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

Example 6 with RegDataSet

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

the class RegressionSynthesizer method univarStepFeatureNoise.

public RegDataSet univarStepFeatureNoise() {
    NormalDistribution featureNoise = new NormalDistribution(0, 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 = 0.7;
        } else {
            label = 0.2;
        }
        label += noise.sample();
        featureValue += featureNoise.sample();
        dataSet.setFeatureValue(i, 0, featureValue);
        dataSet.setLabel(i, label);
    }
    return dataSet;
}

Example 7 with RegDataSet

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

the class RegressionSynthesizer method univarExp.

public RegDataSet univarExp() {
    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 = Math.exp(featureValue);
        label += noise.sample();
        dataSet.setFeatureValue(i, 0, featureValue);
        dataSet.setLabel(i, label);
    }
    return dataSet;
}

Example 8 with RegDataSet

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

the class RegressionSynthesizer method univarQuadratic.

public RegDataSet univarQuadratic() {
    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 = Math.pow(featureValue, 2);
        label += noise.sample();
        dataSet.setFeatureValue(i, 0, featureValue);
        dataSet.setLabel(i, label);
    }
    return dataSet;
}

Example 9 with RegDataSet

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

the class RegressionSynthesizer method univarPiecewiseLinear.

public RegDataSet univarPiecewiseLinear() {
    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 = -featureValue + 0.5;
        } else {
            label = featureValue;
        }
        label += noise.sample();
        dataSet.setFeatureValue(i, 0, featureValue);
        dataSet.setLabel(i, label);
    }
    return dataSet;
}

Example 10 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;
}