LFDMM源码剖析(融入词向量的概率图模型)

本文作者：合肥工业大学 管理学院 钱洋 email：1563178220@qq.com 内容可能有不到之处，欢迎交流。
未经本人允许禁止转载。

原文地址：https://blog.csdn.net/qy20115549/article/details/79675572

论文来源

Nguyen D Q, Billingsley R, Du L, et al. Improving topic models with latent feature word representations[J]. Transactions of the Association for Computational Linguistics, 2015, 3: 299-313.

发表在15年的ACL会议对应的一个期刊上，应该说还是很不错的，后面有一些文章在这个基础上进行了改进。下面，对作者提供的源码进行解析。

源码解读

package models;
import java.io.BufferedReader;
import java.io.BufferedWriter;
import java.io.FileReader;
import java.io.FileWriter;
import java.io.IOException;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Set;
import java.util.TreeMap;
import utility.FuncUtils;
import utility.LBFGS;
import utility.Parallel;
import cc.mallet.optimize.InvalidOptimizableException;
import cc.mallet.optimize.Optimizer;
import cc.mallet.types.MatrixOps;
import cc.mallet.util.Randoms;
/**
 * Implementation of the LF-DMM latent feature topic model, using collapsed Gibbs sampling, as
 * described in:
 * 
 * Dat Quoc Nguyen, Richard Billingsley, Lan Du and Mark Johnson. 2015. Improving Topic Models with
 * Latent Feature Word Representations. Transactions of the Association for Computational
 * Linguistics, vol. 3, pp. 299-313.
 * 
 * @author Dat Quoc Nguyen
 */
public class LFDMM
{
    public double alpha; // Hyper-parameter alpha  超参数
    public double beta; // Hyper-parameter beta   超参数
    // public double alphaSum; // alpha * numTopics
    public double betaSum; // beta * vocabularySize  V*beta
    public int numTopics; // Number of topics  主题数目
    public int topWords; // Number of most probable words for each topic  每个主题取多少个靠前的单词
    public double lambda; // Mixture weight value  混合权重值
    public int numInitIterations;  //
    public int numIterations; // Number of EM-style sampling iterations  迭代次数
    public List<List<Integer>> corpus; // Word ID-based corpus  语料单词的id
    public List<List<Integer>> topicAssignments; // Topics assignments for words  单词的主题分配
                                                 // in the corpus
    public int numDocuments; // Number of documents in the corpus  文档的数量
    public int numWordsInCorpus; // Number of words in the corpus  整个语料单词的数量
    public HashMap<String, Integer> word2IdVocabulary; // Vocabulary to get ID  单词的编号
                                                       // given a word
    public HashMap<Integer, String> id2WordVocabulary; // Vocabulary to get word  将编号转化为单词  用于输出
                                                       // given an ID
    public int vocabularySize; // The number of word types in the corpus  整个语料中单词的总数
    // Number of documents assigned to a topic  分配到一个主题文档的数量
    public int[] docTopicCount;
    // numTopics * vocabularySize matrix
    // Given a topic: number of times a word type generated from the topic by
    // the Dirichlet multinomial component  主题对应的单词数量  该单词是由多项式分布产生
    public int[][] topicWordCountDMM;
    // Total number of words generated from each topic by the Dirichlet
    // multinomial component 主题对应的总的单词数量  这些单词是由多项式分布产生
    public int[] sumTopicWordCountDMM;
    // numTopics * vocabularySize matrix
    // Given a topic: number of times a word type generated from the topic by
    // the latent feature component  单词是由隐特征部分产生  统计一个主题对应的单词数量
    public int[][] topicWordCountLF;
    // Total number of words generated from each topic by the latent feature
    // component  单词由隐特征产生  统计一个主题对应的总单词数量
    public int[] sumTopicWordCountLF;
    // Double array used to sample a topic   概率 用于抽样
    public double[] multiPros;
    // Path to the directory containing the corpus  
    public String folderPath;
    // Path to the topic modeling corpus
    public String corpusPath;
    public String vectorFilePath;
    public double[][] wordVectors; // Vector representations for words  词向量表示
    public double[][] topicVectors;// Vector representations for topics 主题向量表示
    public int vectorSize; // Number of vector dimensions  向量的维度
    public double[][] dotProductValues;   //点乘法的值
    public double[][] expDotProductValues;  //指数变化后的值
