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package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics
import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph.GraphStatistic
import java.text.DecimalFormat
import java.util.ArrayList
import java.util.HashMap
import org.eclipse.emf.ecore.EObject
class MultiplexParticipationCoefficientMetric extends Metric {
public static val countName = "MPCCount";
public static val valueName = "MPCValue";
override evaluate(GraphStatistic g) {
//because the precision issue of double, we translate double values into String to be the key
val formatter = new DecimalFormat("#0.00000");
//get number of different types
val typeCounts = g.allTypes.size;
val map = new HashMap<String, Integer>();
//calculate the metric distribution
g.allNodes.forEach[n|
var coef = calculateMPC(n, g, typeCounts);
//format number to String
val value = formatter.format(coef);
if(!map.containsKey(value)){
map.put(value, 1);
}else{
map.put(value, map.get(value) + 1);
}
]
//convert it into a 2 dimentional array
val String[][] datas = newArrayOfSize(2, map.size+1);
datas.get(0).set(0, valueName);
datas.get(1).set(0, countName)
var count = 1;
for(entry : map.entrySet.sortBy[it.key]){
datas.get(0).set(count, entry.key+"");
datas.get(1).set(count, entry.value+"");
count++;
}
return datas;
}
override evaluateSamples(GraphStatistic g){
val samples = new ArrayList<Double>();
val typeCounts = g.allTypes.size;
//calculate the metric distribution
g.allNodes.forEach[
samples.add(calculateMPC(it, g, typeCounts));
]
return samples;
}
def double calculateMPC(EObject n, GraphStatistic g, int typeCounts){
val edgeCounts = g.outDegree(n) + g.inDegree(n);
var coef = 0.0;
for(type : g.allTypes){
val degree = g.dimentionalDegree(n, type) as double;
coef += Math.pow(degree / edgeCounts, 2);
}
coef = 1 - coef;
coef = coef * typeCounts / (typeCounts-1);
//Consider the case that either typeCounts-1 or the edgeCounts could be 0 in some situation
//in this case the metric should be evaluated to 0
if(Double.isNaN(coef)){
coef = 0;
}
return coef;
}
}
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