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

Example 11 with RegDataSet

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

the class RegressionSynthesizer method gaussianMixture.

public RegDataSet gaussianMixture() {
    NormalDistribution leftGaussian = new NormalDistribution(0.2, 0.01);
    NormalDistribution rightGaussian = new NormalDistribution(0.7, 0.1);
    RegDataSet dataSet = RegDataSetBuilder.getBuilder().numDataPoints(numDataPoints).numFeatures(1).dense(true).missingValue(false).build();
    for (int i = 0; i < numDataPoints; i++) {
        double featureValue = Sampling.doubleUniform(0, 1);
        double label;
        if (featureValue > 0.5) {
            label = leftGaussian.sample();
        } else {
            label = rightGaussian.sample();
        }
        dataSet.setFeatureValue(i, 0, featureValue);
        dataSet.setLabel(i, label);
    }
    return dataSet;
}

Example 12 with RegDataSet

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

the class RegressionSynthesizer method univarBeta.

public RegDataSet univarBeta() {
    BetaDistribution betaDistribution = new BetaDistribution(2, 5);
    RegDataSet dataSet = RegDataSetBuilder.getBuilder().numDataPoints(numDataPoints).numFeatures(1).dense(true).missingValue(false).build();
    for (int i = 0; i < numDataPoints; i++) {
        double featureValue = Sampling.doubleUniform(0, 1);
        double label;
        label = betaDistribution.density(featureValue);
        label += noise.sample();
        dataSet.setFeatureValue(i, 0, featureValue);
        dataSet.setLabel(i, label);
    }
    return dataSet;
}

Example 13 with RegDataSet

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

the class RegressionSynthesizer method multivarLine.

public RegDataSet multivarLine() {
    int numFeatures = 2;
    RegDataSet dataSet = RegDataSetBuilder.getBuilder().numDataPoints(numDataPoints).numFeatures(2).dense(true).missingValue(false).build();
    for (int i = 0; i < numDataPoints; i++) {
        for (int j = 0; j < numFeatures; j++) {
            double featureValue = Sampling.doubleUniform(0, 1);
            dataSet.setFeatureValue(i, j, featureValue);
        }
        double label = 0;
        for (int j = 0; j < numFeatures; j++) {
            label += dataSet.getRow(i).get(j);
        }
        label += noise.sample();
        dataSet.setLabel(i, label);
    }
    return dataSet;
}

Example 14 with RegDataSet

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

the class RegressionSynthesizer method linear.

public static RegDataSet linear() {
    int numData = 50;
    RegDataSet dataSet = RegDataSetBuilder.getBuilder().numDataPoints(numData).numFeatures(16000).dense(true).missingValue(false).build();
    Vector weights = new DenseVector(16000);
    weights.set(0, 0.001);
    weights.set(1, 0.001);
    weights.set(2, 0.001);
    weights.set(3, 0.001);
    for (int i = 0; i < numData; i++) {
        for (int j = 0; j < 16000; j++) {
            BernoulliDistribution bernoulliDistribution = new BernoulliDistribution(0.5);
            int sample = bernoulliDistribution.sample();
            if (sample == 0) {
                dataSet.setFeatureValue(i, j, -1);
            } else {
                dataSet.setFeatureValue(i, j, 1);
            }
        }
        double label = weights.dot(dataSet.getRow(i));
        dataSet.setLabel(i, label);
    }
    return dataSet;
}

Example 15 with RegDataSet

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

the class RegressionSynthesizer method univarSine.

public RegDataSet univarSine() {
    RegDataSet dataSet = RegDataSetBuilder.getBuilder().numDataPoints(numDataPoints).numFeatures(1).dense(true).missingValue(false).build();
    for (int i = 0; i < numDataPoints; i++) {
        double featureValue = Sampling.doubleUniform(-Math.PI, Math.PI);
        double label;
        label = Math.sin(featureValue);
        label += noise.sample();
        dataSet.setFeatureValue(i, 0, featureValue);
        dataSet.setLabel(i, label);
    }
    return dataSet;
}