    public double[] sumExpValues; // Partition function values 求和的值
    public final double l2Regularizer = 0.01; // L2 regularizer value for learning topic vectors L2正则化
    public final double tolerance = 0.05; // Tolerance value for LBFGS convergence  LBFGS收敛
    public String expName = "LFDMM";
    public String orgExpName = "LFDMM";
    public String tAssignsFilePath = "";
    public int savestep = 0;
    public LFDMM(String pathToCorpus, String pathToWordVectorsFile, int inNumTopics,
            double inAlpha, double inBeta, double inLambda, int inNumInitIterations,
            int inNumIterations, int inTopWords)
        throws Exception
    {
        this(pathToCorpus, pathToWordVectorsFile, inNumTopics, inAlpha, inBeta, inLambda,
                inNumInitIterations, inNumIterations, inTopWords, "LFDMM");
    }
    public LFDMM(String pathToCorpus, String pathToWordVectorsFile, int inNumTopics,
            double inAlpha, double inBeta, double inLambda, int inNumInitIterations,
            int inNumIterations, int inTopWords, String inExpName)
        throws Exception
    {
        this(pathToCorpus, pathToWordVectorsFile, inNumTopics, inAlpha, inBeta, inLambda,
                inNumInitIterations, inNumIterations, inTopWords, inExpName, "", 0);
    }
    public LFDMM(String pathToCorpus, String pathToWordVectorsFile, int inNumTopics,
            double inAlpha, double inBeta, double inLambda, int inNumInitIterations,
            int inNumIterations, int inTopWords, String inExpName, String pathToTAfile)
        throws Exception
    {
        this(pathToCorpus, pathToWordVectorsFile, inNumTopics, inAlpha, inBeta, inLambda,
                inNumInitIterations, inNumIterations, inTopWords, inExpName, pathToTAfile, 0);
    }
    public LFDMM(String pathToCorpus, String pathToWordVectorsFile, int inNumTopics,
            double inAlpha, double inBeta, double inLambda, int inNumInitIterations,
            int inNumIterations, int inTopWords, String inExpName, int inSaveStep)
        throws Exception
    {
        this(pathToCorpus, pathToWordVectorsFile, inNumTopics, inAlpha, inBeta, inLambda,
                inNumInitIterations, inNumIterations, inTopWords, inExpName, "", inSaveStep);
    }
    public LFDMM(String pathToCorpus, String pathToWordVectorsFile, int inNumTopics,
            double inAlpha, double inBeta, double inLambda, int inNumInitIterations,
            int inNumIterations, int inTopWords, String inExpName, String pathToTAfile,
            int inSaveStep)
        throws Exception
    {
        alpha = inAlpha;
        beta = inBeta;
        lambda = inLambda;
        numTopics = inNumTopics;
        numIterations = inNumIterations;
        numInitIterations = inNumInitIterations;
        topWords = inTopWords;
        savestep = inSaveStep;
        expName = inExpName;
        orgExpName = expName;
        //word2vec语料
        vectorFilePath = pathToWordVectorsFile;
        //语料的路径
        corpusPath = pathToCorpus;
        folderPath = pathToCorpus.substring(0,
                Math.max(pathToCorpus.lastIndexOf("/"), pathToCorpus.lastIndexOf("\\")) + 1);
        //输入语料的路径
        System.out.println("Reading topic modeling corpus: " + pathToCorpus);
        //词转化为编号
        word2IdVocabulary = new HashMap<String, Integer>();
        //编号转化为词
        id2WordVocabulary = new HashMap<Integer, String>();
        //语料
        corpus = new ArrayList<List<Integer>>();
        //文档数目
        numDocuments = 0;
        //语料中单词的数目
        numWordsInCorpus = 0;
        //读取语料
        BufferedReader br = null;
        try {
            int indexWord = -1;
            br = new BufferedReader(new FileReader(pathToCorpus));
            //每一行表示一个文档
            for (String doc; (doc = br.readLine()) != null;) {
                if (doc.trim().length() == 0)
                    continue;
                //文档单词拆分
                String[] words = doc.trim().split("\\s+");
                //文档表示成集合
                List<Integer> document = new ArrayList<Integer>();
                //对文档的所有单词进行循环
