Examples with LDA_Gibbs topicmodels.LDA.LDA_Gibbs used on opensource projects

Example 1 with LDA_Gibbs

use of topicmodels.LDA.LDA_Gibbs in project IR_Base by Linda-sunshine.

the class TransductiveMain method main.

public static void main(String[] args) throws IOException, ParseException {
    // Define the number of classes in this Naive Bayes.
    int classNumber = 5;
    // The default value is unigram.
    int Ngram = 2;
    // Document length threshold
    int lengthThreshold = 5;
    // each sentence should have at least 2 sentences for HTSM, LRSHTM
    int minimunNumberofSentence = 2;
    /**
     ***parameters for the two-topic topic model****
     */
    // pLSA, LDA_Gibbs, LDA_Variational
    String topicmodel = "pLSA";
    int number_of_topics = 30;
    // these two parameters must be larger than 1!!!
    double alpha = 1.0 + 1e-2, beta = 1.0 + 1e-3, eta = 5.0;
    // negative converge means do need to check likelihood convergency
    double converge = -1, lambda = 0.7;
    int number_of_iteration = 100;
    boolean aspectSentiPrior = true;
    /**
     ***The parameters used in loading files.****
     */
    String folder = "./data/amazon/tablet/topicmodel";
    String suffix = ".json";
    String stopword = "./data/Model/stopwords.dat";
    // Token model.
    String tokenModel = "./data/Model/en-token.bin";
    // Sentence model. Need it for pos tagging.
    String stnModel = "./data/Model/en-sent.bin";
    String tagModel = "./data/Model/en-pos-maxent.bin";
    String sentiWordNet = "./data/Model/SentiWordNet_3.0.0_20130122.txt";
    // Added by Mustafizur----------------
    String pathToPosWords = "./data/Model/SentiWordsPos.txt";
    String pathToNegWords = "./data/Model/SentiWordsNeg.txt";
    String pathToNegationWords = "./data/Model/negation_words.txt";
    // String category = "tablets"; //"electronics"
    // String dataSize = "86jsons"; //"50K", "100K"
    // String fvFile = String.format("./data/Features/fv_%dgram_%s_%s.txt", Ngram, category, dataSize);
    // String fvStatFile = String.format("./data/Features/fv_%dgram_stat_%s_%s.txt", Ngram, category, dataSize);
    // String aspectlist = "./data/Model/aspect_output_simple.txt";
    String fvFile = String.format("./data/Features/fv_%dgram_topicmodel.txt", Ngram);
    String fvStatFile = String.format("./data/Features/fv_%dgram_stat_topicmodel.txt", Ngram);
    String aspectSentiList = "./data/Model/aspect_sentiment_tablet.txt";
    String aspectList = "./data/Model/aspect_tablet.txt";
    /**
     ***Parameters in learning style.****
     */
    // "SUP", "SEMI"
    String style = "SEMI";
    // "RW", "RW-ML", "RW-L2R"
    String method = "RW-L2R";
    /**
     ***Parameters in transductive learning.****
     */
    String debugOutput = "data/debug/topical.sim";
    // String debugOutput = null;
    boolean releaseContent = false;
    // k fold-cross validation
    int CVFold = 10;
    // choice of base learner
    String multipleLearner = "SVM";
    // trade-off parameter
    double C = 1.0;
    /**
     ***Parameters in feature selection.****
     */
    // String featureSelection = "DF"; //Feature selection method.
    // double startProb = 0.5; // Used in feature selection, the starting point of the features.
    // double endProb = 0.999; // Used in feature selection, the ending point of the features.
    // int DFthreshold = 30; // Filter the features with DFs smaller than this threshold.
    // 
    // System.out.println("Performing feature selection, wait...");
    // jsonAnalyzer analyzer = new jsonAnalyzer(tokenModel, classNumber, null, Ngram, lengthThreshold);
    // analyzer.LoadStopwords(stopwords);
    // analyzer.LoadDirectory(folder, suffix); //Load all the documents as the data set.
    // analyzer.featureSelection(fvFile, featureSelection, startProb, endProb, DFthreshold); //Select the features.
    System.out.println("Creating feature vectors, wait...");
    AspectAnalyzer analyzer = new AspectAnalyzer(tokenModel, stnModel, tagModel, classNumber, fvFile, Ngram, lengthThreshold, aspectList, true);
    // Added by Mustafizur----------------
    analyzer.setMinimumNumberOfSentences(minimunNumberofSentence);
    // Load the sentiwordnet file.
    analyzer.LoadStopwords(stopword);
    // analyzer.loadPriorPosNegWords(sentiWordNet, pathToPosWords, pathToNegWords, pathToNegationWords);
    analyzer.setReleaseContent(releaseContent);
    // Added by Mustafizur----------------
    // Load all the documents as the data set.
    analyzer.LoadDirectory(folder, suffix);
    analyzer.setFeatureValues("TF", 0);
    // Get the collection of all the documents.
