Examples with Classifier.supervised.modelAdaptation._AdaptStruct Classifier.supervised.modelAdaptation._AdaptStruct used on opensource projects

Example 16 with Classifier.supervised.modelAdaptation._AdaptStruct

use of Classifier.supervised.modelAdaptation._AdaptStruct in project IR_Base by Linda-sunshine.

the class CLinAdaptWithDP method loadUsers.

@Override
public void loadUsers(ArrayList<_User> userList) {
    m_userList = new ArrayList<_AdaptStruct>();
    // Init each user.
    for (_User user : userList) m_userList.add(new _DPAdaptStruct(user, m_dim));
    m_pWeights = new double[m_gWeights.length];
}

Example 17 with Classifier.supervised.modelAdaptation._AdaptStruct

use of Classifier.supervised.modelAdaptation._AdaptStruct in project IR_Base by Linda-sunshine.

the class CLRWithHDP method loadUsers.

@Override
public void loadUsers(ArrayList<_User> userList) {
    m_userList = new ArrayList<_AdaptStruct>();
    for (_User user : userList) m_userList.add(new _HDPAdaptStruct(user));
    m_pWeights = new double[m_gWeights.length];
}

Example 18 with Classifier.supervised.modelAdaptation._AdaptStruct

use of Classifier.supervised.modelAdaptation._AdaptStruct in project IR_Base by Linda-sunshine.

the class CLRWithMMB method loadUsers.

@Override
public void loadUsers(ArrayList<_User> userList) {
    m_userList = new ArrayList<_AdaptStruct>();
    for (_User user : userList) m_userList.add(new _MMBAdaptStruct(user));
    m_pWeights = new double[m_gWeights.length];
    m_indicator = new _HDPThetaStar[m_userList.size()][m_userList.size()];
}

Example 19 with Classifier.supervised.modelAdaptation._AdaptStruct

use of Classifier.supervised.modelAdaptation._AdaptStruct in project IR_Base by Linda-sunshine.

the class CLRWithMMB method trainTrace.

@Override
public double trainTrace(String data, int iter) {
    m_numberOfIterations = iter;
    m_thinning = 1;
    System.out.print(String.format("[Info]Joint Sampling for all zero edges: %b\n", m_jointAll));
    System.out.print(toString());
    double delta = 0, lastLikelihood = 0, curLikelihood = 0;
    double likelihoodX = 0, likelihoodY = 0;
    int count = 0;
    double likelihoodE = 0;
    // double[] likelihoodE;
    // clear user performance, init cluster assignment, assign each review to one cluster
    init();
    initThetaStars_Edges_Joint();
    sanityCheck();
    // Burn in period for doc.
    while (count++ < m_burnIn) {
        calculate_E_step();
        calculate_M_step();
    }
    try {
        String traceFile = String.format("%s_iter_%d_burnin_%d_thin_%d_%b_%d.txt", data, iter, m_burnIn, m_thinning, m_jointAll, System.currentTimeMillis());
        PrintWriter writer = new PrintWriter(new File(traceFile));
        // EM iteration.
        for (int i = 0; i < m_numberOfIterations; i++) {
            // Cluster assignment, thinning to reduce auto-correlation.
            calculate_E_step();
            likelihoodY = calculate_M_step();
            // accumulate the likelihood
            likelihoodX = accumulateLikelihoodX();
            // likelihoodE = accumulateDecomposedLikelihoodEMMB();
            // likelihoodE[3] = (m_MNL[2]/2)*Math.log(1-m_rho);
            likelihoodE = accumulateLikelihoodEMMB();
            likelihoodE += (m_MNL[2] / 2) * Math.log(1 - m_rho);
            // curLikelihood = likelihoodY + likelihoodX + likelihoodE[0] + likelihoodE[1] + likelihoodE[3];
            curLikelihood = likelihoodY + likelihoodX + likelihoodE;
            delta = (lastLikelihood - curLikelihood) / curLikelihood;
            // evaluate the model
            if (i % m_thinning == 0) {
                evaluateModel();
                test();
                for (_AdaptStruct u : m_userList) u.getPerfStat().clear();
            }
            // writer.write(String.format("%.5f\t%.5f\t%.5f\t%.5f\t%d\t%.5f\t%.5f\n", likelihoodE[0], likelihoodE[1], likelihoodE[2], likelihoodE[3], m_kBar, m_perf[0], m_perf[1]));
            writer.write(String.format("%.5f\t%.5f\t%.5f\t%.5f\t%d\t%.5f\t%.5f\n", likelihoodY, likelihoodX, likelihoodE, delta, m_kBar, m_perf[0], m_perf[1]));
            System.out.print(String.format("\n[Info]Step %d: likelihood: %.4f, Delta_likelihood: %.3f\n", i, curLikelihood, delta));
            if (Math.abs(delta) < m_converge)
                break;
            lastLikelihood = curLikelihood;
        }
        writer.close();
    } catch (IOException e) {
        e.printStackTrace();
    }
    // we do not want to miss the last sample?!
    evaluateModel();
    return curLikelihood;
}

Example 20 with Classifier.supervised.modelAdaptation._AdaptStruct

use of Classifier.supervised.modelAdaptation._AdaptStruct in project IR_Base by Linda-sunshine.

the class CoRegLR method loadUsers.

@Override
public void loadUsers(ArrayList<_User> userList) {
    int vSize = m_featureSize + 1;
    // step 1: create space
    m_userList = new ArrayList<_AdaptStruct>();
    for (int i = 0; i < userList.size(); i++) {
        _User user = userList.get(i);
        m_userList.add(new _CoRegLRAdaptStruct(user, i, vSize, m_topK));
    }
    // huge space consumption
    _CoRegLRAdaptStruct.sharedW = new double[vSize * m_userList.size()];
    // step 3: construct neighborhood graph
    constructNeighborhood(m_sType);
}