                for (String word : words) {
                    //文档中单词编号-----编号是全局而言
                    if (word2IdVocabulary.containsKey(word)) {
                        //如果包含了该单词，将该单词直接添加到文档集合中
                        document.add(word2IdVocabulary.get(word));
                    }
                    else {
                        //加1表示从0开始对单词进行编号，并将编号对应的单词加入到id2WordVocabulary
                        indexWord += 1;
                        word2IdVocabulary.put(word, indexWord);
                        id2WordVocabulary.put(indexWord, word);
                        //文档添加该单词
                        document.add(indexWord);
                    }
                }
                //文档数目++
                numDocuments++;
                //语料中所有单词的数量
                numWordsInCorpus += document.size();
                //将所有文档添加到集合中
                corpus.add(document);
            }
        }
        catch (Exception e) {
            e.printStackTrace();
        }
        //语料不重复单词的总量
        vocabularySize = word2IdVocabulary.size();
        //主题对应的文档统计
        docTopicCount = new int[numTopics];
        //主题-单词统计  来自多项式分布
        topicWordCountDMM = new int[numTopics][vocabularySize];
        //主题对应的单词总数目统计 来自多项式分布
        sumTopicWordCountDMM = new int[numTopics];
        //主题-单词统计 来自于隐特征分布
        topicWordCountLF = new int[numTopics][vocabularySize];
        //主题对应的单词数目总计 来自于隐特征
        sumTopicWordCountLF = new int[numTopics];
        //多项式分布的先验
        multiPros = new double[numTopics];
        //先验为1/K,后面要轮盘赌的，在初始化的时候
        for (int i = 0; i < numTopics; i++) {
            multiPros[i] = 1.0 / numTopics;
        }
        // alphaSum = numTopics * alpha;  
        betaSum = vocabularySize * beta;  
        //读取词向量 word2vec文件
        readWordVectorsFile(vectorFilePath);
        topicVectors = new double[numTopics][vectorSize];
        dotProductValues = new double[numTopics][vocabularySize];
        expDotProductValues = new double[numTopics][vocabularySize];
        sumExpValues = new double[numTopics];
        System.out
                .println("Corpus size: " + numDocuments + " docs, " + numWordsInCorpus + " words");
        System.out.println("Vocabuary size: " + vocabularySize);
        System.out.println("Number of topics: " + numTopics);
        System.out.println("alpha: " + alpha);
        System.out.println("beta: " + beta);
        System.out.println("lambda: " + lambda);
        System.out.println("Number of initial sampling iterations: " + numInitIterations);
        System.out.println("Number of EM-style sampling iterations for the LF-DMM model: "
                + numIterations);
        System.out.println("Number of top topical words: " + topWords);
        tAssignsFilePath = pathToTAfile;
        if (tAssignsFilePath.length() > 0)
            initialize(tAssignsFilePath);
        else
            initialize();
    }
    //读取词向量文件
    public void readWordVectorsFile(String pathToWordVectorsFile)
        throws Exception
    {
        //输出需要读取词向量文件的相对地址
        System.out.println("Reading word vectors from word-vectors file " + pathToWordVectorsFile
                + "...");
        BufferedReader br = null;
        try {
            br = new BufferedReader(new FileReader(pathToWordVectorsFile));
            //以空格分开
            String[] elements = br.readLine().trim().split("\\s+");
            //词向量的长度，这里减1是因为第一维度是词
            vectorSize = elements.length - 1;
            //word2vec向量的维度，只去语料中有的词vocabularySize
            wordVectors = new double[vocabularySize][vectorSize];
            //单词为第一维度
            String word = elements[0];
            //如果这个词语在语料中的话，将该词的词向量存入数组wordVectors
            if (word2IdVocabulary.containsKey(word)) {
                for (int j = 0; j < vectorSize; j++) {
                    wordVectors[word2IdVocabulary.get(word)][j] = new Double(elements[j + 1]);
                }
            }
            //继续读文本，上面之所以要先读一行是为了初始化，获取词向量的维度
            for (String line; (line = br.readLine()) != null;) {
                elements = line.trim().split("\\s+");
