Examples with MultiLabel edu.neu.ccs.pyramid.dataset.MultiLabel used on opensource projects

Example 71 with MultiLabel

use of edu.neu.ccs.pyramid.dataset.MultiLabel in project pyramid by cheng-li.

the class CBMNoiseOptimizerFixed method updateBinaryTarget.

private void updateBinaryTarget(int dataPointIndex) {
    double[] comProb = targets[dataPointIndex];
    double[] marginals = new double[cbm.getNumClasses()];
    for (int c = 0; c < comProb.length; c++) {
        MultiLabel multiLabel = combinations.get(c);
        double prob = comProb[c];
        for (int l : multiLabel.getMatchedLabels()) {
            marginals[l] += prob;
        }
    }
    for (int l = 0; l < cbm.getNumClasses(); l++) {
        // we need to add some protection
        if (marginals[l] > 1) {
            marginals[l] = 1;
        }
        binaryTargetsDistributions[l][dataPointIndex][0] = 1 - marginals[l];
        binaryTargetsDistributions[l][dataPointIndex][1] = marginals[l];
    }
}

Example 72 with MultiLabel

use of edu.neu.ccs.pyramid.dataset.MultiLabel in project pyramid by cheng-li.

the class CBMS method predictByMarginals.

/**
     * sort marginals, and keep top few
     * @param vector
     * @param top
     * @return
     */
public MultiLabel predictByMarginals(Vector vector, int top) {
    double[] probs = predictClassProbs(vector);
    int[] sortedIndices = ArgSort.argSortDescending(probs);
    MultiLabel prediction = new MultiLabel();
    for (int i = 0; i < top; i++) {
        prediction.addLabel(sortedIndices[i]);
    }
    return prediction;
}

Example 73 with MultiLabel

use of edu.neu.ccs.pyramid.dataset.MultiLabel in project pyramid by cheng-li.

the class CBMS method predictLogAssignmentProbs.

/**
     * for batch jobs, use this to save computation
     * @param x
     * @param assignments
     * @return
     */
private double[] predictLogAssignmentProbs(Vector x, List<MultiLabel> assignments) {
    BMDistribution bmDistribution = computeBM(x);
    double[] probs = new double[assignments.size()];
    for (int c = 0; c < assignments.size(); c++) {
        MultiLabel multiLabel = assignments.get(c);
        probs[c] = bmDistribution.logProbability(multiLabel);
    }
    return probs;
}

Example 74 with MultiLabel

use of edu.neu.ccs.pyramid.dataset.MultiLabel in project pyramid by cheng-li.

the class CBMUtilityOptimizer method updateGamma.

private void updateGamma(int n) {
    Vector x = dataSet.getRow(n);
    BMDistribution bmDistribution = cbm.computeBM(x);
    // size = combination * components
    List<double[]> logPosteriors = new ArrayList<>();
    for (int c = 0; c < combinations.size(); c++) {
        MultiLabel combination = combinations.get(c);
        double[] pos = bmDistribution.logPosteriorMembership(combination);
        logPosteriors.add(pos);
    }
    double[] sums = new double[cbm.numComponents];
    for (int k = 0; k < cbm.numComponents; k++) {
        double sum = 0;
        for (int c = 0; c < combinations.size(); c++) {
            sum += targets[n][c] * logPosteriors.get(c)[k];
        }
        sums[k] = sum;
    }
    double[] posterior = MathUtil.softmax(sums);
    for (int k = 0; k < cbm.numComponents; k++) {
        gammas[n][k] = posterior[k];
        gammasT[k][n] = posterior[k];
    }
}

Example 75 with MultiLabel

use of edu.neu.ccs.pyramid.dataset.MultiLabel in project pyramid by cheng-li.

the class CBMUtilityOptimizer method updateBinaryTarget.

private void updateBinaryTarget(int dataPointIndex) {
    double[] comProb = targets[dataPointIndex];
    double[] marginals = new double[cbm.getNumClasses()];
    for (int c = 0; c < comProb.length; c++) {
        MultiLabel multiLabel = combinations.get(c);
        double prob = comProb[c];
        for (int l : multiLabel.getMatchedLabels()) {
            marginals[l] += prob;
        }
    }
    for (int l = 0; l < cbm.getNumClasses(); l++) {
        // we need to add some protection
        if (marginals[l] > 1) {
            marginals[l] = 1;
        }
        binaryTargetsDistributions[l][dataPointIndex][0] = 1 - marginals[l];
        binaryTargetsDistributions[l][dataPointIndex][1] = marginals[l];
    }
}