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package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance
import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.app.Domain
import java.util.List
import org.apache.commons.math3.stat.inference.KolmogorovSmirnovTest
import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io.RepMetricsReader
class KSDistance {
var static ksTester = new KolmogorovSmirnovTest();
var double[] mpcSamples;
var double[] naSamples;
var double[] outDegreeSamples;
new(Domain d){
var metrics = RepMetricsReader.read(d);
mpcSamples = metrics.mpcSamples;
naSamples = metrics.naSamples.stream.mapToDouble([it]).toArray();
outDegreeSamples = metrics.outDegreeSamples.stream.mapToDouble([it]).toArray();
}
def double mpcDistance(List<Double> samples){
// map list to array
var arr = samples.stream.mapToDouble([it]).toArray();
//if the size of array is smaller than 2, ks distance cannot be performed, simply return 1
if(arr.size < 2) return 1;
return ksTester.kolmogorovSmirnovStatistic(mpcSamples, arr);
}
def double naDistance(List<Double> samples){
// map list to array
var arr = samples.stream.mapToDouble([it]).toArray();
//if the size of array is smaller than 2, ks distance cannot be performed, simply return 1
if(arr.size < 2) return 1;
return ksTester.kolmogorovSmirnovStatistic(naSamples as double[], arr);
}
def double outDegreeDistance(List<Double> samples){
// map list to array
var arr = samples.stream.mapToDouble([it]).toArray();
//if the size of array is smaller than 2, ks distance cannot be performed, simply return 1
if(arr.size < 2) return 1;
return ksTester.kolmogorovSmirnovStatistic(outDegreeSamples, arr);
}
}
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