    _Corpus c = analyzer.returnCorpus(fvStatFile);
    pLSA tModel = null;
    if (topicmodel.equals("pLSA")) {
        tModel = new pLSA_multithread(number_of_iteration, converge, beta, c, lambda, number_of_topics, alpha);
    } else if (topicmodel.equals("LDA_Gibbs")) {
        tModel = new LDA_Gibbs(number_of_iteration, converge, beta, c, lambda, number_of_topics, alpha, 0.4, 50);
    } else if (topicmodel.equals("LDA_Variational")) {
        tModel = new LDA_Variational_multithread(number_of_iteration, converge, beta, c, lambda, number_of_topics, alpha, 10, -1);
    } else {
        System.out.println("The selected topic model has not developed yet!");
        return;
    }
    tModel.setDisplayLap(0);
    tModel.setSentiAspectPrior(aspectSentiPrior);
    tModel.LoadPrior(aspectSentiPrior ? aspectSentiList : aspectList, eta);
    tModel.EMonCorpus();
    // construct effective feature values for supervised classifiers
    analyzer.setFeatureValues("BM25", 2);
    // how to set this reasonably
    c.mapLabels(3);
    if (style.equals("SEMI")) {
        // perform transductive learning
        System.out.println("Start Transductive Learning, wait...");
        double learningRatio = 1.0;
        // k nearest labeled, k' nearest unlabeled
        int k = 30, kPrime = 20;
        // labeled data weight, unlabeled data weight
        double tAlpha = 1.0, tBeta = 0.1;
        // convergence of random walk, weight of random walk
        double tDelta = 1e-5, tEta = 0.6;
        boolean simFlag = false, weightedAvg = true;
        // bound for generating rating constraints (must be zero in binary case)
        int bound = 0;
        // top K similar documents for constructing pairwise ranking targets
        int topK = 25;
        double noiseRatio = 1.0;
        boolean metricLearning = true;
        // training LambdaRank with multi-threads
        boolean multithread_LR = true;
        GaussianFieldsByRandomWalk mySemi = null;
        if (method.equals("RW")) {
            mySemi = new GaussianFieldsByRandomWalk(c, multipleLearner, C, learningRatio, k, kPrime, tAlpha, tBeta, tDelta, tEta, weightedAvg);
        } else if (method.equals("RW-ML")) {
            mySemi = new LinearSVMMetricLearning(c, multipleLearner, C, learningRatio, k, kPrime, tAlpha, tBeta, tDelta, tEta, weightedAvg, bound);
            ((LinearSVMMetricLearning) mySemi).setMetricLearningMethod(metricLearning);
        } else if (method.equals("RW-L2R")) {
            mySemi = new L2RMetricLearning(c, multipleLearner, C, learningRatio, k, kPrime, tAlpha, tBeta, tDelta, tEta, weightedAvg, topK, noiseRatio, multithread_LR);
        }
        mySemi.setSimilarity(simFlag);
        mySemi.setDebugOutput(debugOutput);
        mySemi.crossValidation(CVFold, c);
    } else if (style.equals("SUP")) {
        // perform supervised learning
        System.out.println("Start SVM, wait...");
        SVM mySVM = new SVM(c, C);
        mySVM.crossValidation(CVFold, c);
    }
}

Example 2 with LDA_Gibbs

use of topicmodels.LDA.LDA_Gibbs in project IR_Base by Linda-sunshine.

the class Execution method main.

public static void main(String[] args) throws IOException, ParseException {
    Parameter param = new Parameter(args);
    System.out.println(param.toString());
    String stnModel = (param.m_model.equals("HTMM") || param.m_model.equals("LRHTMM")) ? param.m_stnModel : null;
    String posModel = (param.m_model.equals("HTMM") || param.m_model.equals("LRHTMM")) ? param.m_posModel : null;
    _Corpus corpus;
    Analyzer analyzer;
    /**
     *Load the data from vector file**
     */
    if (param.m_fvFile != null && (new File(param.m_fvFile)).exists()) {
        analyzer = new VctAnalyzer(param.m_classNumber, param.m_lengthThreshold, param.m_featureFile);
        // Load all the documents as the data set.
        analyzer.LoadDoc(param.m_fvFile);
        corpus = analyzer.getCorpus();
    } else {
        /**
         *Load the data from text file**
         */
        analyzer = new DocAnalyzer(param.m_tokenModel, stnModel, posModel, param.m_classNumber, param.m_featureFile, param.m_Ngram, param.m_lengthThreshold);
        ((DocAnalyzer) analyzer).setReleaseContent(!param.m_weightScheme.equals("PR"));
        if (param.m_featureFile == null) {
            /**
             **Pre-process the data.****
             */
            // Feture selection.
