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

Example 1 with structures._UserReviewPair

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

the class asyncMTLinAdapt method trainByReview.

void trainByReview() {
    LinkedList<_UserReviewPair> reviewlist = new LinkedList<_UserReviewPair>();
    double gNorm, gNormOld = Double.MAX_VALUE;
    int predL, trueL, counter = 0;
    _Review doc;
    _CoLinAdaptStruct user;
    // collect the training/adaptation data
    for (int i = 0; i < m_userList.size(); i++) {
        user = (_CoLinAdaptStruct) m_userList.get(i);
        for (_Review r : user.getReviews()) {
            if (r.getType() == rType.ADAPTATION || r.getType() == rType.TRAIN)
                // we will only collect the training or adaptation reviews
                reviewlist.add(new _UserReviewPair(user, r));
        }
    }
    // sort them by timestamp
    Collections.sort(reviewlist);
    for (_UserReviewPair pair : reviewlist) {
        user = (_CoLinAdaptStruct) pair.getUser();
        // test the latest model before model adaptation
        if (m_testmode != TestMode.TM_batch) {
            doc = pair.getReview();
            predL = predict(doc, user);
            trueL = doc.getYLabel();
            user.getPerfStat().addOnePredResult(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;
            if (++counter % 120 == 0)
                System.out.println();
        }
    }
}