                word = elements[0];
                //语料中出现的每个单词的词向量
                if (word2IdVocabulary.containsKey(word)) {
                    for (int j = 0; j < vectorSize; j++) {
                        wordVectors[word2IdVocabulary.get(word)][j] = new Double(elements[j + 1]);
                    }
                }
            }
        }
        catch (Exception e) {
            e.printStackTrace();
        }
        //防止语料中的词在word2vec文件中不存在
        for (int i = 0; i < vocabularySize; i++) {
            if (MatrixOps.absNorm(wordVectors[i]) == 0.0) {
                System.out.println("The word \"" + id2WordVocabulary.get(i)
                        + "\" doesn't have a corresponding vector!!!");
                throw new Exception();
            }
        }
    }
    //初始化方法
    public void initialize()
        throws IOException
    {
        //随机对文档进行主题分配
        System.out.println("Randomly initialzing topic assignments ...");
        topicAssignments = new ArrayList<List<Integer>>();
        //循环每篇文档
        for (int docId = 0; docId < numDocuments; docId++) {
            List<Integer> topics = new ArrayList<Integer>();
            //基于轮盘赌获取主题编号（前面已经初始化了），这里multiPros必须有值，否则则会报错
            int topic = FuncUtils.nextDiscrete(multiPros);
            //分配到该主题的文档数量+1
            docTopicCount[topic] += 1;
            //文档的单词个数
            int docSize = corpus.get(docId).size();
            //循环每个单词
            for (int j = 0; j < docSize; j++) {
                //获取单词编号
                int wordId = corpus.get(docId).get(j);
                //随机产生false or true,用来初始化该文档是来自于隐特征还是多项式分布
                boolean component = new Randoms().nextBoolean();
                int subtopic = topic;   //这里是什么意思呢
                if (!component) { // Generated from the latent feature component
                    //主题-单词数量增加1  由隐特征主题生成
                    topicWordCountLF[topic][wordId] += 1;
                    // 该主题生成的单词总数增加1 由隐特征主题生成
                    sumTopicWordCountLF[topic] += 1;
                }
                else {// Generated from the Dirichlet multinomial component
                    //主题-单词数量增加1 由多项式分布生成
                    topicWordCountDMM[topic][wordId] += 1;
                    //主题生成的单词总数增加1
                    sumTopicWordCountDMM[topic] += 1;
                    subtopic = subtopic + numTopics;  
                }
                topics.add(subtopic);
            }
            topicAssignments.add(topics);
        }
    }
    //输入参数初始化
    public void initialize(String pathToTopicAssignmentFile)
        throws Exception
    {
        System.out.println("Reading topic-assignment file: " + pathToTopicAssignmentFile);
        topicAssignments = new ArrayList<List<Integer>>();
        BufferedReader br = null;
        try {
            br = new BufferedReader(new FileReader(pathToTopicAssignmentFile));
            int docId = 0;
            int numWords = 0;
            for (String line; (line = br.readLine()) != null;) {
                String[] strTopics = line.trim().split("\\s+");
                List<Integer> topics = new ArrayList<Integer>();
                int topic = new Integer(strTopics[0]) % numTopics;
                docTopicCount[topic] += 1;
                for (int j = 0; j < strTopics.length; j++) {
                    int wordId = corpus.get(docId).get(j);
                    int subtopic = new Integer(strTopics[j]);
                    if (subtopic == topic) {
                        topicWordCountLF[topic][wordId] += 1;
                        sumTopicWordCountLF[topic] += 1;
                    }
                    else {
                        topicWordCountDMM[topic][wordId] += 1;
                        sumTopicWordCountDMM[topic] += 1;
                    }
                    topics.add(subtopic);
                    numWords++;
                }
                topicAssignments.add(topics);
                docId++;
            }
            if ((docId != numDocuments) || (numWords != numWordsInCorpus)) {
                System.out
