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

Example 1 with SparseDataSet

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

the class StumpSelector method scores.

/**
 * @param index
 * @param labels size = num labels * num data
 * @param feature
 * @param idTranslator
 * @param matchScoreType
 * @param docFilter
 */
public static double[] scores(ESIndex index, double[][] labels, Ngram feature, IdTranslator idTranslator, FeatureLoader.MatchScoreType matchScoreType, String docFilter, Map<String, float[]> fieldLength) {
    Ngram ngram = null;
    try {
        ngram = (Ngram) Serialization.deepCopy(feature);
    } catch (IOException e) {
        e.printStackTrace();
    } catch (ClassNotFoundException e) {
        e.printStackTrace();
    }
    ngram.setIndex(0);
    DataSet dataSet = new SparseDataSet(labels[0].length, 1, false, null);
    FeatureLoader.loadNgramFeature(index, dataSet, ngram, idTranslator, matchScoreType, docFilter, fieldLength);
    double[] scores = new double[labels.length];
    for (int l = 0; l < scores.length; l++) {
        double score = score(dataSet, labels[l]);
        scores[l] = score;
    }
    return scores;
}

Example 2 with SparseDataSet

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

the class IntervalSplitterTest method test1.

private static void test1() {
    SparseDataSet dataSet = new SparseDataSet(5, 2, false);
    dataSet.setFeatureValue(0, 0, 0);
    dataSet.setFeatureValue(1, 0, 0);
    dataSet.setFeatureValue(2, 0, 0);
    dataSet.setFeatureValue(3, 0, 1);
    dataSet.setFeatureValue(4, 0, 1);
    double[] labels = { 1, 2, 3, 3, 1 };
    RegTreeConfig regTreeConfig = new RegTreeConfig();
    double[] probs = { 1, 1, 1, 1, 0 };
    regTreeConfig.setMinDataPerLeaf(1).setNumSplitIntervals(2);
    Splitter.GlobalStats globalStats = new Splitter.GlobalStats(labels, probs);
    System.out.println(IntervalSplitter.split(regTreeConfig, dataSet, labels, probs, 0, globalStats));
}

Example 3 with SparseDataSet

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

the class IntervalSplitterTest method test3.

private static void test3() {
    SparseDataSet dataSet = new SparseDataSet(5, 2, false);
    dataSet.setFeatureValue(0, 0, 0);
    dataSet.setFeatureValue(1, 0, 0);
    dataSet.setFeatureValue(2, 0, 0);
    dataSet.setFeatureValue(3, 0, 1);
    dataSet.setFeatureValue(4, 0, 1);
    double[] labels = { 1, 2, 3, 3, 1 };
    RegTreeConfig regTreeConfig = new RegTreeConfig();
    double[] probs = { 1, 1, 1, 1, 0 };
    regTreeConfig.setMinDataPerLeaf(2).setNumSplitIntervals(200);
    Splitter.GlobalStats globalStats = new Splitter.GlobalStats(labels, probs);
    System.out.println(IntervalSplitter.split(regTreeConfig, dataSet, labels, probs, 0, globalStats));
}

Example 4 with SparseDataSet

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

the class IntervalSplitterTest method test4.

private static void test4() {
    SparseDataSet dataSet = new SparseDataSet(8, 1, false);
    dataSet.setFeatureValue(0, 0, 0);
    dataSet.setFeatureValue(1, 0, 0);
    dataSet.setFeatureValue(2, 0, 0);
    dataSet.setFeatureValue(3, 0, 1);
    dataSet.setFeatureValue(4, 0, 1);
    dataSet.setFeatureValue(5, 0, 2);
    dataSet.setFeatureValue(6, 0, 2);
    dataSet.setFeatureValue(6, 0, 3);
    double[] labels = { 1, 2, 3, 3, 1, 5, 5, 5 };
    RegTreeConfig regTreeConfig = new RegTreeConfig();
    double[] probs = { 1, 1, 1, 1, 1, 1, 1, 1 };
    regTreeConfig.setMinDataPerLeaf(2).setNumSplitIntervals(4);
    Splitter.GlobalStats globalStats = new Splitter.GlobalStats(labels, probs);
    System.out.println(IntervalSplitter.split(regTreeConfig, dataSet, labels, probs, 0, globalStats));
}

Example 5 with SparseDataSet

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

the class IntervalSplitterTest method test2.

private static void test2() {
    SparseDataSet dataSet = new SparseDataSet(5, 2, false);
    dataSet.setFeatureValue(0, 0, 0);
    dataSet.setFeatureValue(1, 0, 0);
    dataSet.setFeatureValue(2, 0, 0);
    dataSet.setFeatureValue(3, 0, 1);
    dataSet.setFeatureValue(4, 0, 1);
    double[] labels = { 1, 2, 3, 3, 1 };
    RegTreeConfig regTreeConfig = new RegTreeConfig();
    double[] probs = { 1, 1, 1, 1, 0 };
    regTreeConfig.setMinDataPerLeaf(1).setNumSplitIntervals(200);
    Splitter.GlobalStats globalStats = new Splitter.GlobalStats(labels, probs);
    System.out.println(IntervalSplitter.split(regTreeConfig, dataSet, labels, probs, 0, globalStats));
}