            System.out.println("Performing feature selection, wait...");
            param.m_featureFile = String.format("./data/Features/%s_fv.dat", param.m_featureSelection);
            param.m_featureStat = String.format("./data/Features/%s_fv_stat.dat", param.m_featureSelection);
            System.out.println(param.printFeatureSelectionConfiguration());
            ((DocAnalyzer) analyzer).LoadStopwords(param.m_stopwords);
            // Load all the documents as the data set.
            analyzer.LoadDirectory(param.m_folder, param.m_suffix);
            // Select the features.
            analyzer.featureSelection(param.m_featureFile, param.m_featureSelection, param.m_startProb, param.m_endProb, param.m_maxDF, param.m_minDF);
        }
        // Collect vectors for documents.
        System.out.println("Creating feature vectors, wait...");
        // Load all the documents as the data set.
        analyzer.LoadDirectory(param.m_folder, param.m_suffix);
        analyzer.setFeatureValues(param.m_featureValue, param.m_norm);
        corpus = analyzer.returnCorpus(param.m_featureStat);
    }
    if (param.m_weightScheme.equals("PR")) {
        System.out.println("Creating PageRank instance weighting, wait...");
        PageRank myPR = new PageRank(corpus, param.m_C, 100, 50, 1e-6);
        myPR.train(corpus.getCollection());
    }
    // Execute different classifiers.
    if (param.m_style.equals("SUP")) {
        BaseClassifier model = null;
        if (param.m_model.equals("NB")) {
            // Define a new naive bayes with the parameters.
            System.out.println("Start naive bayes, wait...");
            model = new NaiveBayes(corpus);
        } else if (param.m_model.equals("LR")) {
            // Define a new logistics regression with the parameters.
            System.out.println("Start logistic regression, wait...");
            model = new LogisticRegression(corpus, param.m_C);
        } else if (param.m_model.equals("PR-LR")) {
            // Define a new logistics regression with the parameters.
            System.out.println("Start posterior regularized logistic regression, wait...");
            model = new PRLogisticRegression(corpus, param.m_C);
        } else if (param.m_model.equals("SVM")) {
            // corpus.save2File("data/FVs/fvector.dat");
            System.out.println("Start SVM, wait...");
            model = new SVM(corpus, param.m_C);
        } else {
            System.out.println("Classifier has not been developed yet!");
            System.exit(-1);
        }
        model.setDebugOutput(param.m_debugOutput);
        model.crossValidation(param.m_CVFold, corpus);
    } else if (param.m_style.equals("SEMI")) {
        BaseClassifier model = null;
        if (param.m_model.equals("GF")) {
            System.out.println("Start Gaussian Field by matrix inversion, wait...");
            model = new GaussianFields(corpus, param.m_classifier, param.m_C, param.m_sampleRate, param.m_kUL, param.m_kUU);
        } else if (param.m_model.equals("GF-RW")) {
            System.out.println("Start Gaussian Field by random walk, wait...");
            model = new GaussianFieldsByRandomWalk(corpus, param.m_classifier, param.m_C, param.m_sampleRate, param.m_kUL, param.m_kUU, param.m_alpha, param.m_beta, param.m_converge, param.m_eta, param.m_weightedAvg);
        } else if (param.m_model.equals("GF-RW-ML")) {
            System.out.println("Start Gaussian Field with distance metric learning by random walk, wait...");
            model = new LinearSVMMetricLearning(corpus, param.m_classifier, param.m_C, param.m_sampleRate, param.m_kUL, param.m_kUU, param.m_alpha, param.m_beta, param.m_converge, param.m_eta, param.m_weightedAvg, param.m_bound);
        // ((LinearSVMMetricLearning)model).setMetricLearningMethod(false);
        // ((LinearSVMMetricLearning)model).verification(param.m_CVFold, corpus, param.m_debugOutput);
        } else {
            System.out.println("Classifier has not been developed yet!");
            System.exit(-1);
        }
        model.setDebugOutput(param.m_debugOutput);
        model.crossValidation(param.m_CVFold, corpus);
    } else if (param.m_style.equals("TM")) {
        TopicModel model = null;
        if (param.m_model.equals("2topic")) {
            model = new twoTopic(param.m_maxmIterations, param.m_converge, param.m_beta, corpus, param.m_lambda);