                        .println("The topic modeling corpus and topic assignment file are not consistent!!!");
                throw new Exception();
            }
        }
        catch (Exception e) {
            e.printStackTrace();
        }
    }
    //模型推断
    public void inference()
        throws IOException
    {
        System.out.println("Running Gibbs sampling inference: ");
        //初始化迭代
        for (int iter = 1; iter <= numInitIterations; iter++) {
            System.out.println("\tInitial sampling iteration: " + (iter));
            //单词初始化迭代
            sampleSingleInitialIteration();
        }
        for (int iter = 1; iter <= numIterations; iter++) {
            System.out.println("\tLFDMM sampling iteration: " + (iter));
            //优化主题向量
            optimizeTopicVectors();
            //迭代
            sampleSingleIteration();
            if ((savestep > 0) && (iter % savestep == 0) && (iter < numIterations)) {
                System.out.println("\t\tSaving the output from the " + iter + "^{th} sample");
                expName = orgExpName + "-" + iter;
                write();
            }
        }
        expName = orgExpName;
        //保存模型相关参数
        writeParameters();
        System.out.println("Writing output from the last sample ...");
        //保存信息
        write();
        System.out.println("Sampling completed!");
    }
    //优化主题向量
    public void optimizeTopicVectors()
    {
        System.out.println("\t\tEstimating topic vectors ...");
        sumExpValues = new double[numTopics];
        dotProductValues = new double[numTopics][vocabularySize];
        expDotProductValues = new double[numTopics][vocabularySize];
        Parallel.loop(numTopics, new Parallel.LoopInt()
        {
            @Override
            public void compute(int topic)
            {
                int rate = 1;
                boolean check = true;
                while (check) {
                    double l2Value = l2Regularizer * rate;
                    try {
                        //主题向量表示  主题包含的单词个数  词向量 正则化值(这里是传入参数-----以便执行TopicVectorOptimizer)
                        TopicVectorOptimizer optimizer = new TopicVectorOptimizer(
                                topicVectors[topic], topicWordCountLF[topic], wordVectors, l2Value);
                        //通过LBFGS优化
                        Optimizer gd = new LBFGS(optimizer, tolerance);
                        gd.optimize(600);
                        //需要优化的参数
                        optimizer.getParameters(topicVectors[topic]);
                        //输入的是两个特征的乘积以及其加和-----针对每个主题计算一个向量值（为了更新主题使用）
                        sumExpValues[topic] = optimizer.computePartitionFunction(
                                dotProductValues[topic], expDotProductValues[topic]);
                        check = false;
                        if (sumExpValues[topic] == 0 || Double.isInfinite(sumExpValues[topic])) {
                            double max = -1000000000.0;
                            for (int index = 0; index < vocabularySize; index++) {
                                if (dotProductValues[topic][index] > max)
                                    max = dotProductValues[topic][index];
                            }
                            for (int index = 0; index < vocabularySize; index++) {
                                expDotProductValues[topic][index] = Math
                                        .exp(dotProductValues[topic][index] - max);
                                sumExpValues[topic] += expDotProductValues[topic][index];
                            }
                        }
                    }
                    catch (InvalidOptimizableException e) {
                        e.printStackTrace();
                        check = true;
                    }
                    rate = rate * 10;
                }
            }
        });
    }
    //每一代分配主题
    public void sampleSingleIteration()
    {
        //对每一篇文档进行循环
        for (int dIndex = 0; dIndex < numDocuments; dIndex++) {
            //获取文档的所有单词
