Examples with SamplingPredictor edu.neu.ccs.pyramid.multilabel_classification.crf.SamplingPredictor used on opensource projects

Example 1 with SamplingPredictor

use of edu.neu.ccs.pyramid.multilabel_classification.crf.SamplingPredictor in project pyramid by cheng-li.

the class MultiLabelSynthesizer method crfSample.

public static MultiLabelClfDataSet crfSample() {
    int numData = 10000;
    int numClass = 4;
    int numFeature = 2;
    MultiLabelClfDataSet dataSet = MLClfDataSetBuilder.getBuilder().numFeatures(numFeature).numClasses(numClass).numDataPoints(numData).build();
    List<MultiLabel> support = Enumerator.enumerate(numClass);
    CMLCRF cmlcrf = new CMLCRF(numClass, numFeature, support);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(0).set(0, 0);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(0).set(1, 10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(1).set(0, 10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(1).set(1, 10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(2).set(0, 10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(2).set(1, 0);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(3).set(0, -10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(3).set(1, -10);
    // generate features
    for (int i = 0; i < numData; i++) {
        for (int j = 0; j < numFeature; j++) {
            dataSet.setFeatureValue(i, j, Sampling.doubleUniform(-1, 1));
        }
    }
    SamplingPredictor samplingPredictor = new SamplingPredictor(cmlcrf);
    // assign labels
    for (int i = 0; i < numData; i++) {
        MultiLabel label = samplingPredictor.predict(dataSet.getRow(i));
        dataSet.setLabels(i, label);
    }
    return dataSet;
}