        } else if (param.m_model.equals("pLSA")) {
            if (param.m_multithread == false) {
                model = new pLSA(param.m_maxmIterations, param.m_converge, param.m_beta, corpus, param.m_lambda, param.m_numTopics, param.m_alpha);
            } else {
                model = new pLSA_multithread(param.m_maxmIterations, param.m_converge, param.m_beta, corpus, param.m_lambda, param.m_numTopics, param.m_alpha);
            }
            ((pLSA) model).LoadPrior(param.m_priorFile, param.m_gamma);
        } else if (param.m_model.equals("vLDA")) {
            if (param.m_multithread == false) {
                model = new LDA_Variational(param.m_maxmIterations, param.m_converge, param.m_beta, corpus, param.m_lambda, param.m_numTopics, param.m_alpha, param.m_maxVarIterations, param.m_varConverge);
            } else {
                model = new LDA_Variational_multithread(param.m_maxmIterations, param.m_converge, param.m_beta, corpus, param.m_lambda, param.m_numTopics, param.m_alpha, param.m_maxVarIterations, param.m_varConverge);
            }
            ((LDA_Variational) model).LoadPrior(param.m_priorFile, param.m_gamma);
        } else if (param.m_model.equals("gLDA")) {
            model = new LDA_Gibbs(param.m_maxmIterations, param.m_converge, param.m_beta, corpus, param.m_lambda, param.m_numTopics, param.m_alpha, param.m_burnIn, param.m_lag);
            ((LDA_Gibbs) model).LoadPrior(param.m_priorFile, param.m_gamma);
        } else if (param.m_model.equals("HTMM")) {
            model = new HTMM(param.m_maxmIterations, param.m_converge, param.m_beta, corpus, param.m_numTopics, param.m_alpha);
        } else if (param.m_model.equals("LRHTMM")) {
            model = new LRHTMM(param.m_maxmIterations, param.m_converge, param.m_beta, corpus, param.m_numTopics, param.m_alpha, param.m_C);
        } else {
            System.out.println("The specified topic model has not been developed yet!");
            System.exit(-1);
        }
        if (param.m_CVFold <= 1) {
            model.EMonCorpus();
            // fixed: print top 10 words
            model.printTopWords(10);
        } else
            model.crossValidation(param.m_CVFold);
    } else if (param.m_style.equals("FV")) {
        corpus.save2File(param.m_fvFile);
        System.out.format("Vectors saved to %s...\n", param.m_fvFile);
    } else
        System.out.println("Learning paradigm has not developed yet!");
}

Example 3 with LDA_Gibbs

use of topicmodels.LDA.LDA_Gibbs in project IR_Base by Linda-sunshine.

the class TopicModelMain method main.

public static void main(String[] args) throws IOException, ParseException {
    int mb = 1024 * 1024;
    Runtime rTime = Runtime.getRuntime();
    System.out.println("totalMem\t:" + rTime.totalMemory() / mb);
    // Define the number of classes in this Naive Bayes.
    int classNumber = 5;
    // The default value is unigram.
    int Ngram = 1;
    // The way of calculating the feature value, which can also be "TFIDF", "BM25"
    String featureValue = "TF";
    // The way of normalization.(only 1 and 2)
    int norm = 0;
    // Document length threshold
    int lengthThreshold = 5;
    // each document should have at least 2 sentences
    int minimunNumberofSentence = 2;
    /**
     ***parameters for the two-topic topic model****
     */
    // ACCTM, ACCTM_TwoTheta, ACCTM_C, ACCTM_CZ, ACCTM_CZLR, LDAonArticles, ACCTM_C,
    // correspondence_LDA_Gibbs, LDA_Gibbs_Debug, LDA_Variational_multithread
    // 2topic, pLSA, HTMM, LRHTMM, Tensor, LDA_Gibbs, LDA_Variational, HTSM, LRHTSM,
    // LDAGibbs4AC_test, DCMCorrLDA_multi_E_test,DCMLDA4AC_test, DCMDMCorrLDA_multi_E_test
    // DCMDMCorrLDA_test, DCMDMMCorrLDA_test, corrLDA_Gibbs_test,
    // DCMCorrLDA_Multi_EM, sparseDCMLDA_test, DCMLDA_test, sparseLDA_test, LDA_Gibbs_test
    // sparseClusterDCMLDA, sparseClusterDCMLDA_test
    String topicmodel = "weightedCorrespondenceModel_test";
    String category = "tablet";
    int number_of_topics = 15;
    // false means in training there is no reviews from NewEgg
    boolean loadNewEggInTrain = true;
    // false means no shuffling and true means shuffling
    boolean setRandomFold = true;
    // 0 means nothing loaded as prior; 1 = load both senti and aspect; 2 means load only aspect
    int loadAspectSentiPrior = 0;
    // these two parameters must be larger than 1!!!
