Examples with structures._Word structures._Word used on opensource projects

Example 56 with structures._Word

use of structures._Word in project IR_Base by Linda-sunshine.

the class DCMLDA4AC method initialize_probability.

protected void initialize_probability(Collection<_Doc> collection) {
    m_alpha = new double[number_of_topics];
    m_beta = new double[number_of_topics][vocabulary_size];
    m_totalAlpha = 0;
    m_totalBeta = new double[number_of_topics];
    m_topic_word_prob = new double[number_of_topics][vocabulary_size];
    for (_Doc d : collection) {
        if (d instanceof _ParentDoc4DCM) {
            _ParentDoc4DCM pDoc = (_ParentDoc4DCM) d;
            pDoc.setTopics4Gibbs(number_of_topics, 0, vocabulary_size);
            for (_Word w : pDoc.getWords()) {
                int wid = w.getIndex();
                int tid = w.getTopic();
                word_topic_sstat[tid][wid]++;
            }
            for (_ChildDoc cDoc : pDoc.m_childDocs) {
                cDoc.setTopics4Gibbs_LDA(number_of_topics, 0);
                for (_Word w : cDoc.getWords()) {
                    int wid = w.getIndex();
                    int tid = w.getTopic();
                    pDoc.m_wordTopic_stat[tid][wid]++;
                    pDoc.m_topic_stat[tid]++;
                    word_topic_sstat[tid][wid]++;
                }
            }
        }
    }
    initialAlphaBeta();
    imposePrior();
}

Example 57 with structures._Word

use of structures._Word in project IR_Base by Linda-sunshine.

the class DCMLDA4AC method cal_logLikelihood_partial4Child.

protected double cal_logLikelihood_partial4Child(_ChildDoc d) {
    double likelihood = 0;
    _ParentDoc4DCM pDoc = (_ParentDoc4DCM) d.m_parentDoc;
    for (_Word w : d.getWords()) {
        int wid = w.getIndex();
        double wordLikelihood = 0;
        for (int k = 0; k < number_of_topics; k++) {
            wordLikelihood += d.m_topics[k] * pDoc.m_wordTopic_prob[k][wid];
        }
        likelihood += Math.log(wordLikelihood);
    }
    return likelihood;
}

Example 58 with structures._Word

use of structures._Word in project IR_Base by Linda-sunshine.

the class DCMLDA4AC method initTestDoc.

public void initTestDoc(ArrayList<_Doc> sampleTestSet, _Doc d) {
    _ParentDoc4DCM pDoc = (_ParentDoc4DCM) d;
    for (_Stn stnObj : pDoc.getSentences()) {
        stnObj.setTopicsVct(number_of_topics);
    }
    int testLength = 0;
    pDoc.setTopics4GibbsTest(number_of_topics, 0, testLength, vocabulary_size);
    sampleTestSet.add(pDoc);
    for (_ChildDoc cDoc : pDoc.m_childDocs) {
        testLength = (int) (m_testWord4PerplexityProportion * cDoc.getTotalDocLength());
        cDoc.setTopics4GibbsTest(number_of_topics, d_alpha, testLength);
        for (_Word w : d.getWords()) {
            int wid = w.getIndex();
            int tid = w.getTopic();
            pDoc.m_wordTopic_stat[tid][wid]++;
            pDoc.m_topic_stat[tid]++;
        }
        sampleTestSet.add(cDoc);
        cDoc.createSparseVct4Infer();
    }
}

Example 59 with structures._Word

use of structures._Word in project IR_Base by Linda-sunshine.

the class DCMLDA4AC_test method printParentTopicAssignment.

protected void printParentTopicAssignment(_Doc d, File topicFolder) {
    String topicAssignmentFile = d.getName() + ".txt";
    try {
        PrintWriter pw = new PrintWriter(new File(topicFolder, topicAssignmentFile));
        for (_Word w : d.getWords()) {
            int index = w.getIndex();
            int topic = w.getTopic();
            String featureName = m_corpus.getFeature(index);
            pw.print(featureName + ":" + topic + "\t");
        }
        pw.flush();
        pw.close();
    } catch (FileNotFoundException e) {
        e.printStackTrace();
    }
}

Example 60 with structures._Word

use of structures._Word in project IR_Base by Linda-sunshine.

the class LDAGibbs4AC method cal_logLikelihood_partial4Child.

protected double cal_logLikelihood_partial4Child(_Doc d) {
    double docLogLikelihood = 0.0;
    for (_Word w : d.getTestWords()) {
        int wid = w.getIndex();
        double wordLogLikelihood = 0;
        for (int k = 0; k < number_of_topics; k++) {
            double wordPerTopicLikelihood = d.m_topics[k] * topic_term_probabilty[k][wid];
            wordLogLikelihood += wordPerTopicLikelihood;
        }
        docLogLikelihood += Math.log(wordLogLikelihood);
    }
    return docLogLikelihood;
}