
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if (len < minLength) {
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storeys = new int[len];
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for (int i = 0; i < len; i++) {
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storeys[i] = i + startIndex;
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}
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} else {
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List<Integer> list = new ArrayList<>();
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for (int i = 1; i < len; i++) {
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list.add(i);
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}
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int myLen = (int) (minLength + (float) Math.random() * (len - minLength + 1));
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storeys = new int[myLen];
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if (startIndex > 0) {
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storeys[0] = startIndex;
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}
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for (int i = 1; i < myLen; i++) {
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int index = random.nextInt(list.size());
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storeys[i] = list.get(index) + startIndex;
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list.remove(index);
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}
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Arrays.sort(storeys);
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}
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return storeys;
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}
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private int[] pushArray(int[] arr, int value) {
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int[] myArr = new int[arr.length + 1];
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for (int i = 0; i < myArr.length; i++) {
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if (i < myArr.length - 1) {
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myArr[i] = arr[i];
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} else {
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myArr[i] = value;
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}
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}
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return myArr;
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}
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private Matrix insertZero(Matrix feature, int index) throws Exception {
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Matrix matrix = new Matrix(feature.getX() + 1, feature.getY());
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int x = matrix.getX();
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int y = matrix.getY();
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for (int i = 0; i < x; i++) {
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for (int j = 0; j < y; j++) {
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if (i < index) {
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matrix.setNub(i, j, feature.getNumber(i, j));
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} else if (i > index) {
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matrix.setNub(i, j, feature.getNumber(i - 1, j));
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}
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}
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}
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return matrix;
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}
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private void semanticsStudy(MyWordFeature myWordFeature, String question, String answer, long eventID, Random random) throws Exception {
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Matrix featureMatrix = insertZero(myWordFeature.getFeatureMatrix(), question.length());
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List<Float> firstFeatureList = myWordFeature.getFirstFeatureList();
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System.out.println("训练question:" + question + ",answer:" + answer);
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if (question.length() > 1 && !answer.isEmpty()) {
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int[] questionStoreys = getStoreys(question.length(), random, 0);
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int[] answerStoreys = getStoreys(answer.length() + 1, random, maxFeatureLength);
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Map<Integer, Float> E = new HashMap<>();
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for (int i = 0; i < answerStoreys.length - 1; i++) {
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//System.out.println("=====================================");
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E.clear();
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int index = answerStoreys[i + 1] - maxFeatureLength;
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questionStoreys = pushArray(questionStoreys, answerStoreys[i]);
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String myAnswer = answer.substring(index - 1, index);
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int wordID = wordEmbedding.getID(myAnswer) + 1;
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E.put(wordID, 1f);
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studySemanticsNerve(eventID, firstFeatureList, true, E, null, featureMatrix, questionStoreys, question.length());
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}
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}
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}
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private void studySemanticsNerve(long eventId, List<Float> featureList, boolean isStudy, Map<Integer, Float> E, SemanticsBack semanticsBack,
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Matrix rnnMatrix, int[] storeys, int questionLength) throws Exception {
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List<SensoryNerve> sensoryNerves = semanticsManager.getSensoryNerves();
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if (sensoryNerves.size() == featureList.size()) {
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for (int i = 0; i < sensoryNerves.size(); i++) {
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sensoryNerves.get(i).postMessage(eventId, featureList.get(i), isStudy, E, semanticsBack, rnnMatrix, storeys, questionLength);
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}
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} else {
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throw new Exception("1size not equals,feature size:" + featureList.size() + "," +
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"sensorySize:" + sensoryNerves.size());
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}
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}
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}
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