            List<Integer> document = corpus.get(dIndex);
            //文档的长度
            int docSize = document.size();
            //文档的初始主题分布，接下来是移除该单词
            int topic = topicAssignments.get(dIndex).get(0) % numTopics;
            //主题对应的文档数目减1
            docTopicCount[topic] = docTopicCount[topic] - 1;
            //接下来对对个单词进行循环，做相关单词的统计工作
            for (int wIndex = 0; wIndex < docSize; wIndex++) {
                //获取单词的id
                int word = document.get(wIndex);// wordId
                int subtopic = topicAssignments.get(dIndex).get(wIndex);
                if (subtopic == topic) {
                    topicWordCountLF[topic][word] -= 1;
                    sumTopicWordCountLF[topic] -= 1;
                }
                else {
                    topicWordCountDMM[topic][word] -= 1;
                    sumTopicWordCountDMM[topic] -= 1;
                }
            }
            // 对文档单词的主题进行抽样
            for (int tIndex = 0; tIndex < numTopics; tIndex++) {
                multiPros[tIndex] = (docTopicCount[tIndex] + alpha);
                for (int wIndex = 0; wIndex < docSize; wIndex++) {
                    int word = document.get(wIndex);
                    //依据公式进行计算，不过论文公式有问题  N_{d,w}+K_{d,w}的次方有问题，推理的公式应该是这样的
                    multiPros[tIndex] *= (lambda * expDotProductValues[tIndex][word]
                            / sumExpValues[tIndex] + (1 - lambda)
                            * (topicWordCountDMM[tIndex][word] + beta)
                            / (sumTopicWordCountDMM[tIndex] + betaSum));
                }
            }
            //基于轮盘赌抽样
            topic = FuncUtils.nextDiscrete(multiPros);
            //开始做相关统计
            docTopicCount[topic] += 1;
            for (int wIndex = 0; wIndex < docSize; wIndex++) {
                int word = document.get(wIndex);
                int subtopic = topic;
                //这里是对s_{di}的抽样，采用的是直接计算，并没有使用轮盘赌
                if (lambda * expDotProductValues[topic][word] / sumExpValues[topic] > (1 - lambda)
                        * (topicWordCountDMM[topic][word] + beta)
                        / (sumTopicWordCountDMM[topic] + betaSum)) {
                    //来自隐特征的相关统计
                    topicWordCountLF[topic][word] += 1;
                    sumTopicWordCountLF[topic] += 1;
                }
                else {
                    //来自多项式分布的相关统计
                    topicWordCountDMM[topic][word] += 1;
                    sumTopicWordCountDMM[topic] += 1;
                    subtopic += numTopics;
                }
                // 更新主题分配
                topicAssignments.get(dIndex).set(wIndex, subtopic);
            }
        }
    }
    //初始化迭代
    public void sampleSingleInitialIteration()
    {
        //对每篇文档循环
        for (int dIndex = 0; dIndex < numDocuments; dIndex++) {
            //获取文档
            List<Integer> document = corpus.get(dIndex);
            //文档的长度，即文档包含的所有单词数
            int docSize = document.size();
            //文档主题分配，这里需要理解一下
            int topic = topicAssignments.get(dIndex).get(0) % numTopics;
            //主题生成的文档统计，移除该文档
            docTopicCount[topic] = docTopicCount[topic] - 1;
            //循环文档的每一个单词
            for (int wIndex = 0; wIndex < docSize; wIndex++) {
                //获取单词的编号
                int word = document.get(wIndex);
                //获取subtopic
                int subtopic = topicAssignments.get(dIndex).get(wIndex);
                //如果subtopic和topic相同，来自于隐变量，否则来自于多项式分布
                if (topic == subtopic) {
                    //主题-单词 数目减1
                    topicWordCountLF[topic][word] -= 1;
                    //主题对应的总的单词数-1
                    sumTopicWordCountLF[topic] -= 1;
                }
                else {
                    //主题-单词 数目减1
                    topicWordCountDMM[topic][word] -= 1;
                    //主题对应的总的单词数-1
                    sumTopicWordCountDMM[topic] -= 1;
                }
            }
            // 抽取文档所属的主题，计算该篇文档属于每个主题的概率，然后基于轮盘赌进行选择
            for (int tIndex = 0; tIndex < numTopics; tIndex++) {
