Examples with Parameter Classifier.supervised.liblinear.Parameter used on opensource projects

Example 1 with Parameter

use of Classifier.supervised.liblinear.Parameter in project IR_Base by Linda-sunshine.

the class MultiTaskSVM method train.

@Override
public double train() {
    init();
    // Transfer all user reviews to instances recognized by SVM, indexed by users.
    int trainSize = 0, validUserIndex = 0;
    ArrayList<Feature[]> fvs = new ArrayList<Feature[]>();
    ArrayList<Double> ys = new ArrayList<Double>();
    // Two for loop to access the reviews, indexed by users.
    ArrayList<_Review> reviews;
    for (_AdaptStruct user : m_userList) {
        reviews = user.getReviews();
        boolean validUser = false;
        for (_Review r : reviews) {
            if (r.getType() == rType.ADAPTATION) {
                // we will only use the adaptation data for this purpose
                fvs.add(createLibLinearFV(r, validUserIndex));
                ys.add(new Double(r.getYLabel()));
                trainSize++;
                validUser = true;
            }
        }
        if (validUser)
            validUserIndex++;
    }
    // Train a liblinear model based on all reviews.
    Problem libProblem = new Problem();
    libProblem.l = trainSize;
    libProblem.x = new Feature[trainSize][];
    libProblem.y = new double[trainSize];
    for (int i = 0; i < trainSize; i++) {
        libProblem.x[i] = fvs.get(i);
        libProblem.y[i] = ys.get(i);
    }
    if (m_bias) {
        // including bias term; global model + user models
        libProblem.n = (m_featureSize + 1) * (m_userSize + 1);
        // bias term in liblinear.
        libProblem.bias = 1;
    } else {
        libProblem.n = m_featureSize * (m_userSize + 1);
        // no bias term in liblinear.
        libProblem.bias = -1;
    }
    // solver type: SVM
    SolverType type = SolverType.L2R_L1LOSS_SVC_DUAL;
    m_libModel = Linear.train(libProblem, new Parameter(type, m_C, SVM.EPS));
    setPersonalizedModel();
    return 0;
}

Example 2 with Parameter

use of Classifier.supervised.liblinear.Parameter in project IR_Base by Linda-sunshine.

the class IndividualSVM method train.

@Override
public double train() {
    init();
    // Transfer all user reviews to instances recognized by SVM, indexed by users.
    int trainSize = 0, validUserIndex = 0;
    ArrayList<Feature[]> fvs = new ArrayList<Feature[]>();
    ArrayList<Double> ys = new ArrayList<Double>();
    // Two for loop to access the reviews, indexed by users.
    ArrayList<_Review> reviews;
    for (_AdaptStruct user : m_supFlag ? m_supUserList : m_userList) {
        trainSize = 0;
        reviews = user.getReviews();
        boolean validUser = false;
        for (_Review r : reviews) {
            if (r.getType() == rType.ADAPTATION) {
                // we will only use the adaptation data for this purpose
                fvs.add(createLibLinearFV(r, validUserIndex));
                ys.add(new Double(r.getYLabel()));
                trainSize++;
                validUser = true;
            }
        }
        if (validUser)
            validUserIndex++;
        // Train individual model for each user.
        Problem libProblem = new Problem();
        libProblem.l = trainSize;
        libProblem.x = new Feature[trainSize][];
        libProblem.y = new double[trainSize];
        for (int i = 0; i < trainSize; i++) {
            libProblem.x[i] = fvs.get(i);
            libProblem.y[i] = ys.get(i);
        }
        if (m_bias) {
            // including bias term; global model + user models
            libProblem.n = m_featureSize + 1;
            // bias term in liblinear.
            libProblem.bias = 1;
        } else {
            libProblem.n = m_featureSize;
            // no bias term in liblinear.
            libProblem.bias = -1;
        }
        m_libModel = Linear.train(libProblem, new Parameter(m_solverType, m_C, SVM.EPS));
        // Set users in the same cluster.
        if (m_supFlag)
            setPersonalizedModelInCluster(user.getUser().getClusterIndex());
        else
            setPersonalizedModel(user);
    }
    return 0;
}