    double alpha = 1.0 + 1e-2, beta = 1.0 + 1e-3, eta = topicmodel.equals("LDA_Gibbs") ? 200 : 5.0;
    // negative converge means do not need to check likelihood convergency
    double converge = 1e-9, lambda = 0.9;
    int varIter = 10;
    double varConverge = 1e-5;
    int topK = 20, number_of_iteration = 50, crossV = 1;
    int gibbs_iteration = 1000, gibbs_lag = 50;
    int displayLap = 20;
    // gibbs_iteration = 50;
    // gibbs_lag = 20;
    // displayLap = 20;
    double burnIn = 0.4;
    boolean sentence = false;
    // most popular items under each category from Amazon
    // needed for docSummary
    String[] tabletProductList = { "B008GFRDL0" };
    String[] cameraProductList = { "B005IHAIMA" };
    String[] phoneProductList = { "B00COYOAYW" };
    String[] tvProductList = { "B0074FGLUM" };
    /**
     ***The parameters used in loading files.****
     */
    String amazonFolder = "./data/amazon/tablet/topicmodel";
    String newEggFolder = "./data/NewEgg";
    String articleType = "Tech";
    // articleType = "Reuters";
    // articleType = "Gadgets";
    // articleType = "Yahoo";
    // articleType = "APP";
    String articleFolder = String.format("./data/ParentChildTopicModel/%sArticles", articleType);
    // articleFolder = String.format(
    // "./data/ParentChildTopicModel/Reuters",
    // articleType);
    String commentFolder = String.format("./data/ParentChildTopicModel/%sComments", articleType);
    String suffix = ".json";
    // Token model.
    String tokenModel = "./data/Model/en-token.bin";
    String stnModel = null;
    String posModel = null;
    if (topicmodel.equals("HTMM") || topicmodel.equals("LRHTMM") || topicmodel.equals("HTSM") || topicmodel.equals("LRHTSM")) {
        // Sentence model.
        stnModel = "./data/Model/en-sent.bin";
        // POS model.
        posModel = "./data/Model/en-pos-maxent.bin";
        sentence = true;
    }
    String fvFile = String.format("./data/Features/fv_%dgram_topicmodel_%s.txt", Ngram, articleType);
    String fvStatFile = String.format("./data/Features/fv_%dgram_stat_%s_%s.txt", Ngram, articleType, topicmodel);
    String aspectList = "./data/Model/aspect_" + category + ".txt";
    String aspectSentiList = "./data/Model/aspect_sentiment_" + category + ".txt";
    String pathToPosWords = "./data/Model/SentiWordsPos.txt";
    String pathToNegWords = "./data/Model/SentiWordsNeg.txt";
    String pathToNegationWords = "./data/Model/negation_words.txt";
    String pathToSentiWordNet = "./data/Model/SentiWordNet_3.0.0_20130122.txt";
    File rootFolder = new File("./data/results");
    if (!rootFolder.exists()) {
        System.out.println("creating root directory" + rootFolder);
        rootFolder.mkdir();
    }
    SimpleDateFormat dateFormatter = new SimpleDateFormat("yyyyMMdd-HHmm");
    String filePrefix = String.format("./data/results/%s", dateFormatter.format(new Date()));
    filePrefix = filePrefix + "-" + topicmodel + "-" + articleType;
    File resultFolder = new File(filePrefix);
    if (!resultFolder.exists()) {
        System.out.println("creating directory" + resultFolder);
        resultFolder.mkdir();
    }
    String outputFile = filePrefix + "/consoleOutput.txt";
    PrintStream printStream = new PrintStream(new FileOutputStream(outputFile));
    System.setOut(printStream);
    String infoFilePath = filePrefix + "/Information.txt";
    // //store top k words distribution over topic
    String topWordPath = filePrefix + "/topWords.txt";
    /**
     ***Parameters in feature selection.****
     */
    String stopwords = "./data/Model/stopwords.dat";
    // Feature selection method.