                //这里这个公式是哪里来的呢,这里作者弄得词都是来自于多项式分布
                multiPros[tIndex] = (docTopicCount[tIndex] + alpha);
                for (int wIndex = 0; wIndex < docSize; wIndex++) {
                    int word = document.get(wIndex);
                    multiPros[tIndex] *= (lambda * (topicWordCountLF[tIndex][word] + beta)
                            / (sumTopicWordCountLF[tIndex] + betaSum) + (1 - lambda)
                            * (topicWordCountDMM[tIndex][word] + beta)
                            / (sumTopicWordCountDMM[tIndex] + betaSum));
                }
            }
            //基于轮盘赌进行选择
            topic = FuncUtils.nextDiscrete(multiPros);
            //新主题对应的文档数量加1
            docTopicCount[topic] += 1;
            //判断该主题是来自于隐特征还是多项式分布
            for (int wIndex = 0; wIndex < docSize; wIndex++) {
                int word = document.get(wIndex);// wordID
                int subtopic = topic;
              //这里是对s_{di}的抽样，采用的是直接计算，并没有使用轮盘赌
                if (lambda * (topicWordCountLF[topic][word] + beta)
                        / (sumTopicWordCountLF[topic] + betaSum) > (1 - lambda)
                        * (topicWordCountDMM[topic][word] + beta)
                        / (sumTopicWordCountDMM[topic] + betaSum)) {
                    topicWordCountLF[topic][word] += 1;
                    sumTopicWordCountLF[topic] += 1;
                }
                else {
                    topicWordCountDMM[topic][word] += 1;
                    sumTopicWordCountDMM[topic] += 1;
                    subtopic += numTopics;
                }
                // Update topic assignments
                topicAssignments.get(dIndex).set(wIndex, subtopic);
            }
        }
    }
    public void writeParameters()
        throws IOException
    {
        BufferedWriter writer = new BufferedWriter(new FileWriter(folderPath + expName + ".paras"));
        writer.write("-model" + "\t" + "LFDMM");
        writer.write("\n-corpus" + "\t" + corpusPath);
        writer.write("\n-vectors" + "\t" + vectorFilePath);
        writer.write("\n-ntopics" + "\t" + numTopics);
        writer.write("\n-alpha" + "\t" + alpha);
        writer.write("\n-beta" + "\t" + beta);
        writer.write("\n-lambda" + "\t" + lambda);
        writer.write("\n-initers" + "\t" + numInitIterations);
        writer.write("\n-niters" + "\t" + numIterations);
        writer.write("\n-twords" + "\t" + topWords);
        writer.write("\n-name" + "\t" + expName);
        if (tAssignsFilePath.length() > 0)
            writer.write("\n-initFile" + "\t" + tAssignsFilePath);
        if (savestep > 0)
            writer.write("\n-sstep" + "\t" + savestep);
        writer.close();
    }
    public void writeDictionary()
        throws IOException
    {
        BufferedWriter writer = new BufferedWriter(new FileWriter(folderPath + expName
                + ".vocabulary"));
        for (String word : word2IdVocabulary.keySet()) {
            writer.write(word + " " + word2IdVocabulary.get(word) + "\n");
        }
        writer.close();
    }
    public void writeIDbasedCorpus()
        throws IOException
    {
        BufferedWriter writer = new BufferedWriter(new FileWriter(folderPath + expName
                + ".IDcorpus"));
        for (int dIndex = 0; dIndex < numDocuments; dIndex++) {
            int docSize = corpus.get(dIndex).size();
            for (int wIndex = 0; wIndex < docSize; wIndex++) {
                writer.write(corpus.get(dIndex).get(wIndex) + " ");
            }
            writer.write("\n");
        }
        writer.close();
    }
    public void writeTopicAssignments()
        throws IOException
    {
        BufferedWriter writer = new BufferedWriter(new FileWriter(folderPath + expName
                + ".topicAssignments"));
        for (int dIndex = 0; dIndex < numDocuments; dIndex++) {
            int docSize = corpus.get(dIndex).size();
            for (int wIndex = 0; wIndex < docSize; wIndex++) {
                writer.write(topicAssignments.get(dIndex).get(wIndex) + " ");
            }
            writer.write("\n");
