Examples with structures._HDPThetaStar._Connection structures._HDPThetaStar._Connection used on opensource projects

Example 1 with structures._HDPThetaStar._Connection

use of structures._HDPThetaStar._Connection in project IR_Base by Linda-sunshine.

the class MTCLinAdaptWithHDP method setPersonalizedModel.

@Override
protected void setPersonalizedModel() {
    double[] prob;
    _HDPAdaptStruct user;
    Collection<_HDPThetaStar> thetas;
    setClusterModels();
    for (_AdaptStruct u : m_userList) {
        // we set each user's personalized weights based on the review's cluster assignment
        user = (_HDPAdaptStruct) u;
        thetas = user.getHDPTheta4Rvw();
        prob = new double[user.getHDPTheta4Rvw().size()];
        int count = 0;
        double sum = 0;
        for (_HDPThetaStar theta : thetas) {
            double clusterAssignment = getClusterAssignment(user, theta);
            prob[count++] = clusterAssignment;
            sum += clusterAssignment;
        }
        // normalize the probability for each cluster
        for (int i = 0; i < prob.length; i++) {
            prob[i] /= sum;
        }
        // construct the personalized weights by weighted cluster models
        double[] pWeights = new double[m_gWeights.length];
        count = 0;
        for (_HDPThetaStar theta : thetas) {
            Utils.add2Array(pWeights, theta.getWeights(), prob[count++]);
        }
        user.setPersonalizedModel(pWeights);
    }
}

Example 2 with structures._HDPThetaStar._Connection

use of structures._HDPThetaStar._Connection in project IR_Base by Linda-sunshine.

the class MTCLinAdaptWithHDP method gradientByFunc.

@Override
protected void gradientByFunc(_AdaptStruct u, _Doc review, double weight, double[] g) {
    _Review r = (_Review) review;
    _HDPThetaStar theta = r.getHDPThetaStar();
    // feature index
    int n, k, s;
    int cIndex = theta.getIndex();
    if (cIndex < 0 || cIndex >= m_kBar)
        System.err.println("Error,cannot find the theta star!");
    int offset = m_dim * 2 * cIndex, offsetSup = m_dim * 2 * m_kBar;
    double[] Au = theta.getModel();
    double delta = (review.getYLabel() - logit(review.getSparse(), r)) * weight;
    // Bias term for individual user.
    // a[0] = ws0*x0; x0=1
    g[offset] -= delta * getSupWeights(0);
    // b[0]
    g[offset + m_dim] -= delta;
    // Bias term for super user.
    // a_s[0] = a_i0*w_g0*x_d0
    g[offsetSup] -= delta * Au[0] * m_gWeights[0];
    // b_s[0] = a_i0*x_d0
    g[offsetSup + m_dimSup] -= delta * Au[0];
    // Traverse all the feature dimension to calculate the gradient for both individual users and super user.
    for (_SparseFeature fv : review.getSparse()) {
        n = fv.getIndex() + 1;
        k = m_featureGroupMap[n];
        // w_si*x_di
        g[offset + k] -= delta * getSupWeights(n) * fv.getValue();
        // x_di
        g[offset + m_dim + k] -= delta * fv.getValue();
        s = m_featureGroupMap4SupUsr[n];
        // a_i*w_gi*x_di
        g[offsetSup + s] -= delta * Au[k] * m_gWeights[n] * fv.getValue();
        // a_i*x_di
        g[offsetSup + m_dimSup + s] -= delta * Au[k] * fv.getValue();
    }
}

Example 3 with structures._HDPThetaStar._Connection

use of structures._HDPThetaStar._Connection in project IR_Base by Linda-sunshine.

the class MTCLinAdaptWithHDPMultipleE method gradientByFunc.