    String featureSelection = "DF";
    // Used in feature selection, the starting point of the features.
    double startProb = 0.;
    // Used in feature selection, the ending point of the features.
    double endProb = 0.999;
    // Filter the features with DFs smaller than this threshold.
    int DFthreshold = 5;
    System.out.println("Performing feature selection, wait...");
    // jsonAnalyzer analyzer = new jsonAnalyzer(tokenModel, classNumber, null, Ngram, lengthThreshold);
    // jsonAnalyzer analyzer = new jsonAnalyzer(tokenModel, classNumber, fvFile, Ngram, lengthThreshold, stnModel, posModel);
    // newEggAnalyzer analyzer = new newEggAnalyzer(tokenModel, classNumber, fvFile, Ngram, lengthThreshold, stnModel, posModel, category, 2);
    // analyzer.LoadDirectory(folder, suffix); //Load all the documents as the data set.
    /**
     *** parent child topic model ****
     */
    ParentChildAnalyzer analyzer = new ParentChildAnalyzer(tokenModel, classNumber, fvFile, Ngram, lengthThreshold);
    // analyzer.LoadDirectory(commentFolder, suffix);
    if (topicmodel.equals("LDA_APPMerged"))
        articleFolder = String.format("./data/ParentChildTopicModel/%sDescriptionsReviews", articleType);
    // articleFolder = String.format(
    // "./data/ParentChildTopicModel/%sArticles4Merged",
    // articleType);
    // 
    // commentFolder = String.format(
    // "./data/ParentChildTopicModel/%sComments4Merged",
    // articleType);
    // 
    analyzer.LoadParentDirectory(articleFolder, suffix);
    // analyzer.LoadDirectory(articleFolder, suffix);
    analyzer.LoadChildDirectory(commentFolder, suffix);
    // analyzer.LoadChildDirectory(commentFolder, suffix);
    // if((topicmodel."LDA_APP")&&(topicmodel!="LDA_APPMerged"))
    // analyzer.LoadChildDirectory(commentFolder, suffix);
    // analyzer.featureSelection(fvFile, featureSelection, startProb, endProb, DFthreshold); //Select the features.
    System.out.println("Creating feature vectors, wait...");
    // analyzer.LoadNewEggDirectory(newEggFolder, suffix); //Load all the documents as the data set.
    // analyzer.LoadDirectory(amazonFolder, suffix);
    analyzer.setFeatureValues(featureValue, norm);
    // Get the collection of all the documents.
    _Corpus c = analyzer.returnCorpus(fvStatFile);
    if (topicmodel.equals("2topic")) {
        twoTopic model = new twoTopic(number_of_iteration, converge, beta, c, lambda);
        if (crossV <= 1) {
            for (_Doc d : c.getCollection()) {
                model.inference(d);
                model.printTopWords(topK);
            }
        } else
            model.crossValidation(crossV);
    } else {
        pLSA model = null;
        if (topicmodel.equals("pLSA")) {
            model = new pLSA_multithread(number_of_iteration, converge, beta, c, lambda, number_of_topics, alpha);
        } else if (topicmodel.equals("LDA_Gibbs")) {
            // number_of_topics = 15;
            model = new // in gibbs sampling, no need to compute log-likelihood during sampling
            LDA_Gibbs(// in gibbs sampling, no need to compute log-likelihood during sampling
            gibbs_iteration, // in gibbs sampling, no need to compute log-likelihood during sampling
            0, // in gibbs sampling, no need to compute log-likelihood during sampling
            beta, // in gibbs sampling, no need to compute log-likelihood during sampling
            c, lambda, number_of_topics, alpha, burnIn, gibbs_lag);
        } else if (topicmodel.equals("LDA_Variational_multithread")) {
            model = new LDA_Variational_multithread(number_of_iteration, converge, beta, c, lambda, number_of_topics, alpha, varIter, varConverge);
        } else if (topicmodel.equals("HTMM")) {
            model = new HTMM(number_of_iteration, converge, beta, c, number_of_topics, alpha);
        } else if (topicmodel.equals("HTSM")) {
            model = new HTSM(number_of_iteration, converge, beta, c, number_of_topics, alpha);
        } else if (topicmodel.equals("LRHTMM")) {
            model = new LRHTMM(number_of_iteration, converge, beta, c, number_of_topics, alpha, lambda);
        } else if (topicmodel.equals("LRHTSM")) {
            model = new LRHTSM(number_of_iteration, converge, beta, c, number_of_topics, alpha, lambda);
        } else if (topicmodel.equals("correspondence_LDA_Gibbs")) {
            double ksi = 800;
            double tau = 0.7;
            model = new // in gibbs sampling, no need to compute log-likelihood during sampling
