Examples with BernoulliDistribution edu.neu.ccs.pyramid.util.BernoulliDistribution used on opensource projects

Example 6 with BernoulliDistribution

use of edu.neu.ccs.pyramid.util.BernoulliDistribution in project pyramid by cheng-li.

the class CBMTest method test2.

private static void test2() throws Exception {
    MultiLabelClfDataSet dataSet = MLClfDataSetBuilder.getBuilder().numFeatures(2).numClasses(4).numDataPoints(1000).build();
    BernoulliDistribution bernoulliDistribution = new BernoulliDistribution(0.5);
    for (int n = 0; n < dataSet.getNumDataPoints(); n++) {
        for (int m = 0; m < dataSet.getNumFeatures(); m++) {
            int bit = bernoulliDistribution.sample();
            int flip = bit;
            if (Math.random() < 0.1) {
                flip = 1 - bit;
            }
            dataSet.setFeatureValue(n, m, bit);
            if (m == 0) {
                if (flip == 0) {
                    dataSet.addLabel(n, 0);
                } else {
                    dataSet.addLabel(n, 1);
                }
            } else {
                if (flip == 0) {
                    dataSet.addLabel(n, 2);
                } else {
                    dataSet.addLabel(n, 3);
                }
            }
        }
    }
    MultiLabelClfDataSet testSet = MLClfDataSetBuilder.getBuilder().numFeatures(2).numClasses(4).numDataPoints(100).build();
    for (int n = 0; n < testSet.getNumDataPoints(); n++) {
        for (int m = 0; m < testSet.getNumFeatures(); m++) {
            int bit = bernoulliDistribution.sample();
            testSet.setFeatureValue(n, m, bit);
            int flip = bit;
            if (Math.random() < 0.1) {
                flip = 1 - bit;
            }
            if (m == 0) {
                if (flip == 0) {
                    testSet.addLabel(n, 0);
                } else {
                    testSet.addLabel(n, 1);
                }
            } else {
                if (flip == 0) {
                    testSet.addLabel(n, 2);
                } else {
                    testSet.addLabel(n, 3);
                }
            }
        }
    }
    int numComponents = 4;
    CBM cbm = CBM.getBuilder().setNumClasses(dataSet.getNumClasses()).setNumFeatures(dataSet.getNumFeatures()).setNumComponents(numComponents).setBinaryClassifierType("boost").setMultiClassClassifierType("boost").build();
    cbm.setPredictMode("dynamic");
    CBMOptimizer optimizer = new CBMOptimizer(cbm, dataSet);
    optimizer.setPriorVarianceBinary(10);
    optimizer.setPriorVarianceMultiClass(10);
    CBMInitializer.initialize(cbm, dataSet, optimizer);
    for (int i = 0; i < 3; i++) {
        optimizer.iterate();
        System.out.print("i: " + i + "\t");
        System.out.print("objective: " + optimizer.getTerminator().getLastValue() + "\t");
        System.out.print("trainAcc: " + Accuracy.accuracy(cbm, dataSet) + "\t");
        System.out.println("testAcc: " + Accuracy.accuracy(cbm, testSet));
    }
    System.out.println(cbm.toString());
}

Example 7 with BernoulliDistribution

use of edu.neu.ccs.pyramid.util.BernoulliDistribution in project pyramid by cheng-li.

the class ClusterLabels method getCluster.

private static List<WordFrequency> getCluster(BM bm, int k) throws Exception {
    BernoulliDistribution[][] distributions = bm.getDistributions();
    List<Pair<String, Double>> pairs = new ArrayList<>();
    for (int d = 0; d < bm.getDimension(); d++) {
        Pair<String, Double> pair = new Pair<>(bm.getNames().get(d), distributions[k][d].getP());
        pairs.add(pair);
    }
    Comparator<Pair<String, Double>> comparator = Comparator.comparing(Pair::getSecond);
    List<Pair<String, Double>> sorted = pairs.stream().sorted(comparator.reversed()).collect(Collectors.toList());
    List<WordFrequency> frequencies = new ArrayList<>();
    double sum = sorted.stream().filter(pair -> pair.getSecond() > 0).limit(20).mapToDouble(Pair::getSecond).sum();
    sorted.stream().filter(pair -> pair.getSecond() > 0).limit(20).forEach(pair -> {
        WordFrequency wordFrequency = new WordFrequency(pair.getFirst(), (int) (pair.getSecond() * 200 / sum));
        frequencies.add(wordFrequency);
    });
    return frequencies;
}