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

Example 1 with structures._Node

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

the class GaussianFieldsByRandomWalk method randomWalkByWeightedSum.

// The random walk algorithm to generate new labels for unlabeled data.
// Take the average of all neighbors as the new label until they converge.
double randomWalkByWeightedSum() {
    // construct the sparse graph on the fly every time
    double wL = m_alpha, wU = m_beta, acc = 0;
    _Node node;
    /**
     ** Construct the C+scale*\Delta matrix and Y vector. ***
     */
    for (int i = 0; i < m_U; i++) {
        node = m_nodeList[i];
        double wijSumU = 0, wijSumL = 0;
        double fSumU = 0, fSumL = 0;
        /**
         **Walk through the top k' unlabeled neighbor for the current data.***
         */
        for (_Edge edge : node.m_unlabeledEdges) {
            // get the similarity between two nodes.
            wijSumU += m_simFlag ? edge.getSimilarity() : 1;
            fSumU += m_simFlag ? edge.getSimilarity() * edge.getPred() : edge.getPred();
        }
        /**
         **Walk through the top k labeled neighbor for the current data.***
         */
        for (_Edge edge : node.m_labeledEdges) {
            // get the similarity between two nodes.
            wijSumL += m_simFlag ? edge.getSimilarity() : 1;
            fSumL += m_simFlag ? edge.getSimilarity() * edge.getPred() : edge.getPred();
        }
        node.m_pred = m_eta * (fSumL * wL + fSumU * wU) / (wijSumL * wL + wijSumU * wU) + (1 - m_eta) * node.m_classifierPred;
        if (Double.isNaN(node.m_pred)) {
            System.out.format("Encounter NaN in random walk!\nfSumL: %.3f, fSumU: %.3f, wijSumL: %.3f, wijSumU: %.3f\n", fSumL, fSumU, wijSumL, wijSumU);
            System.exit(-1);
        } else if ((int) node.m_label == getLabel(node.m_pred))
            acc++;
    }
    return acc / m_U;
}

Example 2 with structures._Node

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

the class GaussianFieldsByRandomWalk method calcMeanVar4OneNode.

public double[] calcMeanVar4OneNode(_Node n) {
    double[] stat4OneNode = new double[4];
    double sum = 0, mean = 0, var = 0;
    _Edge neighbor;
    for (int j = 0; j < m_kPrime; j++) {
        neighbor = n.m_unlabeledEdges.get(j);
        sum += neighbor.getSimilarity();
    }
    mean = sum / m_kPrime;
    // mean of unlabeled neighbors' similarities.
    stat4OneNode[0] = mean;
    for (int j = 0; j < m_kPrime; j++) {
        neighbor = n.m_unlabeledEdges.get(j);
        var += (neighbor.getSimilarity() - mean) * (neighbor.getSimilarity() - mean);
    }
    var = Math.sqrt(var / m_kPrime);
    // variance of unlabeled neighbors' similarities.
    stat4OneNode[1] = var;
    // clear for the calculation of the labeled neighbors.
    sum = 0;
    // clear for the calculation of the labeled neighbors.
    mean = 0;
    // clear for the calculation of the labeled neighbors.
    var = 0;
    for (int j = 0; j < m_k; j++) {
        neighbor = n.m_labeledEdges.get(j);
        sum += neighbor.getSimilarity();
    }
    mean = sum / m_k;
    // mean of unlabeled neighbors' similarities.
    stat4OneNode[2] = mean;
    for (int j = 0; j < m_k; j++) {
        neighbor = n.m_labeledEdges.get(j);
        var += (neighbor.getSimilarity() - mean) * (neighbor.getSimilarity() - mean);
    }
    var = Math.sqrt(var / m_k);
    // variance of unlabeled neighbors' similarities.
    stat4OneNode[3] = var;
    return stat4OneNode;
}

Example 3 with structures._Node

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

the class GaussianFieldsByRandomWalk method randomWalkByMajorityVote.

// Take the majority of all neighbors(k+k') as the new label until they converge.
double randomWalkByMajorityVote() {
    // construct the sparse graph on the fly every time
    double similarity = 0, acc = 0;
    int label;
    double wL = m_eta * m_alpha, wU = m_eta * m_beta;
    _Node node;
    /**
     ** Construct the C+scale*\Delta matrix and Y vector. ***
     */
    for (int i = 0; i < m_U; i++) {
        node = m_nodeList[i];
        Arrays.fill(m_cProbs, 0);
        /**
         **Walk through the top k' unlabeled neighbor for the current data.***
         */
        for (_Edge edge : node.m_unlabeledEdges) {
            // Item n's label.
            label = getLabel(edge.getPred());
            similarity = edge.getSimilarity();
            m_cProbs[label] += m_simFlag ? similarity * wU : wU;
        }
        /**
         **Walk through the top k labeled neighbor for the current data.***
         */
        for (_Edge edge : node.m_labeledEdges) {
            label = (int) edge.getLabel();
            similarity = edge.getSimilarity();
            m_cProbs[label] += m_simFlag ? similarity * wL : wL;
        }
        /**
         **Multiple learner's prediction.***
         */
        label = (int) node.m_classifierPred;
        m_cProbs[label] += 1 - m_eta;
        node.m_pred = Utils.argmax(m_cProbs);
        if (node.m_label == node.m_pred)
            acc++;
    }
    return acc / m_U;
}

Example 4 with structures._Node

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

the class GaussianFieldsByRandomWalk method checkSimilarityVariance.

// Check the mean and variance of the
public void checkSimilarityVariance() {
    double[] stat;
    double[] avgStat = new double[4];
    _Node node;
    for (int i = 0; i < m_U; i++) {
        node = m_nodeList[i];
        stat = calcMeanVar4OneNode(node);
        avgStat = addOneNodeStat(avgStat, stat);
    }
    for (int i = 0; i < avgStat.length; i++) {
        avgStat[i] = avgStat[i] / m_U;
    }
    m_simiStat[m_foldCount++] = avgStat;
}

Example 5 with structures._Node

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

the class PairwiseSimCalculator method constructNodes.

void constructNodes() {
    _Doc d;
    for (int i = m_start; i < m_end; i++) {
        d = m_GFObj.getTestDoc(i);
        m_GFObj.m_nodeList[i] = new _Node(i, d.getYLabel(), m_GFObj.predict(d));
    }
}