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

Example 6 with structures._PerformanceStat

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

the class asyncCoRegLR method train.

// this is online training in each individual user
@Override
public double train() {
    double gNorm, gNormOld = Double.MAX_VALUE;
    int updateCount = 0;
    int predL, trueL;
    _Review doc;
    _PerformanceStat perfStat;
    _CoRegLRAdaptStruct user;
    initLBFGS();
    init();
    for (int t = 0; t < m_userOrder.length; t++) {
        user = (_CoRegLRAdaptStruct) m_userList.get(m_userOrder[t]);
        if (user.hasNextAdaptationIns()) {
            // test the latest model
            if (m_testmode != TestMode.TM_batch && (doc = user.getLatestTestIns()) != null) {
                perfStat = user.getPerfStat();
                predL = predict(doc, user);
                trueL = doc.getYLabel();
                perfStat.addOnePredResult(predL, trueL);
            }
            // in batch mode we will not accumulate the performance during adaptation
            // prepare to adapt: initialize gradient
            Arrays.fill(m_g, 0);
            calculateGradients(user);
            gNorm = gradientTest(user);
            if (m_displayLv == 1) {
                if (gNorm < gNormOld)
                    System.out.print("o");
                else
                    System.out.print("x");
            }
            // gradient descent
            gradientDescent(user, m_initStepSize, 1.0);
            gNormOld = gNorm;
            if (m_displayLv > 0 && ++updateCount % 100 == 0)
                System.out.println();
        }
    }
    if (m_displayLv > 0)
        System.out.println();
    setPersonalizedModel();
    // we do not evaluate function value
    return 0;
}

Example 7 with structures._PerformanceStat

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

the class asyncMTRegLR method train.

@Override
public double train() {
    double gNorm, gNormOld = Double.MAX_VALUE;
    int predL, trueL;
    _Review doc;
    _AdaptStruct user;
    _PerformanceStat perfStat;
    double val;
    initLBFGS();
    init();
    try {
        m_writer = new PrintWriter(new File(String.format("train_online_MTRegLR.txt")));
        for (int i = 0; i < m_userList.size(); i++) {
            user = m_userList.get(i);
            while (user.hasNextAdaptationIns()) {
                // test the latest model before model adaptation
                if (m_testmode != TestMode.TM_batch && (doc = user.getLatestTestIns()) != null) {
                    perfStat = user.getPerfStat();
                    val = logit(doc.getSparse(), user);
                    predL = predict(doc, user);
                    trueL = doc.getYLabel();
                    perfStat.addOnePredResult(predL, trueL);
                    m_writer.format("%s\t%d\t%.4f\t%d\t%d\n", user.getUserID(), doc.getID(), val, predL, trueL);
                }
                // in batch mode we will not accumulate the performance during adaptation
                gradientDescent(user, m_initStepSize, 1.0);
                // test the gradient only when we want to debug
                if (m_displayLv > 0) {
                    gNorm = gradientTest();
                    if (m_displayLv == 1) {
                        if (gNorm < gNormOld)
                            System.out.print("o");
                        else
                            System.out.print("x");
                    }
                    gNormOld = gNorm;
                }
            }
            m_writer.flush();
            if (m_displayLv == 1)
                System.out.println();
        }
    } catch (IOException e) {
        e.printStackTrace();
    }
    setPersonalizedModel();
    // we do not evaluate function value
    return 0;
}