Example 3 with Parameter

use of Classifier.supervised.liblinear.Parameter in project IR_Base by Linda-sunshine.

the class SVM method libSVMTrain.

public static Model libSVMTrain(Collection<_Doc> trainSet, int fSize, SolverType type, double C, double bias) {
    Feature[][] fvs = new Feature[trainSet.size()][];
    double[] y = new double[trainSet.size()];
    // file id
    int fid = 0;
    for (_Doc d : trainSet) {
        if (bias > 0)
            fvs[fid] = Utils.createLibLinearFV(d, fSize);
        else
            fvs[fid] = Utils.createLibLinearFV(d, 0);
        y[fid] = d.getYLabel();
        fid++;
    }
    Problem libProblem = new Problem();
    libProblem.l = fid;
    libProblem.n = bias >= 0 ? 1 + fSize : fSize;
    libProblem.x = fvs;
    libProblem.y = y;
    libProblem.bias = bias;
    return Linear.train(libProblem, new Parameter(type, C, SVM.EPS));
}

Example 4 with Parameter

use of Classifier.supervised.liblinear.Parameter in project IR_Base by Linda-sunshine.

the class SVM method libSVMTrain.

public static Model libSVMTrain(ArrayList<Feature[]> featureArray, ArrayList<Integer> targetArray, int fSize, SolverType type, double C, double bias) {
    Feature[][] featureMatrix = new Feature[featureArray.size()][];
    double[] targetMatrix = new double[targetArray.size()];
    for (int i = 0; i < featureArray.size(); i++) {
        featureMatrix[i] = featureArray.get(i);
        targetMatrix[i] = targetArray.get(i);
    }
    Problem libProblem = new Problem();
    libProblem.l = featureMatrix.length;
    libProblem.n = fSize;
    libProblem.x = featureMatrix;
    libProblem.y = targetMatrix;
    libProblem.bias = bias;
    return Linear.train(libProblem, new Parameter(type, C, SVM.EPS));
}

Example 5 with Parameter

use of Classifier.supervised.liblinear.Parameter in project IR_Base by Linda-sunshine.

the class ACCTM_CZLR method updateFeatureWeight.

public void updateFeatureWeight(_ParentDoc pDoc, int iter, File weightIterFolder) {
    int totalChildWordNum = 0;
    int featureLen = 0;
    ArrayList<Double> targetValList = new ArrayList<Double>();
    ArrayList<Feature[]> featureList = new ArrayList<Feature[]>();
    for (_ChildDoc cDoc : pDoc.m_childDocs) {
        for (_Word w : cDoc.getWords()) {
            double[] wordFeatures = w.getFeatures();
            double x = w.getX();
            featureLen = wordFeatures.length;
            Feature[] featureVec = new Feature[featureLen];
            for (int i = 0; i < featureLen; i++) {
                featureVec[i] = new FeatureNode(i + 1, wordFeatures[i]);
            }
            featureList.add(featureVec);
            targetValList.add(x);
        }
    }
    totalChildWordNum = featureList.size();
    double[] targetVal = new double[totalChildWordNum];
    Feature[][] featureMatrix = new Feature[totalChildWordNum][];
    for (int i = 0; i < totalChildWordNum; i++) {
        featureMatrix[i] = featureList.get(i);
    }
    for (int i = 0; i < totalChildWordNum; i++) {
        targetVal[i] = targetValList.get(i);
    }
    Problem problem = new Problem();
    problem.l = totalChildWordNum;
    // featureNum
    problem.n = featureLen + 1;
    problem.x = featureMatrix;
    problem.y = targetVal;
    SolverType solver = SolverType.L2R_LR;
    double C = 1.0;
    double eps = 0.01;
    Parameter param = new Parameter(solver, C, eps);
    Model model = Linear.train(problem, param);
    int featureNum = model.getNrFeature();
    for (int i = 0; i < featureNum; i++) pDoc.m_featureWeight[i] = model.getDecfunCoef(i, 0);
    String weightFile = pDoc.getName() + ".txt";
    File modelFile = new File(weightIterFolder, weightFile);
    try {
        // if((iter>200)&&(iter%100==0))
        model.save(modelFile);
    } catch (Exception e) {
        System.out.println(e.getMessage());
    }
}