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

Example 1 with EmpiricalCDF

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

the class FusedKolmogorovFilterTest method test1.

private static void test1() {
    Vector vector = new DenseVector(10);
    vector.set(0, 0.1);
    vector.set(1, 0.2);
    vector.set(2, 0.15);
    vector.set(3, 0.4);
    vector.set(4, 0.7);
    vector.set(8, 0.9);
    int[] labels = new int[10];
    labels[0] = 0;
    labels[1] = 1;
    labels[2] = 1;
    labels[3] = 1;
    labels[9] = 1;
    FusedKolmogorovFilter filter = new FusedKolmogorovFilter();
    filter.setNumBins(10);
    List<List<Double>> inputsEachClass = filter.generateInputsEachClass(vector, labels, 2);
    System.out.println(inputsEachClass);
    List<EmpiricalCDF> empiricalCDFs = filter.generateCDFs(vector, inputsEachClass);
    System.out.println(empiricalCDFs);
    System.out.println(filter.maxDistance(empiricalCDFs));
}

Example 2 with EmpiricalCDF

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

the class FusedKolmogorovFilterTest method test2.

private static void test2() {
    Vector vector = new DenseVector(10);
    vector.set(0, 0.1);
    vector.set(1, 0.2);
    vector.set(2, 0.15);
    vector.set(3, 0.4);
    vector.set(4, 0.7);
    vector.set(8, 0.9);
    vector.set(9, 0.8);
    int[] labels = new int[10];
    labels[0] = 0;
    labels[1] = 1;
    labels[2] = 2;
    labels[3] = 1;
    labels[9] = 2;
    FusedKolmogorovFilter filter = new FusedKolmogorovFilter();
    filter.setNumBins(10);
    List<List<Double>> inputsEachClass = filter.generateInputsEachClass(vector, labels, 3);
    System.out.println(inputsEachClass);
    List<EmpiricalCDF> empiricalCDFs = filter.generateCDFs(vector, inputsEachClass);
    System.out.println(empiricalCDFs);
    System.out.println(filter.maxDistance(empiricalCDFs));
    System.out.println(EmpiricalCDF.distance(empiricalCDFs.get(0), empiricalCDFs.get(1)));
    System.out.println(EmpiricalCDF.distance(empiricalCDFs.get(0), empiricalCDFs.get(2)));
    System.out.println(EmpiricalCDF.distance(empiricalCDFs.get(1), empiricalCDFs.get(2)));
}