            corrLDA_Gibbs(// in gibbs sampling, no need to compute log-likelihood during sampling
            gibbs_iteration, // in gibbs sampling, no need to compute log-likelihood during sampling
            0, // in gibbs sampling, no need to compute log-likelihood during sampling
            beta - 1, // in gibbs sampling, no need to compute log-likelihood during sampling
            c, lambda, number_of_topics, alpha - 1, burnIn, gibbs_lag);
        } else if (topicmodel.equals("ACCTM")) {
            double mu = 1.0;
            double[] gamma = { 0.5, 0.5 };
            double ksi = 800;
            double tau = 0.7;
            model = new ACCTM(gibbs_iteration, 0, beta - 1, c, lambda, number_of_topics, alpha - 1, burnIn, gibbs_lag);
        } else if (topicmodel.equals("ACCTM_C")) {
            double mu = 1.0;
            double[] gamma = { 0.5, 0.5 };
            beta = 1.001;
            double ksi = 800;
            double tau = 0.7;
            converge = 1e-5;
            model = new ACCTM_C(gibbs_iteration, 0, beta - 1, c, lambda, number_of_topics, alpha - 1, burnIn, gibbs_lag, gamma);
        } else if (topicmodel.equals("ACCTM_CHard")) {
            double mu = 1.0;
            double[] gamma = { 0.5, 0.5 };
            double ksi = 800;
            double tau = 0.7;
            beta = 1.001;
            model = new ACCTM_CHard(gibbs_iteration, 0, beta - 1, c, lambda, number_of_topics, alpha - 1, burnIn, gibbs_lag, gamma);
        } else if (topicmodel.equals("ACCTM_CZ")) {
            double mu = 1.0;
            double[] gamma = { 0.5, 0.5 };
            beta = 1.001;
            alpha = 1.01;
            double ksi = 800;
            double tau = 0.7;
            // number_of_topics = 30;
            model = new ACCTM_CZ(gibbs_iteration, 0, beta - 1, c, lambda, number_of_topics, alpha - 1, burnIn, gibbs_lag, gamma);
        } else if (topicmodel.equals("ACCTM_CZLR")) {
            double mu = 1.0;
            double[] gamma = { 0.5, 0.5 };
            beta = 1.001;
            alpha = 1.01;
            double ksi = 800;
            double tau = 0.7;
            // number_of_topics = 30;
            converge = 1e-9;
            model = new ACCTM_CZLR(gibbs_iteration, converge, beta - 1, c, lambda, number_of_topics, alpha - 1, burnIn, gibbs_lag, gamma);
        } else if (topicmodel.equals("DCMLDA_test")) {
            converge = 1e-3;
            int newtonIter = 50;
            double newtonConverge = 1e-3;
            // number_of_topics = 15;
            model = new DCMLDA_test(gibbs_iteration, converge, beta - 1, c, lambda, number_of_topics, alpha - 1, burnIn, gibbs_lag, newtonIter, newtonConverge);
            String priorFile = "./data/Features/" + articleType + "TopicWord.txt";
            model.LoadPrior(priorFile, eta);
        } else if (topicmodel.equals("LDA_Gibbs_test")) {
            // number_of_topics = 15;
            // in gibbs sampling, no need to compute
            // log-likelihood during sampling
            model = new LDA_Gibbs_test(gibbs_iteration, 0, beta, c, lambda, number_of_topics, alpha, burnIn, gibbs_lag);
        } else if (topicmodel.equals("DCMCorrLDA_multi_E_test")) {
            converge = 1e-2;
            int newtonIter = 30;
            double newtonConverge = 1e-2;
            gibbs_iteration = 40;
            gibbs_lag = 10;
            double ksi = 800;
            double tau = 0.7;
            double alphaC = 0.001;
            model = new DCMCorrLDA_multi_E_test(gibbs_iteration, converge, beta - 1, c, lambda, number_of_topics, alpha - 1, alphaC, burnIn, ksi, tau, gibbs_lag, newtonIter, newtonConverge);
            String priorFile = "./data/Features/" + articleType + "TopWords.txt";
            model.LoadPrior(priorFile, eta);
        } else if (topicmodel.equals("LDAGibbs4AC_test")) {
            double ksi = 800;
            double tau = 0.7;
            model = new LDAGibbs4AC_test(gibbs_iteration, 0, beta - 1, c, lambda, number_of_topics, alpha - 1, burnIn, gibbs_lag, ksi, tau);
        } else if (topicmodel.equals("DCMLDA4AC_test")) {
            // number_of_topics = 5;
            converge = 1e-3;
            double ksi = 800;
            double tau = 0.7;
            int newtonIter = 1000;
            double newtonConverge = 1e-3;
            model = new DCMLDA4AC_test(gibbs_iteration, converge, beta - 1, c, lambda, number_of_topics, alpha - 1, burnIn, gibbs_lag, ksi, tau, newtonIter, newtonConverge);