@Override
protected void gradientByFunc(_AdaptStruct u, _Doc review, double weight, double[] g) {
    int index = -1;
    double confidence = 1;
    _Review r = (_Review) review;
    _HDPThetaStar oldTheta = r.getHDPThetaStar();
    HashMap<_HDPThetaStar, Integer> thetaCountMap = r.getThetaCountMap();
    for (_HDPThetaStar theta : thetaCountMap.keySet()) {
        index = findHDPThetaStar(theta);
        // some of the cluster may disappear, ignore them.
        if (index >= m_kBar || index < 0)
            continue;
        r.setHDPThetaStar(theta);
        confidence = thetaCountMap.get(theta);
        // confidence plays the role of weight here, how many times the review shows in the cluster.
        super.gradientByFunc(u, review, confidence, g);
    }
    r.setHDPThetaStar(oldTheta);
}

Example 4 with structures._HDPThetaStar._Connection

use of structures._HDPThetaStar._Connection in project IR_Base by Linda-sunshine.

the class CLRWithMMB method accumulateDecomposedLikelihoodEMMB.

// traverse all the clusters to get the decomposed likelihood
// 0: m*log(rho*zBz); 1: n*log(1-rho*zBz); 2:n*log(rho(1-zBz))
protected double[] accumulateDecomposedLikelihoodEMMB() {
    double[] likelihoodE = new double[4];
    _Connection connection;
    int e_0, e_1;
    double logRho = Math.log(m_rho), log_zBz = 0, zBz = 0;
    _HDPThetaStar theta_g, theta_h;
    for (int g = 0; g < m_kBar; g++) {
        theta_g = m_hdpThetaStars[g];
        for (int h = g; h < m_kBar; h++) {
            theta_h = m_hdpThetaStars[h];
            if (!theta_g.hasConnection(theta_h))
                continue;
            connection = theta_g.getConnection(theta_h);
            e_1 = connection.getEdge()[1];
            e_0 = connection.getEdge()[0];
            log_zBz = Math.log(m_abcd[0] + e_1) - Math.log(m_abcd[0] + m_abcd[1] + e_0 + e_1);
            zBz = (m_abcd[0] + e_1) / (m_abcd[0] + m_abcd[1] + e_0 + e_1);
            likelihoodE[0] += e_1 * (logRho + log_zBz);
            likelihoodE[1] += e_0 * (Math.log(1 - m_rho * zBz));
            likelihoodE[2] += e_0 * (logRho + Math.log(1 - zBz));
        // likelihoodE: m*log(rho*(a+e_1))/(a+b+e_0+e_1))+n*log(rho*(b+e_0))/(a+b+e_0+e_1))
        // likelihoodE += (e_0+e_1)*Math.log(m_rho)+e_1*Math.log(m_abcd[0]+e_1)+
        // e_0*Math.log(m_abcd[1]+e_0)-(e_0+e_1)*Math.log(m_abcd[0]+m_abcd[1]+e_0+e_1);
        // likelihoodE += e_1*Math.log(m_abcd[0]+e_1)+e_0*Math.log(m_abcd[1]+e_0)
        // -(e_0+e_1)*Math.log(m_abcd[0]+m_abcd[1]+e_0+e_1);
        }
    }
    return likelihoodE;
}

Example 5 with structures._HDPThetaStar._Connection

use of structures._HDPThetaStar._Connection in project IR_Base by Linda-sunshine.

the class CLRWithMMB method checkEdges.

// check if the sum(m_MNL) == sum(edges of all clusters)
protected void checkEdges() {
    int mmb_0 = 0, mmb_1 = 0;
    _HDPThetaStar theta;
    for (int i = 0; i < m_kBar; i++) {
        theta = m_hdpThetaStars[i];
        mmb_0 += theta.getEdgeSize(0);
        mmb_1 += theta.getEdgeSize(1);
    }
    if (mmb_0 != m_MNL[0])
        System.out.println("Zero edges sampled from mmb is not correct!");
    if (mmb_1 != m_MNL[1])
        System.out.println("One edges sampled from mmb is not correct!");
}