        }
        writer.close();
    }
    public void writeTopicVectors()
        throws IOException
    {
        BufferedWriter writer = new BufferedWriter(new FileWriter(folderPath + expName
                + ".topicVectors"));
        for (int i = 0; i < numTopics; i++) {
            for (int j = 0; j < vectorSize; j++)
                writer.write(topicVectors[i][j] + " ");
            writer.write("\n");
        }
        writer.close();
    }
    public void writeTopTopicalWords()
        throws IOException
    {
        BufferedWriter writer = new BufferedWriter(new FileWriter(folderPath + expName
                + ".topWords"));
        for (int tIndex = 0; tIndex < numTopics; tIndex++) {
            writer.write("Topic" + new Integer(tIndex) + ":");
            Map<Integer, Double> topicWordProbs = new TreeMap<Integer, Double>();
            for (int wIndex = 0; wIndex < vocabularySize; wIndex++) {
                //获取概率值，这里可以看出包含两部分的内容，将两部分信息进行融合了
                double pro = lambda * expDotProductValues[tIndex][wIndex] / sumExpValues[tIndex]
                        + (1 - lambda) * (topicWordCountDMM[tIndex][wIndex] + beta)
                        / (sumTopicWordCountDMM[tIndex] + betaSum);
                topicWordProbs.put(wIndex, pro);
            }
            //主题词分布降序排序
            topicWordProbs = FuncUtils.sortByValueDescending(topicWordProbs);
            Set<Integer> mostLikelyWords = topicWordProbs.keySet();
            int count = 0;
            for (Integer index : mostLikelyWords) {
                if (count < topWords) {
                    writer.write(" " + id2WordVocabulary.get(index));
                    count += 1;
                }
                else {
                    writer.write("\n\n");
                    break;
                }
            }
        }
        writer.close();
    }
    public void writeTopicWordPros()
        throws IOException
    {
        BufferedWriter writer = new BufferedWriter(new FileWriter(folderPath + expName + ".phi"));
        for (int t = 0; t < numTopics; t++) {
            for (int w = 0; w < vocabularySize; w++) {
                double pro = lambda * expDotProductValues[t][w] / sumExpValues[t] + (1 - lambda)
                        * (topicWordCountDMM[t][w] + beta) / (sumTopicWordCountDMM[t] + betaSum);
                writer.write(pro + " ");
            }
            writer.write("\n");
        }
        writer.close();
    }
    public void writeDocTopicPros()
        throws IOException
    {
        BufferedWriter writer = new BufferedWriter(new FileWriter(folderPath + expName + ".theta"));
        for (int i = 0; i < numDocuments; i++) {
            int docSize = corpus.get(i).size();
            double sum = 0.0;
            for (int tIndex = 0; tIndex < numTopics; tIndex++) {
                multiPros[tIndex] = (docTopicCount[tIndex] + alpha);
                for (int wIndex = 0; wIndex < docSize; wIndex++) {
                    int word = corpus.get(i).get(wIndex);
                    multiPros[tIndex] *= (lambda * expDotProductValues[tIndex][word]
                            / sumExpValues[tIndex] + (1 - lambda)
                            * (topicWordCountDMM[tIndex][word] + beta)
                            / (sumTopicWordCountDMM[tIndex] + betaSum));
                }
                sum += multiPros[tIndex];
            }
            for (int tIndex = 0; tIndex < numTopics; tIndex++) {
                writer.write((multiPros[tIndex] / sum) + " ");
            }
            writer.write("\n");
        }
        writer.close();
    }
    public void write()
        throws IOException
    {
        //主题词分布
        writeTopTopicalWords();
        writeDocTopicPros();
        writeTopicAssignments();
        writeTopicWordPros();
    }
    public static void main(String args[])
        throws Exception
    {
        //初始化迭代次数----模型迭代次数
        LFDMM lfdmm = new LFDMM("", "", 40, 0.1, 0.01, 0.6, 20,
                20, 20, "LFDMM");
        lfdmm.writeParameters();
        lfdmm.inference();
    }
}