        } else if (topicmodel.equals("DCMCorrLDA_test")) {
            // number_of_topics = 15;
            converge = 1e-3;
            int newtonIter = 50;
            double newtonConverge = 1e-3;
            double ksi = 800;
            double tau = 0.7;
            double alphaC = 0.001;
            model = new DCMCorrLDA_test(gibbs_iteration, converge, beta - 1, c, lambda, number_of_topics, alpha - 1, alphaC, burnIn, ksi, tau, gibbs_lag, newtonIter, newtonConverge);
        } else if (topicmodel.equals("sparseDCMLDA_test")) {
            converge = 1e-3;
            int newtonIter = 50;
            double newtonConverge = 1e-3;
            // number_of_topics = 15;
            double tParam = 1;
            double sParam = 1;
            model = new sparseDCMLDA_test(gibbs_iteration, converge, beta - 1, c, lambda, number_of_topics, alpha - 1, burnIn, gibbs_lag, newtonIter, newtonConverge, tParam, sParam);
        /*
				 * String priorFile = "./data/Features/" + articleType +
				 * "TopicWord.txt"; model.LoadPrior(priorFile, eta);
				 */
        } else if (topicmodel.equals("sparseLDA_test")) {
            converge = 1e-3;
            // number_of_topics = 15;
            double tParam = 1;
            double sParam = 1;
            model = new sparseLDA_test(gibbs_iteration, 0, beta - 1, c, lambda, number_of_topics, alpha - 1, burnIn, gibbs_lag, tParam, sParam);
        } else if (topicmodel.equals("sparseClusterDCMLDA_test")) {
            converge = 1e-3;
            int newtonIter = 50;
            double newtonConverge = 1e-3;
            double tParam = 1;
            double sParam = 1;
            double gammaParam = 0.01;
            int clusterNum = 1;
            model = new sparseClusterDCMLDA_test(gibbs_iteration, converge, beta - 1, c, lambda, number_of_topics, alpha - 1, burnIn, gibbs_lag, newtonIter, newtonConverge, tParam, sParam, clusterNum, gammaParam);
        /*
				 * String priorFile = "./data/Features/" + articleType +
				 * "TopicWord.txt"; model.LoadPrior(priorFile, eta);
				 */
        } else if (topicmodel.equals("weightedCorrespondenceModel_test")) {
            beta = beta - 1;
            alpha = alpha - 1;
            // number_of_iteration = 2;
            double lbfgsConverge = varConverge;
            converge = 1e-3;
            model = new weightedCorrespondenceModel_test(number_of_iteration, converge, beta, c, lambda, number_of_topics, alpha, varIter, varConverge, lbfgsConverge);
        // 
        // String priorFile = "./data/Features/" + articleType + "TopicWord.txt";
        // model.LoadPrior(priorFile, eta);eta
        }
        model.setDisplayLap(displayLap);
        model.setInforWriter(infoFilePath);
        if (loadAspectSentiPrior == 1) {
            System.out.println("Loading aspect-senti list from " + aspectSentiList);
            model.setSentiAspectPrior(true);
            model.LoadPrior(aspectSentiList, eta);
        } else if (loadAspectSentiPrior == 2) {
            System.out.println("Loading aspect list from " + aspectList);
            model.setSentiAspectPrior(false);
            model.LoadPrior(aspectList, eta);
        } else {
            System.out.println("No prior is added!!");
        }
        if (crossV <= 1) {
            model.EMonCorpus();
            if (topWordPath == null)
                model.printTopWords(topK);
            else
                model.printTopWords(topK, topWordPath);
        } else {
            model.setRandomFold(setRandomFold);
            double trainProportion = 0.8;
            double testProportion = 1 - trainProportion;
            model.setPerplexityProportion(testProportion);
            model.crossValidation(crossV);
            model.printTopWords(topK, topWordPath);
        }
        model.closeWriter();
        if (sentence) {
            String summaryFilePath = "./data/results/Topics_" + number_of_topics + "_Summary.txt";
            model.setSummaryWriter(summaryFilePath);
            if (category.equalsIgnoreCase("camera"))
                ((HTMM) model).docSummary(cameraProductList);
            else if (category.equalsIgnoreCase("tablet"))
                ((HTMM) model).docSummary(tabletProductList);
            else if (category.equalsIgnoreCase("phone"))
                ((HTMM) model).docSummary(phoneProductList);
            else if (category.equalsIgnoreCase("tv"))
                ((HTMM) model).docSummary(tvProductList);
        }
    }
}